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It’s clear that AI, including generative AI, will be tested in the courts. Cloud and AI architects must practice defensive design and governance to stay out of trouble.
Our unrealistic expectations of genAI are like hoping a two-year-old will calmly act like an adult. Try patiently experimenting with prompts and spending ‘quality time’ with this developing technology.
Cloud and AI are the most important technologies today, and both have outstripped open source licenses. It’s time for a new open source definition.
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?
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