The Strawberry Problem: Can AI Really Think? "Imagine you're at a dinner party and someone offers you a strawberry. But, there's a catch - it's been genetically modified to taste like a combination of a strawberry and a pineapple. Would you eat it? This thought experiment, known as the 'Strawberry Problem,' is a classic challenge for AI systems. Can they truly understand the nuances of human reasoning and decision-making? OpenAI's latest research tackles this very question, pushing the boundaries of AI's ability to think and reason like humans. The results are fascinating! Read more about the Strawberry Problem and the future of AI reasoning: https://lnkd.in/gbdA-E3Z What do you think? Can AI truly replicate human thought processes, or will it always be limited by its programming? Share your thoughts! #AI #Reasoning #StrawberryProblem #OpenAI #FutureOfWork #Innovation"
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Remember the movie "Her"? Where an AI became an incredibly lifelike personal assistant and companion, blurring the lines between technology and human emotion? With OpenAI's newest release, GPT-4O, we're stepping closer to that reality. OpenAI has just unveiled GPT-4O, a groundbreaking advancement in AI that pushes the boundaries of what we thought possible. This new model utilizes diffusion models to better understand the intricate structures and interactions of not just proteins, but smaller molecules like DNA, RNA, and ligands. It's a leap forward in scientific research, potentially revolutionizing drug development and personalized medicine. Imagine an AI that can not only comprehend and respond to complex human queries but also assist in scientific discoveries that can change lives. GPT-4O promises enhanced precision, deeper understanding, and a more human-like interaction. As we embrace this future, it's crucial to balance the benefits with ethical considerations. How we integrate such powerful AI into our lives and industries will define our technological and societal progress. Let's discuss: How do you envision using GPT-4O in your field? What are your hopes and concerns about this next step in AI evolution? #ArtificialIntelligence #OpenAI #GPT4O #FutureTech #Innovation #AIInScience #TechEthics #HerMovieInRealLife
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Hack Your Genes with AI! (Wait, what?) Imagine a future where diseases are edited out of your DNA with the power of AI. That future just got a whole lot closer! Profluent, a company at the cutting edge of AI and protein design, just launched OpenCRISPR - an "open-source" platform that lets AI design tools to edit human genes! This isn't science fiction - this is groundbreaking tech with the potential to cure diseases and change lives forever. But is it safe? What are the risks? We're diving DEEP into this one! Follow @metavenger for more mind-blowing tech updates and credit to @theaipage for the heads-up! Let us know in the comments - would you edit your genes? #AI #ArtificialIntelligence #Reel #ViralReel
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🔍#OpenAI has sparked an intriguing debate with news that its upcoming #GPT4 could potentially assist in creating bioweapons. While it’s a somewhat tongue-in-cheek consideration, it rightfully underscores the critical ethical questions in AI technology’s use and development. 💡Imagine the merger of high-power AI with fields such as DNA sequencing or pathogen engineering - it treads a thin line between groundbreaking innovation and dangerous consequences. On the flip side, GPT-4 could revolutionize the translation of complex scientific papers and progress medical treatments. 🔐 As #AI advances, keeping tools like GPT-4 accessible only to an elite few seems to be the path forward. Responsible AI usage is no longer a mere ethical debate – it’s a matter of survival in a world where technology can be both a boon and a potential bane. Join the conversation about this fascinating and somewhat dystopian topic here 👉: https://lnkd.in/eeWkyy9d #ArtificialIntelligence #GPT4 #BioWeapons #Technology
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"The most important thing you can do to prepare your data for AI is to know what you're capturing and why it matters. Writing down your data model is mission critical!" - Emerson Huitt, CEO and Founder, Snthesis Inc. Find out why and how to unleash the power of AI in your biotech research by watching the full webinar: https://hubs.li/Q02qLgM70 #ELN #Biotech #AI
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Did you know that the first use of the term "artificial intelligence" was in 1956? It was coined during a conference at Dartmouth College, where researchers gathered to explore the possibility of machines mimicking human intelligence. Today, AI is now at the top, and it's great at picking the perfect movie for you. #ArtificialIntelligence #AIHistory #TechInnovation #MachineLearning
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That's exactly why we have doubled down on our investments in novel experimental approaches for high-throughput characterization of Intrinsically Disordered Proteins and novel chemical matter that would bind to them in a selective and high-affinity fashion. Let's make no mistakes here, ML-based approaches are as good as training data. No data = No sensible models.
