8 Types of #AIAgents You Should Know by @PythonPr #LLM #ArtificialIntelligence #ML #MachineLearning
→ View original post on X — @ronald_vanloon, 2026-04-07 00:20 UTC
MACHINE LEARNING
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Context Rot Demands Specialization in AI Systems
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prima facie as long as context rot is a thing you are going to need specialization or you're gonna have a bad time
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ICML 2026 announces 44 workshops and 4 affinity workshops
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We're excited to announce the 44 workshops and 4 affinity workshops to be held at #ICML2026! Read the blog post to see what they are, and learn more about the selection process. This year was exceptionally selective, with 247 submissions but only capacity to host 44 workshops
→ View original post on X — @thegautamkamath, 2026-04-06 23:58 UTC
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Neural Network Expert Disputes AI Model Hallucination Claims
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I am blocking you unless you retract this. I have been working on neural networks for 30 years. A recent survey showed my technical predictions have been over 90% correct. Your own data (ie the data that you pointed) supported my claim that current models still hallucinate.
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Gary Marcus Challenges Claims of Near-Zero LLM Hallucination Rates
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This ML Prof told me that the hallucination rate for frontier reasoning LLMs is “next to nil” And then gave me data, only after I pushed him, showing a best-case rate of 4.6% (which of course is benchmark specific). 4.6% is not “next to nil”. Imagine if your accountant hallucinated 4.6% of the time. Or worse, your pilot. Aran Nayebi (@aran_nayebi) Have you had a chance to try the latest reasoning models? You'll see their hallucination rate is next to nil. In fact, there’s a big difference between frontier reasoning models & the base LLMs that're freely available to the public, see e.g. here: nitter.net/aran_nayebi/status/202… — https://nitter.net/aran_nayebi/status/2041249684698648922#m
→ View original post on X — @garymarcus, 2026-04-06 22:25 UTC
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Symbolic Learning vs Curve-Fitting: Reverse-Engineering Generative Programs
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With curve-fitting, you are recording a lossy approximation of the output of some generative program. With symbolic learning, you are losslessly reverse-engineering the source code of the generative program. Symbolic learning won't be the best fit for all problems, but for the ones where the latent program is reasonably simple, it will outperform by many orders of magnitude.
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Self-Improvement in AI Research Versus Biology Discussion
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when do we see self-improvement in AI research vs. biology? @LiamFedus, Cofounder @periodiclabs and former lead of post-training OpenAI, on @NoPriorsPod pic.twitter.com/NdyTDy1ynR
— sarah guo (@saranormous) 6 avril 2026when do we see self-improvement in AI research vs. biology? @LiamFedus, Cofounder @periodiclabs and former lead of post-training OpenAI, on @NoPriorsPod
→ View original post on X — @ceobillionaire, 2026-04-06 21:44 UTC
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Artificial Intelligence Detects Heart Failure Through Voice Analysis
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Source: https://pudgycat.io/ai-voice-heart-failure-detection-noah-labs-vox/ [Translated from EN to English]
→ View original post on X — @kimmonismus, 2026-04-06 21:31 UTC
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FDA AI Tool Vox Detects Heart Failure from Voice Analysis
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Very exciting breaktrough: An FDA-designated AI tool called Vox can analyze just five seconds of a patient’s voice to detect signs of worsening heart failure, using patterns linked to fluid buildup that humans cannot hear. Trained on more than 3 million voice samples and supported by five clinical trials, it points to a huge shift in healthcare: cheaper, earlier, phone-based detection for a disease affecting 64 million people worldwide and costing the U.S. over $30 billion a year. I love it.
→ View original post on X — @kimmonismus, 2026-04-06 21:31 UTC
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AI Systems Outperform Humans in Data Analysis and Writing
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You are so wrong it isn't funny. You can't read 20,000 posts every day and write up an analysis. My AI can. And do it with writing that's better than 99% of humans.