✳flâneur — a map of the web's best reading

Adaption

adaptionlabs.ai · 342 words · saved by 1 readers

Today’s AI is expensive, static, and slow to change. A handful of companies build monolithic, one-size-fits-all-models optimized for the average use case. Averages erase the exceptional. Whether that’s because of your country, language, industry or your pursuit at the edge of what’s possible. Tools should exist to extend human capability. Instead, we contort. We rephrase. We mould our requests to compensate for AI limitations. Most models work well until your use case doesn’t fit. Then you’re on your own. AI as it is: AI as it should be: Going beyond brute force scaling The last decade has been characterized by brute force: larger and larger volumes of compute to build larger and larger monolithic systems. Intelligence shouldn’t be frozen in training data or updated in slow expensive cycles. We are betting against scaling, and instead building efficient AI that continually learns. Where others chase scale, we are building adaptability-first systems. The way forward: Intelligence shoul

Adaption | Adaptive AI That Continuously Learns Login The Days of Monolithic AI are Over. Most AI is frozen in place - it doesn't adapt. Around the world, people share the same longing: AI that adapts to them, not the other way around. It comes through as small cries into the night: Why have we become elevated prompt engineers? Today’s AI is expensive, static, and slow to change. A handful of companies build monolithic, one-size-fits-all-models optimized for the average use case. Averages erase the exceptional. Whether that’s because of your country, language, industry or your pursuit at the e

Explore this link on the map →

related reading