The Thousand Brains Project: A New Paradigm in AI that is Challenging Deep Learning with Inspiration from Human Brain

We have established notable milestones in AI understanding over the past decade, especially with rapid research booming in deep learning. However, much of the ocean remains unexplored, and the shore still seems far off for real-world applications. Every moment, researchers from different parts of the world aspire and strive to innovate AI solutions that are implementable in real and challenging applications. In this article, we talk about the Thousand Brains Project. This ongoing research project aims to develop a complementary form of AI based on the working principles of the human brain, specifically the neocortex. Imagine your machine spares a few glances on an object, and the next second, it knows how to grab and interact with the object via touch for final execution. This is something that the Thousand Brains Project is working on. A team of researchers from Numenta Inc. aims to develop a universal platform for flexible, capable AI and robotics solutions that differ from deep learning approaches. Besides, this project seamlessly enables models to use inputs from multiple modalities.

This project differs from contemporary technologies because it uses sensorimotor learning at its core. Sensorimotor interactive systems learn by sensing different parts of the world while consistently interacting; here, the learnings represent the foundational knowledge supporting other functions. Furthermore, the researchers have chosen a design that makes sensory and motor processing distinct with separate architectures. They choose actively generated temporal sequences of sensory and motor inputs to achieve this. Three components constitute the architecture  -: sensor modules, learning modules, and the motor system. A final key component, a common communication protocol, ties together these three elements.

This project uses reference frames and explicit coordinate systems in their learned structured models, providing a second order of differentiation. Reference frames keep track of sensor positions relative to the environment and are used throughout all levels of information processing, including the environment, objects, and even abstract ideas. These models follow a policy of ‘know’ and ‘grow’ as they constantly learn from their surroundings and manipulate objects accordingly based on the estimates of structured data.

The Thousand Brains Project is quite ambitious, striving to build a universal platform and messaging protocol for intelligent sensorimotor systems in the long run. For this, the team is developing an interface called Cortical Messaging Protocol. CMP enables rapid project scaling and helps with inter-modular communication through the defined channel.

Now, we will talk about the first implementation of TBP:  Monty. Monty has Sensor Modules to turn raw sensory data into a common language, Learning Modules to model incoming data streams and use them for interaction, and a motor system for execution. CMP further ties all its components. At present, authors have focussed on object recognition and pose detection tasks. For the experiment, agents interacted with the environment and collected observations. The authors describe the differentiated training and evaluation phase for Monty. The paper then mentions various applications of Monty under multiple configurations.

Conclusion: The Thousand Brains Project is developing a new field of Artificial Intelligence inspired by human brains. The highly ambitious project aims to present a holistic AI and robotics solution. Models under the project are currently working on object recognition tasks; however, this has a vast scope, and a few future potential tasks could be modeling language, learning without explicit supervision, generalizing modeling, etc.

Check out the Paper and GitHub Page. All credit for this research goes to the researchers of this project. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Group. Don’t Forget to join our 60k+ ML SubReddit.

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