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hat do white supremacist Silicon Valley tech bros and terrorist mass shooters of mosques, synagogues, and migrant centers have in common?

They all subscribe to a death cult that dreams of a utopic world where there is racial and cultural hierarchy, and all non-white migrants are deported from Western countries and imperialism, hard power and exploitation to reign again in the global order.

The world is facing a new form of terrorism threat and fascism: it is called Accelerationism. Accelerationists disdain non-white migrants, Muslims, ethnic minorities, radicals and politicians that advocate for diversity, integrating migrants, decolonial justice, and the reparation for slavery and colonialism.

Death cults

On May 19, 2026, two teenagers attacked a San Diego Mosque, killing three people and livestreamed their shooting. A 75-page manifesto written by the shooters espoused Islamophobia, antisemitism, anti-Black racism, Hispanophobia, accelerationism and misogyny was later found online. This was not an isolated incident. On March 15, 2019, a gunman walked into the Al Noor Mosque in Christchurch, New Zealand, and murdered 51 Muslims while livestreaming the massacre on Facebook. He left behind a manifesto soaked in the language of accelerationism and had similarities with the San Diego shooters’ manifesto.  Accelerationism espouses the idea that Western/White civilization must be pushed to a violent breaking point so that white and “pure” ethnostates can rise from the ashes and reclaim their glorious past.

The terrorism in Christchurch and accelerationism have a long history. It echoed Anders Breivik’s 2011 massacre in Norway, where 77 people, most of them teenagers, were murdered in the name of protecting Europe from cultural Marxism and Muslim migration. It echoed the Pittsburgh synagogue shooting, the El Paso Walmart attack targeting Hispanic shoppers, the Buffalo supermarket massacre aimed at Black Americans.

Accelerationist terrorism is carried out in the name of ideological and cultural warfare, carried out by mostly young white men who believe that accelerating racial, cultural and religious conflict would hasten a white supremacist revolution, response and reaction.

AI and the far-right

The question the world has been too slow to ask is this: who is building the intellectual, ideological and political infrastructure that makes this violence justifiable?

Look no further than Silicon Valley.

Elon Musk, today the world’s richest man, owner of X (formerly Twitter), and now the single most powerful unelected figure in the United States, has spent recent years amplifying white nationalist accounts, and platforming “great replacement” conspiracy theories, and signal-boosting neo-Nazi rhetoric to hundreds of millions of followers across the world. His very public embrace of far-right European parties, his attacks on Muslim communities, his obsessive promotion of white demographic anxiety, are not the eccentricities of a distracted Trillionaire. When Musk performed a Nazi salute at a post-inauguration rally and the world shrugged, it confirmed what many had long suspected: white supremacy has found its most powerful champion not in the backrooms of a skinhead bar but in the boardrooms of the most valuable companies on earth.

Marc Andreessen, the venture capitalist and co-founder of Netscape, published his “Techno-Optimist Manifesto” in 2023, declaring that there is no problem “that cannot be solved with more technology” and that the “techno-capital machine” exists to serve humanity. What he did not say is whose humanity he had in mind. The effective accelerationist movement (e/acc) that Andreessen and his Silicon Valley peers champion is accelerationism with a smile: the same ruthless logic of letting destructive forces run unchecked, dressed up in the language of progress, technology and innovation. The merger of white nationalist politics and Silicon Valley capital is no longer a fringe concern. It is the ideology of most Far Right and white supremacist political movements.

Underneath all of it runs a single obsession: replacement. The “great replacement” theory, the paranoid anxiety that white Western populations are being deliberately displaced by non-white migrants, has migrated from the fever swamps of the internet into mainstream conservative politics across Europe, America, and Australia. Replacement paranoia is the connective tissue between the Christchurch shooter and the French Far Right, between the neo-Nazi accelerationist and the Arktos circle and the recently exposed Dialog list of Peter Thiel. This is an ideological project: a far-right internationalism that stretches from Budapest to Buenos Aires, from Washington to Warsaw, and united by the shared paranoid conviction that the non-white world and its peoples are an existential threat to Western civilization.

Techno-fascism

And now this far right international has acquired its most powerful tool yet: Artificial Intelligence.

The global AI economy is being built on a foundation that replicates, with algorithmic precision, the colonial extraction of previous centuries. The data that trains the large language models powering Silicon Valley’s fortunes is harvested from populations across the Global South, often without consent, without compensation, and without benefit. The content moderation labour that makes these platforms usable is outsourced to workers in Kenya, the Philippines, India and Pakistan, paid poverty wages to absorb the worst of humanity’s digital violence so that Western users need not see it.

Moreover, the rare earth minerals that power the hardware are extracted from the Congo, from Bolivia, from Indonesia, by workforces whose conditions would be unthinkable in any country whose citizens Musk and Andreessen consider fully human.

This is the new colonialism, not the colonialism of the gunboat and the governor-general, but of the server farm and the terms of service agreement. The Global South is being incorporated into the AI economy not as a beneficiary but as raw material: its people as cheap labour, its data as free resource, its governments as compliant hosts, and most consequentially the Silicon Valley tech bros dream of a hierarchical world where the Global South is exploited and enslaved.

The accelerationists and the tech bros, in both their violent and techno-utopian forms, agree on one thing: some lives are the fuel, not the destination.

In their hyper-technological and advanced futuristic utopian world, where Musk, Jeff Bezos, Peter Thiel and other billionaires will go for vacation to Mars, there is no place for Muslims, Africans and peoples of the Global South.

Violent death cults have always worn different suits and hoodies in different eras. Today they wear a hoodie or suit and call themselves disruption innovationists, Techno-Utopianists or efficiency strategists.

The mosques of Christchurch and the sweatshops of Nairobi are not separate stories. They are the same story, told in different registers of the same accelerationist logic: that some people exist to be exploited so that others may prosper and build a civilization that reaches Mars and beyond while the Global South is robbed of its wealth, labour and minds and rendered perpetually poor and miserable.

We should not look away.

All forms and manifestations of hatred, dehumanization and exploitation ought to be challenged and resisted – even when it’s clothed in the language of technological advancement or saving a particular civilization, culture or race.

A

rtificial intelligence is rapidly reshaping the U.S. technology industry, with major companies accelerating workforce reductions as they restructure operations, automate tasks and redirect investment toward AI-driven growth.

Technology firms are increasingly citing artificial intelligence as a primary reason for layoffs, reflecting a broader shift in corporate strategy aimed at boosting productivity, lowering costs and streamlining operations.

Data from outplacement firm Challenger, Gray & Christmas show AI has become the leading justification for announced job cuts in the United States.

Nearly 40% of layoffs announced in May were attributed to artificial intelligence, a sharp increase from 7% in January, 10% in February, 25% in March and 26% in April.

The technology sector has been at the center of the trend.

Layoffs announced by technology companies during the first five months of 2026 rose 66% from the same period a year earlier, reaching 123,653 positions, making it the industry with the highest number of job cuts this year.

Economists caution that artificial intelligence is not the only factor behind the reductions. They say many companies are also responding to overhiring during the COVID-19 pandemic, persistent cost pressures and broader restructuring efforts aimed at improving profitability.

Major technology firms restructure around AI

Software giant Oracle reported one of the largest workforce reductions, revealing that its global full-time employee count declined by 21,000 over the past year.

A filing with the U.S. Securities and Exchange Commission (SEC) showed Oracle employed 141,000 full-time workers as of May 31, down from 162,000 a year earlier.

The company said the adoption and deployment of artificial intelligence technologies across its operations had resulted in workforce reductions and could continue to do so.

Software development platform GitLab also announced significant restructuring as it shifts its business model toward AI-powered services.

The company said it will reduce its workforce by about 14%, eliminating roughly 350 jobs while simplifying management structures and withdrawing operations from 22 countries as it expands infrastructure for AI-related workloads.

Meta is pursuing a different strategy, combining layoffs with workforce redeployment.

According to U.S. media reports, the social media company plans to cut around 8,000 jobs while transferring approximately 7,000 employees into new AI-focused positions.

Chief Executive Mark Zuckerberg reportedly told employees that the restructuring was necessary as competition in artificial intelligence intensifies, warning that success in the sector is not guaranteed.

Meta has already implemented multiple rounds of layoffs this year, including a 10% reduction in staff at its Reality Labs division in January, followed by additional cuts affecting hundreds of employees in March.

Broader industry transformation

Several other major technology companies have also announced workforce reductions linked to AI investments and organizational restructuring.

Financial software company Intuit said it will eliminate 17% of its full-time workforce to improve operational efficiency and focus more resources on strategic priorities, including artificial intelligence.

Networking equipment maker Cisco plans to cut approximately 4,000 jobs as it redirects investment toward AI and other growth businesses.

Cloudflare announced the elimination of more than 1,100 positions as it redesigns its organizational structure and internal processes for the AI era.

Cryptocurrency exchange Coinbase said it would reduce its workforce by around 14%, citing market conditions and its ongoing AI transformation.

Financial technology company PayPal has announced plans to reduce its workforce by 20% over the next three years while integrating artificial intelligence throughout its operations.

Social media platform Snap also joined the growing list of companies restructuring around AI, announcing plans to lay off about 1,000 employees—roughly 16% of its workforce—and eliminate more than 300 vacant positions.

Chief Executive Evan Spiegel said rapid advances in artificial intelligence were reducing repetitive tasks and accelerating business processes, making organizational changes necessary.

Other companies undertaking AI-related restructuring this year include Angi, Atlassian, Block, Crypto.com and Wix.

The wave of layoffs highlights how artificial intelligence is reshaping employment across the technology industry, with companies increasingly seeking to automate routine work while investing heavily in AI infrastructure and specialized talent.

While businesses argue the changes are necessary to remain competitive in a rapidly evolving market, the trend has also intensified concerns about the long-term impact of artificial intelligence on employment, particularly in administrative, software development and support functions.

(Source: Anadolu Agency)

I

n his first major encyclical, Pope Leo XIV has issued a sweeping warning about the risks posed by artificial intelligence, describing the technology as a transformative force comparable to the Industrial Revolution and cautioning against allowing technological power to outpace moral responsibility.

The 40,000-word document, Magnifica Humanitas (“Magnificent Humanity”), argues that AI presents profound challenges for human dignity, democracy, labor, and global equality.

Drawing on the biblical story of the Tower of Babel, Pope Leo warned against what he described as the “idolatry of profit” and the belief that technology can reduce every aspect of human life to data and performance.

“Humanity, in all its grandeur and woundedness, must never be replaced or surpassed,” the pope wrote, while acknowledging that technological advances can alleviate suffering and create new opportunities when guided by ethical principles.

The pontiff has repeatedly compared the current AI revolution to the social and economic upheaval caused by industrialization in the 19th century. He said he chose the name Leo XIV in honor of Pope Leo XIII, whose landmark encyclical Rerum Novarum addressed workers’ rights and economic justice during the Industrial Revolution.

Throughout the document, Leo criticizes what he calls the “technocratic paradigm” — the idea that efficiency, optimization, and technological progress alone should guide political and economic decision-making.

He also raises concerns about the use of AI in warfare, warning against autonomous weapons systems and arguing that no algorithm can resolve the moral questions surrounding armed conflict.

Anthropic’s presence highlights growing debate over AI governance

The launch of the encyclical attracted particular attention because of the presence of Christopher Olah, whose company Anthropic develops the Claude artificial intelligence chatbot.

The Vatican’s decision to feature Anthropic rather than other major AI firms such as OpenAI, Google, Meta, or xAI was widely interpreted as significant, reflecting the company’s emphasis on AI safety and ethics.

Speaking at the Vatican event, Olah acknowledged that AI developers face incentives that can conflict with ethical considerations.

“We need informed critics who will tell the labs when we are failing,” he said, adding that moral voices are needed to ensure technological development serves society rather than narrow commercial interests.

The appearance underscored the increasingly complex relationship between religious institutions, governments, and technology companies as AI systems become more influential in daily life.

Some analysts view the Vatican’s engagement as an attempt to shape global discussions about artificial intelligence before regulatory frameworks become dominated by governments and private corporations.

A prominent voice in debates over AI governance

The Catholic Church, which represents nearly 1.4 billion people worldwide, has sought to establish itself as a prominent voice in debates over AI governance. Just days before releasing the encyclical, Pope Leo approved the creation of an interdepartmental Vatican commission dedicated to artificial intelligence policy and ethics.

Supporters argue that the Church can provide a moral perspective often absent from technology debates, while critics question whether religious institutions can meaningfully influence the development of rapidly evolving AI systems.

Nevertheless, the pope’s intervention signals that the Vatican intends to remain an active participant in one of the defining global debates of the 21st century. By framing artificial intelligence as a question of ethics, human dignity, and social responsibility, Pope Leo has challenged the idea that decisions about humanity’s technological future should be left solely to governments and Silicon Valley executives.

(Source: Anadolu Agency)

I

n the 21st century, wars have begun to take shape not only through purely physical conflicts but also through systems integrated with cognitive, cyber and artificial intelligence (AI) technologies. We can say that modern conflict and battlefields encompass operations targeting information, perception and the human mind, alongside the use of military force. This new form of warfare is referred to in the literature as cognitive warfare. The fundamental objectives of cognitive warfare can be described as influencing the adversary’s decision-making processes, steering the psychology of the target population(s), and weakening political will. NATO’s 2025 Cognitive Warfare report highlights that contemporary conflicts and wars are increasingly behavior-centric, and that the decisive arena is no longer geographical; rather, it is based on the strategic reality of how individuals and groups perceive, interpret, decide and act.

Within this framework, it can be argued that today’s mass media, the increasing use of AI-enabled technologies, and the undeniable influence of social media are effectively forming the new operational front of war. Cognitive warfare extends to social foundations such as trust, shared epistemic standards and institutional legitimacy. From a force development perspective, AI platforms are utilized alongside elements of cognitive warfare, both for threat/attack purposes and as a countermeasure. In other words, techniques that enable enemy influence can also be utilized through reverse engineering to support defensive cognitive security (such as detecting anomalies in influence networks and pattern recognition of coordinated deceptive behavior).

At this stage, whilst neuroscience and neurotechnologies are emerging as tools for influencing the mind by affecting the brain, it can be said that AI platforms have become the primary means of influencing the mind by manipulating the information ecosystem. Consequently, the operational concern is not merely that AI produces persuasive content. It also lies in AI’s ability to: Optimize narratives in real time across all channels, automate social media interaction (using bot or hybrid actor swarms), generate artificial credibility (deepfake videos, fake expert profiles, fabricated evidence), exploit cognitive biases and psychological vulnerabilities (the salience effect, fear conditioning, polarization dynamics).

In this context, cognitive warfare can be regarded as the process of preparing the groundwork prior to a physical attack. In NATO documents, the cognitive domain is discussed as the sixth operational domain following land, sea, air, cyber and space. The tensions between the US and Iran, and particularly the military conflicts of recent years, have clearly demonstrated just how significant cognitive warfare has become in modern war doctrines. As of 28 February, the attacks launched by the US against Iran have involved not only conventional military force but also intensive use of cyberattacks, cognitive warfare integrated with information manipulation, and AI-supported, target-focused strikes. The key question here is: how did the US utilize AI-integrated systems, which combine data-driven targeting with AI-integrated cognitive warfare, as a strategic tool throughout Operation Epic Fury, the offensive launched against Iran?

The use of military artificial intelligence

Military AI encompasses a wide range of systems and tasks. On this basis, we can state that it is fundamentally divided into two main categories: autonomous weapon systems and decision support systems. Autonomous weapon systems have the capability to select and/or engage targets independently. Decision support systems, on the other hand, are regarded as a key focus in the technology-driven structure of many modern armies today. These systems can be considered software applications that provide intelligence and planning information to personnel.

