The group used deep learning to zero in on the molecules most likely to bind to their targets. Google in particular has become a magnet for deep learning and related ai talent. In March the company bought a startup cofounded by geoffrey hinton, a university of Toronto computer science professor who was part of the team that won the merck contest. Hinton, who will split his time between the university and google, says he plans to take ideas out of this field and apply them to real problems such as image recognition, search, and natural-language understanding, he says. All this has normally cautious ai researchers hopeful that intelligent machines may finally escape the pages of science fiction. Indeed, machine intelligence is starting to transform everything from communications and computing to medicine, manufacturing, and transportation.
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This is the culmination of literally 50 years of my focus on artificial intelligence, he says. Kurzweil was attracted not just by googles computing resources but also by the startling progress the company has made in a branch of ai called deep learning. Deep-learning software attempts to mimic the activity in layers of neurons in the neocortex, the wrinkly 80 percent of the brain where thinking occurs. The software learns, in a very real sense, owner to recognize patterns in digital representations of sounds, images, and other data. The basic idea—that software can simulate the neocortexs large array of neurons in an artificial neural network—is decades old, and it has led to as many disappointments as breakthroughs. But because of improvements in mathematical formulas and increasingly powerful computers, computer scientists can now model many more layers of virtual neurons than ever before. With this greater depth, they are producing remarkable advances in speech and image recognition. Last June, a google deep-learning system that had been shown 10 million images from videos proved almost twice as good as any previous image recognition effort at identifying objects such as cats. Google also used the technology to cut the error rate on speech recognition in its latest Android mobile software. In October, microsoft chief research officer Rick rashid wowed attendees at a lecture in China with a demonstration of speech software that transcribed his spoken words into English text with an error rate of 7 percent, translated them into Chinese-language text, and then simulated his. That same month, a team of three graduate students and two professors won a contest held by merck to identify molecules that could lead to new drugs.
How to Create a mind. He told Page, who had read an early draft, that he wanted to start a company to develop his ideas about guaranteed how to build a truly intelligent computer: one that could understand language and then make inferences and decisions on its own. It quickly became obvious that such an effort would require nothing less than google-scale data and computing power. I could try to give you some access to it, page told Kurzweil. But its going to be very difficult to do that for an independent company. So page suggested that Kurzweil, who had never held a job anywhere but his own companies, join google instead. It didnt take kurzweil long to make up his mind: in January he started working for google as a director of engineering.
They say that this is exactly what their algorithm—called deepCube—does. By contrast, conventional deep-learning machines simply recognize certain patterns. DeepCube is able to teach itself how to reason in order to solve a complex environment with only one reward state using pure reinforcement learning, they say. The real test, of course, will be how this approach copes with more complex problems such as protein folding. Well be watching to see how it does. Org/abs/1805.07470 : Solving the rubik's Cube without Human Knowledge. When ray kurzweil met with google ceo larry page last July, he wasnt looking for a job. A respected inventor whos become a machine-intelligence futurist, kurzweil wanted to discuss his upcoming book.
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To do this, it must be able to evaluate the move. Autodidactic iteration does this by starting with the finished cube and working backwards to find a configuration that is similar to the proposed move. This process is not gbr perfect, but deep learning helps the system figure out which moves are generally better than others. Having been trained, the network then uses a standard search tree to hunt for suggested moves for each configuration. The result is an algorithm that performs remarkably well. Our algorithm is able to solve 100 of randomly scrambled cubes while achieving a median solve length of 30 moves—less than or equal to solvers that employ human domain knowledge, say mcAleer and.
Thats interesting because it has implications for a variety of other tasks that deep learning has struggled with, including puzzles like sokoban, games like montezumas revenge, and problems like prime number factorization. Indeed, McAleer and co have other goals in their sights: we are working on extending this method to find approximate solutions to other combinatorial optimization problems such as prediction of protein tertiary structure. Whether these problems will be as amenable to this approach is not clear. They do not generally benefit from a proof that they can be solved in a small number of moves, as the rubiks Cube problem does. That undoubtedly worked in the teams favor here. McAleer and co argue that their approach is a form of reasoning about problems. They point out that one definition of reasoning is: algebraically manipulating previously acquired knowledge in order to answer a new question.
This reward process is hugely important because it helps the machine to distinguish good play from bad play. In other words, it helps the machine learn. But this doesnt work in many real-world situations, because rewards are often rare or hard to determine. For example, random turns of a rubiks Cube cannot easily be rewarded, since it is hard to judge whether the new configuration is any closer to a solution. And a sequence of random turns can go on for a long time without reaching a solution, so the end-state reward can only be offered rarely. In chess, by contrast, there is a relatively large search space but each move can be evaluated and rewarded accordingly.
That just isnt the case for the rubiks Cube. Enter Stephen McAleer and colleagues from the University of California, irvine. These guys have pioneered a new kind of deep-learning technique, called autodidactic iteration, that can teach itself to solve a rubiks Cube with no human assistance. The trick that McAleer and co have mastered is to find a way for the machine to create its own system of rewards. Heres how it works. Given an unsolved cube, the machine must decide whether a specific move is an improvement on the existing configuration.
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Another common challenge is to design algorithms that can solve the cube from any position. Rubik himself, within a month of inventing the toy, came up with an algorithm that could do this. But attempts to automated the process have supermarket all relied on algorithms that have been hand-crafted by humans. More recently, computer scientists have tried to find ways for machines to solve the problem themselves. One idea is to use the same kind of approach that has been so successful with games like chess and. In these paper scenarios, a deep-learning machine is given the rules of the game and then plays against itself. Crucially, it is rewarded at each step according to how it performs.
For full access to this pdf, sign in to an existing account, or purchase an annual subscription. Yet another bastion of human skill and intelligence has fallen to the onslaught of the machines. A new kind of deep-learning machine has taught itself to solve a rubiks Cube without any human assistance. The milestone is emotional significant because the new approach tackles an important problem in computer science—how to solve complex problems when help is minimal. The rubiks Cube is a three-dimensional puzzle developed in 1974 by the hungarian inventor Erno rubik, the object being to align all squares of the same color on the same face of the cube. It became an international best-selling toy and sold over 350 million units. The puzzle has also attracted considerable interest from computer scientists and mathematicians. One question that has intrigued them is the smallest number of moves needed to solve it from any position. The answer, proved in 2014, turns out to.
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