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AI agents no longer forget. EvoSC’s self-consolidation lets them learn from successes and failures, turning experience into lasting intuition — without memory overload.

TranscriptFormer was trained on 112 million cells and can compare types across species, including cells the model has never seen.

CoALA reimagines LLMs as intelligent agents with memory, actions, and decision loops, enabling real-world interaction, learning from experience, and moving far beyond static chatbot capabilities.

Evolution in code has always been clumsy and wasteful. Now, GP-GOMEA teaches programs to protect what works and evolve with purpose.

As data grows complex, pattern mining faces six key challenges—reshaping algorithms, efficiency, and insight discovery—unlocking smarter, faster, and more targeted decision-making in modern data-driven systems.

AI is becoming the doctor for billions. Can intelligent diagnostics finally end medical deserts and bring expert healthcare to the world’s most remote places?

Hruy argues that today’s online/social media platforms are deliberately designed to engineer artificial stupidity, quietly cultivating a future population optimized for content consumption rather than analysis and thought.

Traditional genetic programming relies on blind crossover, often breaking useful code. New methods like GP-GOMEA learn structure, preserve building blocks, and enable scalable software evolution.

In an infinite data library, AI learns to spend its thinking wisely—prioritizing surprising patterns over common ones, transforming brute-force search into strategic, curiosity-driven discovery.