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AI PROBLEMS Gizmodo: Google Is in Full-On Damage Control Over Its New Health App. "If there's one thing you don't want to screw around with as a tech company, it's apps. Just […]
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 Privacy-focused search engine DuckDuckGo has seen a surge in demand for its "No AI" search option in the wake of Google's May 19th I/O announcements. Google debuted a new "intelligent" search box reimagined with AI. It features AI suggestions as an upgrade to autocomplete, support for follow-up questions, expanded Personal Intelligence for connecting Gmail and Google Photos, and Search agents.
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There's also a new 360-degree convertible model, the Swift Spin 14 AI.
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Artificial intelligence is not flash in the pan — it is here to stay. Gartner says more than 80% of enterprises will have used some form of generative AI APIs or applications by 2026. If you plan to be among those 80%, then you have to determine the best way to train and deploy it, on premises or in the cloud.
AI training requires specialized hardware that is very, very expensive compared to standard server equipment. It starts at the mid-six figures and can run into the several-million-dollar range. And that hardware cannot be repurposed for other uses such as databases.
In addition to purchasing and maintaining the AI hardware, there is the model on which your AI application is based. Training is the difficult part of AI and the most process intensive. Training can take weeks or even months, depending on the size of the data set. That could be months you don't have.
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