Efstratios Gavves

Last Friday, I had a fantastic discussion with Prof. Henk Corporaal at TU/e. As a young AI research, I had the opportunity to consider how we can scale alongside the evolution of artificial intelligence, 𝘵𝘩𝘪𝘯𝘬𝘪𝘯𝘨 and 𝘭𝘦𝘢𝘳𝘯𝘪𝘯𝘨. I've read the bitter lesson (https://lnkd.in/eDJFn5Ks) many times, and learned that search and learning are two methods that continue to scale with increased computation even as the available computation becomes very great. This is clearly evident in how AI systems have expanded their capacity to search and learning with increased computation. So we are also thinking what is the future direction of AI research. We cannot predict. I’m not even sure if there is renewed interest in traditional computer vision models amidst the current dominance of foundational models. But I think that, as humans, the ways to scaling with the evolution of AI are 𝘵𝘩𝘪𝘯𝘬𝘪𝘯𝘨 and 𝘭𝘦𝘢𝘳𝘯𝘪𝘯𝘨. A nice shared book of Reinforcement Learning: https://lnkd.in/eenGRskD. Will learn reinforcement learning again:)

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Early research in computational neural networks were based on a simple model of spiking neurons, developed by Wilson and Cowan in the late 60s/early 70s. (Cowan was also a good friend of my PhD advisor) This simple model has been very instructive in understanding long-term memory, (visual) hallucinations, Epilepsy, and even elementary decision-making processes In relation to NNs, this model showed that neural populations can settle into stable patterns of activity, leading to the conceptual basis for Hopfield's idea that networks could have stable attractor states representing memory patterns. Moreover, the idea that neural activity can be described in terms of an energy landscape ala Hopfield was influenced by understanding neural dynamics from models like Wilson-Cowan. It also inspired the development of RNNs, as these networks incorporate feedback loops, mimicking the recurrent connections in biological neural circuits modeled by the Wilson and Cowan model. Simple models can go a long way https://lnkd.in/g58HJ4K4

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.The metropolitan-scale quantum link. Cartographic layout of the distant quantum link and the route of the deployed fiber bundle, with similar quantum processor nodes in Delft and The Hague. Fiber length between node Delft and midpoint is 15 km and between node The Hague and midpoint is 10 km, with losses on the quantum channels at 5.6 and 5.2 dB, respectively. #Delft #Hague #Coherence times #solid-state qubits #Quantum networks #Diamond spin qubits #Quantum frequency converters(QFC) https://lnkd.in/gzqWcY4f

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(POST DECALE DU VENDREDI) Exciting Breakthrough in AI: Sarcasm Detection! Researchers at the Campus Fryslân - University of Groningen have crafted an algorithm that accurately detects sarcasm 74% of the time by analyzing audio and text, a game-changer for AI communication. This innovation has vast implications, especially in AI healthcare, aiding those with autism, dementia, and schizophrenia and enhances sentiment analysis. More info in the paper from The Guardian where some journalists and columnists do indeed master sarcasm 😎 #AI #MachineLearning #HealthcareInnovation #SarcasmDetection #TechNews https://lnkd.in/eXpvA8CB

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A peek inside art objects🔎💻 An X-ray scanner, some small metal balls, and a newly developed algorithm. That is all you need to make a 3D model that enables you to look inside art objects without dismantling them. Thanks to the research of Francien Bossema (Centrum Wiskunde & Informatica and Leiden Institute of Advanced Computer Science - LIACS), museums can now use existing X-ray equipment as CT scanners, without having to buy such a costly and complicated device. The picture shows an example of the scans that can be made using Bossema's method 🖼📸 Read more about Bossema’s research ➡ https://lnkd.in/eTqcJQkg #museums #algorithm #LeidenScience #LIACS #CWI

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Today, we celebrate the birth of a titan in the world of computer science—Edsger Dijkstra, born on May 11, 1930. A visionary who quite literally helped us find our way in the maze of computing! Dijkstra was the brain behind many of the algorithms that still dictate the flow of our digital lives today. Most famously, he developed the shortest path algorithm that bears his name—the Dijkstra’s algorithm. This brilliant piece of logic helps determine the shortest path from point A to point B efficiently and is crucial in fields ranging from networking to geographical mapping. This was one of the first algorithms that l learned as an undergrad student. Beyond his technical prowess, Dijkstra was known for his essays on programming, advocating for clarity and simplicity in code long before it was in vogue. His thoughts on the discipline of programming and software development have influenced countless developers and are still revered in academic and professional circles. As we navigate through complex projects and tight deadlines, let’s channel a bit of Dijkstra’s clarity and foresight in our work. Whether you're coding, strategizing, or just trying to find the most efficient route to your next meeting, remember his contributions and how they’ve made doing what we do a lot easier!

