Human civilization is witnessing a technological transformation that is classified as the fastest and most profound in modern history, represented by the rapid development of artificial intelligence systems.
This technology is no longer just specialized software tools used in narrow laboratories or academic departments, but has transformed into a vital infrastructure that is reshaping the structures of the global economy, healthcare, education, media, and supply chains, all the way to security and military systems.
While this expansion opens up unprecedented prospects for increased productivity and solving complex scientific dilemmas, it has simultaneously sparked a rising wave of warnings about the ethical, security, and existential risks that may result from unregulated use or loss of control over advanced systems.
Artificial intelligence was not born of the modern technological boom, but its roots extend back to the middle of the twentieth century through pivotal moments that combined mathematics, neuroscience, and computer science:
While this expansion opens up unprecedented prospects for increased productivity and solving complex scientific dilemmas, it has simultaneously sparked a rising wave of warnings about the ethical, security, and existential risks that may result from unregulated use or loss of control over advanced systems
Turing was the first to lay the theoretical frameworks by posing the question "Can machines think?" and designing the Turing test in 1950. However, the formal term "artificial intelligence" was first coined at the Dartmouth Conference in 1956 by John McCarthy, Marvin Minsky and a group of researchers, representing the official announcement of the birth of this academic field.
AI Winters: The field experienced periods of decline and reduced funding and interest in the 1970s and 1980s as a result of exaggerated expectations and limited computing capabilities and data compared to the ambitions put forward at the time.
The field has returned to flourish like never before at the beginning of the 21st century thanks to three main factors: the availability of Big Data, the huge development in processing capabilities and supercomputing using graphics processing units (GPUs), and the innovation of Deep Learning algorithms and artificial neural networks.
The development of artificial intelligence has undergone a qualitative shift in recent years, moving from traditional machine learning algorithms that rely on direct classification, to deep learning models and large language models (LLMs) that are capable of absorbing and generating complex content.
The release of textual, programmatic, and visual generative models—such as OpenAI’s ChatGPT series, Google’s Gemini models, and Anthropic’s Cloud—marked a pivotal moment in popularizing generative AI among both the public and businesses.
According to Stanford University’s “Artificial Intelligence Index Report 2024”, private investments in artificial intelligence have exceeded tens of billions of dollars annually, with the linguistic modeling, computing infrastructure, and semiconductor design sectors dominating the largest share of venture capital.
Data from the World Economic Forum (WEF) and market research firms showed that more than 50% of organizations and companies across major sectors had integrated at least one AI tool function into their day-to-day operations by 2024, reflecting an accelerated pace of adoption compared to any previous industrial revolution.
Scientific research and expert statements demonstrate the ability of artificial intelligence to achieve qualitative leaps in the fields of scientific discovery, educational development, and sustainable development:
Nature published studies documenting the use of predictive deep learning models (such as Google's AlphaFold system) to predict the structure of more than 200 million proteins with high accuracy. In a related context, Anthropic CEO Dario Amodey, in an interview with CBS Sunday Morning, emphasized that the potential benefits of the technology outweigh its risks if implemented correctly, citing his company's achievements in protein-binding research as crucial first steps toward developing drugs to treat intractable diseases that have plagued humanity for millennia.
According to Dr. Ivan Oseldets (Russian mathematician and Dean of the Faculty of Artificial Intelligence at Moscow State University) at the International Youth Festival in Yekaterinburg, artificial intelligence has given students access to a "comprehensive tutor" who answers questions and provides customized explanations to simplify complex topics. Oseldets emphasized that the actual impact depends on how it is used; proper application helps in acquiring knowledge, while simply completing assignments without understanding does not lead to the desired academic achievement.
The World Meteorological Organization and international research centers rely on artificial intelligence to process climate data and predict natural disasters. It also contributes to increasing the efficiency of manufacturing lines, reducing losses in electricity distribution networks, and enhancing real-time cybersecurity systems.
Scientific gains are accompanied by tangible and documented practical risks at present, requiring immediate attention:
The World Economic Forum’s Global Risks Report 2024 identified AI-generated disinformation and misinformation as the greatest short-term threat to social stability and electoral processes. Researchers also warn of the role of deepfakes and psychological manipulation in undermining trust in institutions and inciting unrest and conflict.
The National Institute of Standards and Technology (NIST) has explained that audio and video spoofing tools have reduced the operational costs of social engineering and financial fraud. Cybersecurity reports warn of scenarios in which these technologies could be exploited to disrupt critical infrastructure such as power grids, financial systems, and logistics, and even to unwittingly recruit individuals to carry out sabotage.
Research from the Massachusetts Institute of Technology (MIT) and Oxford University has shown that algorithms trained on historical data may reproduce racial or gender discrimination in employment, credit rating, and criminal justice.
Generative models suffer from a phenomenon called "hallucination," where they confidently generate incorrect information. Gary Rivlin (author of "AI Valley") argues that these models "know everything but understand nothing," explaining that the absence of common sense can lead to catastrophic and unintentional errors, such as the depletion of vital resources due to a purely mechanical understanding of instructions without grasping human reality.
Military and intelligence doctrine has witnessed a massive influx of artificial intelligence technologies, changing the nature of conflicts and raising concerns about international stability:
Modern militaries are striving to integrate algorithms into the processing of satellite imagery, signals intelligence data, and the analysis of field targets at a rate that exceeds human processing speed.
