Patric Fulop

Generalisation is Key to AGI 🗝️ I keep hearing that we're on the verge of achieving AGI, but I believe current approaches won't get us there. Right now, we're placing all our hopes on neural networks, thinking that by pouring in more compute and data, we'll evolve them into AGI. Neural networks are effective tools for pattern recognition and prediction but often struggle with out-of-distribution generalisation. This means they excel in tasks similar to their training data but can falter when encountering novel or unfamiliar scenarios. The root of this issue lies in their focus on learning specific patterns rather than broader, underlying principles that could be applied to diverse contexts. For example, a neural network trained to identify cat breeds might perform well on familiar images of cats but could struggle with recognising a cat in unusual situations, such as wearing a costume or captured from a unique angle. This challenge in generalisation can impact their ability to reason and plan effectively, given the complexity and unpredictability of real-world scenarios. These limitations also have significant ethical implications. The demand for extensive data can lead to privacy concerns and amplify existing biases within the data. Furthermore, the potential for failures in unfamiliar contexts, such as in autonomous vehicles or medical diagnostics, underscores the risks associated with current neural network technologies. To move toward Artificial General Intelligence, it is crucial to explore alternative approaches that can better generalise to new and diverse data. These methods should focus on understanding fundamental principles and relationships rather than merely memorising patterns from training sets. #ArtificialGeneralIntelligence #DataEthics #TechInnovation

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One of the best blogs I've come across on quantization, with outstanding visualizations. Highly recommend a read for anyone looking to understand LLM quantization in more depth! 📐 - https://lnkd.in/e_WTgq6x At Felafax AI (YC S24), we are building Infra to make full precision multi-GPU fine-tuning easier. #AI #Infra #LLM

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The MVP for Fakt AI is live (heavy emphasis on the M!) We are one step closer to a world where any statement can be fact checked instantly. It's taken a while but it's nice to finally get something 'done'. It's barebones and only focuses on questions that require academic papers to answer them. Since this was the main issue that inspired me to build Fakt AI in the first place, I think that's fine. I will continue development over the coming weeks and months. Next step will be deploying this as a front-end app so it's easier for people to try. Sooooooo many areas I can still improve. Give it a whirl and tell me your thoughts. https://lnkd.in/e_mW5S9Z

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Thanks for sharing Brent Hoberman … wonderful (but not surprising) how personal networks and amazing mentors have helped the remarkable Alex Kendall and Wayve team become #GlobalChampions plus .. “The company also benefited from five aspects of being in the UK: 🎓Academic thought leadership, with great Professors such as Zoubin Ghahramani and Roberto Cipolla 👩‍💻Talent density, thanks to multiple leading global companies w large UK AI centres 🏛️Regulatory regime & supportive government, with testing allowed on UK roads & autonomous vehicle bill going through parliament 🇬🇧Leading corporates with UK HQs willing to partner 🌎Skilled immigration regime allowing immigrant founders to move to the UK I hope that Wayve acts as a beacon to other international founders, to show how a once contrarian approach can attract the world’s best brains and capital, and how the UK can focus on technology opportunities and encourage broader entrepreneurship across the country to drive more growth. Wayve announces Europe's largest AI raise - $1bn led by SoftBank, Microsoft and NVIDIA. This should be celebrated as a pivotal deal for the UK to stake its claim to be a global leader in the platform shift for embodied AI.”  #ScaleUp #AI #7Cs #Leadership #EveryoneDeservesACoachMentor

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In an essay last week, Sam Altman, the CEO of OpenAI, argued that the accelerating capabilities of AI will usher in an idyllic “Intelligence Age,” unleashing “unimaginable” prosperity and “astounding triumphs” like “fixing the climate.” It’s a promise that no one is in a position to make—and one that, when it comes to the topic of climate change, fundamentally misunderstands the nature of the problem. More maddening, the argument suggests that the technology’s massive consumption of electricity today doesn’t much matter, since it will allow us to generate abundant clean power in the future. That casually waves away growing concerns about a technology that’s already accelerating proposals for natural-gas plants and diverting major tech companies from their corporate climate targets . ... https://lnkd.in/e9HxNcRP

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When I talk about BaldrAI, many people ask if we're using LLMs (Large Language Models). If I ask them where they think LLMs would be useful it appears that they believe the hype and assume that LLMs are AGI (Artificial General Intelligence). There is only one way to say this: LLMs are not and can never be AGI. The long and short of this is that Kaspar Von Grünberg is right! LLMs are massively over-rated, and I think this mostly comes from the way that OpenAI talks about GPT and how the next version will have "PhD-level intelligence". What OpenAI claims is fundamentally impossible as the critical reasoning skills of LLMs are literally non-existent. It doesn't matter how much data you train it on, it cannot think, it can only guess the next word. This gives the illusion of thought, but it is not actually thinking, it's like a child who has studied for a test by memorizing the answer sheet - it has no understanding, only the answers to the questions. We must not hand the world to these models or treat them like gods. If we do, Azimov will be right and the robots will lead to the fall of humanity.

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