{"id":14633,"date":"2024-03-20T18:08:56","date_gmt":"2024-03-20T18:08:56","guid":{"rendered":"http:\/\/TheNextWeb=1404938"},"modified":"2024-03-20T18:08:56","modified_gmt":"2024-03-20T18:08:56","slug":"this-startup-is-building-ai-that-can-fly-drones-and-make-its-own-decisions","status":"publish","type":"post","link":"https:\/\/www.londonchiropracter.com\/?p=14633","title":{"rendered":"This startup is building AI that can fly drones and make its own decisions"},"content":{"rendered":"\n

The debate on how far artificial intelligence can go revolves around questions about what actually constitutes human intelligence, and can a machine function similarly enough to a human brain?<\/span><\/p>\n

<\/span>While not shooting for AGI, UK-based Stanhope AI is building its models according to neuroscience principles, and using the predictive, hierarchic machinery that make up our brains for inspiration. <\/span><\/p>\n

The result is an <\/span>AI<\/span><\/a> that doesn\u2019t need training. It basically just needs to be told that it exists, provided a prior system of beliefs  \u2014 and then take off (literally) into the real world and learn from its surroundings using sensors. <\/span>Not unlike how you see, hear, and feel things that expand your knowledge, causing you to update (or reinforce) your worldview.<\/span><\/p>\n

A spinout from <\/span>University College London, the <\/span>startup<\/span><\/a> just raised \u00a32.3mn for its neuroscience-inspired \u201cagentic AI.\u201d We caught up with co-founder and CEO, professor of computational neuroscience Rosalyn Moran, to learn more about the startup\u2019s tech and vision for the future.<\/span>
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The layered \u2018brain\u2019 of Stanhope\u2019s AI<\/strong> <\/span><\/h2>\n

Stanhope AI\u2019s method builds on a theory that says that the brain has a model of the world, and is continuously trying to gather evidence to validate and update said model. <\/span><\/p>\n

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\u201cThe AI has a \u2018brain\u2019 a few levels deep, and at the very bottom of the brain are its sensors,\u201d Moran explains. The sensors, which for you and me would be our eyes, in this case are cameras and LiDAR. <\/span><\/p>\n

\u201cAnd then those feed into a predictive layer that will try and say, \u2018Okay, I saw a wall over there. Now I don\u2019t need to keep looking\u2019. And it\u2019s built into a more interesting cognitive prediction at the higher levels. So it\u2019s very much like a hierarchical brain.\u201d <\/span><\/p>\n

This is the same kind of prediction that our human brains engage in in order to make sense of the world and save energy (the brain is the most energy-demanding organ we have). This is a neuroscience principle called \u201cactive inference,\u201d part of the Free Energy Theory, developed by Moran\u2019s co-founder, pr<\/span>ofessor of theoretical neurobiology, Karl Friston. <\/span><\/p>\n

\u201cI don\u2019t need to check every pixel on the wall to make sure it\u2019s a wall \u2014 I can fill in a bit. So that\u2019s why we think the human brain is so efficient,\u201d Moran adds. <\/span>
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<\/span>Essentially, the way you experience the world is a result of how your brain <\/span>predicts<\/span><\/i> you will see it, in the service of energy efficiency. But credit to our brains, they then refines those predictions based on incoming sensory data. Stanhope AI\u2019s model does the same, using the visual input from the world around it. <\/span>It then makes autonomous decisions based on the new, real-time data. <\/span><\/p>\n

No massive training data sets required<\/strong> <\/span><\/h2>\n

Using this approach to AI differs significantly from traditional machine learning methods such as those used to train LLMs, which can only operate with the data they are provided by those who train them. <\/span><\/p>\n

\u201cWe don\u2019t train [our model],\u201d Moran says. \u201cThe heavy lifting is done in establishing the generative model, and making sure that it is correct and has consistent priors with where you might want it to operate.\u201d <\/span><\/span><\/p>\n

This is all theoretically fascinating, but for a startup to spin out of the lab, there needs to be real-world applications. Stanhope AI says that its AI can sit on autonomous machines, such as delivery drones and robots. The tech is currently in testing on drones with partners including Germany\u2019s Federal Agency for Disruptive Innovation and the Royal Navy.<\/span><\/p>\n

The greatest technological challenge the startup surmounted thus far was scaling from smaller models working in lab settings, to larger ones that can learn to navigate a much more expansive landscape.<\/span><\/p>\n

\u201cWe had to use three mathematical routes to do free energy calculations that were much more efficient, so that we could build much larger worlds for our drones,\u201d Moran states. She also adds that finding the right hardware that the company could access and control without having to rely on third parties also presented a significant engineering hurdle. <\/span><\/p>\n

New wave of agentic AI<\/strong><\/h2>\n

Stanhope AI\u2019s \u201cActive Inference Models\u201d are, the company says, truly autonomous and can rebuild and refine their predictions. This is part of a new wave of \u201cagentic AI\u201d which are, just as the human brain, <\/span>always trying to \u201cguess what will happen next\u201d by continuously learning from discrepancies between predictions and real-time data. There is no need for extensive (and expensive) prior training, and the approach also lowers the risk of AI \u201challucinations.\u201d  <\/a><\/span><\/p>\n

Notably, Stanhope\u2019s AI are white box models, with the \u201cexplainability built into its architecture.\u201d As Moran elaborates, \u201cWe make sure that it\u2019s working absolutely perfectly in simulation. If the AI, or the drone, does something strange then we really drill down on what it believed there, why it did what it did. So it\u2019s a very different way of developing AI.\u201d The idea, she says, is to transform the capabilities of AI and robotics and make them more impactful in real-world scenarios. <\/span><\/span><\/p>\n

The UCL Technology Fund<\/span> led Stanhope AI\u2019s \u00a32.3mn funding round. <\/span>Creator Fund, MMC Ventures, Moonfire Ventures, and Rockmount Capital also participated, along with several industry investors. <\/span>
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<\/span>Stanhope AI was founded in 2021 by Professor Rosalyn Moran, Director Professor Karl Friston and Technical Advisor Dr Biswa Sengupta.<\/span><\/p>\n

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