NEW: Results from a protein binding contest suggest AI models still have ways to go in solving some of the more complex biological questions. I talked to Leash Bio CEO Ian Quigley, PhD, whose startup ran the Kaggle contest over the past few months, about how ~2000 competing teams fared: “They are less good, and I mean pretty poor but still doable, at predicting molecules that look different from the others but share a very common central core,” Quigley told me. “What the computers cannot do at this current stage is predict on molecules that don’t look anything like the ones they’ve been shown already.” “They are pretty good at memorizing and pretty bad at extrapolating into novel chemical space,” he added. “When I say pretty bad, you could randomly choose molecules and ask if they are binders or not from a collection, and you would be doing as well as the winning models in this competition.” A lot of interesting implications to consider from the so-so results: AI hype, generalization capabilities of models, capturing the right biological complexity in datasets, proper benchmarks for the field and more. Leash's big bet is that generating far more data will make better AI predictions. Quigley compared to to playing chess or folding proteins — ML problems that were only cracked when there were massive databases to train on. My latest for Endpoints News:
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The title says it all. Whatever people tell you, AI's performance in predicting what small molecules bind to protein targets - a necessary step for discovering new drugs - is underwhelming. This isn't the first challenge that indicates that result, though. The #CACHEChallenges run by Conscience in partnership with the The Structural Genomics Consortium (SGC) is working on exactly this problem. What makes the CACHE Challenges unique (as far as I'm aware) is that they are testing predictions with experiments AND sharing all of the predictions and experimental data openly. I highlight this mainly because the conclusion most folks seem to reach is that "we need more data." Feels nice to be part of an effort that, in it's own small way, is trying to generate it and share it with the world. You can find the preprint for CACHE Challenge 1 here, if you want to read more: https://lnkd.in/gvNkDzw4
NEW: Results from a protein binding contest suggest AI models still have ways to go in solving some of the more complex biological questions. I talked to Leash Bio CEO Ian Quigley, PhD, whose startup ran the Kaggle contest over the past few months, about how ~2000 competing teams fared: “They are less good, and I mean pretty poor but still doable, at predicting molecules that look different from the others but share a very common central core,” Quigley told me. “What the computers cannot do at this current stage is predict on molecules that don’t look anything like the ones they’ve been shown already.” “They are pretty good at memorizing and pretty bad at extrapolating into novel chemical space,” he added. “When I say pretty bad, you could randomly choose molecules and ask if they are binders or not from a collection, and you would be doing as well as the winning models in this competition.” A lot of interesting implications to consider from the so-so results: AI hype, generalization capabilities of models, capturing the right biological complexity in datasets, proper benchmarks for the field and more. Leash's big bet is that generating far more data will make better AI predictions. Quigley compared to to playing chess or folding proteins — ML problems that were only cracked when there were massive databases to train on. My latest for Endpoints News:
In a reality check for the field, AI underwhelms in Leash Bio's binding contest: 'No one did well'
endpts.com
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Humans are going to have better tools : 5 Must Watch AI Conversations in World Economic Forum 📹 In the recent World Economic Forum, the center stage topic was impact of Ai on world and what lies ahead. 1. Satya Nadella : Impact of AI will unfold in science - chemistry and biology. 2. Expanding universe of Gen AI Models : Andrew Ng, Yann LeCun discussed expanding LLM models beyond Openai Models. 3. Gen AI : Steam engine of 4th Industrial revolution ? Checkout the full details in this comprehensive article by Analytics India Magazine . Bhasker Gupta Link in comments. 👇 #ai #wef #davos
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Attention Biosciences Industry professionals! Have you heard about the NIMBL AI session? This is a game-changer! Over 2100 work tasks will be taken over by AI, freeing up workers to be more productive than ever before. Don't miss out on this opportunity to stay ahead of the curve in the industry. #NIMBLAI #BiosciencesIndustry #ProductivityBoost
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