We consider the problem of optimally composing a set of primitives to tackle control tasks. To address this problem, we introduce Neo-FREE: a control architecture inspired by the Thousand Brains Theory and Free Energy Principle from cognitive sciences. In accordance with the neocortical (Neo) processes postulated by the Thousand Brains Theory, Neo-FREE consists of functional units returning control primitives. These are linearly combined by a gating mechanism that minimizes the variational free energy (FREE). The problem of finding the optimal primitives' weights is then recast as a finite-horizon optimal control problem, which is convex even when the cost is not and the environment is nonlinear, stochastic, non-stationary. The results yield an algorithm for primitives composition and the effectiveness of Neo-FREE is illustrated via in-silico and hardware experiments on an application involving robot navigation in an environment with obstacles.

For a long time, I have been following Numenta, a startup of neuroscientists whose goal is to understand the neocortex to reproduce the mechanisms in learning algorithms. The founder, Jeff Hawkins, wrote the book A Thousand Brains: A New Theory of Intelligence. Attractive title for me who loves neuroscience and artificial intelligence. In his book, the author gives a history of theories about the brain and intelligence. He explains with anecdotes and experiences how he arrived at his theory. The hypothesis I explore in this chapter is that the brain stores all knowledge using reference frames, and thinking is a form of moving.

Using algorithms derived from neuroscience, AI research company Numenta has achieved a dramatic performance improvement in deep learning networks, without any loss in accuracy. Their breakthrough is also vastly more energy efficient. Today's deep learning networks have accomplished a great deal but are running into fundamental limitations – including their need for enormous compute power. A large, complex model can cost millions of dollars to train and to run, and the power required is growing at an exponential rate. New algorithms are essential to break through this performance bottleneck.

My name is Vincenzo Lomonaco and I'm a Postdoctoral Researcher at the University of Bologna where, in early 2019, I obtained my PhD in computer science working on "Continual Learning with Deep Architectures" in the effort of making current AI systems more autonomous and adaptive. Personally, I've always been fascinated and intrigued by the research insights coming out of the 15 years of Numenta research at the intersection of biological and machine intelligence. Now, as a visiting research scientist at Numenta, I've finally gotten the chance to go through all its fascinating research in much greater detail. I soon realized that, given the broadness of the Numenta research scope (across both neuroscience and computer science), along with the substantial changes made over the years to both the general theory and its algorithmic implementations, it may not be really straightforward to quickly grasp the concepts around them from a pure machine learning perspective. This is why I decided to provide a single-entry-point, easy-to-follow, and reasonably short guide to the HTM algorithm for people who have never been exposed to Numenta research but have a basic machine learning background.

Jeff Hawkins is the founder of Redwood Center for Theoretical Neuroscience in 2002 and Numenta in 2005. In his 2004 book titled On Intelligence, and in his research before and after, he and his team have worked to reverse-engineer the neocortex and propose artificial intelligence architectures, approaches, and ideas that are inspired by the human brain. These ideas include Hierarchical Temporal Memory (HTM) from 2004 and The Thousand Brains Theory of Intelligence from 2017. This conversation is part of the Artificial Intelligence podcast. Audio podcast version is available on https://lexfridman.com/ai/

Neuroscience and artificial intelligence (AI) are two very different scientific disciplines. Neuroscience traces back to ancient civilizations, and AI is a decidedly modern phenomenon. At a cursory glance, it would seem that a branch of science of living systems would have little in common with one that springs from inanimate machines wholly created by humans. Yet discoveries in one field may result in breakthroughs in the other-- the two fields share a significant problem, and future opportunities. The origins of modern neuroscience is rooted in ancient human civilizations. One of the first descriptions of the brain's structure and neurosurgery can be traced back to 3000 - 2500 B.C. largely due to the efforts of the American Egyptologist Edwin Smith.

Recent advancement in artificial intelligence, namely in deep learning, has borrowed concepts from the human brain. The architecture of most deep learning models is based on layers of processing– an artificial neural network that is inspired by the neurons of the biological brain. Yet neuroscientists do not agree on exactly what intelligence is, and how it is formed in the human brain -- it's a phenomena that remains unexplained. Technologist, scientist, and co-founder of Numenta, Jeff Hawkins, presented an innovative framework for understanding how the human neocortex operates, called "The Thousand Brains Theory of Intelligence," at the Human Brain Project Summit in Maaastricht, the Netherlands, in October 2018. The neocortex is the part of the human brain that is involved in higher-order functions such as conscious thought, spatial reasoning, language, generation of motor commands, and sensory perception.