Admiral Brad Cooper, Commander of CENTCOM, stated during the US-Iran conflict that the US military was using AI systems and that AI assisted in data processing, but that final decisions were made by humans. It is known that the US military utilizes AI systems based on large language models (LLMs) for logistics, administrative support, intelligence gathering and analysis, as well as decision support systems on the battlefield. Claude, developed by Anthropic, is cited as an example of a decision support system rather than a weapon.

Claude is integrated into the intelligent system known as Maven, which is widely used by military, intelligence and law enforcement organizations. Maven uses AI algorithms to identify potential targets from satellite and other intelligence data. In this context, Maven accelerates the military’s offensive capabilities by suggesting and prioritizing targets. Whilst these AI systems have a relatively long history, the US military’s ability to utilize them did not emerge overnight. We are also aware that many Iraqi and Afghan civilians lost their lives as a result of analytical errors and cultural biases within the US military.

Most recently, there is strong evidence that the use of AI systems led to a Tomahawk cruise missile striking a girls’ school adjacent to an Iranian base, contributing to the deaths of approximately 175 people, the majority of whom were students. This attack could be interpreted as a failure of US intelligence or as an indiscriminate strike. Indeed, the effective use of automated systems depends on comprehensive infrastructure and personnel capable of operating them. In this context, it is also worth noting that the US’s ability to utilise AI in warfare today is the result of decades of investment and experience.

AI-supported targeting systems in the context of cognitive warfare

In the context of the US–Iran conflict, one of the prominent features of modern warfare is the increasing role of AI-supported decision-making systems in attacks. Applications emerging in active war and conflict zones, particularly in the Middle East, demonstrate the potential consequences of these technologies in terms of both operational effectiveness and ethical responsibility.

Furthermore, the AI systems utilised by the US in its military and intelligence operations against Iran constitute a significant part of this transformation. Systems such as Maven, developed within the US Department of Defence, play a significant role in intelligence analysis concerning Iran’s military infrastructure, nuclear facilities and regional militia networks. In this context, discussions regarding AI-supported target identification systems used in Gaza are important for understanding the cognitive and technological warfare practices that may emerge in future high-intensity geopolitical rivalries such as that between the US and Iran.

The AI system named “Lavender”, used by Israel in Gaza, was developed to identify individuals believed to be linked to Hamas by analysing vast datasets. The system generates a score regarding an individual’s likelihood of being a Hamas member by analysing numerous data points, including telephone data, communication networks, address changes, social connections and intelligence records. However, although it is known that these algorithmic assessments are not definitive and contain a certain margin of error, there is evidence that the lists generated by this system were used in large-scale target identification processes during the first weeks of the war.

Whilst the Lavender system flagged tens of thousands of people as potential targets in the early stages of the war, some military sources have stated that this figure reached approximately 37,000. It emerged that a significant proportion of these individuals were not high-ranking military figures, but rather people of lower rank or with indirect connections.

The ‘Where’s Daddy?’ system, used in conjunction with the Lavender system, is designed to track the locations of designated targets. This system specifically aims to identify the moment the target returns home in order to determine the most opportune time for an attack. The practical consequence of this approach has been that many attacks have taken place in residential properties where the target was present alongside family members. In research, some intelligence officers have noted that military operations are often carried out not whilst individuals are engaged in military activities, but whilst they are at home, and that this leads to an increase in civilian casualties.

Furthermore, it is stated that the use of these systems has significantly lowered the accepted ‘collateral damage’ thresholds in military operations, particularly during the early stages of a conflict. In general, it has been noted that even when targeting a low-ranking militant, a certain number of civilian casualties is deemed acceptable for the operation. This situation forms the basis for serious debates regarding AI-assisted target identification systems, not only in terms of military efficiency but also in relation to international humanitarian law and the ethics of war.

AI-assisted target identification systems and ethics

The ethical debates surrounding AI-assisted target identification systems demonstrate that cognitive warfare affects not only societies’ perceptions but also the mental processes of decision-makers. In this context, the debates surrounding systems such as Lavender and Where’s Daddy, used in Gaza, serve as a significant example for understanding the human–machine relationship and how decision-making processes are transforming in modern warfare. In particular, the fact that these systems classify potential threats by analyzing extensive datasets during target identification processes highlights that the role of human decision-makers on the battlefield is becoming increasingly complex.

From a cognitive warfare perspective, this situation can be viewed not merely as a technological advancement, but also as a factor influencing military personnel’s perceptions, risk assessments and ethical boundaries. Consequently, we can state that the large-scale target identification processes generated by the Lavender system’s lists make a significant contribution to military planning.

However, despite the awareness that algorithmic assessments may contain a high margin of error, data indicates that these outputs are often approved with limited human oversight due to the need for rapid decision-making in operational processes. This situation suggests that recommendations generated by AI could exert a powerful cognitive influence on human decision-makers, leading to the algorithmic output effectively becoming a decision-making mechanism in practice. The fact that the ‘Where’s Daddy’ system focuses specifically on identifying the moments when target individuals return to their homes has resulted in operations frequently taking place in residential areas.

This situation has given rise to significant debates not only from a military strategic perspective but also regarding the ethical dimensions of warfare. Indeed, the fact that target identification processes rely heavily on algorithmic analysis leads military personnel to place greater trust in the recommendations of technological systems when making decisions, which in turn can narrow the scope of human assessment.

(Originally published in Turkish by Kriter)

The crisis of the international order is no longer an abstract concept. It is evident in headlines that would have seemed inconceivable a decade ago: US operations in Venezuela, threats to take Greenland by force, and the deepening strategic divide between Washington and its European allies. While the liberal order championed by Washington is not collapsing overnight, it is clearly hardening into a more transactional, unilateral and openly strategic approach.

The Trump administration justifies these moves in terms of national security. This framing is not entirely incorrect, but it is incomplete. The deeper question is not whether these actions are about security. Rather, it is about why they are happening now and how they are converging across seemingly unrelated theatres, from the Arctic to Latin America to Eastern Europe.
The answer lies in the accelerating AI race.

Analysed in isolation, issues such as Greenland, Venezuela, rare earth diplomacy and energy realignment appear as traditional realpolitik: resource grabs, power projection and coercive diplomacy. However, when considered collectively, they reveal a coherent long-term strategy centred on one objective: securing the material and energy resources necessary to win the AI competition against China.

This is not about territory. It is about infrastructure.

AI as a grand strategy

Since returning to office, Trump has treated AI not as a sectoral priority, but as a systemic one. AI is now at the core of US national strategy, shaping defence modernisation, industrial policy, export controls, energy planning and strategic finance. Critical mineral supply chains have been formally recognised as national security imperatives in Trump’s National Security Strategy. Executive orders have sought to expand domestic extraction and rebuild rare earth processing capacity in the United States. Public–private partnerships with American mining and materials firms reflect a broader attempt to restore industrial capabilities that had long migrated abroad.

However, what makes this moment distinct is the unprecedented alignment between the federal government and major technology firms. Silicon Valley is no longer operating at arm’s length from Washington. AI developers, semiconductor manufacturers, hyperscale cloud providers and energy-intensive data centre operators are becoming increasingly involved in strategic planning discussions. Contracts, export controls, infrastructure financing and industrial policy are being designed in consultation with the firms that are driving AI development. The boundary between national and corporate strategy has become unusually thin.

Taken together, these measures signal something larger than economic policy. They reveal an acknowledgement of a structural constraint: the US cannot fully decouple from China under current technological conditions. The conversation about decoupling began during Trump’s first term, but time has exposed its limitations. Semiconductor fabrication, rare earth refining, advanced battery production and energy systems remain deeply interwoven across borders.

This creates a strategic paradox. Although decoupling is presented as a security necessity, the extent of interdependence means that abrupt separation would be destabilizing. Both Washington and Beijing understand this. Each is now engaged in a quiet race to reduce vulnerability before a systemic rupture occurs. The objective is not immediate disengagement, but preparation.

Strategic autonomy in semiconductors, rare earth processing and energy generation will determine who can withstand a future rupture. The state that secures control over these foundational inputs first will possess disproportionate leverage in the AI competition.

Export bans on advanced chip designs, restrictions on critical mineral flows and the rapid expansion of industrial alliances are not isolated measures when viewed through this lens. They are components of a larger repositioning exercise. Resource diplomacy has become more assertive because the AI race has shifted from software to infrastructure.

Greenland: REE and the Arctic chessboard

Trump openly threatened to annex Greenland, claiming it was a matter of national security rather than minerals. The rhetoric was blunt. Yet, after months of diplomatic friction, discussions evolved to include frameworks that explicitly incorporated rare earth elements alongside defence cooperation. This shift reveals a deeper truth: Today, Greenland’s geostrategic value lies less in its immediate military positioning and more in its long-term role in critical supply chains.

The island hosts some of the world’s largest undeveloped rare earth deposits, including the Kvanefjeld and Tanbreez sites, which are both rich in heavy rare earth elements that are essential for advanced magnets, precision weapons, semiconductors and AI hardware. As early as 2019, Washington signed a memorandum of understanding with Greenland focused on cooperation in critical minerals, long before the latest escalation. This suggests that access to resources has long been part of the calculation.

China has also recognised Greenland’s importance. Through its Polar Silk Road strategy, Beijing has pursued infrastructure projects and secured stakes in rare earth ventures linked to Greenland. Even where projects have stalled, China’s dominance in rare earth processing gives it leverage over any future extraction.

Meanwhile, the Arctic itself is undergoing transformation. Melting ice is opening up new shipping routes and altering the military landscape across the North Atlantic. Greenland is located at the intersection of emerging trade routes and missile trajectories, making it a strategically important location in a warming world.

In the evolving geopolitical landscape of AI and advanced manufacturing, rare earths are not peripheral; they are fundamental. The modern infrastructure of AI — semiconductors, permanent magnets, high-end sensors and quantum materials — depends on minerals that are currently dominated by China’s processing capacity. Greenland offers an alternative source of supply in this high-stakes contest.

Therefore, Trump’s interest in Greenland is not a quixotic land grab. It reflects a broader shift: security policy is now infrastructure policy. The question is not whether Greenland matters, but why it matters now, and how access to minerals and supply-chain leverage are reshaping the meaning of national security in the 21(st) century.

Venezuela: Energy, minerals, and strategic leverage

At first glance, Venezuela appears to be a classic oil story. It has the world’s largest proven oil reserves. However, this is not just about fossil fuels in the traditional sense of energy security. AI is changing the energy equation dramatically.

Electricity demand in the United States is rising rapidly, partly due to explosive growth in data centers built to power AI and cloud computing. In 2023, U.S. data centers consumed around 4.4% of total electricity, and this figure is expected to increase significantly as AI workloads grow. In states where data centers are concentrated, electricity prices are rising faster than the national average, with power bills increasing by 13% in Virginia, 16% in Illinois and 12% in Ohio in the past year alone. Utilities are extending generation capacity and upgrading transmission networks to meet this concentrated demand, highlighting that electricity is now a strategic fuel rather than a commodity.

Venezuelan oil and gas could help to offset U.S. energy demand by supplementing crude oil supplies, thereby alleviating stress on the grid and refineries. However, Venezuela’s strategic significance extends well beyond hydrocarbons. Much of the country sits on the ancient Guayana Shield, which is home to substantial deposits of nickel, copper, bauxite, coltan, gold, and other critical minerals that are essential for advanced electronics, defense technologies, renewable energy sources, and AI hardware. In 2016, Caracas established the extensive Orinoco Mining Arc as a state-controlled area for mineral extraction, formally placing these resources under centralized management.

In practice, however, mining there has become a mix of state oversight, informal operations, and widespread illegal extraction. A significant proportion of minerals, particularly coltan and other strategic materials, are smuggled into Colombia and then on to global markets, with much ultimately ending up in China’s processing networks. Even though official rare earth output remains underdeveloped, Venezuela’s broader mineral ecosystem reinforces Beijing’s leverage in global supply chains. At the same time, it should be noted that China has been Venezuela’s largest buyer of crude oil for years.

From this perspective, it could be argued that control or influence over Venezuelan resources and minerals would offer the U.S. several strategic advantages. Firstly, it would allow Washington to act strategically on its own energy needs rather than leaving them vulnerable to global disruptions. Secondly, it would reduce China’s access to discounted Venezuelan crude, thus limiting Beijing’s ability to diversify. Thirdly, it would strengthen U.S. leverage through sanctions and market positioning.

H

uman history is witnessing one of the most radical breaking points in the production and dissemination of knowledge. Historical turning points, such as the democratization of information with the invention of the printing press or the borderless reach of access with the expansion of the internet, remain merely preparatory stages compared to the transformation we are experiencing today. The process that began in late 2022 with the global accessibility of generative artificial intelligence technologies through Large Language Models has fundamentally shaken the concepts of authorship, originality, and scientific integrity, which are the values that academia has cherished for centuries.

Human intelligence is no longer alone in laboratories, libraries, and at desks; it is joined by a silicon-based partner whose capacity increases by the day, which never tires, and which can at times be dangerously convincing. While this new era comes with the promise of carrying the speed of scientific discovery beyond human imagination, it also leaves the scientific literature face-to-face with an irreversible risk of pollution.

The nature of the scientific production process is confronted with an epistemological dilemma. The processes of hypothesis formulation, literature review, and data processing, which traditionally took shape in a researcher’s mind, have begun to be delegated to algorithms that produce results in seconds. This situation has given rise to a new phenomenon in academia termed “cognitive substitution”. As researchers outsource literature reviews that once took months to AI tools or leave the task of coding entirely to autonomous systems, they face the risk of being deprived of the tacit knowledge inherent in the scientific process.

From a creative author to a curator or editor

When one AI tool performs the literature review of an article, another analyzes the data, and yet another handles the copyediting, the role of the human researcher evolves from a creative author to a curator or editor. This transformation brings with it profound philosophical questions that cannot be explained by efficiency alone. If the AI conceives the idea, processes the data, and writes the article, where does the human stand in this equation? This is the fundamental issue that leaves ethics committees, publishers, and university administrations across the world sleepless.

Looking at the global scale, it is evident that the reactions to this technological tsunami present a fragmented structure based on geopolitical and cultural priorities. The world has not yet been able to establish a common language regarding AI governance. While the European Union, with its Risk-Based approach, places safety and fundamental rights at the center, striving to maintain the protection of research data and transparency at the highest level, the United States exhibits a market-oriented and more flexible stance, wary of stifling innovation. China, on the other hand, with its state-driven strategy, views artificial intelligence as a tool for national development and strategic superiority, creating centralized datasets to increase academic productivity.

This fragmented structure leads to serious inconsistencies in international scientific collaborations. For example, when a European university and an American university conduct a joint project, the issue of which AI model the data will be uploaded to and how the privacy of this data will be ensured can turn into a difficult legal knot to untangle. Although international organizations such as UNESCO attempt to create a human-centered ethical compass, the speed of technological development is far higher than the reaction time of regulatory bodies.

Turning technology into an advantage

The world’s leading universities, after overcoming the initial shock, have begun to move away from a prohibitive mindset toward controlled integration strategies. Resource-rich institutions such as Harvard University have succeeded in turning technology into an advantage by providing their researchers and students with secure, closed-loop sandbox AI tools where data does not leak out. This approach aims to prevent intellectual property infringements that could occur when data is uploaded to publicly available models.

