HAPPY 2024: in this 74th year since The Economist started mediating futures of brainworking machines clued by the 3 maths greats NET (Neumann, Einstein, Turing) people seem to be chatting about 5 wholly different sorts of AI. 1BAD: The worst tech system designers don't deserve inclusion in human intel at all, and as Hoover's Condoleezza Rice . 2 reports their work is result of 10 compound techs of which Ai is but one. Those worst for world system designs may use media to lie or multiply hate or hack, and to perpetuate tribal wars and increase trade in arms. Sadly bad versions of tv media began in USA early 1960s when it turned out what had been the nation's first major export crop, tobacco, was a killer. Please note for a long time farmers did not know bac was bad: western HIStory is full of ignorances which lawyer-dominated societies then cover up once inconvenient system truths are seen. A second AI ecommerce type (now 25 years exponential development strong) ; this involves ever more powerful algorithms applied to a company's data platform that can be app'd to hollow out community making relatively few people richer and richer, or the reverse. You can test a nation's use of this ai by seeing if efinance has invested in the poorest or historically most disconnected - see eg bangladesh's bklash, one of the most populous digital cash systems . Digital money is far cheaper to distribute let alone to manually account for so power AI offers lots of lessons but whether its good or not depends in part on whether there are enough engineers in gov & public service to see ahead of what needs regulating. There are 2 very good ai's which have only scaled in recent years that certainly dont need regulating by non engineers and one curious ai which was presented to congress in 2018 but which was left to multiply at least 100 variants today the so-called chats or LLMs. Lets look at the 2 very good ai's first because frankly if your community is concerned about any extinction risks these AI may most likely save you, One I call science AI and frankly in the west one team is so far ahead that we should count ourselves lucky that its originator Hassabis has mixed wealth and societal growth. His deep mind merged with google to make wealth but open sourced the 200 million protein databank equivalent to a billion hours of doctorate time- so now's the time for biotech to save humanity if it ever does. Alongside this the second very good AI graviates around Fei-Fei Li) in developing 20 million imagenet database so that annual competitions training computers to see 20000 of the most everyday sights we humans view around the world including things and life-forms such as nature's plants and animals. Today, students no longer need to go back to 0.1 programming to ask computer about any of these objects; nor do robots or and autonomous vehicles - see fei-fei li's book worlds i see which is published in melinda gates Entrepreneurial Revolution of girl empowerment
ED , VN Hypothesis: in 21st C brainworking worlds how people's times & data are spent is foundational to place's community health, energy and so natural capacity to grow/destroy wealth -thus species will depend on whether 1000 mother tongue language model mediates intelligence/maths so all communities cooperatively celebrate lifetimes and diversity's deep data ) . Check out "Moore exponential patterns" at year 73 of celebrating Game : Architect Intelligence (Ai) - players welcome .. some jargon

Monday, September 25, 2023

berkeley stanford and deep mind

 Nobel Laureate and gene databank CRISP founder Jennifer Doudna is at Berkeley 

is there an ai person at berkeley who may connect doudna and eg alphafold2 progress in practical areas of protein breakthrough

Bard replies 

Yes, there are several AI researchers at UC Berkeley who could potentially collaborate with Jennifer Doudna on applying AlphaFold2's protein structure prediction capabilities to practical areas of protein engineering. Here are a few examples:

  • Pieter Abbeel: Abbeel's expertise in reinforcement learning could be used to develop algorithms for optimizing protein design and engineering.

  • Anca Dragan: Dragan's expertise in robot learning could be used to develop robotic systems that can perform protein manipulation and synthesis.

  • Michael Jordan: Jordan's expertise in machine learning and Bayesian statistics could be used to develop algorithms for predicting protein interactions and function.

  • Sergey Levine: Levine's expertise in reinforcement learning and control theory could be used to develop algorithms for controlling protein folding and assembly.

  • Stuart Russell: Russell's expertise in artificial intelligence, machine learning, and robotics could be used to develop AI-powered systems for protein design and engineering.

In addition to these specific researchers, there is a large and active AI community at UC Berkeley that could provide valuable expertise and collaboration opportunities for Jennifer Doudna's work on protein engineering. The university's strong research infrastructure and resources would also be a valuable asset for such collaborations.

Here is a specific example of how AI could be used to apply AlphaFold2's protein structure prediction capabilities to practical areas of protein breakthrough:

  • Protein design: AI could be used to design new proteins with desired properties, such as increased stability, catalytic activity, or binding affinity. This could lead to the development of new drugs, enzymes, and materials.

  • Protein engineering: AI could be used to engineer existing proteins to improve their function or to add new functionalities. This could lead to the development of new therapies for diseases or the creation of new industrial enzymes.

  • Protein structure prediction: AI could be used to improve the accuracy of protein structure prediction methods, such as AlphaFold2. This could lead to a deeper understanding of protein function and the development of new drugs and therapies.

The potential applications of AI in protein engineering are vast and are only beginning to be explored. Collaborations between AI researchers and scientists like Jennifer Doudna could lead to significant breakthroughs in this field.

================ Is Ian Stoica - speaker at Radical Ventures Toronto masterclass a top ai person at berkley- who else is ? (Now that Hinton is no longer at To9rnto full time Radical Vnetures is ome of ai event webs to track)

Bard answers

Yes, Ian Stoica is a highly respected AI researcher and professor at UC Berkeley. He is known for his work on machine learning, natural language processing, and reinforcement learning. He is also a co-founder of the Berkeley Artificial Intelligence Research (BAIR) lab, which is one of the leading AI research labs in the world.

Here are some of the other top human AI professors at UC Berkeley:

  • Pieter Abbeel: Abbeel is a professor of computer science and robotics at UC Berkeley. He is known for his work on reinforcement learning and robotics. He is also a co-founder of covariant.ai, a startup that is developing self-driving cars.

  • Anca Dragan: Dragan is a professor of electrical engineering and computer sciences at UC Berkeley. She is known for her work on robot learning and human-robot interaction. She is also the director of the Berkeley Robot Learning Lab.

  • Michael Jordan: Jordan is a professor of computer science and statistics at UC Berkeley. He is known for his work on machine learning, Bayesian statistics, and artificial intelligence. He is also a co-founder of the Data Science Institute at UC Berkeley.

  • Sergey Levine: Levine is a professor of electrical engineering and computer sciences at UC Berkeley. He is known for his work on reinforcement learning, robotics, and control theory. He is also the director of the AUTOLab at UC Berkeley.

  • Stuart Russell: Russell is a professor of computer science at UC Berkeley. He is known for his work on artificial intelligence, machine learning, and robotics. He is also the co-author of the textbook Artificial Intelligence: A Modern Approach, which is one of the most widely used AI textbooks in the world.

These are just a few of the many outstanding AI professors at UC Berkeley. The university is a leader in AI research and education, and its faculty members are making significant contributions to the field.

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