

AI
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@DeepLearn007
Imtiaz Adam CS #AI Postgrad |#Tech #Strategy #MachineLearning #DeepLearning | #RL #Agentic | #LLM Liberal | #GenAI| MBA alum @morganstanley @LBS @Columbia_Biz






Reminder AI reasoning breakthrough In a new analysis, researchers highlight the rise of neurosymbolic AI, a hybrid approach that combines neural networks with formal logic and rule-based systems. Scientists say the next AI breakthrough is not bigger models, but smarter ones. Recent systems from Google DeepMind show how this works in practice: > AlphaGeometry 2 (2025–2026) solves ~83–88% of International Math Olympiad geometry problems, with some solutions generated in seconds > AlphaProof (2024 → current) achieved 28/42 points (silver medal level) at IMO by generating formally verified proofs > AlphaFold predicted 200M+ protein structures with near experimental accuracy, showing how hybrid AI can solve real scientific problems at scale. Instead of relying purely on probability like LLM, these systems integrate symbolic constraints, structured reasoning and verification engines to produce outputs that can be checked and proven correct. The shift is subtle but massive 👀 This new direction suggests AI is moving from sounding intelligent to actually reasoning with verifiable correctness, a change that could redefine progress in science, mathematics and engineering.


Scientists say the next AI breakthrough is not bigger models, but smarter ones. AI reasoning breakthrough In a new analysis, researchers highlight the rise of neurosymbolic AI, a hybrid approach that combines neural networks with formal logic and rule-based systems. Instead of relying purely on probability like LLM, these systems integrate symbolic constraints, structured reasoning and verification engines to produce outputs that can be checked and proven correct. Recent systems from Google DeepMind show how this works in practice: > AlphaGeometry 2 (2025–2026) solves ~83–88% of International Math Olympiad geometry problems, with some solutions generated in seconds > AlphaProof (2024 → current) achieved 28/42 points (silver medal level) at IMO by generating formally verified proofs > AlphaFold predicted 200M+ protein structures with near experimental accuracy, showing how hybrid AI can solve real scientific problems at scale. Unlike LLMs, which generate answers probabilistically, these systems use structured reasoning pipelines where results are validated, constrained, and logically consistent. The shift is subtle but massive 👀 This new direction suggests AI is moving from sounding intelligent to actually reasoning with verifiable correctness, a change that could redefine progress in science, mathematics and engineering.














