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Modelling Brittle-Ductile Transitions
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From the Latin “Capillus Promissus” meaning “long hair”
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Our lines. An ideal multi-purpose line for everyone the types of skin and hair. Specific treatments for colored hair fine or damaged. Treatments designed to cure the main abnormalities of skin and hair. A line designed to counteract thinning and hair loss. Over the last half decade, billions of dollars in research funding and venture capital have flowed towards AI; it is the hottest course in computer science programs at MIT and Stanford.
In Silicon Valley, newly minted AI specialists command half a million dollars in salary and stock. But there are many things that people can do quickly that smart machines cannot. Natural language is beyond deep learning; new situations baffle artificial intelligences, like cows brought up short at a cattle grid. None of these shortcomings is likely to be solved soon.
By itself, it is unlikely to automate ordinary human activities. He is a senior partner at Flagship Pioneering, a firm in Boston that creates, builds, and funds companies that solve problems in health, food, and sustainability. Before that he was the editor of Red Herring magazine, a business magazine that was popular during the dot-com boom. To see why modern AI is good at a few things but bad at everything else, it helps to understand how deep learning works.
Deep learning is math: a statistical method where computers learn to classify patterns using neural networks. Deep learning employs an algorithm called backpropagation, or backprop, that adjusts the mathematical weights between nodes, so that an input leads to the right output. According to skeptics like Marcus, deep learning is greedy, brittle, opaque, and shallow. The systems are greedy because they demand huge sets of training data. They are opaque because, unlike traditional programs with their formal, debuggable code, the parameters of neural networks can only be interpreted in terms of their weights within a mathematical geography.
Consequently, they are black boxes, whose outputs cannot be explained, raising doubts about their reliability and biases.
Finally, they are shallow because they are programmed with little innate knowledge and possess no common sense about the world or human psychology. These limitations mean that a lot of automation will prove more elusive than AI hyperbolists imagine. We need to invent better methods of machine learning, skeptics aver.
Marcus claims that our best model for intelligence is ourselves, and humans think in many different ways.