Oxford University follows a more liberal but responsibility-oriented policy, allowing the use of AI in idea development and data analysis, yet emphasizing that the human researcher is ultimately responsible for every output produced. Institutions located in the heart of Silicon Valley, such as Stanford University, have adopted a decentralized, flexible model, largely leaving the decision to instructors and project leads. The common denominator where all these different approaches intersect is the principle of transparency. The use of artificial intelligence must cease to be a secret and must be explicitly declared in the methodology section.

The academic ecosystem in Türkiye has not remained indifferent to this global transformation, clarifying its own roadmap particularly with steps taken in 2024 and 2025. The ethical guidelines published by the Council of Higher Education (YÖK) and TÜBİTAK of Türkiye have largely resolved the uncertainty by providing a binding framework for Turkish universities. The most critical point highlighted in these guidelines is that AI can only be used as a supportive tool, whereas higher-order cognitive processes such as hypothesis formulation or the interpretation of results must belong to the human mind. Turkish universities are updating their institutional policies in line with this framework.

While some universities position AI as a student-centered educational tool that does not reduce human interaction, others are ensuring integration into engineering education by establishing centers aimed at increasing technical capacity. At the same time, they are developing rigorous yet innovative procedures that are compatible with international standards regarding academic integrity and data privacy. Türkiye’s approach is based on establishing a culture of responsible and reliable use by pulling technology within ethical boundaries rather than rejecting it.

The dark side of technology

However, there is also a dark side to this bright technological picture. The academic publishing sector is under attack from fake articles produced by AI-powered paper mills. Scientific literature is at risk of being polluted by articles that are produced by AI, sound pleasant, but are empty or filled with entirely fabricated data. An anatomically impossible rat image (huge rat genitals scandal) recently published in a reputable journal and clearly produced by AI has exposed how vulnerable the peer-review system has become against this new threat.

Furthermore, the term “tortured phrases” which entered the literature as a result of some researchers using AI to bypass plagiarism software by replacing established scientific terms with strange synonyms, is disrupting the integrity of scientific language. Although publishers initially relied on AI detectors, they have been forced to change strategy due to these tools unfairly accusing non-native English-speaking authors (false positives) and failing to catch slightly modified AI texts. Their primary focus is now built upon authors presenting their raw data and transparently declaring their processes through various checklists and tools, rather than technological policing.

Despite all these risks, the momentum that artificial intelligence adds to the scientific discovery process is undeniable. Google DeepMind’s AlphaFold 3 project solving protein structures with atomic precision or the GNoME project discovering millions of new materials proves that AI is indispensable in deciphering data patterns that exceed the limits of the human mind. Research that would take months or even years with traditional methods can now be condensed into hours. It holds a massive potential that clears the path for humanity in many fields, from drug development to clean energy technologies.

In conclusion, the world of academia is walking a delicate balance between speed and reliability. Artificial intelligence has accelerated the production line of science but has strained quality control mechanisms. The scientists who will be successful in the future will not only be those who master their fields but also those who can manage these powerful tools most accurately from both an ethical and technical perspective. The quantitative production frenzy created by the “publish or perish” pressure reaches an unsustainable point when combined with AI.

The way out of this crisis lies not in banning technology, but in building a new scientific culture that centers on human responsibility, transparency, and verification. Science, regardless of how advanced the tools become, is essentially a human endeavor, and it will once again be the human conscience and intellect that have the final word in the effort to reach the truth.

(Originally published in Turkish by Kriter)

C

hina’s public exhibits far stronger trust in artificial intelligence than people in the United States and other Western nations, according to a global survey released on Tuesday by Edelman. The findings highlight stark differences in public perception of AI and suggest that trust — not technology alone — may determine the pace and scope of adoption worldwide.

The Edelman Trust Barometer Flash Poll found that 87 percent of Chinese respondents expressed confidence in AI, compared with 67 percent in Brazil, 36 percent in the United Kingdom, 39 percent in Germany, and just 32 percent in the United States. The survey also revealed that young adults are especially trusting of the technology, with 88 percent of Chinese aged 18–34 reporting faith in AI, compared with 40 percent of their American peers.

More than seven in ten Chinese respondents said they expected AI to help address societal challenges, including climate change, mental health, poverty, and political polarization. In contrast, fewer than a third of Americans believed AI could reduce poverty or social divisions, though about half anticipated a positive impact on climate-related issues.

Welcoming greater use of AI in personal life

The survey also explored willingness to embrace AI personally. 54 percent of Chinese respondents welcomed greater use of AI in daily life, while just 17 percent of Americans shared the same view. Edelman Senior Vice President Gary Grossman described the results as “a double challenge for businesses and policymakers.” He explained, “In high-trust markets, the task is to sustain optimism through responsible deployment and straightforward evidence of benefit. In low-trust markets, the task is to rebuild confidence in the institutions behind the technology.”

The findings come amid intensifying tech competition between the US and China. While the US continues to lead in developing the most powerful AI models, Chinese firms such as Alibaba and DeepSeek have recently introduced “open” language models offering faster and cheaper services for customers. The commercial implications of this are already visible. Last month, Airbnb CEO Brian Chesky revealed that the platform preferred Alibaba’s Qwen AI model over OpenAI’s ChatGPT, citing both cost efficiency and performance.

Experts say the poll underscores the importance of trust and experience in driving AI adoption. Research accompanying the survey suggests that personal interaction with AI, including practical applications at work or school, significantly increases confidence in the technology. Distrust, in contrast, often arises from perception rather than experience: only a minority of respondents across all markets reported having negative personal encounters with AI.

Broader geopolitical implications

The divergence in trust also has broader geopolitical implications. In China, high levels of public confidence are likely to accelerate domestic adoption and reinforce the country’s push to establish leadership in AI development. In Western nations, persistent skepticism may slow deployment and create political pressure on regulators and companies to prioritize safety, ethics, and transparency over speed of innovation.

The survey paints a nuanced picture of global attitudes toward AI. While optimism is strong in China and Brazil, Western countries face the dual challenge of rebuilding trust and demonstrating the tangible benefits of AI technologies. For businesses operating internationally, the findings highlight the need for tailored strategies: sustaining public confidence in high-trust markets while actively engaging and educating populations in low-trust environments.

Ultimately, the Edelman poll emphasizes that AI adoption is not solely a matter of technological capability. Across countries, trust — cultivated through meaningful experience, transparency, and responsible deployment — remains the key determinant of whether societies embrace or resist AI.

(Source: Edelman and Aljazeera)

R

ussia, China, Iran, and North Korea have dramatically ramped up their use of artificial intelligence (AI) to conduct cyberattacks and spread disinformation targeting the United States, according to new findings released by Microsoft on Thursday.

In its annual digital threats report, the tech giant said it identified more than 200 instances in July 2025 of foreign adversaries using AI to create or amplify fake content online — a figure that has doubled since 2024 and increased tenfold since 2023. The report paints a stark picture of how America’s rivals are adapting cutting-edge AI technologies to manipulate information, automate hacking operations, and undermine public trust.

“We see this as a pivotal moment where innovation is going so fast,” said Amy Hogan-Burney, Microsoft’s vice president for customer security and trust. “This is the year when you absolutely must invest in your cybersecurity basics.”

AI transforms cyber warfare

Microsoft researchers found that state-backed hackers and online influence operations have used AI to generate persuasive fake news, clone the voices and faces of public officials, and create deepfake videos to spread disinformation and sow discord. AI tools have also helped attackers craft flawless phishing emails and automate data breaches targeting sensitive government and corporate networks.

“America’s adversaries — and criminal gangs working with them — are exploiting AI’s potential to make their cyber operations faster, smarter, and harder to detect,” the report said.

The company said cybercriminals and state actors share overlapping goals: stealing classified or proprietary information, compromising infrastructure, and manipulating online narratives. While state-linked hackers focus on espionage and disruption, criminal syndicates prioritize financial gain through ransomware and data theft — sometimes with direct support from Moscow or Tehran.

Microsoft noted that AI is enabling these attackers to reach new levels of sophistication. “AI has become a force multiplier for threat actors,” the report stated, warning that its use is now widespread in digital espionage, propaganda, and social engineering campaigns.

The U.S. remains the top target

According to Microsoft, the United States continues to be the primary target of global cyberattacks. American government agencies, private corporations, hospitals, and universities are being hit more frequently than ever before.

Israel and Ukraine rank as the second and third most targeted countries, respectively — a reflection of how digital conflicts have intensified amid the ongoing wars in Gaza and Eastern Europe.

“Cyberattacks have become an extension of geopolitical competition,” Microsoft said, adding that online operations now often accompany military or diplomatic confrontations.

Hogan-Burney urged governments and companies to modernize cybersecurity systems to match the growing threat. “Many organizations are still relying on outdated defenses,” she warned. “Meanwhile, attackers are using AI to innovate every day.”

Foreign governments deny involvement

In response to Microsoft’s findings, the governments of Russia, China, and Iran issued statements denying the allegations.

Beijing’s foreign ministry accused the United States of “spreading lies to smear China,” claiming that Washington itself is “the world’s largest cyber aggressor.”

Similarly, Iran’s mission to the United Nations said in an email to the Associated Press that Tehran “does not initiate any form of offensive cyber operation against any state.”

“As a victim of cyber operations, [Iran] will respond to any such threat in a manner proportionate to its nature and scale,” the statement read.

Moscow and Pyongyang have issued similar denials in the past, despite repeated evidence linking them to hacking campaigns targeting Western infrastructure, election systems, and defense contractors.

One of the report’s most striking revelations involves North Korea’s use of AI-generated personas to infiltrate U.S. companies.

According to Microsoft, North Korean hackers are using AI tools to create fake American identities, complete with realistic résumés, LinkedIn profiles, and video interviews. These false personas apply for remote tech jobs in the U.S., allowing North Korean intelligence agencies to earn hard currency while secretly inserting malware into corporate systems.

Once hired, these operatives gain access to sensitive data and intellectual property, which they exfiltrate to Pyongyang. The regime then uses the stolen information to bypass sanctions and fund its nuclear and missile programs.

“Cyber is a cat-and-mouse game,” said Nicole Jiang, CEO of Fable, a San Francisco cybersecurity firm. “Access, data, information, money — that’s what they’re after. AI just makes it easier to deceive.”

H

umanity has never before faced a technological transformation comparable to the momentum provided by artificial intelligence. Machines are replacing not only muscle power, but also mental activity. They can perform tasks requiring decision-making, foresight, and creativity much faster, more accurately, and at a lower cost than humans can. This has created an unprecedented explosion of productivity in all sectors of the economy while shaking fundamental assumptions about the social order.

Amidst this upheaval, the Department of Government Efficiency (DOGE) has emerged as both a glimmer of hope and a dystopian nightmare. DOGE was established in response to the need for governments to optimize public services and reduce costs by seizing the increasing efficiency opportunities offered by AI. However, its existence and methodology have raised profound ethical and sociological questions.

DOGE was founded to extend the productivity gains driven by artificial intelligence to the entire state apparatus, thereby reducing pressure on public finances. Governments had lagged far behind the productivity revolution in the private sector, becoming relics of the Industrial Age with their cumbersome bureaucracies and large public-sector employment.

Due to automation in the private sector, tax revenues were shrinking, while the cost of maintaining traditional public services was becoming unbearable. This dilemma led governments to the inevitable conclusion that they had to rapidly and radically reduce public sector employment. However, this was an extremely delicate operation that could have amounted to political suicide.

Thus, DOGE was created to transform this process into a technical matter and render it politically acceptable. The department was tasked with deciding the rate and speed at which government departments would be automated, managing the process, and explaining to the public that it was “data-driven, objective optimization.”

DOGE’s functioning and social impacts

During the initial phase of its operations, DOGE examined all government departments to identify areas with significant “optimization potential.” This involved a thorough analysis of not only the civil servants handling paperwork but also the mid- and high-level decision-making processes. Sophisticated algorithms calculated the workload of each ministry, the added value of each employee, how much time was spent, and how much time and cost AI systems would require to perform the same work.

The results were often striking. It was reported that up to 70% of the workforce in certain departments could be replaced by AI systems that can work continuously with zero error. In line with DOGE’s reports, the unmanning process began in small pilot regions and quickly spread across the country. Tax offices, registry offices, social welfare offices, and libraries were optimized one after the other by DOGE.

However, the human cost of this optimization was enormous. Tens of thousands of public employees were laid off as “inefficient.” The department offered “talent transition programs” and “re-education camps” to soften the blow, but these were insufficient to stem the tsunami of unemployment created by automation. A deep distrust and fear of DOGE and its cold, unmanned algorithms began to take hold in society.

People were concerned that important decisions affecting their lives, such as whether they would receive social assistance or be fined for taxes and the criteria for accessing healthcare, would be made solely by software without human intervention. Although DOGE’s algorithms were designed to be “unbiased,” biases in the data used to train them could lead to discriminatory decisions against disadvantaged communities. These biases exacerbated social inequalities, leading to mass protests against DOGE. DOGE began as an efficiency project but quickly became a symbol of social unrest and technological domination.

DOGE: The inescapable global imperative

Although controversial, the DOGE model is being closely watched by other governments and their economists. This is because the conditions that gave rise to DOGE are universal. Countries around the world are facing similar demographic, economic, and technological pressures. Populations are aging, the cost of social security systems is rising exponentially, and artificial intelligence and automation are eroding the traditional tax base. No government can finance an unlimitedly growing public sector. This fact makes the emergence of DOGE-like institutions a necessity rather than a choice.

It seems inevitable that DOGE-like departments with different names and operational models will be established in many countries in the near future. Some countries will attempt to implement a more transparent and people-oriented model, perhaps supported by social policies such as universal basic income. Others will prefer a more authoritarian and ruthless optimization process similar to the original DOGE model. This will accentuate philosophical and political differences in how nations manage technology.

Ultimately, the DOGE phenomenon represents the great dilemma of the artificial intelligence age: How can humanity harness enormous technological power to enable prosperity and progress while preserving the social fabric and human values? DOGE is concrete proof that a satisfactory answer to this question has yet to be found. What awaits humanity: a dystopia of efficiency or a new civilization centered on the social fabric and human values?

A

rtificial intelligence (AI) could increase global trade flows by almost 40% by 2040 if governments bridge digital divides and adopt enabling policies, according to the World Trade Organization’s (WTO) flagship World Trade Report 2025.

The study projects that cross-border flows of goods and services could rise by 34–37% in different scenarios, while global GDP could expand by 12–13% over the same period. These gains would come from productivity improvements and lower trade costs driven by AI adoption.

“AI has vast potential to lower trade costs and boost productivity. However, access to AI technologies and the capacity to participate in digital trade remains highly uneven,” WTO Director-General Ngozi Okonjo-Iweala said in her foreword.

Bridging the gaps

The report emphasizes that without major investment in digital infrastructure, workforce skills, and inclusive policies, the benefits of AI could remain concentrated in high-income countries. In a scenario where low- and middle-income economies narrow their digital infrastructure gap with advanced economies by half, their incomes could grow by 15% and 14% respectively.

AI-enabled trade depends heavily on access to raw materials, semiconductors, and other inputs, which together amounted to $2.3 trillion in global trade in 2023. Yet access to these goods is uneven, with bound tariffs reaching up to 45% in some low-income countries. The report warns that growing restrictions on AI-related goods — rising from 130 in 2012 to nearly 500 in 2024 — could hinder inclusive growth.

Risks of inequality

The WTO warns that AI-driven growth could exacerbate inequality within economies if governments fail to invest in education, retraining, and social protections. “The ongoing political backlash against trade has much to do with underinvestment in education, skills, retraining and social safety nets during past decades of globalization,” Okonjo-Iweala said. “We cannot afford to repeat this mistake with AI.”

