Three decades ago, David Rumelhart, Geoffrey Hinton, and Ronald Williams wrote about a foundational weight-calculating technique — backpropagation — in a monumental paper titled “Learning Representations by Back-propagating Errors.” Backpropagation, aided by increasingly cheaper, more robust computer hardware, has enabled monumental leaps in computer vision, natural language processing, machine translation, drug design, and material inspection, where some deep neural networks (DNNs) have produced results superior to human experts. Looking at the advances we have made to date, can DNNs be the harbinger of superintelligent robots? From a report: Demis Hassabis doesn’t believe so — and he would know. He’s the cofounder of DeepMind, a London-based machine learning startup founded with the mission of applying insights from neuroscience and computer science toward the creation of artificial general intelligence (AGI) — in other words, systems that could successfully perform any intellectual task that a human can. “There’s still much further to go,” he told VentureBeat at the NeurIPS 2018 conference in Montreal in early December. “Games or board games are quite easy in some ways because the transition model between states is very well-specified and easy to learn. Real-world 3D environments and the real world itself is much more tricky to figure out … but it’s important if you want to do planning.”
Most AI systems today also don’t scale very well. AlphaZero, AlphaGo, and OpenAI Five leverage a type of programming known as reinforcement learning, in which an AI-controlled software agent learns to take actions in an environment — a board game, for example, or a MOBA — to maximize a reward. It’s helpful to imagine a system of Skinner boxes, said Hinton in an interview with VentureBeat. Skinner boxes — which derive their name from pioneering Harvard psychologist B. F. Skinner — make use of operant conditioning to train subject animals to perform actions, such as pressing a lever, in response to stimuli, like a light or sound. When the subject performs a behavior correctly, they receive some form of reward, often in the form of food or water. The problem with reinforcement learning methods in AI research is that the reward signals tend to be “wimpy,” Hinton said. In some environments, agents become stuck looking for patterns in random data — the so-called “noisy TV problem.”
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Source:: Slashdot