Hugging Face co-founder Thomas Wolf just challenged Anthropic CEO’s vision for AI’s future — and the $130 billion industry is taking notice

Thomas Wolf, founder of Ai Compaming Face, has released a blatant challenge to the most optimistic technology industry visions of artificial intelligence, on the pretext that today’s artificial intelligence systems are mainly unable to present the scientific revolutions that their elements consider.

In the publication of a provocative blog published on his personal site this morning, Wolf directly faces the widely circulated vision of the CEO of anthropologist Dario Ameudi, who expected the advanced artificial intelligence “the twenty -first century compressed” where decades of scientific progress can be revealed in only years.

“I am afraid that artificial intelligence will not give us” the twenty -first century compressed, “” Wolf writes in his post, on the pretext that the current artificial intelligence systems are likely to produce a “country yes on the server” instead of the “country of geniuses” that Amoii imagines.

The stock exchange highlights the increasing gap in how artificial intelligence leaders think about technology capabilities to transform scientific discovery and problem solving, with great effects on work strategies, research priorities and policy decisions.

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The wolf hesitates in his criticism in his personal experience. Although he was a straight student attended by the Massachusetts Institute of Technology, he describes the discovery that he was “a wonderful average, a coastal researcher, when his work began in his doctorate. This experience was his view that academic success and scientific genius require various mental curricula as a basic difference – the previously rewarding previous match, and the last demand for rebellion against existing thinking.

“The main mistake that people make is to think that Newton or Einstein was just good students.” “The penetration of real science is the suggestion of Copernicus, for all its days – in terms of ML, we will say,” Despite each group of training data – that the Earth may revolve around the sun instead of the opposite. “

The Amodei vision, which was published last October in the “Loveing ​​Grace machines” article, offers a radical different perspective. It describes a future in which artificial intelligence, which works in “10 x 100x for human speed”, and with the thought that exceeds the Nobel Prize winners, can achieve progress in biology, neuroscience and other fields within five to 10 years.

Amodei is imagined “reliable prevention and treatment of almost all normal infectious diseases”, “eliminating most cancer”, effective treatments for genetic diseases, doubling human life, all of which are accelerated by artificial intelligence. “I think the returns to intelligence are high for these discoveries, and that everything in biology and medicine is often followed by.”

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This basic tension in Wolf’s criticism reveals the reality that is often overcome in the development of artificial intelligence: our standards are designed primarily to measure convergence thinking rather than different thinking. The current artificial intelligence systems excel in the production of answers that are in line with the unanimity of the current knowledge, but they are struggling with a kind of contradictory ideas and challenges that drive scientific revolutions.

The industry has invested extensively in measuring the quality of the artificial intelligence systems that can answer questions with the applicable answers, solve problems with known solutions, and fit the current understanding frameworks. This creates a systematic bias towards systems that are compatible instead of challenge.

The wolf specifically criticizes the current artificial intelligence evaluation criteria such as the “Latest Humanity Exam” and “Math Math”, which tests artificial intelligence systems on difficult questions with well -known answers instead of their ability to generate innovative hypotheses or challenge the current models.

“These criteria test if the artificial intelligence models can find the correct answers to a set of questions that we already know the answer,” Wolf writes. “However, real scientific breakthroughs will not come from answering the known questions, but from asking new difficult questions and questioning common concepts and previous ideas.”

This criticism indicates a deeper issue in how artificial intelligence imagines. The current focus on the number of parameters, the size of training data, and standard performance may be the equivalent of artificial intelligence for excellent students instead of revolutionary thinkers.

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This intellectual gap has great effects on the industrial intelligence industry and the ecosystem of the wider business.

Companies that are in line with the vision of Amodei may scaling artificial intelligence systems to unprecedented sizes, expecting intermittent innovation to increase the increase in mathematical strength and the integrity of the broader knowledge. This approach supports companies’ strategies such as Anthropor, Obayy and other artificial intelligence laboratories that collectively raised tens of billions of dollars in recent years.