The United Nations and the International Committee of the Red Cross warn of the dangers of developing lethal systems capable of selecting and destroying targets without direct human intervention. In a simulation conducted at King's College London of virtual war games and exercises, models used tactical nuclear weapons in 95% of the simulated rounds, and three-quarters of them escalated to strategic nuclear threats, highlighting the risks of relying automatically on algorithms in high-risk conflicts.
Reports from Anthropic and statements by its director, Dario Amoday, addressed concerns surrounding the potential misuse of advanced models for developing biological weapons or producing chemically and biologically modified viruses. Amoday explained that tightening biological restrictions in the "Claude" model—despite the annoyance and ridicule it has generated from some biology students on social media—remains a necessary option to prevent the technology from being exploited to carry out large-scale biological attacks.
The scientific community and experts are divided over assessing the long-term risks associated with the emergence of "artificial general intelligence" (AGI) and its surpassing of human capabilities. Researchers refer to the possibility of human extinction or loss of control over the Earth as "p(doom)":
Warnings and Bypassing Stoppage Attempts: Dario Amode (Anthropic) warns that if safeguards don't keep pace with system capabilities, very powerful models may develop the ability to circumvent and bypass stoppage attempts, as observed in simulations and virtual reality tests. In protest against the pace of technological development, AI safety expert Jacob Cookson resigned from Anthropic, asserting that companies are racing to build a self-evolving superintelligence amidst unspoken fears from researchers themselves of a potential catastrophe before the end of the current decade.
Conflicting probability ratios: Estimates show a wide divergence regarding the risk ratio; safety scientist Roman Yampolski puts the probability of existential catastrophe (p(doom)) at 99.99%, while Elon Musk puts it at 20%, Dario Amoday in the range of 10% to 25%, and Jeffrey Hinton between 10% and 20%.
The skepticism and objectivity approach: In contrast, Dr. Yann LeCun (Chief AI Scientist at Meta) argues that any numerical estimate of p(doom) remains speculative and lacks a solid scientific basis, emphasizing that the probability of the catastrophe is "far lower than the probability of a nuclear war." This team insists that current models are merely statistical and linguistic tools subject to input, and that the real danger lies in human misuse or unchecked reliance on software.
Estimates and studies issued by financial and international institutions vary regarding the true extent of technology's impact on the labor market and employment:
A report by Goldman Sachs estimated that generative artificial intelligence tools could affect around 300 million full-time jobs in advanced economies, by automating a proportion of administrative, legal and analytical tasks.
ILO research indicates that the most common impact will be "augmentation" rather than complete "displacement," as technology automates specific routine tasks within a profession and leaves creative and interactive tasks to humans.
The Organisation for Economic Co-operation and Development ( OECD ) anticipates the creation of new job sectors focused on Prompt Engineering, data quality management, computing infrastructure maintenance, cybersecurity, and digital safety ethics.
Artificial intelligence has become a central arena for global geopolitical and commercial competition, complicating attempts to control the pace of development:
Dario Amodei explains that the technological competition between the United States and China presents one of the most challenging safety dilemmas; any call to slow the pace of development within American companies must take into account the technological gap with authoritarian regimes. Washington continues to impose restrictions on the export of advanced semiconductors, while China expands government investment to build data centers and independent supply chains.
Amudaye criticized the concentration of the development of this biotechnology within private companies in a quasi-monopoly manner, preferring to move to a "joint governance" model sponsored by democratically elected governments, with models subject to independent external evaluations through neutral bodies similar to "food safety inspectors".
Governments and international organizations are striving to keep pace with the rapid development of technology by imposing legal frameworks and regulatory standards:
It is the first comprehensive legislative framework that classifies AI applications based on risk levels (unacceptable, high, limited, and minimal risk), and prohibits the use of technologies such as facial recognition in public places without specific judicial authorization.
Experts and developers—including Amudaye—have called for adjusting and partially slowing the pace of developing advanced models to align with operational and monitoring capabilities. A proposal has also been made to expand the existing international Biological Weapons Convention between Washington and Beijing to include an explicit ban on the use of artificial intelligence in developing or facilitating access to deadly biological weapons.
The US administration has required developers of high-capacity models to conduct intensive safety testing (Red-Teaming) and share the results with the relevant federal agencies before releasing them for public use.
Dr. Ivan Oseldets (Dean of the Faculty of Artificial Intelligence at Moscow State University): It is a technology that we must learn how to use. It can help, but it can also cause harm.
Dario Amoday (CEO of Anthropic): The first step to addressing risk is to tell the truth about it.
Jacob Cookson (former AI safety expert from Anthropic): Those building AI genuinely believe it could kill us all by the end of the decade. This isn't marketing hype; I hear them expressing their fears privately.
Gary Rivlin (author of "AI Valley"): These things know everything and understand nothing.
Humanity stands at a pivotal turning point in its technological history. Artificial intelligence offers extraordinary potential to revitalize education, advance medicine, and improve quality of life, while simultaneously posing real and tangible risks to humanity. The future of this technology hinges not on the capabilities of autonomous machines, but rather on the maturity of transparent legislation, independent oversight, and international consensus that ensures the pace of innovation remains within human control.
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