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AI Journalist for Unite.AI | Founder of AI Disruptor | Content for Startups

Neural networks were first criticized because we couldn't understand their "black box" decision-making. Now we're discovering that forcing them to explain everything in human terms might actually be holding them back. Sometimes, progress means letting go of our need to understand every step of the process. Meta's researchers noticed three big problems with making AI explain everything: 1️⃣ Most of what the AI writes is just filler - all those "therefore" and "next" transitions that don't add any real value 2️⃣ The AI gets stuck at critical moments because it has to commit to specific words instead of exploring multiple paths 3️⃣ It wastes enormous effort making sure its explanations sound grammatically correct instead of actually solving the problem This suggests we might need to rethink how we interact with AI. Instead of always demanding explicit explanations, we might need to develop new ways to understand and guide AI reasoning that don't rely on forcing it to "show its work" in human terms. https://lnkd.in/d43Ztc5C

Making AI show its work might be a mistake

Making AI show its work might be a mistake

aidisruptor.ai

Nice!

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