81–90 of 119 posts
Earlier quoted context omitted.
Related: https://learn.microsoft.com/en-us/office/troubleshoot/excel/...
Excel must be such an incredible codebase. The amount of spaghetti + spaghetti to keep legacy spaghetti behaviour must really be a sight to behold.
Earlier quoted context omitted.
Line go up. The only three words that matter nowadays.
Unless we’re talking about solar’s cost per watt, then line go down. Down down down.
Unfortunately that's good enough for a lot of people, especially when you don't actually care and just need an output to give to someone else (office jobs etc).
Earlier quoted context omitted.
>b) Experts may give wrong answers but it will happen once. LLMs will do it over and over again. Well... Sometimes "experts" will give the wrong answer repeatedly.
Assuming they aren't convinced they are wrong - and multiple opinions can be valid while differing. The world isnt always black and white. An AI can never be convinced it's wrong permanently, and sometimes it can't be convinced ed temporarily, depending on the model
The tech industry is an environment composed almost entirely of companies running a loss to prove viability (and don't see that as ironic) to raise more funding from investors. AI is just the latest in a long series of empty hype to keep the gravy train running, last year it was VR, and it looks like at this point that the whole thing is teetering on a cliff. It's a bunch of MBAs scrambling for a sales pitch.
LLMs are useful. But "extremely lossy compression of documents with natural language lookup built in" doesn't sell endless subscriptions as well as "we created a mind." So they sell hype, which of course they cannot live up to because LLMs aren't minds.
LLMs would be better nomenclature than AI in this context. LLMs are not factual databases. They are not trained to retrieve or produce factual statements. LLMs give you the most likely word after some prior words. They are incredibly accurate at estimating the probabilities of the next word. It is a weird accident that you can use auto-regressive next word prediction to make a chat bot. It's even weirder that you can…
And the utility of a "language generator" without reliable knowledge or facts is extremely limited. The technical term for that kind of language is bullshit.
> People don't care about (or are willing to accept) the "wrong answers" because there are enough use cases for "writing" that don't require factual accuracy. (see for instance, the entire genre of fiction writing)
Fiction, or at least good fiction, requires factual accuracy, just not the kind of factual accuracy you recalling stuff from an encyclopedia. For instance: factual accuracy about what it was like to live in the world in a certain time or place, so you can create a believable setting; or about human psychology, so you can create believable characters.
If this is correct, then it's less of "people don't care" and more "the hype is louder than them."
That said: I, too, am completely perplexed by people within the tech community using LLMs heavily in making software while, unironically, saying that they have to keep an eye on it since it might produce incorrect work.
I haven't found a human that answers every single question correctly, either. You know whom to ask a question based off that person's domain of expertise. Well, AI's domain of expertise is everything (supposedly). What gets difficult is evaluating the response, but let's not pretend that's any easier to do when interacting with a human. Experts give wrong answers all the time. It's generally other experts who point o…
This is a common argument to support the usage of LLMs: "Well, humans do it too." We have many rules, regulations, strategies, patterns, and legions of managers and management philosophy for dealing with humans. With humans, they're incorrect sometimes, yes, and we actively work around their failures. We expect humans to develop over time. We expect them to join a profession and give bad answers a lot. As time goes o…
My graduate research was in this area. My lab group developed swarm robots for various terrestrial and space exploration tasks. I spent a lot of time probing why our swarm robots developed pathological behavioral breakdowns - running away from construction projects, burying each other, etc... The issue was so fundamental to our machine learning methods that we never found a way to reliably address it—by the time I le…
I believe I saw this research featured in a documentary or some other film. Am I remebering incorrrectly.
I’m also curious about this. This morning I needed to generate summaries of news articles when I noticed Bing AI was inserting facts that weren’t present in the source article. (Not correct at all) It really hurts the potential of what AI could do if I have to double check everything it generates. We wouldn’t accept a spreadsheet program that required double checking with a calculator, why do LLMs get a pass?