Generative AI is a small piece of the artificial intelligence pie, not the whole pie itself. Keep paying attention to deep learning and machine learning.


Copyright: – “When The Generative AI Hype Fades”


By now you’ve used a generative AI (GenAI) tool like ChatGPT to build an application, author a grant proposal, or write all those employee reviews you’d been putting off. If you’ve done any of these things or simply played around with asking a large language model (LLM) questions, you’ve no doubt been impressed by just how well GenAI tools can mimic human output.

You’ve also no doubt recognized that they’re not perfect. Indeed, for all their promise, GenAI tools such as ChatGPT or GitHub Copilot still need experienced human input to create the prompts that guide them, as well as to review their results. This won’t change anytime soon.

In fact, generative AI is big not so much for all the exam papers, legal briefs, or software applications it may write, but because it has heightened the importance of AI more generally. Once all the hype around Generative AI fades—and it will—we’ll be left with increased investments in deep learning and machine learning, which may be GenAI’s biggest contribution to AI.

To the person with a GenAI hammer

It’s hard not to get excited about generative AI. On the software developer side, it promises to remove all sorts of drudgery from our work while enabling us to focus on higher-value coding. Most developers are still just lightly experimenting with GenAI coding tools like AWS CodeWhisperer, but others like Datasette founder Simon Willison have gone deep and discovered “enormous leaps ahead in productivity and in the ambition of the kinds of projects that you take on.”

One reason Willison is able to gain so much from GenAI is his experience: He can use tools like GitHub Copilot to generate 80% of what he needs, and he is savvy enough to know where the tool’s output is usable and where he needs to write the remaining 20%. Most lack his level of experience and expertise and may need to be less ambitious with their use of GenAI.[…]

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