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Creating Artificial General Intelligence

What is the compelling question or challenge?

Can we build an artificial general intelligence (AGI) - a “system that outperforms humans at most economically valuable work”[1] - which would unleash rapid progress in science, health, education and art?

What do we know now about this Big Idea and what are the key research questions we need to address?

In the last 6 years, artificial neural network techniques have made rapid progress on previously insurmountable challenges in image recognition, image and sound synthesis, language translation, and robotic control.

Two key algorithmic frameworks that were developed in the 1990s finally began to work well due to greatly increased computing power and the appearance of much larger data sets to train the networks on. Convolutional neural networks (CNNs), based on neuroscience discoveries of how low level visual processing is done in the brains of many animals, allow computers to recognize images at beyond human level, enabling diagnosis of eye diseases and cancers better than expert doctors and autonomous vehicles to recognize other cars, signs and pedestrians. Recurrent neural networks (RNNs) have led to advances in language translation and robotic control. Both techniques allow a computer to autonomously discover high level abstract concepts (like faces or word similarity) from raw pixels or letters.

However, our current state of the art roughly corresponds to the lowest levels of our brains’ instinctive processing of the world. The questions that need to be addressed relate to how to approximate our higher level reasoning abilities to form truly abstract concepts and world models, understand other agents' goals, reason by analogy, transfer knowledge between domains, learn from only a few examples, and imagine plans far into the future. This higher level processing is what separates AGI from current techniques.

These questions can only be addressed by combining knowledge and insight from diverse fields like neuroscience, statistics, computer science, psychology, cognitive science, physics, control theory, robotics, and even game theory. Progress will come by giving researchers the computational resources, long term support and interdisciplinary encouragement to work collaboratively for many years on these key research questions. Currently promising approaches include finding ways to augment neural networks with working memories to store earlier learned skills and notice the larger patterns and getting neural networks to predict what the world will look like in the future to better guide its own actions.

Another major point concerns the safety, interpretability and bias of these artificial neural networks. More research is sorely needed to create confidence in the safety of neural networks, especially when deployed in non-training environments, to better understand how they arrive at decisions (especially in medical, employment and justice applications), and to identify when a network has learned a concept that is different from what was desired due to unrepresentative training data or a faulty algorithm.

Current techniques like CNNs and RNNs already enable dramatically improved algorithms for robotic control, medical diagnosis and more. Even if no further algorithmic progress is made, there are many new areas where established techniques can be applied that would be valuable for society. However, future, improved algorithms and architectures will perform at higher levels of reasoning, and promise to be even more valuable by enabling computers to carry out a much wider set of tasks than they can currently. CNNs and RNNs alone are likely not capable of creating AGI. Much more basic research is required. Unlike the incentives of industry, the National Science Foundation (NSF) is the perfect institution to fund explorations of high risk ideas that could be transformative instead of just incremental improvements of current techniques.

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