The resulting problems aren’t a temporary bug — they’re a basic limitation of training a machine learning model on a dataset where proteins always fold neatly.
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The Google-backed AI company DeepMind made headlines in March 2016 when its AlphaGo game AI engine was able to defeat Lee Sedol, one of the top Go players in the world. DeepMind followed up this great achievement with the AlphaZero engine in 2017, which made the remarkable achievement of soundly beating AlphaGo in Go as well as one of the world’s best chess engines in chess. The interesting difference between AlphaGo and AlphaZero is that AlphaGo uses databases of top human games for learning, while AlphaZero only learns by playing against itself. Using the same AI engine to dominate two different games, while also discarding reliance on human games suggests that DeepMind has found an algorithm that is intrinsically superior Read More ›
DeepMind, a part of Alphabet (i.e., Google), has made many headlines in the past. The biggest was its development of AlphaGo, which used reinforcement learning to beat the number one Go player at the time (2017). DeepMind generalized this into AlphaZero, which is supposedly able to solve any two-player game of perfect information. DeepMind has come back into headlines recently with the attempt to build an AI which can generate any algorithm. While they are starting with map data, the goal is to generalize this and generate any desired algorithm. The search for such a “universal algorithm” has been essentially equivalent to the search for a perpetual motion machine in physics. The allure of both is obvious. In physics, if you Read More ›
Could a computer design itself? Could it design a bigger and better computer? A team at Google says yes. According to a recent article at NewScientist, Google has begun using AI to design AI. “Engineers at Google have tasked an artificial intelligence with designing faster and more efficient processors – and then used its chip designs to develop the next generation of specialised computers that run the very same type of AI algorithms,” writes Matthew Sparkes. Sparkes continues by explaining Google’s chip design, and introducing the reader to Google’s Anna Goldie, a member of the team at the front of this effort that tasks computers with making better computers. “It is conceivable,” says Sparkes, “that this new AI-designed chip will be used Read More ›
Alphabet’s DeepMind team has just scored a breakthrough in finding treatments for diseases. Their latest AlphaFold system won a grand challenge in analyzing the “folds” of proteins. Proteins—large and often very complex chains of amino acids—do the work in our cells. But, like all bodies, they are three-dimensional. We can’t understand them until we can analyze the folds (the third dimension) that are unique to each type among hundreds of thousands. Knowing what a given protein actually does (or doesn’t) is critical to developing many new medical treatments. How hard is the problem? In his acceptance speech for the 1972 Nobel Prize in Chemistry, Christian Anfinsen famously postulated that, in theory, a protein’s amino acid sequence should fully determine its Read More ›
George Gilder and Robert J. Marks discuss the human brain, superintelligent machines, artificial intelligence, and George Gilder’s new book Gaming AI: Why AI Can’t Think but Can Transform Jobs (which you can get for free here). Show Notes Additional Resources
Recently, we’ve been looking at tech philosopher George Gilder’s new Gaming AI about what AI can—and can’t—do for us. It can’t do our thinking for us but it can do many jobs we don’t even try because no human being has enough time or patience to motor through all the calculations. Which brings us to the massive complexity of the proteins that carry out our genetic instructions—better knowledge of which would help us battle many diseases. Gilder notes that when DeepMind’s AlphaGo beat humans at the board game Go in 2016, it wasn’t just for the fun of winning a game. DeepMind cofounder Demis Hassabis (pictured in 2018) is more interested in real-life uses such as medical research (p. 11). Read More ›
AI is good at winning games. But how does this (and other) accomplishments translate to applications in the real world? George Gilder and Robert J. Marks discuss artificial intelligence, games, and George Gilder’s new book Gaming AI: Why AI Can’t Think but Can Transform Jobs (which you can get for free here). Show Notes Additional Resources
Mundie, former Microsoft Chief Research & Strategy Officer, formerly told his audience that Big Data will enable each person to be “completely understood” by machines that can produce a computer facsimile of each detail. It would be far too complex for human physicians to make sense of, he said.
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AI can become our “collaborators” only in the sense that a shovel can collaborate with me to dig a hole: It amplifies my powers to do things which are otherwise difficult.
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The question scientists must ask, especially about an unexpected finding, is, if no one can reproduce your results, did you discover something new or did you just get lucky? With AI that’s not easy, due to dependence on randomness.
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In evolution, “performance” just means the continued survival of a lineage. Thus it can include hybrids between what you might want for your purposes and what you don’t want.
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Co-founder Mustafa Suleyman is a philosopher and social justice activist who hoped to use the technology for fundamental transformations. But his AI ethics board lasted about seven days at Google.
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In an all-out botwar with the other tech Bigs, DeepMind could simply be paying top minds not to work for the competition while readying AI tools that pay better than winning at board games. Maybe.
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Our surprise at AlphaGo’s move says more about our inability to predict what a program will do than about any creative effort of the program. We’ve known for decades that we cannot predict the results of any moderately complex computer program.
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All of the tasks that AI accomplishes require a certain amount of memory, computational power, and time. We have a good enough understanding of the human brain to measure the same quantities used for the same tasks. Thus, we can measure the difference between what minds and machines require to solve the same problem.
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