by Angela Guess
Will Knight recently wrote in the MIT Technology Review, “The latest machine-learning techniques promise to transform whole industries by making it easier for computers to recognize patterns in data, to make accurate predictions, and to generally behave more intelligently. Unfortunately, the experts capable of crafting and optimizing the code needed to make this magic possible are in pretty short supply. Such is the demand for machine-learning talent, in fact, that startups see an opportunity to offer deep technical expertise to companies, from financial and insurance firms to Web startups and carmakers, that are hoping to harness AI. A few startups now offer to accelerate the performance of the machine-learning algorithms so that they work well on arrays of computer chips. At least one is designing its own computer chips to squeeze the best performance out of the latest algorithms.”
Knight goes on, “Speaking at the Neural Information Processing Systems (NIPS) conference in Montreal last month, Tijmen Tielemen, a Dutch machine-learning expert who studied in Hinton’s group, and who specialized in optimizing neural networks, said that it can take many hours or even days to train a deep-learning network on a large data set, and each time a network is tweaked the training process must begin again. Minds.ai offers software libraries that support a deep-learning network by communicating efficiently with graphics chips. This could help businesses perform cutting-edge deep learning without requiring top talent. For example, a company aiming to train an algorithm for self-driving cars to recognize particular objects would normally need a team with strong technical expertise to do it efficiently. ‘When you build a serious neural network these days, it takes a long time to train it,’ Tielemen says. ‘This is a very real concern, and we are training neural networks faster’.”
photo credit: Minds.ai