Marcus Frei

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Marcus Frei

Marcus Frei

@MarcusFrei_

Entrepreneur, co-founder and student. Research on the paradox of machine consciousness. Member @EUdigitalsme FG AI, Advisory Board https://t.co/iO3XXVTOrN

Southern Germany or Cal (: Katılım Mayıs 2013
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Marcus Frei
Marcus Frei@MarcusFrei_·
The future of #robotics isn't about reinventing human, will be less articulated, of non-human #AI -- it's a machine's world we adapt to;
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Andreas Köpf
Andreas Köpf@neurosp1ke·
Daughter: „looks nice, but a bit slow“ .. ok Claude - please auto-research fast inference on Jetson Thor, my cuda graphs baseline is 691ms e2e …5h 130 experiments later auto-research plateaued at 285 ms. Integrating into real robot repo easy >2x speedup 🤯
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Kyber Labs
Kyber Labs@KyberLabsRobots·
🪶Our hand can take a hammer hit but also detect a feather. The finger moves until it meets a tiny resistance and stops, with no tactile sensors. Back drivability and torque transparency let it feel the world through its own drive currents, enabling simple, reliable interaction.
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Borjan (Boki) Milinković
Borjan (Boki) Milinković@MilinkovBorjan·
🎊📜 NEW PAPER 📜🎊 Can we seriously build synthetic consciousness? And if so, where do we start? I’m super excited to present recent publication in Neuroscience & Biobehavioral Reviews where @jaaanaru and I confront this challenge head on. sciencedirect.com/science/articl… 1/n
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a16z
a16z@a16z·
Thinking Machines chief scientist John Schulman on what’s changed in AI since 2015–2017: “Previously the people were a bit weirder.” “Engineering skill matters more now… as opposed to research, taste and the ability to do exploratory research.” “There’s so much low hanging fruit just from scaling the simple ideas and executing on them well.” “People who have more of a software engineering background have more of an advantage now.” @johnschulman2 @thinkymachines @mntruell @cursor_ai
Michael Truell@mntruell

A conversation with @johnschulman2 on the first year LLMs could have been useful, building research teams, and where RL goes from here. 00:20 - Speedrunning ChatGPT 09:22 - Archetypes of research managers 11:56 - Was OpenAI inspired by Bell Labs? 16:54 - The absence of value functions 18:23 - Continual learning 21:09 - Brittle generalization 24:05 - Co-training generators and verifiers, GANs 27:06 - John’s personal use of AI for research 28:54 - Day in the life 33:01 - Slowdowns in consequential ML ideas 36:21 - "Peer review" within the labs 39:19 - Distribution shift in researchers 43:33 - Future of RL 45:33 - Will the labs coordinate if the world needs them to? 44:46 - Forecasting ills in AGI and engineering 47:53 - Thinking Machines

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Nicolas Rouleau
Nicolas Rouleau@DrNRouleau·
My essay on the brain as a electromagnetic receiver of consciousness, entitled "An Immortal Stream of Consciousness" was one of the winners of the Bigelow Institute for Consciousness Studies (BICS) @BigelowInst Essay Competition. Is survival possible? Read it here: tinyurl.com/2hnscxh2
Astronomy Vibes@AstronomyVibes

“My brain is only a receiver, in the Universe there is a core from which we obtain knowledge, strength and inspiration. I have not penetrated into the secrets of this core, but I know that it exists” ~ Nikola Tesla

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Apptronik
Apptronik@Apptronik·
✨ Incredible progress from @GoogleDeepMind on open-ended robot reasoning, featuring Apollo!  Check out Apollo responding to complex instructions and adapting to changing contexts with @FryRsquared
Google DeepMind@GoogleDeepMind

Pre-programmed backflips are fun, but open-ended reasoning is the real challenge for robots. 🤖 @Fryrsquared stepped inside the lab to see how we’re building agents that can understand context and figure things out for themselves – rather than just following a script. Watch our bonus episode ↓ goo.gle/4q1MGuv

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Jaan Aru
Jaan Aru@jaaanaru·
Under challenging conditions, human vision turns into iterative problem solving. Typical deep learning algorithms don't capture this behavior. Our paper out now in Plos Computational Biology Led by @tarunkhajuria journals.plos.org/ploscompbiol/a…
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Ben Goertzel
Ben Goertzel@bengoertzel·
As an AI expert who totally doesn't think LLMs are the path to AGI, but uses LLMs very avidly to get things done, here is my take on the utility of top LLMs currently: 1) If you don't know what you're doing in a certain domain, you can get interesting and valuable results, but they will be mixed up with garbage and you'll have trouble identifying the garbage.... In some cases automated methods can be used to test/filter results but this remains mostly dodgy in real-world cases. 2) If you DO know what you're doing in some domain, but are NOT willing to creatively adapt your work-style to the LLMs, or are oriented toward proving the LLMs are dumb and flawed rather than making the most of them ... then you won't get much out of them except time-saving on some rote-ish tasks 3) If you DO know what you're doing in some domain and ARE willing to open-mindedly and creatively work with the LLMs to route around their shortcomings and leverage up their strengths, you can amplify your abilities and output DRAMATICALLY.. though still with some tradeoffs: in many respects LLM-assisted products are still not as good as expert-human-crafted products, though they are also often better in various respects (and can often be produced SO much faster)
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a16z
a16z@a16z·
Marc Andreessen posted: “High IQ experts work for mid IQ generalists.” We asked him to elaborate. “We probably all underrate intelligence… The people who are in the fields that involve intelligence probably overrate intelligence.” @pmarca
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Sebastian Risi
Sebastian Risi@risi1979·
I’m beyond excited to announce our MIT Press book on Neuroevolution! An HTML version is now available for free on neuroevolutionbook.com, with a print edition coming out later in 2026. Real intelligence is not static; it evolves. For decades, the field of neuroevolution has pursued this necessary adaptability. Our book chronicles its development, from early concepts to its modern integration with deep learning and reinforcement learning, exploring its potential for understanding the origins of intelligence and its real-world applications. And the companion webpage is more than just a book site! It comes equipped with interactive demos, videos, exercises, and tutorials to allow everyone to experience neuroevolution in action. Check it out and let us know what you think! It was a pleasure to work on this book over the last 4+ years with David (@hardmaru), Yujin (@yujin_tang), and Risto. We are incredibly proud of the result and look forward to celebrating! We hope to connect with many of you at NeurIPS. We are very grateful to Melanie Mitchell (@MelMitchell1) who provided a fantastic foreword. To quote her: “The next big thing in AI is coming, and I suspect that neuroevolution will be a major part of it”. We think so too!
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Luiz Pessoa
Luiz Pessoa@PessoaBrain·
𝗜𝘀 𝘁𝗵𝗲 𝗯𝗿𝗮𝗶𝗻 𝗮 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝗻𝗲𝘁𝘄𝗼𝗿𝗸? In a sense yes, but does network science help us understand the brain as a complex system? Intriguing paper. If anything the paper has 800+ refs! doi.org/10.1016/j.plre…
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