Very few organizations have enough iron to train a large language model in a reasonably short amount of time, and that is why most will be grabbing pre-trained models and then retraining the parameters in the models with much smaller datasets that are important to them. …
There is more than one way to get to a large language model with over 1 trillion parameters that can do lots of different things and enterprises can use to create AI training and inference infrastructure to extend and enrich their thousands of applications. …
It would be hard to find something that is growing faster than the Nvidia datacenter business, but there is one contender: OpenAI. …
If you have the entire corpus of the Internet scrubbed of nonsense plus whatever else you can scrounge up in whatever language all put into the right format so you can chew on that data one token at a time with trillions of parameters of interconnections between those tokens to build a large language model for generative AI applications, you have an enormous problem. …
Google is a big company with thousands of researchers and tens of thousands of software engineers, who all hold their own opinions about what AI means to the future of business and the future of their own jobs and ours. …
If you are a Global 20,000 company and you want to build a large language model that is specifically tuned to your business, the first thing you need is a corpus of your own textual data on which to train that LLM. …
When it comes to building any platform, the hardware is the easiest part and, for many of us, the fun part. …
When we hear about the massive GPT-3 language model, it is natural to assume it is open source and ready for the taking. …
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