Generative Pre-Trained Transformers (GPTs) are hyped to revolutionize robotics. Here we question their utility. GPTs for autonomous robotics demand enormous and costly compute, excessive training times and (often) offboard wireless control. We contrast GPT state of the art with how tiny insect brains have achieved robust autonomy with none of these constraints. We highlight lessons that can be learned from biology to enhance the utility of GPTs in robotics.

Researchers understand the structure of brains and have mapped them out in some detail, but they still don't know exactly how they process data -- for that, a detailed "circuit map" of the brain is needed. Now, scientists have created just such a map for the most advanced creature yet: a fruit fly larva. Called a connectome, it diagrams the insect's 3016 neurons and 548,000 synapses, Neuroscience News has reported. The map will help researchers study better understand how the brains of both insects and animals control behavior, learning, body functions and more. The work may even inspired improved AI networks.

We have developed a model for online continual or lifelong reinforcement learning (RL) inspired on the insect brain. Our model leverages the offline training of a feature extraction and a common general policy layer to enable the convergence of RL algorithms in online settings. Sharing a common policy layer across tasks leads to positive backward transfer, where the agent continuously improved in older tasks sharing the same underlying general policy. Biologically inspired restrictions to the agent's network are key for the convergence of RL algorithms. This provides a pathway towards efficient online RL in resource-constrained scenarios.

Neuromorphic architectures are ideally suited for the implementation of smart sensors able to react, learn, and respond to a changing environment. Our work uses the insect brain as a model to understand how heterogeneous architectures, incorporating different types of neurons and encodings, can be leveraged to create systems integrating input processing, evaluation, and response. Here we show how the combination of time and rate encodings can lead to fast sensors that are able to generate a hypothesis on the input in only a few cycles and then use that hypothesis as secondary input for more detailed analysis.

Eleni Vasilaki is Professor of Computational Neuroscience and Neural Engineering and Head of the Machine Learning Group in the Department of Computer Science, University of Sheffield. Eleni has extensive cross-disciplinary experience in understanding how brains learn, developing novel machine learning techniques and assisting in designing brain-like computation devices. In this interview, we talk about bio-inspired machine learning and artificial intelligence. I am interested in bio-inspired machine learning. I enjoy theory and analysis of mathematically tractable systems, particularly they can be relevant for neuromorphic computation.

WHERE are all the intelligent robots? Despite huge recent strides in artificial intelligence, autonomous robots answering our every beck and call are still a long way off. To make that leap, we are going to need a revolution in AI – and I believe insects will be at the heart of it. Big ideas in AI seem to come in waves. The first was the notion that creating an intelligent machine involves writing down enough rules for it to follow.

Today the UK natural intelligence company Opteran has raised around €2.3 million in seed funding to pioneer its lightweight, silicon-based approach to autonomy, created by testing insect brains, in what to some would sound a little like a Black Mirror episode. Opteran is a University of Sheffield spin-out based on eight years of research by Professor James Marshall and Dr. Alex Cope into insect brains as part of the Green Brain and Brains on Board projects. Although insects have smaller brains, they are still capable of sophisticated decision making and navigation using optic flow to perceive depth and distance. The Opteran team state that this is a far more efficient, robust and transparent way to achieve autonomy than current deep learning techniques, enabling the team to reverse-engineer insect brains to produce algorithms requiring no data centre or extensive pre-training. It means Opteran can mimic tasks such as seeing, sensing objects, obstacle avoidance, navigation and decision making.

Scientists at the Energy Department's Argonne National Laboratory have pioneered a cutting-edge neuromorphic computer chip--modeled off the brains of bees, fruit flies and other insects--that can rapidly learn, adapt and use substantially less power than its conventional computer chip counterparts. The physicist leading an interdisciplinary team that developed the state-of-the-art design recently spoke to Nextgov about the chips' potential to advance artificial intelligence. "If we start from a biology standpoint, we use ourselves, humans, as a model for intelligent systems, of course. But there are many other branches that evolution has taken where you can sort of reach big computational power," Angel Yanguas-Gil, principal materials scientist in Argonne's Applied Materials division, said. "Insects are one of these areas."

The Pentagon's research arm is looking beyond the human brain to build artificial intelligence. In a recent call for submissions, DARPA revealed that it's looking for ways to take the brains of'very small flying insects' and model their functions in AI robots. The proposal looks to pave the way for robots that are smaller, energy-efficient and easier to train. DARPA is looking beyond the human brain to build artificial intelligence. In a call for proposals, it revealed that it's looking for ways to take insect brains and model their functions in AI robots DARPA is looking for proposals that understand the sensory and nervous systems in miniature insects and can turn them into'prototype computational models.' These models would then be integrated in some type of hardware that emulates how insects think and behave, the agency explained.

The Pentagon's emerging technologies unit put out a call last week for proposals that use insect brains to control robots -- because they could be used to create efficient new models for artificial intelligence, but also because they could be used to explore the meaning of consciousness. "Nature has forced on these small insects drastic miniaturization and energy efficiency, some having only a few hundred neurons in a compact form-factor, while maintaining basic functionality," reads a document in the proposal. "Furthermore, these organisms are possibly able to display increased subjectivity of experience." It goes on to say that there's evidence suggesting that "even small insects have subjective experiences, the first step towards a concept of'consciousness.'" The Defense Advanced Research Projects Agency (DARPA) is famed for funding projects that led to the early internet.