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OPEN AI Chief Scientist Ilya Sutzkaber: The role of academia in artificial intelligence research is changing

At a meeting held this week at Tel Aviv University with the participation of OPEN AI CEO Sam Altman, Sutzkober said that the academy cannot compete with industry in computing power or engineering ability, but instead investigate how to better measure the performance of artificial intelligence, and use artificial intelligence to solve large problems

Artificial intelligence research in the laboratory. Figure using DALEE 2
Artificial intelligence research in the laboratory. Figure using DALEE 2

In a conversation that took place yesterday (Monday) at Tel Aviv University with the participation of Sutzkober, the CEO of OPEN AI Sam Altman and Prof. Nadav Cohen, from the host university, addressed the questions that lie in the seam between the academy and the technology industry when the academy cannot establish a large computing capacity, and although it does not have an engineering heritage it is Can definitely answer the difficult questions that accompany artificial intelligence

OPEN AI's chief scientist, Ilya Sutzkaber says that the role of academia in artificial intelligence research is changing. OPEN AI CEO Sam Altman and Chief Scientist Ilya Sutzkover participated in the event moderated by Dr. Nadav Cohen from Tel Aviv University.  

The two, especially Sotskovich, addressed issues that are at the intersection between academia and industry, on the one hand, the contribution of universities to the study of artificial intelligence, as well as the role of artificial intelligence in solving major problems of humanity, primarily the climate crisis.

Sutzkaber explains: "Academia used to be the place for the most innovative research in the field, but now it is not so much for two reasons: computing and engineering capacity. Academia has lesser computing capabilities than those that industry can afford and usually there is no engineering culture. Still academia can make very dramatic and significant contributions to artificial intelligence: there are so many mysteries about how neural networks work. Producing these complex objects of unimaginable deep learning, which is like an alchemical process where we take the raw materials of data plus the energy source of computing and get the artificial intelligence.

Measuring the quality of artificial intelligence is impossible today

"But what is it, how does it work, what are its characteristics, how do we control it, how do we make it so that we can contain it and also how do we measure all these parameters, these are disappearing. Even the simple task of quality measurements is impossible. In the past the problem was not critical because artificial intelligence was not that important. But now that AI is important, we realize we can't measure it. To answer these questions you don't need a huge computing cluster, you don't need a huge engineering team to ask these questions. There will be a dramatic and significant contribution that everyone will immediately notice." He told the audience that was made up of Israeli high-tech leaders, lecturers and students from Tel Aviv University.

CEO Altman also addressed the issue: there is confusion about what we do and how the academy should treat it. Many articles have been written but the important thing is to think about the most important problems, simply focus on them, change thinking about focus; About the most important problems: what still can't be done, what we still don't know, and how to measure the operation of artificial intelligence."

"First of all, you need to understand the problem, because understanding the problem is a prerequisite for moving towards a solution. This is where we can help. We have academy access programs where you can get computing power and access to our most advanced models. They research them, write articles. They did this even with GPT3. Even before we had the first product, researchers at many universities wrote papers, studied their models, their properties, their biases. We'd love to hear any ideas.

Quick solution of difficult problems

In another context related to the academy, Sutzkever explained: "We are going to understand the mysteries of the universe and more than that, I truly believe that scientific and technological progress is the only sustainable way in which life improves, the world improves."

"These capabilities will allow us to unlock a huge amount of new science, new technological progress. We are already seeing the beginnings of people using these tools to be more efficient, but if you imagine a world where you can ask AI to find cures for diseases or address the climate crisis.”

"For example, climate change is very serious. We believe that dealing with climate change will not be difficult for future generations of artificial intelligence. We will explain to him that we want to build efficient carbon capture systems.

If we can speed up scientific progress, which is something that strong artificial intelligence can do, we can achieve very advanced carbon capture, much faster, produce cheap energy much faster and we can achieve cheap manufacturing. The combination of all three will create a system that would not be possible without the artificial intelligence that accelerates the process."

"We will ask artificial intelligence to tell us how to produce a lot of clean energy cheaply, how to capture carbon efficiently and how to build a plant that will do it on a planetary scale."

Sutzkober later added that today's artificial intelligence, despite the miracle that has come, is only a fraction of what artificial intelligence will be able to do in the future.

More of the topic in Hayadan:

2 תגובות

  1. An interesting article and interesting if chatgpt had written it with fewer errors. In Hebrew, probably not yet

  2. It's all a matter of the governments' priorities. The Weizmann Institute had one of the first computers in the world. Search for Viček. And it's not something was about a fifth of the institute's budget

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