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As the big vendors overstate the capabilities of their generative AI projects, maybe it’s time to use open source to keep them honest.
The new program allows researchers to cut through waiting queues or wait times while providing the option to connect with experts to seek guidance on quantum workloads.
The high costs of development and training and the lack of pricing transparency put commercial large language models out of reach for many companies. Open source models could change that.
Billionaires’ promises of a utopian AI future aren’t helping us solve the serious problems with today’s generative AI models. Security is top of the list.
Large language models trained on questionable stuff online will produce more of the same. Retrieval augmented generation is one way to get closer to truth.
Forty years ago, AI was largely shelved because of its high price tag. By finding the real business benefits, you can do better than the developers of yesterday.
There’s a lot of talk but not many actual implementations of generative AI in the cloud. Better to have all the pieces in place before launching expensive projects.
From system design to daily performance tuning, here’s a checklist of ways to make your systems run effectively.
As part of the learning curve with AI and LLMs, experiment all you want, but take the results with some skepticism, especially if you’re using it to write your code.
With the explosion of interest (and money) in generative AI, what will be left for traditional cloud service development and enhancement that companies need?