The Thousand Brains Project
About the Project
The efforts of Jeff Hawkins and Numenta to understand how the brain works started over 30 years ago and culminated in the last two years with the publication of the Thousand Brains Theory of Intelligence. Since then, we’ve been thinking about how to apply our insights about the neocortex to artificial intelligence. As described in this theory, it is clear that the brain works on principles fundamentally different from current AI systems. To build the kind of efficient and robust intelligence that we know humans are capable of, we need to design a new type of artificial intelligence. This is what the Thousand Brains Project is about.
In the past Numenta has been very open with their research, posting meeting recordings, making code open-source and building a large community around our algorithms. We are happy to announce that we are returning to this practice with the Thousand Brains Project. With funding from the Gates Foundation, among others, we are significantly expanding our internal research efforts and also calling for researchers around the world to follow, or even join this exciting project.
Today we are releasing a short technical document describing the core principles of the platform we are building. To be notified when the code and other resources are released, please sign up for the newsletter below. If you have a specific inquiry please send us an email to ThousandBrains@numenta.com.
Reverse Engineering the Neocortex
If we can understand how the neocortex implements intelligence, we can construct alternative AI systems that have the potential to revolutionize AI. Evolution has spent billions of years optimizing this efficient and incredibly adaptive system called the neocortex. Reverse-engineering it is no small feat, but if we can, it will serve as a blueprint for building artificial general intelligence.
We have made significant progress in understanding the neocortex, the basis of intelligence in mammals. Now we are taking the lessons from years of dedicated in-house research and the wealth of insights from neuroscience and using them to build a truly intelligent system. We believe that such a system will be the basis for applications that simply are not possible today.
A New, Open-Source, AI Framework
Our research team has been implementing a general AI framework that follows the principles of the Thousand Brains Theory. This framework will soon be available as an open-source code base. In addition, we will begin to actively publish our design and engineering progress. In order to further enable adoption, in the near future we will be pledging to not assert our patents related to the Thousand Brains Theory.
To encourage people to build on our ideas and apply our brain-based solutions and algorithms to their problems, we are putting together an easy-to-use SDK. We also want to encourage and foster an active research community and exchange around the Thousand Brains Theory and its application to AI.
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Thousand Brains Principles
We have identified several core neocortical principles that are guiding our software architecture design. These principles are fundamentally different from those used by current AI systems. We believe that by adopting these principles, future AI systems will mitigate many of the problems that plague today’s AI such as the need for huge datasets, long training, high energy consumption, brittleness, inability to learn continuously, lack of explicit reasoning and ability to extrapolate, bias, and lack of interpretability. In addition, future AI systems built on the Thousand Brains principles will enable applications that cannot even be attempted with today’s AI.
Sensorimotor Learning
Learning in every intelligent species known is a fundamentally sensorimotor process. Neuroscience teaches us that every part of the neocortex is involved in processing movement signals and sends outputs to subcortical motor areas. We therefore believe that the future of AI lies in sensorimotor learning and models designed to deal with such settings.
Most AI systems today learn from huge, static, datasets, with major implications. First, this process is costly given the need to create and label these datasets. Second, these systems are unable to learn continuously from new information without retraining on the entire dataset. A system based on the Thousand Brains Theory will be able to ingest a continuous stream of (self-generated) sensory and motor information like a child experiences when playing with a new toy, exploring it with all her senses, and building a model of the object. The system actively interacts with the environment to obtain the information it needs to learn or to perform a certain task. Active learning makes the system efficient and allows it to quickly adapt to new inputs.
Reference Frames
The brain evolved efficient mechanisms, such as grid and place cells, to understand and store the constant stream of sensorimotor inputs. Representations that are learned by the brain are structured and make it possible to perform necessary tasks for survival quickly, such as path integration and planning.
While some existing AI systems are trained in sensorimotor settings (such as in reinforcement learning), the vast majority use the same modeling principles (like ANNs) as in supervised settings, bringing with it the same problems. In contrast, our models are specifically designed to learn from sensorimotor data and to build up structured representations. Those structured models allow for quick and sample-efficient learning and generalization. They also make the system more robust, give it the ability to reason, and enable easy interaction with environments and novel situations.
Modularity (A Thousand Brains)
The neocortex is made up of thousands of cortical columns. Each of these columns is a sensorimotor modeling system on its own with a complex and intricate structure. Neuroscientists talk about 6 different layers which are often divided into more sub-layers the closer you look. Over the past years we have studied the structure of cortical columns and the connections within and between them in detail.
In our implementation, we have cortical column-like units that can each learn complete objects from sensorimotor data. They communicate with each other using a common communication protocol. Inspired by long-range connections in the neocortex, they can communicate with each other laterally to reach a quick consensus and can also be arranged hierarchically to model compositional objects. The common communication protocol makes the system modular and allows for easy cross-modal communication and scalability.
The SDK
In our SDK we will provide a general framework for learning with two different modules: Learning modules (LM) and sensor modules (SM). Learning modules are like cortical columns and learn structured models of the world from sensorimotor data. Sensor modules are the interface between the environment and the system and convert input from a specific modality into the common communication protocol.
An LM does not care from which modality it receives input and multiple LMs receiving input from different modalities can communicate effortlessly. Practitioners can implement custom SMs for their specific sensors and plug them into the system. A system can also use different types of LMs, as long as they all adhere to the common communication protocol. This makes the system extremely general and flexible.
More Information
We are in the process of preparing all our progress from the past years in a digestible format and will release it over the coming months. If you would like to be notified about updates, please email us, follow us on X, or subscribe to our Youtube channel. In the meantime we have compiled a document detailing the core principles and goals of the project.