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I am excited to present at ACM CHI Conference about our recent publication on Explainable AI! If you are attending CHI, I would love to swing by and say hi! Augment HCI KU Leuven #explainableai

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Remember tomb M1093 from 秦家嘴？ New article just appeared with a few more details about this tomb on https://lnkd.in/gBfMk9cc with thanks to Rens Krijgsman 武致知 for alerting me to this new article First, more information about the tomb itself. It seems that the conditions of preservation are not of the best kind; but a lot has been preserved from it nonetheless. The tomb is dated mid-Warring States, from the Chu 楚 state. As noted, it has been looted before the archeologists could get to it. But, because a lot of the material was positioned is a sort of separate chamber (to the right of the coffin), a lot has been preserved (see images #2, one of which below) Objects preserved in it are lacquer; a few metal ones; and pottery vases. The tomb occupant is said to be a common person; their remains are gone (likely because of the looting).

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"Researchers at the University of Twente have developed a new method that allows them to precisely control chemical reactions using metal ions. This marks an important step toward computers that function like the human brain. They recently published their findings in Nature Communications. All living things can respond to their environment through a sequence of complex chemical reactions. This form of information processing requires far less energy than that of digital computers. As a result, the underlying processes are fundamentally different. For decades, researchers have been searching for a way to recreate nature's information processing using molecules." #chemistry #analyticalchemistry #informationprocessing

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With the recent Nobel prizes in physics and chemistry going to AI and AI respectively, this is the time to talk about the opportunities (and risks) of using AI in all science and humanities research and knowledge production. Looking forward to discussing 'Science, Shadows & Simulacra' at this event today, and listening to the other interesting talks that are part of the program. Bonus points for anyone who guesses what shadows and simulacra I am referring to here ;).

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Researchers at Radboud University have significantly advanced brain-computer interface technology, developing an AI system capable of reconstructing highly accurate images based on brain activity recordings. This advancement represents a major step forward in our ability to interpret and visualize neural signals. The study utilized both functional Magnetic Resonance Imaging (fMRI) scans of humans and direct electrode recordings from a macaque monkey to capture brain activity while subjects viewed various images. ➜ Check the news at: https://lnkd.in/dAnkgHEn ➜ Vendorvise's official Instagram: https://lnkd.in/ebK2KQ2C ➜ Vendorvise's Linktree: https://lnkd.in/dQdbMdth 🤖 If you enjoy reading my posts about AI and tech news, hit the like button and share with your friends to support my content! #ai #artificialintelligence #ainews #aipowered #news #blog #researchers #radbound #university #brain #computer #interface #aisystem #fmri #neuralsignals #generativeai #aitech #technology #content #socialmedia #future #new #business #techtrends #innovation #launch #success #markets #marketing #people #industry

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Two of my favorite things: machine learning and turbulence. Interesting read by Rik, Wouter, and Daan Crommelin. "Fully resolving turbulent flows is computationally expensive. Therefore, we typically work with under resolved simulations with added turbulence model. Recently, Neural Networks are being used as turbulence model with as target to reproduce the fully resolved flow field. This is a high dimensional problem. However, scientists are often only interested in a few quantities which are derived from these flow fields, such as the average energy. We propose a simple method which only uses these quantities as "target", resulting in a much lower dimensional problem. We compare this method with convolutional neural networks and find that both can successfully correct long-term simulations." Read here: https://lnkd.in/etBe9BnX Aside, if you are interested in this overlap of CFD/AI and want to get started in your learning journey -> checkout my book I have written on this topic: https://geni.us/JaXp #turbulence #ai #machinelearning #datascience #aerospace #aerodynamics #cfd #cae

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Great opportunities

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Will we never learn? "By 2027, the global AI sector could annually consume as much electricity as the Netherlands, according to one recent estimate. And a new study in Nature Computational Science identifies another concern: AI’s outsize contribution to the world’s mounting heap of electronic waste. The study found that generative AI applications alone could add 1.2 million to five million metric tons of this hazardous trash to the planet by 2030, depending on how quickly the industry grows." Meanwhile ~every company is scrambling to find more nails to smash with their new AI hammers. Maybe we could lay off using enormously wasteful AI products to generate banal and meaningless social media posts, or generating the first two useless lines of dialog in a chat session before the user asks "can I just talk to a f***ing human?!"