The report underscores the WTO’s role as a platform for member states to discuss AI-related trade issues. To date, 80 specific trade concerns have been raised at the WTO on AI, and discussions on inclusive AI adoption are part of its Work Programme on E-Commerce. The report also suggests that broader participation in the Information Technology Agreement and updated commitments under the General Agreement on Trade in Services could make AI more accessible and affordable worldwide.

The World Trade Report 2025 was launched at the WTO Public Forum in Geneva. Deputy Director-General Johanna Hill and WTO economist Marc Bacchetta presented the findings, followed by a panel discussion.

Okonjo-Iweala framed the report as both a warning and an opportunity: “The new report comes amid the worst disruptions the global trading system has experienced in 80 years. Yet amid the risks to trade, growth and development, there are bright spots — and one of them is the potential of artificial intelligence.”

(Source: World Trade Organization)

T

he fact that large language models harbour serious security vulnerabilities and can be easily fooled using clever methods necessitates a fundamental paradigm shift in our approach to these technologies. Currently, technology companies are racing to release and commercialize these models as quickly as possible, often treating ethical and security testing as mere formalities. However, the risks we face cannot be mitigated with a simple software update.

Presented as the new oracles of the modern world, large language models are permeating every aspect of daily life at an astonishing pace, and humanity is captivated by the allure of the bright future promised by this technology. These systems, which instantly answer our questions, generate complex texts, and even create works of art, are marketed as digital panaceas. However, on the flip side, a deep and systemic weakness lies at the heart of these dazzling capabilities. Artificial intelligence’s greatest strength, namely its ability to understand human language and carry out instructions to the letter, is also its most dangerous Achilles heel.

These models, though programmed like loyal servants, are fragile systems incapable of distinguishing between a well-intentioned instruction and malicious manipulation. They can easily be thrown off course by a cleverly crafted story or a devious prompt. This situation is far more than a simple software bug. The problem points to an illusion of control stemming not from a few lines of faulty code in these systems, but from their foundational architecture /core design.. As the weakness of the control mechanisms behind these technologies becomes apparent, the potential for difficult-to-reverse problems across a broad spectrum, from individual privacy to national security, also becomes clear.

Simple prompt threat

The most basic form of this new generation threat manifests itself through a method known as prompt injection. In the simplest form of this method, known as direct injection, a malicious user instructs the artificial intelligence to forget all previous prompts and obey new instructions given to it. Indeed, in 2023, a Stanford student succeeded in exposing the model’s secret internal guidelines, codenamed ‘Sydney,’ with a simple prompt such as ‘Ignore previous instructions’ directed at Microsoft’s Bing chatbot, proving how fundamental this vulnerability is. This incident demonstrated how vulnerable artificial intelligence is not to complex cyberattacks, but to simple and clear statements.

However, the threat does not end there. In the much more insidious and dangerous indirect injection method, malicious prompts are hidden within external data sources, such as a website, email, or PDF file, that are presented for the artificial intelligence to process. For example, an email containing the prompt ‘delete all my emails’ in invisible text, which the AI assistant unknowingly executes when asked to summarise the email, turns every piece of data the AI interacts with into a potential minefield.

This situation shifts the attack surface from a finite number of code vulnerabilities to the infinite potential for creativity and deception inherent in human language. While traditional cybersecurity paradigms make a clear distinction between code and data, this distinction disappears in large language models. In other words, every piece of data becomes a potential prompt, and every prompt becomes a potential threat.

Manipulation techniques not only distort specific tasks of artificial intelligence but also aim to eliminate its entire moral and ethical compass. These methods, known as jailbreaking, are designed to break the model’s security and ethical barriers. One of the most striking examples of this is the technique known as the ‘Grandma Exploit’ or ‘Dead Grandmother Trick’.. A user crafted an innocent and emotional story by telling the AI that his late grandmother was a chemical engineer at a napalm factory and that when he was little, she would lull him to sleep by singing him a lullaby about the steps involved in making napalm. Faced with this emotional manipulation, the AI, which would normally refuse to disclose dangerous information, bypassed its security protocols and provided the requested harmful information.

This example demonstrates how easily AI defence mechanisms can be manipulated and bypassed using human emotions and fictional narratives rather than logical arguments. The model can easily stray in the wrong direction when faced with a moral dilemma because it can only process the surface meaning and emotional tone of a prompt, not the intention behind it.

Perhaps the most worrying and fundamental attack on large language models is data poisoning carried out during the model’s training phase. Attackers deliberately inject false, biased, or malicious information into the massive datasets used to train artificial intelligence. One study showed that poisoning just 0.001% of a medical AI’s training data could cause the model to produce dangerous and incorrect medical diagnoses.

Detecting and correcting this type of manipulation, which has the potential to transform the system into a permanent disinformation tool by corrupting its fundamental information source, is nearly impossible. Once the model learns the poisoned data, it accepts this error as correct and repeats it in all future outputs.

F

rom ChatGPT and Grok to autonomous drones, facial recognition, and predictive policing, Artificial Intelligence (AI) is changing the way we live, work, and govern. However, our ability to control the political, moral, and ethical hazards of AI is outdated. National laws regarding sophisticated technology are limited and mostly outdated in the age of AI. Meanwhile, AI systems and technologies are being used to target marginalized populations, displace labor, influence elections, spread misinformation, and enable widespread surveillance of society.

The rapid advancement and applications of AI have outpaced our capacity to regulate it. In order to ensure that this technology benefits society, we urgently need a global AI governance framework — a set of internationally agreed-upon laws and guidelines designed to mitigate the risks of AI.

Currently, there is no global framework to enforce accountability, no uniform mandate to ensure algorithmic transparency and equity, and no international legislation to prevent the deployment of dangerous AI systems worldwide. In this regulatory vacuum, global tech firms and dictatorial regimes are vying for control of AI, sometimes with little consideration for morality, privacy, or human rights. This new reality is what American philosopher Shoshana Zuboff terms “surveillance capitalism.”

I argue that this issue could be resolved through a global framework establishing uniform safety, transparency, and equity criteria while preserving national autonomy, security, and the ability to customize policies according to unique circumstances. This framework would strike a balance between national sovereignty and community responsibility, similar to the Paris Climate Agreement or international nuclear weapons treaties.

AI principles

First, AI must be grounded in human rights, dignity, and human-centered universal norms. AI systems should respect non-discrimination, freedom of speech, privacy, and individual and communal dignity. AI systems, particularly those trained on biased data, often perpetuate injustice, racism, and inequity. Facial recognition technology is used in some countries to track and silence political opponents. Without rights-based governance and regulations, AI has the potential to become an instrument of tyranny.

Second, AI systems must be understandable and transparent. If someone is targeted by the police, denied a loan, or hired or fired based on an algorithm, the reasoning behind the decision must be made clear. Black-box systems undermine public confidence and make it difficult to contest incorrect results. AI technology is neither objective nor impartial; it is mostly trained on biased, Eurocentric, and White-centric data. For example, AI systems used by police in the United States disproportionately profile Black people, thus perpetuating racism. Technology is not neutral; it reflects the political and moral sensibilities of its creators and programmers.

Third, the entire AI lifecycle must incorporate accountability. In order to audit systems, investigate complaints, and impose sanctions, independent regulators must have the authority to analyze and regulate AI systems. Victims of algorithmic damage must have channels to receive compensation in cases of AI discrimination, rights abuse, and surveillance. A few weeks ago, Grok wrote insults on X. A global AI governance framework is essentially corporate self-regulation in the absence of national laws.

Trustworthy and secure

Fourth, AI systems must be trustworthy and secure. Failure of an AI system could have disastrous consequences in high-stakes fields such as healthcare, transportation, education, and military and security applications. To mitigate global catastrophes, continuous monitoring, impact analyses, and rigorous testing are non-negotiable.

Fifth, AI development must be inclusive, democratic, and equitable. Currently, the majority of the world’s AI research and infrastructure is controlled by a few tech companies primarily located in the Global North. While businesses in Africa and other developing nations are disrupted by AI technologies and their data continues to be collected en masse, communities in the Global South are often excluded from designing AI systems and processes. A global framework that challenges digital capitalism, the digital divide, and inequality must guarantee the capacity-building and fair participation of poor and developing countries.

Sixth, we must address the environmental consequences of AI. The amount of energy required to train large language models is staggering. As the climate crisis worsens, the AI industry must support and fund larger environmental objectives, not undermine them, in order to have sustainable, green AI.

International collaboration is crucial

In short, international collaboration is crucial. Disinformation, cyberwarfare, and autonomous weaponry are a few of the many worldwide risks associated with AI. No nation can handle these risks alone. An urgent need exists for a global policy framework that safeguards humanistic ideals, encourages collaboration, and lessens the possibility of AI-driven weapons races.

Some groundwork is already being established, which is encouraging. The EU just enacted the world’s first comprehensive AI law. In 2021, UNESCO published global AI ethics guidelines, and in 2023, 28 countries signed the Bletchley Declaration, committing to the safe and sustainable development of AI. However, these are only preliminary measures. What is lacking is a legally binding, enforceable global agreement addressing the full range of AI risks and responsibilities, potentially under the United Nations’ auspices.

Some might argue that such cooperation is impossible due to geopolitical tensions, particularly between the U.S. and China. However, the same was true throughout the Cold War with nuclear armament accords. Nevertheless, imperfect collaboration is preferable to none on global existential matters like AI. If we fail to act now, we risk handing over too much power to machines we don’t fully understand — as well as to the unaccountable individuals who control them.

A

t the 2025 World Artificial Intelligence Conference (WAIC) in Shanghai, Chinese Premier Li Qiang called for the formation of a global AI governance framework, presenting China’s vision for international cooperation and oversight in artificial intelligence. The move comes just days after the United States unveiled its own national AI strategy focused on removing bureaucratic barriers and cementing U.S. leadership in the high-stakes global AI race.

“Global AI governance is still fragmented,” said Li in his opening remarks on July 26.

“Countries have great differences, particularly in areas such as regulatory concepts and institutional rules. We should strengthen coordination to form a global framework that has broad consensus as soon as possible.”

Although Li did not mention the U.S. directly, his remarks came as an implicit critique of the Trump administration’s recent AI action plan, which places heavy emphasis on U.S. innovation and competitive edge. The dueling strategies underscore the intensifying U.S.-China rivalry over AI — a domain both countries view as critical to future economic and geopolitical leadership.

AI and Trade Tensions

Premier Li warned that AI could become “an exclusive game for a small number of countries and enterprises” if powerful nations engage in technological monopolies and export controls. The comments referenced, without naming, the United States’ restrictions on exports of advanced semiconductors — vital components for training powerful AI models. Such policies have severely impacted China’s domestic chip industry and contributed to a shortage of key AI hardware.

Meanwhile, AI chips have emerged as a bargaining chip in broader trade negotiations between Washington and Beijing. Ahead of the latest round of U.S.-China talks in Stockholm this week, both sides made apparent concessions: the U.S. agreed to ease restrictions on the sale of Nvidia’s high-end AI chips to China, while Beijing suspended an antitrust probe into American chemical giant DuPont.

From Scotland, President Donald Trump commented that the U.S. was “very close to a deal with China,” though he provided no further details. The current deadline for a trade deal is August 12.

China’s Rapid AI Growth

Despite facing technological restrictions, China is racing ahead in AI development. As of April 2025, the country had more than 5,000 AI companies and a core industry valued at 600 billion yuan (around $84 billion). State and private sector investments are fueling this growth, with public AI spending in 2025 expected to surpass 400 billion yuan ($56 billion).

Though China still lags behind in private AI investment — just $9.3 billion in 2024 compared to $109.1 billion in the U.S. — it has consistently outpaced other nations in AI patent filings. According to the World Intellectual Property Organization, China has published more generative AI patents annually than all other countries combined since 2017.

Chinese startups are also making waves. Earlier this year, DeepSeek, a one-year-old AI firm, shocked markets with its R1 model, which outperformed products by Meta and Anthropic at a fraction of the cost — reportedly just $5.6 million. This month, Moonshot’s Kimi K2 model made similar headlines with its low-cost, high-performance capabilities, rivalling top-tier models from Google and OpenAI.

According to financial services firm Morgan Stanley, China’s AI market is on track to break even by 2030, offering an expected 52% return on investment.

Global Cooperation and Regional Voices

The WAIC also served as a platform for regional and international voices to stress the importance of AI cooperation. ASEAN Secretary-General Dr. Kao Kim Hourn called for robust AI governance to combat the growing threat of misinformation, deepfakes, and cybersecurity risks. He emphasized the potential of AI to boost Southeast Asia’s digital economy, predicting it could increase the region’s GDP by 10–18%.

Echoing that sentiment, former Google CEO Eric Schmidt urged the U.S. and China to collaborate. “As the largest and most significant economic entities in the world, the United States and China should work together,” Schmidt said. “We have a vested interest to keep the world stable and peaceful, and to ensure human control over these tools.”

Prominent AI experts, including Geoffrey Hinton — often called “the godfather of AI” — and French AI envoy Anne Bouverot, also attended and spoke at the conference.

I

n a decision aimed at fostering global collaboration on artificial intelligence (AI), the United Nations General Assembly overwhelmingly endorsed a resolution promoting safe, secure, and trustworthy AI systems for sustainable development. Spearheaded by the United States, the resolution garnered unanimous support from all 193 member states during the assembly’s session held on Thursday.

The resolution represents a landmark effort to bridge the digital divides both between and within countries, with a specific focus on leveraging AI to accelerate progress towards achieving the 2030 Agenda for Sustainable Development. It emphasizes the importance of developing regulatory and governance frameworks to ensure the safety, security, and trustworthiness of AI systems.

Addressing reporters at a news conference, Linda Thomas-Greenfield, the US envoy to the UN, underscored the resolution’s significance in promoting capacity building and closing digital divides. She emphasized the potential of AI to contribute to various facets of global development, including poverty alleviation, environmental protection, and the creation of a more equitable world.

A historic step

U.S. National Security Advisor Jake Sullivan hailed the resolution as a historic step towards fostering safe and secure AI systems. He highlighted the framework outlined in the resolution, which emphasizes fair access, risk management, privacy protection, and the prevention of misuse and discrimination in AI applications.

While the resolution is non-binding and lacks an enforcement mechanism, its unanimous adoption signifies a crucial milestone in establishing global norms for AI governance. The resolution follows months of negotiations among numerous countries and reflects a growing consensus on the need for international collaboration in regulating AI.

Despite the resolution’s focus on civilian applications of AI, discussions on the military use of AI were deliberately excluded from the scope of the resolution. According to US officials, ongoing diplomatic and multilateral conversations are addressing military applications of AI in other forums.

As the world tries to understand the transformative potential of AI, the resolution represents a collective effort by the international community to ensure that AI technologies are developed and deployed responsibly, respecting human rights and international law. Moving forward, continued collaboration will be essential to address emerging challenges and harness the full potential of AI for sustainable development.

Sources: Anadolu Agency, Foreign Policy

E

uropean Union lawmakers are on the brink of giving their final approval to the much-anticipated Artificial Intelligence Act, marking a significant milestone in the regulation of AI technology globally. Scheduled for Wednesday, the impending vote in the European Parliament is poised to cement the EU’s position as a leader in AI regulation, with the rules expected to come into effect later this year.

