“So, I use AI to do the things that I’m bad at,” Torvalds went on,
I guess the key is to use AI to do something you're bad at. Apparently you are good at making lovely documents, so that wasn't effective.
It's far from perfect, but it's better than many a junior dev at this point.
People who talk about "junior devs" don't know much about the software industry.
Unit tests are generally a mechanical translation of the requirements into software that verifies each requirement has been met.
Those are functional tests, not unit tests (unless your specification was so detailed it was written in UML).
We need better tools to understand, manage and reduce complexity, not tools that allow us to create more.
That is begging the question. What I mean is, a lot of people want more complexity, because they want things to take longer, because they make more money. Middle managers want programmers to be busy but go slowly so they can justify hiring more people. There was an interview with Marc Andreesen about that, and Bjarne Stroustrup earlier.
Bottom line: faster isn't better.
Without buying into the conspiracy theory, but this is why I dislike these short lifetimes. It makes my otherwise 100% autonomous website dependent on an external party. I think I'll go back to also offer the site on unencrypted http instead of making an https redirect.
While I agree that AI is not truly deterministic, that doesn't mean you will get widely divergent results from teh same data.
Widely? No. Divergent? Yes. Run the same query multiple times and you'll get different answers. The more input you give, the more subtle the differences are likely to be, but there's always a chance it will go completely off its nut and follow some line of "reasoning" that goes someplace utterly unrelated to what you asked for. This is less likely in special-purpose models, but never impossible.
Of course, which is why for special purpose models this can work; especially since you can train it to be more accurate once you have enough real life data. This tool is not likely using the commercial ChatGpt but a bespoke setup.
Tailoring the LLM to taht can save a lot of time for an analyst/engineer/operator by identifying and collecting teh information needed for analysis and reporting
It can't, though, and for two reasons. One, a person is required to check off on the data. Two, LLMs don't faithfully represent data. They ingest it as tokens and then what they crap out is influenced by random numbers produced internally as part of the token selection process. That means a person cannot certify that the data was faithfully reproduced without checking literally every value, because that's simply not how LLMs work.
You can potentially address this with a hybrid approach, where the LLM uses a tool to collect and manipulate the data, but this is shifting the problem to whether the LLM used the tool correctly. It might be more obvious when it doesn't, though, so it might still be a win.
What LLMs seem best at when it comes to text processing, and they are still not infallible, is summarization. But you can't trust its negatives, so it's only useful for identifying other possible positives. That's not worthless, it can help you catch things, but you can't trust it to do the parts you'd most want it to do.
While I agree that AI is not truly deterministic, that doesn't mean you will get widely divergent results from teh same data. There is some run to run variant, but in my experience, working with very specific criteria, it tends to be in the ranking order and not a failure to identify relationships. That can be addressed in a number of ways. While positive hits are easier, negative ones can be checked in a number of ways to increase the confidence level that they truly are negative, in the sense they are not relevant to the tested data set. To me, one of teh keys is to work with a well defined set of criteria, something I think is possible with tehy type of information they are dealing with, especially since much of the input data will be well structured and in a consistent format.
A human must always be the final arbitrator, and needs to realize the AI is not fool proof and you can't accept everything at face value.
I wouldn't want them to be relying on AI to do reviews though. Maybe two AI, looking only for issues and reporting them back, like you can have AI look for security issues with computer code. And a proper review done by a human.
I was think more along the lines of "Here is the periodic report on X" or "Here all the similar events and corrective actions..." to basically act like a research librarian; not to review techical documentation to identify design issues.
The story isn't that sales have fallen 20%. It's that 80% of the demand still exists.
What we've really taught these companies is that they've traditionally undervalued their products, and if component prices return to "normal" there is no reason at all that retail prices need to track it. The supply side has all the power, and they know it now.
The new normal sucks.
What's frustrating is how many people do not realize they're about to lose everything.
Probably not.
In 1869 the waffle iron was invented for people who had wrinkled waffles.
