Technology

The technology is fascinating and useful - for specific use cases and with an understanding of what it's doing and what you can get out of it.

From LLMs to diffusion models to GANs there are really, really interesting use cases, but the technology simply isn't at the point where it makes any fucking sense to have it plugged into fucking everything.

Leaving the questionable ethics many paid models' creators have used to make their models aside, the backlash against so is understandable because it's being shoehorned into places it just doesn't belong.

I think eventually we may "get there" with models that don't make so many obvious errors in their output - in fact I think it's inevitable it will happen eventually - but we are far from that.

I do think that the "fuck ai" stance is shortsighted though, because of this. This is happening, it's advancing quickly, and while gains on LLMs are diminishing we as a society really need to be having serious conversations about what things will look like when (and/or if, though I'm more inclined to believe it's when) we have functional models that can are accurate in their output.

When it actually makes sense to replace virtually every profession with ai (it doesn't right now, not by a long shot) then how are we going to deal with this as a society?

The key differentiator is almost always the people, though that’s not as sexy as cutting edge technology.

Evidently you haven't worked with me. I'm actually quite sexy.

I had to do this myself at one point and it can be very frustrating.

It's basically the "tech makes lots of money" effect, which attracts lots of people who don't really have any skill at programming and would never have gone into it if it weren't for the money.

We saw this back in earlier tech booms and see it now in poorer countries to were lots of IT work has been outsourced - they still have the same fraction of natural techies as the rest but the demand is so large that masses of people with no real tech skill join the profession and get given actual work to do and they suck at it.

Also beware of cultural expectations and quirks - the team I had to manage were based in India and during group meetings on the phone would never admit if they did not understood something of a task they were given or if there was something missing (I believe that it was so as not to lose face in front of others), so ended up often just going with wrong assumptions and doing the wrong things. I solved this by, after any such group meeting, talking to each member of that outsourced team, individually and in a very non-judgemental way (pretty much had to pass it as "me, being unsure if I explained things correctly") to tease from them any questions or doubts, which helped avoid tons of implementation errors from just not understanding the Requirements or the Requirements themselves lacking certain details and devs just making assumptions on their own about what should go there.

That said, even their shit code (compared to what us on the other side, who were all senior developers or above, produced) actually had a consistent underlying logic throughout the whole thing, with even the bugs being consistent (humans tend to be consistent in the kind of mistakes they make), all of which helps with figuring out what is wrong. LLMs aren't as consistent as even incompetent humans.

As someone who sometimes makes demos of our own AI products at work for internal use, you have no idea how much time I spend on finding demo cases where LLM output isn’t immediately recognizable as bad or wrong…

To be fair it’s pretty much only the LLM features that are like this. We have some more traditional AI features that work pretty well. I think they just tagged on LLM because that’s what’s popular right now.