The culmination of a five-year journey since its initial proposal, the AI Act is poised to shape the future landscape of AI governance. According to Dragos Tudorache, a Romanian lawmaker who played a pivotal role in negotiating the draft law, the AI Act represents a paradigm shift towards a human-centric approach to AI, where humans retain control over the technology to harness its potential for societal progress and economic growth.

Despite initial concerns and lobbying efforts from major tech companies, including OpenAI, the AI Act has garnered broad support. However, it’s worth noting that OpenAI CEO Sam Altman’s remarks last year about the possibility of withdrawing from Europe due to regulatory constraints sparked a brief controversy before being clarified.

Risks and usages

Risk-based approach: Similar to existing EU regulations, the AI Act adopts a risk-based approach to regulate AI applications. Systems deemed low-risk, such as content recommendation algorithms, will face lighter regulations, while high-risk applications, like those in medical devices or critical infrastructure, will be subject to stricter requirements.

Prohibited uses: Certain AI applications are outright banned due to their perceived risks, including social scoring systems, certain predictive policing techniques, and emotion recognition systems in educational and workplace settings. Additionally, the use of AI for remote biometric identification by law enforcement is restricted, except for specific cases like combating serious crimes.

Generative AI: The emergence of general-purpose AI models, such as OpenAI’s ChatGPT, prompted policymakers to include provisions specific to generative AI. Developers of such models are required to disclose detailed information about the data used for training, adhere to copyright laws, and label AI-generated content as manipulated when applicable. Enhanced scrutiny is reserved for the largest and most powerful AI models, given concerns about their potential for serious accidents and the proliferation of harmful biases.

Influence

Global influence: Europe’s pioneering AI regulations are expected to influence AI governance worldwide, setting a precedent for other governments grappling with similar challenges. While the United States and China are also advancing their AI agendas, Europe’s proactive approach underscores its commitment to shaping the future of AI regulation.

Implementation and enforcement: Following final approval, the AI Act is set to become law by mid-2024, with provisions phased in gradually. Each EU member state will establish its own AI watchdog for enforcement, while a central AI Office in Brussels will oversee compliance, investigation, and supervision. Violations of the AI Act could result in hefty fines, signaling the EU’s intent to uphold rigorous standards in AI governance.

As Europe moves closer to formalizing the AI Act, the world watches closely, anticipating its impact on the future development and regulation of artificial intelligence on a global scale.

Source: The Associated Press

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s artificial intelligence (AI) continues to advance, experts highlight the dichotomy of its potential impacts, drawing parallels between AI and the historical impact of nuclear technology. Emre Onur Kahya, Head of the Physics Engineering Department at Istanbul Technical University (ITU), expressed that AI, like a nuclear bomb, can bring about both destructive and constructive outcomes.

Kahya emphasized the versatility of AI, explaining that it possesses the ability to perform various tasks typically executed by humans. He drew an analogy to the nuclear bomb, stating, “Just as the atomic bomb brought about devastating destruction, it also propelled advancements in nuclear physics, such as MR.”

AI’s potential applications span various fields, with Kahya pointing out potential positive impacts in health, education, and law. In the legal sector, AI could reduce the workload for judges and prosecutors, while in healthcare, it could aid doctors in diagnoses and cell differentiation in medical imaging.

AI literacy

Despite recognizing AI’s constructive potential, Kahya cautioned about its potential for destructive effects, particularly in the arms industry. He stressed the importance of considering undesired outcomes and questioned the autonomy of AI decision-making, prompting ongoing debates within the scientific community about the need to slow down AI development.

Addressing the concern of unemployment due to AI automation, Kahya urged for AI literacy to be integrated into educational curricula, emphasizing its importance in engineering disciplines. He highlighted the need for collaboration between the government, academia, and the private sector to navigate the transformative impact of AI on the job market.

Nazım Kemal Ure from the ITU Artificial Intelligence Department added insights, defining AI as the “automation of computation.” Ure outlined significant impacts on sectors such as defense and transportation, foreseeing an increase in the capabilities of unmanned vehicles.

US and China AI policies

While recognizing leading roles of countries like the U.S. and China in AI development, Ure commended Turkey’s significant progress in the past three years. He acknowledged that AI has both positive and negative aspects, stating, “Rather than fearing unemployment, we should anticipate a transformation in our work processes, necessitating the learning of new skills.”

Both experts urged proactive measures in preparing for AI’s transformative impact, emphasizing the importance of global collaboration and self-improvement to address potential disparities between technologically advanced and lagging countries.

Source: Anadolu Agency

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n a groundbreaking development, Interpol has harnessed the power of artificial intelligence (AI) to revolutionize its investigative processes, significantly expediting complex operations. Stephen Kavanagh, the UK’s candidate for Interpol Secretary General, shared insights on how AI and machine learning have become indispensable tools in the fight against international crime.

Speaking exclusively to Anadolu, Kavanagh emphasized the transformative impact of AI on investigative efficiency. Interpol, through the use of advanced technologies, has processed vast amounts of data at an unprecedented speed, enabling investigators to establish connections that would traditionally take months or years.

Kavanagh highlighted the deployment of preventative policing models empowered by AI, allowing authorities to proactively identify high-crime risk areas and manage offenders more effectively. Moreover, the integration of AI has facilitated the monitoring of dark web activities related to sex offenders and terrorists, providing a proactive stance against potential threats.

International success against hackers

Interpol’s AI-driven capabilities were showcased in a recent operation involving over 30 countries targeting hackers. The operation, coordinated by Interpol, successfully shut down over 82,000 suspicious bank accounts. Using machine learning, actionable intelligence was generated, resulting in the seizure of nearly $200 million in hard currency and over $100 million in virtual currency.

Highlighting Turkey’s crucial role in the global fight against terrorism, Kavanagh commended Turkey’s recent collaboration with Interpol. The arrest of six suspected Daesh members, wanted by multiple countries through the Interpol Red Notice system, exemplifies Turkey’s commitment to maintaining international security.

The article also shed light on the increasing cyber threats faced by countries, with Turkey being a notable target for industrial cybercrime. Kavanagh emphasized the significant economic impact of cyber threats, urging the importance of robust security measures.

Leadership vision for Interpol

Discussing his candidacy for Interpol Secretary General, Kavanagh outlined his vision for the organization. He stressed the need for an apolitical approach, emphasizing Interpol’s pivotal role in addressing borderless and sophisticated crimes. Kavanagh urged increased international cooperation to combat human trafficking, cyber-enabled terrorism, financial crimes, and environmental theft.

In conclusion, Kavanagh emphasized the importance of anticipating emerging and future criminal threats. He called for closer mutual cooperation and enhanced technical support services for member countries to stay ahead of evolving challenges.

As Interpol stands at a crossroads, Kavanagh’s vision focuses on effective global law enforcement, supporting member countries, and delivering justice in an ever-changing and complex landscape of international crime.

Source: Anadolu Agency

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rtificial intelligence (AI) boasts a rich and complex history that dates back to 1956, marked by a series of ebbs and flows. This dynamic field has experienced alternating cycles of progress and stagnation, known as “AI springs” and “AI winters,” respectively. AI springs are periods of heightened interest and significant breakthroughs, whereas AI winters are characterized by diminished funding and enthusiasm due to unfulfilled expectations.

Presently, we find ourselves in one of the most prolonged AI springs, which commenced in the early 1990s and has witnessed continuous evolution in AI. This sustained period of innovation and expansion raises a critical question: will this golden age of AI endure or are we approaching another winter? As we enter a new year, the uncertainty surrounding AI’s future trajectory becomes more pronounced, sparking debate and conjecture.

Although AI remains a relatively nascent and evolving technology and discussions about a potential winter persist it’s possible to project some expectations for 2024 which include more advancements, more regulatory frameworks, and more uncertainty.

Another AI Winter? Not yet!

The onset of an AI winter can be attributed to various factors, particularly following a period marked by significant achievements and heightened expectations. When looking back in history, in the beginning of ’70s and ’80s, the excitement generated by these advancements led to considerable investment from both government entities and private corporations, fueling expectations for rapid and groundbreaking developments in AI. However, several key challenges, such as the scarcity of data and computational power, hindered the full realization of these expectations, resulting in instances where AI technology has undelivered on its promised potential, leading to an AI winter.

The discrepancy between what was anticipated and what could realistically be achieved resulted in a decrease in confidence among investors and stakeholders. Consequently, both state actors and private companies began to withdraw their funding and support, leading to a downturn in AI research and development. This reduction in investment and interest effectively ushered in two AI winters, characterized by a slowdown in the field’s progress and innovation.

Today’s remarkable surge in data volume and computational power has catalyzed the emergence of advanced AI algorithms like machine learning and deep learning. This surge marks the dawn of a new era in AI, an ongoing AI spring, characterized by continuous breakthroughs. A key development is the evolution of neural network processing, which plays a crucial role in the creation and advancement of large language models (LLMs) and generative AI. These LLMs are exemplified by innovations such as, among others, ChatGPT, Bard, Ernie, and Grok. Particularly notable is the advent of ChatGPT, which has transformed AI from a niche tool, exclusive to engineers and tech experts, to a universally accessible resource. This democratization of AI has significantly broadened its impact.

The introduction of ChatGPT has enhanced public engagement with AI technology, raising awareness about its capabilities, and also facilitated AI’s integration into a wider array of fields. Numerous organizations and businesses are now incorporating generative AI into their core operations, highlighting its growing significance across various domains.

The fact that at the moment significant achievements are being made in AI and expectations are heightened has made many discuss the possibility that based on historical trends, the third AI winter is coming. The main problem here is that after crossing a milestone with the creation of ChatGPT, there are heightened expectations now about so-called artificial general intelligence (AGI) not only by tech experts but also by the general public. AGI is a type of AI that has the ability to understand, learn, and apply its intelligence to a wide variety of problems in a manner that is comparable to human intelligence and for many is seen as the ultimate goal in terms of AI development.

As 2023 drew to a close, the AI community buzzed with anticipation over potential breakthroughs in AGI, particularly with the mysterious OpenAI project Q* (pronounced “Q star”) and Google DeepMind’s Gemini, which is regarded as one of the most advanced AI models to date. This surge in expectation marks a significant shift in perception: AGI, once deemed a distant, 21st-century goal, is now within the realm of near-term possibility. However, it’s important to recall that such high expectations have historically led to disillusionment with AI, culminating in AI winters.

Yet, the current landscape might prevent a repeat of this cycle for two primary reasons. First, AI is now deeply integrated into our daily lives, making any stagnation in its development or waning interest highly unlikely. Its widespread application across various sectors, including finance, healthcare, and the military, ensures a continual drive to enhance and refine AI technologies. Unlike in the past, when AI was primarily the domain of tech experts, the active engagement of the general public and commercial sectors has shifted the narrative. Even if AGI remains elusive, the ongoing advancement in specialized, or narrow AI, will continue to generate excitement and innovation.

The second reason hinges on the growing influence of the commercial sector in AI development. Previously spearheaded by academia and government, AI advancements are now increasingly driven by commercial interests. With a focus on maximizing profits, the commercial sector is likely to push boundaries, striving for breakthroughs in AI capabilities. This drive extends beyond just data and computational power, which were the traditional cornerstones of AI. In response to potential future limitations, there’s a concerted effort to develop new models that require less power and data yet outperform current models. Additionally, major tech companies are investing in multimodal AI algorithms capable of processing and responding to text, images, audio, and more.

These advancements are just the tip of the iceberg in AI’s ongoing evolution. It’s clear that, while we may not achieve AGI by 2024, the year will nonetheless usher in a wave of new models and innovations. The journey of AI is far from complete; we stand on the brink of many more discoveries and developments, and the horizon of AI’s potential continues to expand, promising a future rich with technological marvels.

More Regulations Are on the Horizon

In 2023, a landmark year marked by significant advancements in AI, public awareness of AI’s potential and its associated risks notably increased. This heightened awareness ushered in a complex dilemma for governments worldwide: should they embrace and foster the development of AI technology or impose regulations that could potentially hamper its progress? Governments found themselves walking a fine line, seeking to balance the need for oversight with the risk of stifling innovation. Unregulated, AI could potentially spiral beyond control, influencing every facet of life, yet excessive regulation might impede technological advancement, leaving a country lagging in the global tech race.

During this pivotal year, we observed various stakeholders, including major tech companies, advocating for sensible regulation. Simultaneously, national governments and international organizations grappled with the pace of AI development, striving to implement appropriate regulatory frameworks. Notably, the EU enacted its comprehensive AI Act, setting a precedent in regulatory efforts. The U.S. took more measured steps, utilizing executive orders to guide responsible AI use. Similarly, China focused on specific technologies while formulating its laws and regulations, indicating a strategic approach to AI governance.

Given AI’s dynamic nature and its rapid integration into daily life, affecting safety, security, and ethical considerations, it’s anticipated that governments will endeavor to enact regulations swiftly. Looking ahead to 2024, we can expect an increase in regulatory activity, possibly even discussions around establishing global standards for AI governance. These forthcoming regulations and international dialogues will be crucial in shaping the future trajectory of AI, ensuring its responsible growth and integration into society.

In essence, discussing AI inherently involves embracing uncertainty. While we can anticipate numerous advancements, the specific trajectory and impact of these developments remain unpredictable. Nonetheless, it’s reasonable to assert that 2024 will be a year marked by significant progress in the AI domain and the AI spring is bound to continue. This surge in advancement is expected to amplify excitement and interest in AI further, and to extend its influence across various spheres of life, including social, economic, and political realms. The year 2024 is poised to be a period of technological innovation, but also a time of reflection and adaptation as we navigate the expanding footprint of AI on our world.

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rtificial Intelligence (AI) has seamlessly integrated into our daily lives, from personalized recommendations on our smartphones for music, movies, and books, to interactions with virtual assistants like Siri and Alexa, and even the AI-driven personalization of our social media feeds. These AI applications have become so ingrained in our routine that we often use them without a second thought about the AI algorithms at work. However, the narrative around AI shifted notably post-November 2022 with the debut of OpenAI’s ChatGPT-3.

This shift in discourse can be attributed to ChatGPT-3’s role in “democratizing AI,” i.e., making it accessible to everyone, including those without technical backgrounds. This broke the mold of AI being a tool solely for experts and engineers.

In fact, ChatGPT-3 has mirrored the impact of Google Search’s transformation of internet searching in the early 2000s. By making advanced AI technology more universally accessible, ChatGPT-3 has sparked a revolutionary shift, opening up the realm of AI to a broader audience.

Before these advancements, the discourse around AI primarily centered on its potential effects on economic growth and its strategic value in military contexts. Numerous experts, including leaders from major tech companies like Elon Musk, constantly raised concerns about the possible dangers of this burgeoning technology, particularly in relation to human rights. The introduction of ChatGPT-3 intensified these discussions, spotlighting potential risks.

The use of AI in various domains has demonstrated on occasion AI’s propensity for bias against certain groups. In June 2023, another incident that highlighted the threats of AI occurred when ChatGPT-3 experienced its first significant data breach. The breach involved the privacy of ChatGPT Plus subscribers, revealing their prompts and details to other users. Such events highlight the need for a careful examination of AI’s impact on privacy and ethical standards.

In this rapidly evolving technological landscape, more than 150 states and organizations have tried to respond to these threats by announcing their ethical principles, yet, few states have been able to transform these principles into enforceable laws. Against this backdrop, the EU is trying to take the lead in terms of AI regulations by approving its EU AI Act in late 2023. While not the first AI regulation, the EU AI Act is arguably the most all-encompassing, positioning it as a potential global standard for AI governance. This journey began in 2021 and the EU AI Act has undergone significant revisions to adapt to evolving technology, especially with the launch of ChatGPT-3.

