Neuromorphic computing, a field that aims to make artificial intelligence more brainlike, is gaining traction among researchers. The inspiration for this new type of AI comes from studying the tiny worm Caenorhabditis elegans, whose brain is just about the width of a human hair. Despite its minuscule size, this worm’s brain efficiently coordinates complex movements as it forages for food.
Daniela Rus, a computer scientist at MIT, is so impressed by the elegance and efficiency of the worm’s brain that she co-founded Liquid AI, a company dedicated to building a new type of artificial intelligence inspired by it. Rus is part of a growing wave of researchers who believe that incorporating insights from neuroscience can lead to leaner, nimbler, and perhaps smarter technology.
While neuromorphic technology is not expected to completely replace traditional computers or AI models, experts like Mike Davies from Intel’s Neuromorphic Computing Lab envision a future where various systems coexist. The goal is to create AI that can adapt quickly to new situations and learn from single experiences, much like the human brain.
One of the key components of neuromorphic computing is the development of artificial neurons that mimic the behavior of real neurons. These neurons are part of spiking neural networks, which simulate the signals of an actual brain by creating discrete spikes that carry information through the network. This approach differs from traditional AI models, which rely on deep learning networks composed of interconnected artificial neurons that process information in a more brute force and inefficient manner.
Efforts to make AI more brainlike have led to the creation of hardware, architecture, and algorithms that closely resemble biological brains. For example, the neuromorphic computer chip BrainScaleS-2, developed as part of the Human Brain Project, combines sparsely connected spiking neurons physically built into hardware. This chip stores memories and performs computations in the neural connections, unlike traditional computer hardware that separates memory and processing, leading to energy inefficiencies.
Researchers are optimistic about the potential of neuromorphic systems to improve AI efficiency. For example, a spiking network running on the BrainScaleS-2 hardware used significantly less energy than a simulation of the same network running on a CPU. This demonstrates the promise of neuromorphic computing in creating more energy-efficient and faster AI systems that can rival traditional deep learning networks.
Training the spiking system to recognize handwriting with a hundredth of the typical system’s energy consumption has opened up new possibilities in the world of artificial intelligence. The team behind this breakthrough has paved the way for spiking neural network hardware to be scaled up and distributed, making it more accessible for a wider range of applications. This advancement could revolutionize computation and make AI more energy-efficient.
While the academic teams working on BrainScaleS-2 are not currently planning to scale up the chip, major tech companies like Intel and IBM are already taking steps in that direction. IBM introduced its NorthPole neuroÂmorphic chip in 2023, combining memory and processing for energy savings. Intel’s Hala Point, launched in 2024, boasts 1.15 billion electronic neurons, making it the largest neuromorphic system in the world.
The Loihi 2 chips inside Hala Point, similar to BrainScaleS-2, use sparsity and combine memory and processing to improve efficiency compared to traditional computer hardware. Researchers have demonstrated significant energy savings while running deep learning algorithms on these chips, showing their potential for a wide range of tasks.
Developers can reconfigure spiking neural networks within these chips in various ways to create new architectures and implement different algorithms. This flexibility allows for innovation and the development of more energy-efficient computing methods. By introducing sparsity in network activity and utilizing memory efficiently, these neuromorphic systems can achieve remarkable energy savings.
Recent research has introduced new ways to model neurons in spiking networks, further enhancing the capabilities of these systems. These advancements open up new possibilities for developing algorithms that could make the most of this hardware and offer even higher energy savings in the future.
Neuromorphic hardware has the potential to revolutionize computing by making it more energy efficient and capable. With ongoing research and development, this technology could lead to the creation of algorithms that have not even been imagined yet. The adaptability and efficiency of these systems offer a glimpse into the future of computing and AI.
One of the key features of a living brain is its ability to learn continuously and efficiently. Researchers have drawn inspiration from the simple yet effective learning mechanisms of organisms like C. elegans to develop more adaptable AI models. Liquid neural networks, inspired by the learning capabilities of small organisms, offer a new approach to AI that is fluid and dynamic.
Unlike traditional deep learning networks that remain static after training, liquid neural networks can adapt and change their parameters over time. This flexibility allows for continuous learning and adjustment, making them more efficient and versatile. By incorporating techniques from standard deep learning with a more adaptive approach, researchers are exploring new frontiers in AI development.
Liquid AI, a cutting-edge artificial intelligence company founded by Rus and Hasani, is revolutionizing the field of AI with their innovative approach inspired by biological systems. The company has developed liquid neural networks that learn and adapt based on the inputs they receive, much like how biological systems evolve.
Hasani and his team created mathematical equations that mimic the activation of neurons in a worm’s brain in response to changing information. These equations govern the behavior of the liquid neural network, which can run in real time thanks to an approximate solution found by the team.
In a groundbreaking study in 2023, Rus, Hasani, and their colleagues demonstrated that liquid neural networks outperformed traditional AI models in adapting to new situations. They trained both liquid neural networks and deep learning networks to pilot a drone towards different objects in various environments. The smallest liquid network, with only 34 artificial neurons, surpassed the largest standard AI network tested.
Liquid AI has even collaborated with the U.S. military to test their model on an actual aircraft, showcasing the practical applications of their technology. The company has scaled up its models with the introduction of LFM-7B, a 7-billion-parameter liquid neural network that outperforms traditional language models of similar size.
Rus expresses excitement about the potential of Liquid AI to transform the future of AI and computing. While the approach may be computationally intensive, it represents a significant step towards more realistic AI that closely mimics the brain’s functioning.
In parallel, other researchers are drawing inspiration from the human brain’s neocortex, a region crucial for higher-order thinking. The neocortex contains minicolumns that play a key role in intelligence, according to neuroscientist Jeff Hawkins. His theory suggests that these minicolumns track and model sensations and ideas, contributing to our understanding of the world.
Numenta, a company co-founded by Hawkins, is exploring the Thousand Brains Project that aims to combine computing architecture with new algorithms inspired by the neocortex. Early simulations show that this architecture can recognize complex objects and learn about the world in real-time, akin to Liquid AI’s algorithms.
The success of these innovative approaches hinges on the synergy between algorithms, architecture, and hardware. Custom hardware designed for specific AI architectures could enhance efficiency and effectiveness, paving the way for new possibilities in AI and computing.
As researchers continue to explore new combinations of neuromorphic hardware, architectures, and algorithms, the future of AI and computing holds exciting prospects. By investing time and resources into these cutting-edge technologies, the field stands to unlock new realms of innovation and advancement. The world is constantly evolving, and with it, so are the ways in which we communicate and connect with one another. In today’s fast-paced society, technology plays a crucial role in facilitating these interactions. From social media platforms to messaging apps, the options for staying connected are endless.
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