Unsupervised learning, transfer learning and curriculum design
Another major discrepancy between AI systems and human-like learning lies in the vastly larger amounts of labelled data required of AI systems to even approach human-level performance. For example, a recent speech recognition system [9] was trained on 11,940 hours of speech with aligned transcriptions. If we both saw and heard another human read text aloud to us for two hours a day, it would take us 16 years to be exposed to that dataset. AlphaGo zero [10] practiced 4.9 million games of self play to beat human Go masters. If a human would play Go every day for 30 years, he or she would have to play 450 games a day to practice as much as AlphaGo zero. Also, a recent dataset on visual question answering [11] contains 0.25M images, 0.76M questions, and ~10M answers. If we received answers to 100 questions about images each day, it would take us 274 years to be exposed to a dataset of this size. It is clear in all three cases that humans receive vastly smaller amounts of labelled training data, yet they can recognize speech, play Go and answer questions about images pretty well.
Several keys to bridging this gap between artificial and biological intelligence lie in the human ability to learn from unlabelled data (unsupervised learning), as well as to build on strong prior knowledge gained from solving previous tasks, and to transfer that knowledge to new tasks (transfer learning). Finally, human society has set up systems of education that involve the design of carefully chosen sequences of tasks to facilitate knowledge acquisition (curriculum design). In order to efficiently instantiate these concepts in artificial systems, we need a deeper understanding and mathematical formalization of how both humans and other animals do unsupervised learning, how knowledge can be transferred between tasks [12,13], and how we can optimize curricula. Advances in these areas, which will require the interactions of computer scientists, psychologists, and educators, will likely be key to reducing the prohibitive data requirements of current AI systems. And they will be essential in empowering AI in other domains where labelled data is scarce.
Building world models for understanding, planning, and active causal learning
Much current AI success in commercial settings is achieved via supervised methods, where an AI system passively receives inputs, is told the correct output, and it adjusts its parameters to match each input-output combination. Babies in contrast behave like active scientists interrogating their environment [14]. Consider for example, the following experiment: through sleight of hand, a baby is shown two “magical” objects: Object A, which appears to move through walls, and object B, which does not fall when dropped. The baby is given both objects to play with. The baby will specifically attempt to push object A through solid surfaces, and drop object B to see if it will fall (and not the other way around). This remarkable experiment suggests that babies act like scientists who actively interrogate their world. In particular they: (1) already have an internal model of how the physical world should behave, (2) pay attention to events which violate that world model, and (3) perform active experiments to gather further data about these violations, thereby actively choosing their own training data based on their current world model.
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Thus even babies, unlike most current commercial AI systems, have remarkable capabilities to learn and exploit world models. We need further research in both neuroscience and AI on learning world models from experience, using such world models to plan (i.e., imagine different futures contingent upon current actions), and use such future plans to make decisions. Such model-based planning and decision making is likely to be a powerful aid to current model-free reinforcement learning systems which simply map world states to values, or expected future rewards. This work in AI can advance hand in hand with work in neuroscience which reveals how neural activity in animals can relate to imagined as well as actualized futures [15]. Also, fundamental drives like curiosity can be formalized into reinforcement learning systems to facilitate learning and exploration [16]. More generally, a deep understanding of multiple systems and intrinsic biological drives that facilitate both animal and human learning is likely to be highly beneficial for speeding up learning in artificial systems.
A scientist detects a novel change in the statistics of his sensory experience.
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