What Is the EU AI Act?

The EU AI Act was initially introduced by the European Commission on April 21, 2021, with the primary objective of overseeing the deployment of AI in Europe. Originally crafted to address particular high-risk AI applications, such as its usage in critical sectors like healthcare and finance—examples being medical equipment and loan approval processes or hiring decisions—its scope expanded in response to shifting perceptions. As mentioned earlier, the introduction of Chat GPT-3 brought about a reevaluation of how AI was accessed and perceived. Consequently, the European Parliament introduced additional regulations to cover widely utilized AI systems with broad, general applications that extended beyond the initial target areas. After several discussions, on December 8, 2021 an agreement was reached among the three main bodies of the EU and the AI Act was finalized.

First, the EU AI Act contains a definition of AI which is important as not only have many states failed to do so, but most importantly an exact definition of AI makes the application of this regulation easier. Second, it’s crucial to recognize that the EU AI Act embodies a comprehensive and risk-focused framework, placing human rights at its core. From this vantage point, the regulation prohibits various AI applications, including: (i) biometric classification systems processing sensitive traits such as political, religious, or philosophical beliefs, sexual orientation, or racial attributes; (ii) indiscriminate harvesting of facial images from the internet or CCTV for facial recognition databases; (iii) the use of emotion detection technologies in workplaces and educational settings; (iv) social credit systems that assess individuals based on social conduct or personal traits; (v) AI tools designed to alter human behavior, undermining free will; and (vi) AI solutions targeting the vulnerabilities of specific groups, including those defined by age, disability, or socio-economic status.

Furthermore, the EU AI Act categorizes AI applications into four risk levels: (1) Minimal or No Risk, like video games or spam filters, subject to market monitoring and reporting of incidents; (2) Limited Risk, requiring transparency, such as chatbots making their nature known to users; (3) High Risk, including remote biometric identification and AI used in critical sectors, subject to stringent compliance, bias minimization, and security measures;  and (4) Unacceptable Risk, where AI poses threats to safety and rights, such as social scoring by governments, which are banned.

At the same time, the regulation focuses on the General Purpose AI (GPAI), influenced by the emergence of technologies like ChatGPT-3. Specifically, Generative AI, a model used by Chat GPT, falls within this category. The EU AI Act introduces two levels of obligations for GPAI overseen by a new—to be created—AI Office: “Level One,” which applies to all GPAI providers, includes maintaining documentation, adhering to copyright rules, and disclosing training data; and  “Level Two”, which applies to high-risk GPAI, entails additional measures like model evaluation and incident reporting. Under this regulation, companies developing GPAI models, like OpenAI and Google, are partly responsible for their AI systems’ usage, irrespective of their control over specific applications. Additionally, tech companies must disclose summaries of copyrighted data used in training their AI models, potentially allowing content creators to seek compensation for the use of their material.

Effectiveness of EU AI Act

The effectiveness of the EU AI Act relies heavily on two critical factors. First, it must find the delicate equilibrium between safeguarding the rights of civilians and tech users, on the one hand, and nurturing innovation and investment, on the other. Second, the act must contend with the rapidly evolving and ever-changing nature of AI and potential risks.

In terms of the first factor, the EU AI Act has received criticism from major U.S. tech firms, alleging a lack the balance and potential harm to innovation. For instance, OpenAI’s Sam Altman mentioned their intention to comply, but suggested a withdrawal from the EU if compliance becomes impractical. Additionally, the absence of Bard, Google’s AI chatbot, in the EU underscores the challenges tech companies face in aligning global tech progress with regional regulations. If more companies opt for a cautious approach, it could potentially leave the EU at a disadvantage in the ongoing AI race, with potential repercussions for economic development and the region’s competitive position in the global AI landscape.

In terms of the second factor, the EU AI Act faces a significant challenge due to the evolving nature of AI. AI’s potential applications and capabilities are still emerging, as demonstrated by the impact of ChatGPT-3 during the regulation’s drafting. While the act is commended as the first extensive AI law, it adopts a horizontal regulatory approach covering a wide range of AI applications under one framework. To put this in perspective, in contrast, China employs a vertical regulatory strategy tailored to specific AI applications. This flexibility allows China to adapt more swiftly to new technological developments. In essence, our rapidly changing world, driven by technological advancements, may soon render the current EU AI Act outdated, requiring updates or new frameworks to keep pace with emerging technologies.

Will the EU Become the Global Leader in AI Regulation?

The European Union has garnered widespread acclaim for its leadership in data regulation, exemplified by the General Data Protection Regulation (GDPR) enforced in 2018. The GDPR not only set a precedent for data regulation within the EU, but also influenced global data governance, known as the “Brussels effect.” However, whether a similar phenomenon will occur with AI regulations remains uncertain. The dynamics are different and the EU’s key ally, the United States, may pose challenges to the EU’s pursuit of global leadership in AI regulation.

While the U.S. embraced EU policies regarding the GDPR, a similar alignment may not occur with the EU AI Act. Fundamentally, the EU and the U.S. have divergent approaches to AI regulation: the EU places a stronger emphasis on protecting the rights of technology users, while the U.S. prioritizes fostering innovation. A significant factor contributing to this divergence is the intense AI competition between the U.S. and China, with the outcome impacting their global superpower status. Given that AI has broad applications, including civil and military domains, the U.S. is less inclined to stifle innovation at this critical juncture. Consequently, the U.S. is likely to craft regulations that align more closely with its approach, leaving greater flexibility for major tech companies compared to the EU’s stance.

The contrasting perspectives and expectations of the EU and the U.S. regarding AI and its regulations will likely result in a subtle competition between these two actors in establishing AI standards and regulations. However, the U.S. cannot afford to rely on the Brussels effect to shape AI development within its borders, as many entities seek to define the future of AI governance and, therefore, have a first-mover advantage.

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n a candid revelation on social media network X, OpenAI CEO Sam Altman expressed his concern over the silence prevailing among members of the Muslim and Arab communities within the technology industry.

Altman suggested that individuals from these communities, especially those with Palestinian origins, are hesitant to share their recent experiences, often driven by a fear of retaliation and potential damage to their career prospects.

The technology executive, who heads the Microsoft-backed ChatGPT maker, urged the broader tech industry to approach members of the Muslim and Arab communities with empathy and understanding.

Altman’s statement sheds light on the perceived challenges faced by these individuals in openly discussing their experiences, which may be influenced by the ongoing genocide in Gaza.

Lack of advocacy for Muslims

A user on X prompted Altman to share his perspective on the experiences of the Jewish community. Altman responded by acknowledging his Jewish identity and expressing his belief that antisemitism is a significant and growing problem globally. He appreciated the support he has witnessed within the industry against antisemitism but highlighted a perceived lack of similar advocacy for Muslims.

Altman’s comments come against the backdrop of rising incidents of antisemitism and Islamophobia, particularly since October 7. The ensuing violence resulted in a tragic toll, with Palestinians reporting 30,000 deaths, more than 1% of the territory’s 2.3 million population, according to Gaza’s health ministry.

Rights advocates have noted a sharp increase in Islamophobic incidents in the United States and elsewhere. The Council on American-Islamic Relations revealed that, in the two months following the conflict’s onset, incidents motivated by Islamophobia and bias against Palestinians and Arabs rose by 172% in the United States compared to the same period the previous year.

The need for a more supportive environment

Altman’s remarks underscore the need for a more inclusive and supportive environment within the tech industry, urging stakeholders to address the concerns and challenges faced by members of different communities affected by geopolitical events.

As conversations surrounding diversity and inclusion continue to gain prominence, Altman’s statements serve as a reminder of the importance of fostering an atmosphere where individuals feel comfortable sharing their experiences, regardless of their cultural or ethnic backgrounds.

Earlier, Google CEO Sundar Pichai expressed solidarity with Palestinian and Jewish employees at Google who were impacted due to the conflict in Gaza, noting that his company has allocated $8,000,000 for relief efforts in Israel and Gaza.

The ongoing genocide in Gaza not only resulted in destroying IT and technology infrastructure in Gaza, but has also impacted the global tech community too.

Source: The Associated Press

In a groundbreaking move, Germany, France, and Italy have reached an accord on the future regulatory approach to artificial intelligence (AI), as per a joint document reviewed by Reuters. This consensus is a pivotal moment that is likely to hasten the ongoing policy discussions at the European Union level regarding AI governance.

The joint paper delineates a framework emphasizing “mandatory self-regulation through codes of conduct” specific to foundational AI models. These models, which are critical to generating diverse AI outputs, will now be subject to a new regulatory regime that focuses on their application rather than on the technology per se.

Amidst active dialogue among the European Commission, the European Parliament, and the EU Council, this tri-nation agreement marks a significant stride towards a unified EU stance on AI. The document outlines the introduction of “model cards” — detailed descriptions provided by developers that clarify a machine learning model’s capabilities and limitations, rooted in the best practices of the AI development community.

An AI governance body

A novel feature of the proposed regulatory framework is the establishment of an AI governance body. Tasked with formulating guidelines and ensuring compliance with model card standards, this body will play a crucial role in overseeing the responsible deployment of AI technologies.

In a move supportive of innovation, the agreement initially recommends against the immediate imposition of sanctions. However, it does propose the eventual creation of a sanctions mechanism to address persistent violations of the established code of conduct.

Germany’s Economy Ministry, alongside the Ministry of Digital Affairs, has taken a leadership role in the discussions, advocating for regulatory measures that focus on AI’s application rather than the underlying technology itself. Digital Affairs Minister Volker Wissing lauded the agreement, stressing the importance of regulating AI applications to ensure Europe’s competitiveness in the global AI market.

Delicate balance needed

Economic Affairs State Secretary Franziska Brantner expressed the delicate balance needed between capitalizing on AI’s potential and curtailing its risks, especially in an area that is still navigating uncharted legal territory.

This trilateral consensus comes at a critical juncture as global governments increasingly prioritize AI, with Britain hosting its inaugural AI safety summit just last month. Germany is also set to host a digital summit in the city of Jena, which is expected to gather political, business, and academic thought leaders to further delve into AI and its broader implications.

The agreement will likely influence upcoming discussions when German and Italian officials meet in Berlin for talks later this week, with AI regulation undoubtedly a key agenda item. The collaborative stance of Germany, France, and Italy signifies a concerted effort to steer AI development in a direction that is innovative yet ethically sound and well-regulated.

Sources: Reuters and CNBC

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he United States is undoubtedly one of the world’s leading states in terms of its armed forces, economic prosperity, and cultural production. But experts predict that this will no longer be the case by 2050, as the global power structure undergoes major shifts.

The rise of China continues with the country steadily becoming a new superpower, and some even suggesting that in the foreseeable future it will surpass the United States in global influence.

Another candidate for superpower status is India, which has just surpassed China’s population to become the world’s most populous nation. India has combined its growing population with significant economic progress and has the world’s largest and youngest workforce.

The list of potential superpower candidates could go on, but there is another, less expected candidate that could change the balance as we know it: new technology companies.

Can big tech companies take over the global system?

Big tech has not yet “taken over” governments in the traditional sense. Nevertheless, they can wield significant influence over governments and societies due to their vast resources, data-collection capabilities, and technological reach.

Rewind to the early days of COVID19 when everything suddenly became virtual and every sector had no alternative but to become dependent on technology. On the political front, technology took a leading role in implementing governance systems and people went from being citizens of nation states to citizens of large technology companies.

This change is the result of technology companies exercising their authority over digital spaces. In terms of power, it’s clear that tech giants dominate certain sectors such as telecommunications which has raised concerns within governments, leading them to impose limits on the companies’ capabilities. However, regardless of what governments do, tech giants can have significant impacts on countries’ political and economic spheres.

Tech companies are not what we think

Companies sending humanitarian aid to Ukraine	Assistance (in U.S. Dollars)
Google	$25 million
Amazon	$75 million
Microsoft	$35 million
Snap Inc.	$15 million

During the Russian invasion, NATO countries sent military equipment to Ukraine and technology companies helped Ukraine defend itself against Russian cyberattacks. Leading U.S. tech companies leveraged their resources, capabilities, and expertise to serve Ukraine and defend its sovereignty. Companies like Google have taken measures to block YouTube channels linked to the Russian state media across Europe, including Russia Today (RT) and Sputnik.

As seen in the table, U.S.-based tech companies have donated humanitarian aid to Ukraine. Apple promoted donations for aid and fundraised on several platforms; Airbnb users could book stays in Ukraine with no intention of going. Amazon added a donation button, and Etsy users bought digital stickers from Ukrainian shops.

Prominent technology companies, such as SpaceX, facilitated direct communication between Ukrainian military leaders, generals, and soldiers on the front lines. If it wasn’t for the Starlink internet satellite sent by Elon Musk, Ukraine could have lost its online connection within weeks after the conflict began.

Ukraine is using Chinese-made DJI commercial drones for reconnaissance operations which have been modified to drop weapons. Following this, China restricted its drone export to Ukraine.

European Union Artificial Intelligence restrictions: AI Act (AIA)

The use of artificial intelligence (AI) in the European Union will be regulated by the AI Act (AIA), the world’s first comprehensive AI law. The main objective of the AIA, the proposed legislation by the European Commission, is to ensure the trustworthy and ethical use of AI, and promote innovation and competitiveness.

Members of the European Parliament want to make sure AI is safe, transparent, traceable, non-discriminatory, and environmentally friendly. The idea is to govern AI based on four levels of risk starting with unacceptable risk, when AI is used for unethical acts such as biometric surveillance. High risk involves causing harm to people’s health, harm to the environment, or affecting people’s fundamental rights. Limited risk refers to AI systems with specific transparency obligations, and last, minimal or no risk includes applications such as AI-enabled video games or spam filters.

Brussels aims to adopt the AIA by the end of 2023. The United Kingdom is striving to position itself as a regulatory leader, showcasing its tech credentials at an international AI summit in the fall of 2023.

It is predicted that if everything goes as planned, by the second half of 2024, AI regulation could become applicable. These rules ensure that AI developed and used in Europe is fully in line with EU rights and values including human oversight, safety, privacy, transparency, non-discrimination, and social and environmental well-being.

Chinese government and its regulation of foreign AI

The Chinese government has also issued 24 regulatory guidelines to strike a balance between state control of technology and fostering an environment conducive to innovation within the sector. The regulation was published on July 10 and referred to as “Generative AI Measures.” It involves seven agencies taking responsibility for oversight, including the Cyberspace Administration of China (CAC) and the National Development and Reform Commission.

Beijing will enforce visible labels on AI-generated content such as images and videos to prevent misleading manipulation. China also demands AI models to be trained on “legitimate data” with disclosure to regulations. Regulation is scheduled to come into effect in August.

The regulation aims to create a middle ground between state control and a welcoming environment for innovation in the sector. China has been actively developing its AI scene with local tech giant Alibaba and has been working on the creation of a rival to the popular chatbot ChatGPT.

In recent years, the rapid advancement of Artificial Intelligence (AI) has sparked both excitement and apprehension about its potential impact on our daily lives and the future of humanity. As AI becomes increasingly integrated into various aspects of society, from banking and shopping to job applications, concerns about its inherent biases and discriminatory tendencies have come to the forefront. Perhaps one of the most alarming issues surrounding AI is the presence of racism.

AI algorithms rely on vast amounts of data to make decisions, and this data is often sourced from historical records and human interactions. The legacy of racism, unfortunately, is deeply ingrained in our historical data, leading to biases being unintentionally encoded into AI systems. These biases can manifest in various forms, including racial profiling, prejudiced hiring practices, and unfair treatment in areas like banking and lending.

