The evolution of new technology involves a cycle where the purpose and use cases expand beyond original expectations. This is evident in the development of AI technology like Transformers and LLMs. The gap between what the technology can currently achieve and what people expect from it is widening as expectations increase due to improvements in the technology's utility. Despite significant advancements in AI in the past seven years, surpassing expectations and debunking doubts, there are still limitations to the technology. The similarity of AI to human interaction sometimes leads to overestimation and premature deployment in scenarios where it may not be fully ready.
Cohere is one of the buzziest AI startups around right now. It's not making consumer products; it's focused on the enterprise market and making AI products for big companies. And there's a huge tension there: up until recently, computers have been deterministic. If you give computers a certain input, you usually know exactly what output you’re going to get. There’s a logic to it. But if we all start talking to computers with human language and getting human language back, well, human language is messy. And that makes the entire process of knowing what to put in and what exactly we’re going to get out of our computers different than it ever has been before.
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Transcript: https://www.theverge.com/e/23937899
Credits:
Decoder is a production of The Verge and part of the Vox Media Podcast Network.
Our producers are Kate Cox and Nick Statt. Our editor is Callie Wright. Our supervising producer is Liam James.
The Decoder music is by Breakmaster Cylinder.
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