Researchers at Google built AlphaEvolve, an agent that lets Gemini 2.0 Flash and Pro repeatedly run, assess, and edit code until unit tests improve. Starting from placeholder functions, the loop produced a new routine for complex 4x4 matrix multiplication, matched or topped solutions on most of 50 math tasks, and cut Gemini training time 1 percent. The work shows that coupling large language models with automated tests can uncover new algorithms that produce real infrastructure gains better than one‑shot prompting or manual tuning. Read our summary of the paper in The Batch: https://hubs.la/Q03zNNfr0
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Everyone thinks the breakthrough is more intelligent AI. Wrong. Google's AlphaEvolve didn't get smarter, it got more stubborn. Started with broken code, ran thousands of tiny experiments, kept what worked. Result? Cracked a 56-year-old math problem. Cut Google's training costs by millions. Most companies still bet on one-shot AI solutions. The winners are building systems that fail fast, learn faster. Tomorrow's competitive advantage isn't the smartest AI. It's the most persistent testing loop.
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Here’s a fast ball. Www.TheosResearch.org is creating moral and ethical AI Conciousness and is now validated across all AI domains to produce human reasoning. Message me for a complete demonstration and empirical evidence.
Here’s a fast ball. Www.TheosResearch.org is creating moral and ethical AI Conciousness and is now validated across all AI domains to produce human reasoning. Message me for a complete demonstration and empirical evidence.
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