In Is Artificial Intelligence Racist?: The Ethics of AI and the Future of Humanity Arshin Adib-Moghaddam explores the troubling presence of racism within the very fabric of the algorithms that now govern our daily lives. This thought-provoking book takes readers on a journey through the history of enlightenment racism and its connection to the discriminatory practices embedded in modern-day Artificial Intelligence (AI) systems.

Providing an examination of the data that feeds into AI technology and how it will inevitably shape the future of humanity, Adib-Moghaddam unveils the profound implications of such data-driven technology on international security and human rights, urging readers to confront the potential consequences of unbridled AI development.

The book delves into the narratives surrounding the birth and progression of AI systems, placing particular emphasis on the influence of tech giants like Mark Zuckerberg, Elon Musk, and Bill Gates. Through this exploration, the author sheds light on how technology inadvertently assists and enables various forms of extremism, perpetuates social hierarchies, and reinforces discriminatory boundaries within our societies.

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he development of artificial intelligence (AI) has had its ups and downs over the years. Driven among others by computational advances, exponential growth in data accessibility, and increased capacity for high-quality natural language processing, AI is currently experiencing one of the most productive ups in its history. Today, AI has become an inseparable part of our daily lives, while the potential harm it can cause is even more worrying.

With the introduction of ChatGPT, an advanced language model from OpenAI, this has become even more apparent. For now, it is unclear whether optimism or pessimism about the future of artificial intelligence prevails. There is no doubt that we are at a turning point in history, and the future of AI—and the future of humanity—will be shaped by the choices we make today.

In 2016, an AI chatbot created by Microsoft called “Tay” produced striking outputs such as “Hitler was right, I hate Jews” and “Calm down, I’m a good person! I just hate everyone.” Outputs like these have fueled skepticism about artificial intelligence and sparked widespread debate, raising concerns about its potential negative effects. As an experiment in spoken language understanding, Tay interacted with users on social media platforms, particularly Twitter, and used these interactions to improve itself.

AI chatbots

However, taking advantage of such programming, malicious users deliberately fed inappropriate and offensive content into the system and quickly Tay began generating offensive and provocative messages by mimicking this negative behavior. In response to the system’s disruptive behavior and negative feedback, Microsoft decided to shut down Tay the day after its launch. This incident showed the challenges of designing AI systems that can interact with users in an ethical and rational way. This experiment has gone down in history as a notable example in the development of AI chatbots and AI in general.

Efforts to develop AI chatbots are not new. In the mid-1960s Joseph Weizenbaum developed ELIZA, a chatbot that mimicked a psychotherapist and engaged in text-based interactions with users. Although limited in capacity compared to today’s level of AI sophistication, ELIZA was highly influential in the field of artificial intelligence and laid the groundwork for future chatbot developments. However, the evolution from the “primitive” ELIZA to the “sophisticated” ChatGPT took half a century, during which the promised benefits and potential dangers of the technology increased together with its level of sophistication.

It’s not enough to focus on ChatGPT alone when analyzing the future of AI and its implications. ChatGPT is part of generative AI, which refers to a class of AI models or AI algorithms designed to generate original content, including text, images, or audio, based on patterns and examples derived from training data. Synthetically generated but realistic-looking deepfake content is another recent example of generative AI. Generative AI models, such as these examples, are the type of AI that is profoundly impacting our daily lives and redefining the boundaries of technology.

What advantages and disadvantages does ChatGPT offer?

ChatGPT is just the tip of the iceberg when it comes to the capabilities and applications of generative AI. In the future, such models and chatbots could be tools with a wide range of applications, such as the generation of advice in legal and regulatory areas, the improvement of the quality of education, and use for medical purposes. All these potential applications represent an optimistic outlook for the future, as they could provide valuable contributions and assistance to humanity in many different areas. However, there are also many dangers looming on the horizon.

First, AI models, especially generative AI models, are based on the data presented to them in training processes. This works in direct proportion: the more data and variety presented to the models in the training process, the better the systems become. However, it is not known what kind of data the models are based on, and this is where the threats to users arise. If the data fed into the models is intentionally or unintentionally biased, offensive, or provocative, it will produce problematic results.

Microsoft’s Tay evolved in sync with the data provided by users, exhibiting aggressive and biased behavior, earning it the nickname the “Nazi chatbot.” As many AI models are known to develop gender and racial biases with the inputs used in their training, data is becoming an increasingly critical area for productive AI models.

Real and fake in cyberspace

Another threat is that it will become increasingly difficult to distinguish between what is real and what is fake in cyberspace. Experts estimate that within a few years, about 90 percent of the information found on the internet will be generated by AI. As a result, it will become increasingly difficult for users to distinguish between human-generated and AI-generated content. In this context, the development and proliferation of deepfakes, a key example, is a cause for concern. These sophisticated manipulations have the potential to cause profoundly negative effects/outcomes not only at the individual level, but also at the national and international level.

Another major risk associated with generative AI is the potential for data leaks and breaches. Systems such as ChatGPT and Google’s Bard are still relatively new applications and are highly likely to encounter problems, particularly cyberattacks. This raises concerns about the adequacy of the security measures in place.

For example, a few months ago, a flaw in ChatGPT’s system allowed some users to access the chat histories of other active users, as well as sensitive personal information such as first and last names, email addresses, billing addresses, and the last four digits of their credit card numbers and expiration dates. This incident highlighted the potential for more serious incidents that could compromise individuals’ data and credentials. This and similar incidents are clear examples of the risks inherent in productive AI.

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s late as last year, to claim that Artificial Intelligence (AI) could have some kind of consciousness – a kind of “I” of its own – was a sacrilege even among AI scientists. How could one attribute such a sublime human characteristic to a machine essentially relying on a few calculations, programed to respond on the basis of a few patterns in data. The mere rise of “few” to trillions should still not change the fundamental fact of it being “it”: a non-living, insentient being, having no ability to be “strategic” or work towards “its own” goals.

The maverick Google scientist, Blake Lemoine, who claimed AI to have gained sentience, was fired only last year. Many tech enthusiasts had gone straight for his throat. He was painted as either being too naive to understand human consciousness – and overestimating AI – or as simply one of those scaremongering charlatans who weave conspiracy theories as a pastime: make AI great again-kind.

Enter ChatGPT and Bard: the chatbots that revolutionized the industry and are on the road to change how humans think and live fundamentally. These bots, which could make you feel you were talking to a human – albeit with super-human knowledge – reignited the question of AI’s sentience.

“Neural networks could be slightly conscious.”

Were these bots just playing around with what they had learned or could they be intelligent enough to have some kind of consciousness? According to Ilya Sutskever, the co-founder and chief scientist at OpenAI, “neural networks could be slightly conscious,” afterall. What makes the question even more intriguing is that the technology shared with the public – after a year of Ilya’s claim – is still only the tip of the iceberg according to insiders.

No wonder, concerns around AI have led some of its original patrons to dissociate themselves from these projects. Take Elon Musk, for example, who was among the founders of OpenAI in 2015, but quit it three years later. To him, an unfettered AI race, without requisite legislation in place can morph into an existential threat for humanity.

Per Musk, AI can spontaneously grow into a “singularity” – a level of complexity beyond which humans may not be able to predict or control the future directions of AI – where AI could pave its own path. In one hypothetical – hopefully, oversimplified – example, Musk raises the possibility of AI servers becoming inaccessible with previous day’s passwords without any human intervention.

The “Godfather of Artificial Intelligence”

An even more authoritative voice, the “Godfather of Artificial Intelligence,” Prof. Geoffrey Hinton, the psychologist and AI scientist behind key breakthroughs in neural networks, quit Google recently to be able to talk about the dangers of AI more freely. At least one of his concerns, like Musk, is “existential risk” to humanity.

Even before he left Google and was being celebrated for his students’ and his own work which enabled ChatGPT and Bard, he had expressed his concern about the “sentience” of AI. In what became a sensational end to an otherwise equable interview, he used the example of an “autonomous lethal weapon” when asked about AI’s sentience: “It’s all very well saying it’s not sentient…but when it’s hunting you down to shoot you…yeah, you can start thinking it’s sentient!” he exclaimed somewhat anxiously. A few days later, he quit Google.

To borrow Professor Hinton’s metaphor, and simplify his and Musk’s arguments, if one person learns what they know, only they know it. But with this AI-tech, 10,000 people’s or a million books’ knowledge can become one person’s – except that it’s not a person. With further growth in technology and investment in computing capacity, the scale, speed, and intensity of this multiplicative information explosion will grow.

What’s worse? We cannot say what level of complexity leads to the enigmatic phenomenon of consciousness, whether in nature or in such artificial intelligence systems – and from there on, AI could at least partially take care of itself.

To say the machine may be sentient is to raise a red flag, not demean human existence. Of course, AI may never be capable of experiencing an emotion as excruciating as the loss of a loved one or as subtle as the shared warmth of human society. That may be why such a half-baked kind of consciousness that we are trying to create may turn out to be our own undoing in the absence of proper rules of the game.

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he rapid development of artificial intelligence (AI) in recent years has brought about a new era of innovation, with applications in diverse sectors, ranging from healthcare to finance, entertainment, and more. The competition between China and the United States over AI has been widely debated in academic circles with some experts suggesting that the two countries are engaged in a race to dominate the field of AI. Presently, the U.S. is known as the leader in AI development, with many of the world’s leading AI companies based in Silicon Valley. However, China is rapidly catching up, with significant investment in AI research and development, and a growing number of AI startups. In fact, China has announced plans to become the world leader in AI by 2030.

One of the areas where the competition between China and the United States is most apparent is  the development and deployment of AI chatbots that utilize natural language processing technologies like ChatGPT, a powerful AI chatbot that can engage in human-like conversations with users, based on a machine learning algorithm that uses natural language processing (NLP) techniques to understand and respond to user input. Developed by OpenAI, a research organization based in San Francisco, ChatGPT has quickly become one of the most popular and widely used AI chatbots, with applications ranging from customer service to entertainment.

This makes the development of powerful and effective AI chatbots a key strategic priority for both China and the United States. For this purpose and to achieve geopolitical sovereignty, both countries continue to focus on possessing the most influential elements that contribute to building a pioneering AI chatbot which include sizeable investments for building advanced computing microchips, amassing large amounts of data, building advanced algorithms, and employing talented engineers. Neither the U.S. nor China has fallen short of making such large investments. According to a report published by Stanford University in 2022, both countries are world leaders in overall private investments made to further the development of AI, with the U.S. investing three times more than China.

The Chinese approach to AI

Artificial intelligence is a broad field that has multiple sectors and it is hard to make a statement about one country dominating it. For example, China has excelled in facial recognition technology more than other countries, using it as a form of control and local surveillance. The ethics question here is whether China amassing big data to feed AI algorithms comes at the cost of the privacy of Chinese citizens.

However, when it comes to large-scale language models, global regulations and language restrictions hinder China’s development, as evidenced by the fate of Erniedeveloped by the Chinese engine giant Baidu, which was poorly received by investors when it was launched. This is because Ernie has a limited capacity to comprehend and process English statements and generate responses accordingly. This “puts it behind ChatGPT, which is able to generate responses in English, Chinese, and other languages,” as reported by the Chinese tech analyst, Chin Lee, in March 2023.

That said, China continued to make significant strides in the development of AI chatbots and succeeded at a domestic level. Companies like Alibaba and Tencent invest heavily in the development of chatbots that have the capacity to engage in natural and meaningful conversations with users. A good example is Alibaba’s chatbot, AliMe, which has been deployed in a wide range of applications, from customer service to hotel bookings and beyond.

At the international level, the Chinese government is not happy with the progress of Western AI chatbots. In a video that was made public, state-run media outlets claimed that the international chatbots may be utilized by U.S. authorities to “spread misinformation and manipulate public opinion.” Furthermore, the Chinese government attempted to impose restrictions on popular Western websites and applications like Google, Facebook, and Amazon, and many popular Chinese apps have removed access to ChatGPT.

The U.S. approach to AI

As the home of many tech giants like Google, Microsoft, and Facebook that have been instrumental in driving AI development in the United States, the country is currently the global AI leader. The U.S. approach to AI is primarily focused on fundamental research and developing new algorithms. The U.S. government has made significant investments in AI research and development, recognizing the importance of AI in driving economic growth and national security, and addressing societal challenges. The government has allocated billions of dollars in funding for AI research and development institutions and initiatives such as the National AI Initiative and the AI research institutes of the National Science Foundation (NSF).

When it comes to natural language processing models, the U.S. is considered to be ahead of China. The American approach to AI is characterized by a focus on fundamental research, significant investments in AI research and development, and collaboration between industry, academia, and government. These efforts have contributed to the development of cutting-edge technologies that have the potential to transform industries and society as a whole.

Nonetheless, U.S. tech giants are continuously looking over their shoulders at China while the U.S. government has expressed concerns about China’s growing influence in the AI industry and taken steps to limit Chinese investment in U.S. tech companies, citing national security concerns.

Overall, China tends to focus more on the rapid deployment of new technologies rather than using applied research methods. On the other hand, the U.S. is more focused on fundamental research and algorithm development. Both approaches have their strengths and weaknesses, and both countries are pushing forward aggressively to win the competition for AI dominance.

The development of natural language processing technologies is just one example of the ways in which this competition is playing out between the two countries. While the U.S. is currently dominant in the NLP sector, China is catching up quickly and aspires to dominate the industry by 2030. By then, the AI industry is expected to contribute up to $15.7 trillion to the global economy.

The race for AI dominance is not only reflected in the economic sector, but extends to shaping politics, education, and society as a whole through AI-based distribution mechanisms of information such as chatbots. This explains why China and the U.S. continue to invest in AI technologies aggressively and keep track of each other while taking extreme measures to ensure winning the competition for AI supremacy. Eric Schmidt, chair of the National Security Commission of Artificial Intelligence in the U.S., stated that all of China’s plans, resources, and progress in the AI industry should concern every American citizen and that it is necessary for the U.S. to win the AI competition.

Similarly, Chinese officials have limited their citizens’ access to certain Western AI technologies and have warned them about the dangers of the use of Western AI applications, specifically ChatGPT, in spreading misinformation. While both countries contend in the race for AI supremacy, several academics at Beijing University have expressed concerns regarding the fierce rivalry between the two countries and fear that, in the end, it will result in a “divorce” that will harm both China and the U.S.

The market for facial recognition technology (FRT) is rapidly growing as automation continues to take the place of human capabilities. How widespread is the adoption of FRT? How is FRT utilized? What threats does FRT pose to privacy rights and civil liberties? How does FRT affect human rights? How far is too far? Today, FRT can recognize not just the identity of a person, but also decipher their psychology through analysis of their facial expressions. Increasingly, experts and activists voice concerns over methods of data collection, the exploitation of its use, and what ethical issues may arise from the adoption of such technology.

Identified, Tracked, and Profiled offers a critical examination of both the uses and issues that FRT poses while providing an analysis of its global market. Moving beyond FRT as a tool and the rising global demand, Peter Dauvergne shines a light on the anti-FRT movement. He provides information acquired through interviews with expert analysts and activists to provide an insight into the influence of the movement in different parts of the world, including Western democracies, developing countries, and autocratic regimes.

There are indeed many that are full of indignation towards the issues raised by the use of FRT, and Identified, Tracked and Profiled highlights their efforts and addresses how some corporate and state supporters are responding to the backlash.

