Naresh Gali | Data, AI, and the Future of Human Potential

Subtitle: Cloud AI is slow, expensive, and public. Edge AI is fast, free, and private. (This is post #39 in the #DataDailySeries) For the last 3 years, the AI narrative was "Bigger is Better."  * GPT-3 (175 Billion Parameters).  * GPT-4 (1.8 Trillion Parameters).  * "We need more GPUs! We need nuclear power plants!" But in 2026, the trend has reversed. The smartest companies (Apple, Microsoft, Google) are racing to make models Smaller. Welcome to the era of SLMs ( Small Language Models ) and Edge AI. The Problem: The Cloud is Too Slow (and Public) Running everything in the cloud has three fatal flaws:  * Latency : You can't have a real-time conversation if your voice has to travel to a server in Virginia and back.  * Privacy: Companies (and users) don't want to send their health data, financial records, or private photos to OpenAI 's servers.  * Cost: Every query costs money. Running a 1 Trillion parameter model for a simple question like "Set ...

Subtitle: Why the " Human Bottleneck " is forcing AI to teach itself—and where you fit in. (This is post #38 in the #DataDailySeries ) We are hitting a Human Bottleneck. To train the next ChatGPT , we need millions of high-quality examples of "reasoning." But hiring humans to manually write millions of complex SQL queries , medical diagnoses , or Python scripts is too slow and too expensive. The industry solution for 2026 is RLAIF ( Reinforcement Learning from AI Feedback ). Instead of humans grading the AI, the AI grades itself based on a " Constitution " (a set of rules). But is this actually safe? Or will it lead to an echo chamber where AI just congratulates itself on bad answers? The Realistic Shift: From Labeling to Legislating In 2024, the job was RLHF (Human Feedback) : You: Look at two answers. Click " Answer A is better ." Repeat 500 times. Reality: This is tedious. Humans get tired, bored, and inconsistent after 1 hour. In 2026, t...