Arjun Prakash on the Real Implications of Generative AI скачать в хорошем качестве

Arjun Prakash on the Real Implications of Generative AI

This is a short talk by Arjun Prakash, CEO & Cofounder of Distyl AI, a new Gen AI company & a longtime AI expert with more than a decade of experience at Palantir. Prakash was one of about a dozen remarkable innovators who spoke at the event, which was created and produced by Reinvent Futures, and held at the new Shack15 club in the Ferry Building of San Francisco in May of 2023. You can find out more about The Great Progression series through the written essays in Substack here: https://peterleyden.substack.com You can find out more about what we learned at this specific event on the Real Implications of Generative AI here: https://peterleyden.substack.com/p/th... You can read extended quotes from Arjun and the other speakers at this event here: https://peterleyden.substack.com/p/th... You can find out more about the strategic foresight company Reinvent Futures that created and produced the events here: https://www.reinvent.net Some key extended quotes from this short talk: So there's a lot of different capabilities that this Generative AI technology has. First is its ability to interact with you in your language. We have progressively moved from talking to the computer in the computer's language to now talking to the computer in our language. And I think that's a pretty big capability shift. A second really, really big deal is Generative AI’s ability to follow instructions. So now I can talk to the model and I can give it instructions. That's what fundamentally allows it to actually do things in a very helpful way for you. This is what makes ChatGPT so helpful for you as an assistant. I think over the next two, three decades, the ability to think with clarity and give clear instructions much the same way you would give instructions to a new employee at your company is going to become an increasingly more relevant skill. … I think Generative AI has three implications on enterprises. Number one is your ability to create insights. So all of a sudden we have increased the number of people at an enterprise that can communicate with an AI model. Many people can ask questions and get answers. Historically, you had to always go through the data scientists or the machine learning engineers. That is still a very important role, but now we have democratized this in many ways. Number two is faster time to value. Historically, if I wanted to deploy an AI application, I had to build a model from scratch. It's pretty expensive. Now I can start with something like GPT 4, which out of the box gives me the ability to get a working prototype in days, not months. That's an incredibly fast time to value. Number three, total cost of ownership. I can now maintain just one model, a GPT 4 model for example, and instead have 200 different prompt templates for 200 different tasks. That's a game changer. I historically needed 200 different applications and models for 200 different tasks. It is significantly easier and cheaper to maintain 200 different prompt templates than 200 different models. I think this is going to be a game changer for enterprises.