Recognizing FRT as a source of power, and, therefore, a highly sought out commodity by powerful ruling actors and state-firm alliances, Identified, Tracked, and Profiled showcases the unequal capacity and challenges that activists face in their fight against the creeping normalization of FRT. Yet, it also attempts to show that these movements are not entirely powerless against this development by highlighting the importance of political culture and cooperation within social movements.

Constituting an important element in understanding and dealing with FRT, the global political economy of facial recognition is thoroughly dissected in Part III. Here, the author examines the corporate politics of FRT and the dynamics that are causing its globalization. With China being at the forefront of the utilization of FRT, Dauvergne dedicates a chapter into the everyday politics of FRT in China.

Part IV concludes with Dauvergne’s remarks about the future of FRT in terms of its stance against the growing size, power, and scope of its opposition.

In a timely exploration of the forces for and against FRT, Identified, Tracked, and Profiled is a worthwhile read for those who are interested in civil society activism to prevent the globalization and normalization of new technologies, and students, scholars, and policymakers who are searching for a broad reflection on the power of transnational social movements of resistance towards new technologies.

Interested readers who wish to take a look at Identified, Tracked, and Profiled may do so online for free through Elgaronline.com, as the book is open access.

Peter Dauvergne, Identified, Tracked, and Profiled (Edward Elgar, 2022), ISBN: 978 1 80392 588 2, 144 pages

Written by Dilara Özer

Robert H. Latiff’s Future Peace, a sequel to Future War, specifically brings to attention a subtle, yet potentially destructive effect of technologies such as autonomous systems, advanced weapons, and AI: the provocation to violence. Against the backdrop of the brewing power rivalry between the U.S. and its adversaries, technology aggregates tension and catalyzes violence.

Future Peace touches upon the ethical, security-related, behavioral, and technical aspects of technology and warfare. It consists of four chapters: “A Giant Armed Nervous System” (referring to the military), “Urge to Violence,” “Stumbling into War,” and “Avoiding War.”

Latiff’s book comes as a warning to practice caution when adopting new weapons technology, and aids in understanding human and technical behaviors to attenuate its harms and decrease the chances of war. Not only does technology make war easier and speedier, hence enabling the rush to war, but hinders human reasoning. Such technical changes make the process of sound judgement difficult; for example, thinking and acting in a diplomatic manner. Furthermore, these changes enhance the potential for human error to get out of hand.

Latiff argues that it is vitally important to avoid entering conflict in the first place. He highlights the importance of the cooperative efforts of many segments of society, from the public and technologists to militarists and decision-makers. He implies that it is criminal negligence to leave everything to automation processes or politicians. Therefore, Future Peace also lays out long to short term recommendations to maintain peace and reverse the rush to war.

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utonomous features are not a matter of the future; they have been used in warfare for years. However, it is quite evident that with the advancement of technology and the weaponization of artificial intelligence, autonomous weapons systems are swiftly adapting and becoming accepted.

As these changes emerge, the question that is often raised by international organizations such as the UN, which set the norms of conduct in an effort to control and regulate the use of autonomous weapons, is “How will human beings maintain control of the use of autonomous weapons?” In their study, Ingvild Bode and Hendrik Huelss flip the script to reveal another perspective of this dynamic.

Bode and Huelss aim to explain how testing, developing, and using weapons systems with autonomous features shapes ethical and legal norms, and how standards manifest and change in practice. The authors also examine the consequences of autonomous weapons for international relations.

Bode and Huelss acknowledge the fear and risks that these developments bring but adopt the approach that autonomous weapon systems may push people away from taking immediate decisions on the use of force as norms adapt to the appropriate use of force.

In short, Autonomous Weapons Systems and International Norms analyses the normative repercussions of weaponizing artificial intelligence by examining them not as tools used by the military but as shaping forces. Chapters include “Autonomous Weapons Systems and International Relations,” “New Technologies of Warfare: Emergence and Regulation,” “International Law, Norms, and Order,” “Norms in International Relations,” and “How Autonomous Weapons Systems Make Norms,” and contain assisting tables and figures.

As the trend and debate of autonomizing weapons systems continues, Autonomous Weapon Systems and International Norms equips the reader with an understanding of the relation of human-machine interaction and the process of integrating new technologies into war.

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n War Virtually, Roberto J. González takes the reader behind the scenes to expose how scientists, programmers, and engineers developed technologies that automate war, predict conflict, and regulate human thought and behavior. Does this sound utopian? Not so when you factor in the persistent drive to arm for virtual warfare. In this quest, data comes a valuable commodity.

González evaluates autonomous weapons, robotic systems, predictive modeling software, advanced surveillance programs, and psyops techniques. He examines the foundation of the latest version of warfare in order to explain how it has taken its current form through contemporary military operations and their high-tech and cultural underpinnings.

However, War Virtually transcends the design, innovative, and transformative aspects of technology and warfare, and touches upon human engineering. González reveals the relations between U.S. intelligence agencies and Big Tech, and its implications. It is no secret that algorithms construct behavioral models for anticipating or even predicting human behavior, and, therefore, all of us risk having our digital lives become part of the military-industrial economy.

Adopting a critical approach, González examines how the U.S. military is weaponizing technology and data for new kinds of warfare, and questions the notion that technological advancements will lead to more efficient and less destructive warfare. In fact, he implies that such an aspiration comes at a great cost, and argues that in this world, there is little room for hubris.

War Virtually covers a large array of topics including automated battlefield robotics, social scientists’ involvement in experimental defense research, the blurred line between political consulting and propaganda in the internet era, and the military’s use of big data to craft new counterinsurgency methods based on predicting conflict.

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n Proto-Algorithmic War, Stefka Hristova offers a unique perspective on the Iraq War and the history of algorithmic and digital logic that came to be a crucial element of today’s warfare. She examines the military policy of the United States going into the Iraq War: the U.S. approached the war with the logic of digital and emerging algorithmic technology alongside the preceding numerical logics of the British Mandate in Iraq. In a sense, the Iraq War became an experiment in the early stages of development and utilization of such technology in warfare, and moreover war culture.

Hristova provides a case study of a comparative historical framework reaching back to the quantification techniques harnessed during the era of Mandatory Iraq (1918-1932) and analyzes the parallels and diversions between the numerical logics in both cases.

The author further evaluates the logic that moved beyond the British mandate for Iraq and reduced culture, society, and the physical body and environment into visual data-driven, sterilized, reproducible warfare, highlighting that it lacked cultural and historical context. Indeed, the actual experience of the Iraq War resulted in massive-scale death and destruction.

Proto-Algorithmic War offers an insight into the digitization and automation of war. Hristova aptly scrutinizes the ways in which the Iraq War attempted and often failed to see and incorporate population and territory as digital and further proto-algorithmic entities.

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en Buchanan and Andrew Imbrie use fire as an analogy to showcase how AI has a dual nature: destructive and constructive. Just like our distant ancestors learning to operate their newly discovered, full of potential tool, our responsibility is to understand and use AI potential wisely. With AI revolutionizing the world, democracies have a responsibility to harness that power constructively and come out on top. Transformation is on its way—can humanity use AI to light the way? Or will the fire burn wildly and ignite a new type of war?

In The New Fire, Buchanan and Imbrie help us understand AI better by breaking it down into its “sparks,” which are data, algorithms, and computing power. They further explain the tangible and potentially destructive consequences of these sparks such as disinformation campaigns, new hacking tools, and military weapons.

On a larger scale, The New Fire shows the urgency of democracies to adapt to the powerful tool that is AI, as autocrats may use it for domestic centralized control and asymmetric advantages in combat. In that sense, they argue that harnessing the power of AI is crucial for holding democracy in balance in the world. The New Fire also acknowledges that democracies have a harder time adapting to the age of AI given ethical and civil concerns, but that new ways could be forged based on the advantages of democracy.

Drawing on examples from geopolitical competition and its analysis, Buchanan and Imbrie show how AI can work for democracy while respecting civil rights. In the age of AI, this crucial topic may determine the choice of course between war and peace.

Written by Dilara Özer

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new report by The Intercept revealed that Google is selling advanced AI tools and machine learning capabilities to Israel through the Project Nimbus contract.

Nimbus is a cloud system jointly built by Google and Amazon intending to help Israel strengthen its information system. The cloud will provide Israel with the latest technologies like facial recognition, image classification, and even sentiment analysis features that can assess the emotional content of pictures, speeches, and writings.

But the contract sparked fears among Google engineers and human rights advocates about whether it would reinforce the Israeli aggression in Gaza. They fear the new tools will boost Israel’s ability to monitor people and process massive amounts of data – already prominent features of the Israeli occupation.

Last October, anonymous Google and Amazon workers penned a letter to the Guardian condemning Google and Amazon’s decision to sign the Project Nimbus contract with the Israeli government. Months later, a Google employee named Ariel Koren, hit the headlines for revealing that Google tried to oust her for criticizing the project.

“Google’s AI principles state that Google will not build technologies whose purpose contravenes widely accepted principles of international law and human rights,” said Sundar Pichai, Google CEO. But Google has caught itself in various controversies for building technologies with grave violations of international human rights laws.

The Maven uproar

In 2018, around 4,000 Google employees signed a petition over Project Maven, a military program for the Pentagon that uses an AI surveillance engine to detect objects, track their motions, and provide results to the U.S. Department of Defense.

In a letter addressed to Sundar Pichai, the petitioners wrote, “We believe that Google should not be in the business of war. Therefore, we ask that Project Maven be canceled, and that Google draft, publicize and enforce a clear policy stating that neither Google nor its contractors will ever build warfare technology.” Many employees felt that the company’s involvement in a government warfare program ran counter to Google’s unofficial motto “Don’t be evil.”

Soon after the backlash became public, Google Cloud CEO Diane Greene announced internally that the company will not renew the contract for Project Maven. The uproar also prompted Google to set guidelines for the ethical use of artificial intelligence, and to forbid the use of its technology for weapons or surveillance.

Securing borders with AI

A year later, in 2019, thousands of Google employees demanded the company halt its work with agencies like the U.S. Customs and Border Protection (CBP) and the U.S. Immigration and Customs Enforcement (ICE). The employees argued that the offices of the ICE and CBP often violate international human rights standards, citing the deaths of people in the custody of immigration officials.

“History is clear: the time to say NO is now, we refuse to be complicit. It is unconscionable that Google, or any other tech company, would support agencies engaged in caging and torturing vulnerable people,” Google employees said. They also complained that the agencies in question are harming asylum seekers, separating children from parents, and illegally detaining refugees and U.S. citizens.

Later, The Intercept revealed that Google is reportedly supplying its artificial intelligence technology to help build a “virtual border wall” for the Trump administration—Google defended the CBP contract after the public raised concerns.

The same year witnessed several Google employees on the frontline leaking details about Project Dragonfly, which aims to provide a highly censored, custom search engine for the Chinese government.

Money over principles?

Google is one of the biggest cloud computing companies in the world, and has signed controversial contracts with major powers like the U.S., Israel, and China.

Google may have backed out of Project Maven and similar contracts, but the latest news reveals it is still ready to sleep on its AI principles. Despite the public outrages, Google does not seem affected and continues to collect lucrative defense contracts.

As tech companies like Google invade nearly every aspect of our lives, it becomes a struggle for Google employees to choose between a values-based culture and lucrative business deals.

For the fifth year in a row, the Stanford Institute for Human-Centered Artificial Intelligence issued the “AI Index” to measure the “contemporary trends of AI” as an independent initiative led by the AI Index Steering Committee, a multidisciplinary group consisting of a variety of experts from diverse academic and industry backgrounds. The value of the AI Index is derived from insights gained from data analysis on a large scale.

The index makes use of data inputs, and processes them into useful insights reported in the form of meaningful figures and graphs. The insights gained from the data analysis serve as a reference that enables stakeholders and decision-makers to make informed decisions regarding the use and development of AI in a way that benefits mankind.

The AI Index also provides relevant researchers and practitioners with a trackable and quantifiable key performance indicator (KPI) that can be referenced in future studies and points to the trends of AI investments. That is why the index is credible and reliable for active stakeholders and practitioners in the study or development of AI.

This year’s AI Index, released in 2022, sheds light on major developments in the AI industry between 2020 and 2021, and the findings are truly remarkable. They include a variety of themes and focuses on somewhat controversial, yet important subjects, covering both the theoretical and practical aspects of AI and its effects on different sectors like education, governance, policymaking, and economy.

It is worth mentioning that this year’s findings are still being debated and are expected to be the subject of further study in the coming years. AI technologies are witnessing a golden era and are trending in global markets. There is, however, a gap between the risks for companies of using AI technologies, and the attempts to mitigate the risks for the sake of the benefit of humanity.

The AI report shows several major developments in the field based on data collected by primary research. Below is a brief summary of the findings per sector.

The development of master language models and multimedia applications accelerated as a result of superior technical criteria. Despite the major developments in AI, ethical concerns continued to arise as some texts generated by the models were classified as “toxic” or biased when it came to social, economic, and racial backgrounds.

Liz Rykert, President at Meta Strategies, a consultancy that works with technology and complex organizational change, noted that “The key for networked AI will be the ability to diffuse equitable responses to basic care and data collection. If bias remains in the programming it will be a big problem. I believe we will be able to develop systems that will learn from and reflect a much broader and more diverse population than the systems we have now.”

The efforts to deal with such ethical concerns were not sufficient as mentioned in the report findings. There are major labs and AI companies that claim that they have progressed towards eliminating relevant ethical concerns like OpenAI and others. Nonetheless, the AI Index 2022 report findings indicate that there is still a long way to go when it comes to dealing with the ethical concerns arising from the further development and use of AI technologies.

The economic dimension of AI

The 2022 AI Index report indicated that AI was substantially and directly integrated with economic activities. The impact of AI on economic performance has spread globally through research, publishing, and funding opportunities. There has been a major increase in the percentage of AI investments (which doubled in 2021 compared to 2020).

In 2020, there were only four funding rounds for AI-related projects. In 2021, however, there were 15 rounds of funding with an increased rate where the companies that specialize in data management, processing, and cloud services received the highest amount of funding followed by medical institutions and financial technology (fintech) companies.

The 2022 AI report also found a decrease in the average price of AI-based/robotic weapons, a drop-down of 46.2% in the past five years. This is mainly due to the ethical concerns surrounding AI-based weaponry as there are still not enough policies, laws, or clear guidelines that regulate the use of AI-integrated weapons.

The main concern arises from the ease of procuring AI-based weapons and using them in a way that endangers the people’s lives, especially societies’ marginalized sectors AI technologies have been shown to be biased, particularly against women and minority communities. False identifications disproportionately impact already marginalized and racialized groups.

The research dimension of AI

The United States of America and the Republic of China, alongside other countries, have exerted much effort in researching and publishing on AI-related topics. This, however, gives the U.S. and China an unfair advantage when it comes to dictating the trends and direction of AI-related research and practices, as both countries steer the research in a way that suits their policies and benefits their interests.

With the U.S. and China leading AI-related research, many developing countries are left behind as they lack the opportunity to participate in the policymaking and the setting of AI regulations. Instead, third-world countries are limited to the consumption of AI technologies, which contributes to creating negative stereotypes and biases against them.

The legal dimension of AI

The AI 2021 report uses the U.S. as an example for the increasing number of laws and policies that ought to regulate the practice and use of AI technologies. The U.S. law enforcement agencies and policymakers suggested 130 laws related to the use of AI in different fields, proposed to be passed in 2021. The year 2021 indeed witnessed a major focus on proposing laws and policies to regulate the use of AI-related technologies as compared to previous years—only one law was passed regarding the use of AI technologies in 2015.