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✅️ #Reference van Rooij, I., Guest, O., Adolfi, F. et al. Reclaiming AI as a Theoretical Tool for Cognitive Science. Comput Brain Behav (2024). https://lnkd.in/d8typUgS A few days ago, the #SamAltman prediction of an #ASI "in a few thousand days" was made, but what about the #AGI? The Artificial General Intelligence, such as #AI as smart as the human brain, seems instead in each AI Company announcement to be just around the corner, closer than ever, but in reality, that's questionable. Let me clarify from the beginning why #ThisPaperMatters and you can't miss it: 🗣 "Despite the current hype surrounding "impending" AGI, this practical infeasibility actually fits very well with what we observe (for example, running out of quality training data and the non-human-like performance of AI systems when tested rigorously)." The latest publication from Computational Brain & Behavior by the paper's lead author, Prof. Van Rooij (Radboud University ), pours cold water on the hype and marketing promoting the relentless pursuit of AI. 💧 Frequently in my posts, you have found how that challenge is #overshadowing human inalienable principles and polarizing people against Institutions where regulations are protecting privacy and these principles simply because their work is "slowing down" the diffusion or adoption of the "latest features," outlined for achieving human-like performance in "reasoning" or breaking down concepts to emulate the underlying mechanisms of the #CognitiveScience of our brain. 💡 #What do we really want to achieve? And #why? 🤷 That's not at all a "wow" effect, as with any new paper I share, but to point out and underline how researchers are well aware of the #complexity of creating human-like or human-level artificial intelligence through current ML approaches is practically #infeasible, despite the current hype surrounding AGI. 💡The risk, in my opinion, is to limit or simply reframe human thinking as a #downgrade simply because an AI, according to some #benchmarks, can be "smarter" and "faster" at solving quizzes. So, is our life just a #puzzle? 🧩 ✅️ And more: ➡️ The #Makeism which is the idea that cognition can be understood as a form of computation: known as "#SyntheticMethodology" or "understanding by design and building" it refers to the quote: 🗣"What I cannot create, I do not understand" by physicist Richard Feynman. ➡️ #Intractability ✔️AI-by-Learning is NP-hard ✔️Resource requirements grow exponentially with input size ✔️Even for moderate inputs (e.g., 15-min!), required samples exceed atoms in universe ✔️"Big Data" is insufficient to cover the astronomical space of possible situations ✔️AI companies are running out of usable data ✔️Datasets becoming more homogeneous, not more complex 💡Isn't it perhaps the case to reconsider the role of AI in cognitive science as a tool to understand the natural cognition rather than replicate it? Let's continue in the comments.👇 #Psychology #Cognitive #AI

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Even the heaviest particles experience the usual quantum weirdness, new experiment shows | by techguru Chriet | Urk Tech Review One of the most surprising predictions of physics is entanglement, a phenomenon where objects can be some distance apart but still linked together. The best-known examples of entanglement involve tiny chunks of light (photons), and low energies. At the Large Hadron Collider in Geneva, the world's largest particle accelerator, an experiment called ATLAS has just found entanglement in pairs of socks: the heaviest particles known to CERN scientists. https://lnkd.in/eKx2zqNn Bertlmann's socks and the nature of reality | CERN server | John Bell, 1980 https://lnkd.in/e_qYr9bx Reinhold Bertlmann | Wikipedia https://lnkd.in/eBmwd3fS #quantum #physics #weirdnss #reality #entanglement #Bertlmann #socks #CERN #LHC #ATLAS #experiment

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Feynman vanuit een andere dimensie : ,,Language isn’t classical, dammit!’’ 🤓 The Origin of Quantum Mechanical Statistics: Some Insights from the Research on Human Language | by Diederik Aerts et al. | ArXiv Identical systems, or entities, are indistinguishable in quantum mechanics (QM), and the symmetrization postulate rules the possible statistical distributions of a large number of identical quantum entities. However, a thorough analysis on the historical development of QM attributes the origin of quantum statistics, in particular, Bose-Einstein statistics, to a lack of statistical independence of the micro-states of identical quantum entities. We have recently identified Bose-Einstein statistics in the combination of words in large texts, as a consequence of the entanglement created by the meaning carried by words when they combine in human language. Relying on this investigation, we put forward the hypothesis that entanglement, hence the lack of statistical independence, is due to a mechanism of contextual updating, which provides deeper reasons for the appearance of Bose-Einstein statistics in human language. However, this investigation also contributes to a better understanding of the origin of quantum mechanical statistics in physics. Finally, we provide new insights into the intrinsically random behaviour of microscopic entities that is generally assumed within classical statistical mechanics. https://lnkd.in/dtTyntfm #Bose #Einstein #statistics #entanglement #quantum #mechanics #physics #QM #human #language #contextual #updating

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Very interesting paper that highlights how readout needs a smart brain able to perform classical computation in real time. Once again proof that quantum computing is a lot more about classical compute than we would like to think.

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