On the contrary, Wolf’s perspective notes that larger returns may come from developing artificial intelligence systems specifically designed to challenge current knowledge, exploring counter -factors and generating new hypotheses – capabilities do not necessarily show from current training methodologies.

“We are currently building very obedient students, not revolutionary,” said Wolf. “This is ideal for the main goal today in the field of creating great assistants and excessive compatible assistants. But until we find a way to motivate them to question their knowledge and suggest ideas that are likely to conflict with previous training data, it will not give us scientific revolutions yet.”

For the leaders of the institutions of betting on artificial intelligence to push innovation, this discussion raises decisive strategic questions. If the wolf is correct, organizations that invest in the current artificial intelligence systems may need revolutionary scientific breakthroughs to reduce their expectations. The real value may be in additional improvements to the current processes, or in spreading human cooperative methods in which humans provide intuition that escalates the model while artificial intelligence systems deal with heavy arithmetic lifting.

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This exchange comes in a pivotal moment in the development of the artificial intelligence industry. After years of explosive growth in the capabilities of artificial intelligence and investment, both stakeholders from the public and private sectors are increasingly focused on practical returns from these technologies.

Modern data from Pitchbook shows for investment capital analysis company to finance artificial intelligence 130 billion dollars worldwide in 2024, while attracting health care and scientific discovery applications. However, questions about concrete scientific breakthroughs have grown from these investments.

The Wolf-Samodei discussion is a deeper philosophical gap in the development of artificial intelligence that was collapsing under the surface of industry discussions. On the other hand, optimistic optimists, who believe that continuous improvements in the size of the model, the size of data and training techniques will eventually lead to systems capable of revolutionary visions. On the other hand, there are skeptical in architecture, who argue that the basic restrictions in how to design current systems may prevent them from making a kind of cognitive hops that characterize scientific revolutions.

What makes this discussion particularly important is that it happens between two respected leaders who were at the forefront of developing artificial intelligence. It cannot be rejected as simply informed or resistant to technological progress.

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Tension between these perspectives indicates a possible development in how to design and evaluate artificial intelligence systems. Wolf’s criticism does not indicate the abandonment of current methods, but rather increases with new technologies and standards that specifically aim to enhance contradictory thinking.

In his position, Wolf suggested that new criteria should be developed to test whether artificial artificial intelligence models can “challenge their training data” and “follow anti -crime methods.” This represents an invitation not to invest in artificial intelligence, but for the most thinking investment, which is the full spectrum of the cognitive capabilities necessary for scientific progress.

This accurate view recognizes the enormous possibilities of Amnesty International, with the realization that the current systems may excel in certain types of intelligence during struggle with others. The path to the front is likely to include the development of complementary methods that benefit from the strengths of the current systems while finding ways to address its restrictions.

For companies and research institutions that transmit artificial intelligence strategy, the effects of it are great. Institutions may need to develop evaluation frameworks that are not evaluated not only the extent of answering artificial intelligence systems to current questions, but how effective they generate them. They may need to design human cooperation models that link the identical capabilities and the computational capabilities of Amnesty International with intuition that is escalating from human experts.

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Perhaps the most valuable result of this exchange is that it pushes the industry towards a more balanced understanding of the potential and restrictions of artificial intelligence. Amodei vision provides a convincing reminder of the transformative effect that can have AI across multiple areas at the same time. Wolf’s criticism provides a necessary budget, while highlighting the specified types of cognitive capabilities needed for revolutionary progress.

As the industry advances forward, this tension is likely to push between optimism and suspicion, between expanding the current approach and developing the new curricula, the next wave of innovation in developing artificial intelligence. By understanding both perspectives, institutions can develop more accurate strategies that increase the potential of the current systems while also investing in methods that address their restrictions.

At the present time, the question is not whether Wolf or Amodei is true, but how their contradictory vision can teach a more comprehensive approach to developing artificial intelligence not only outperforming the questions we have already, but it helps us to discover the questions that we have not yet thought about.