Technology

They can be useful, used "in negative". In a physics course at an institution near me, students are asked to check whether the answers to physics questions given by an LLM/GPT are correct or not, and why.

On the one hand, this puts the students with their back against the wall, so to speak, because clearly they can't use the same or another LLM/GPT to answer, or they'd be going in circles.

But on the other hand, they actually feel empowered when they catch the errors in the LLM/GPT; they really get a kick out of that :)

As a bonus, the students see for themselves that LLMs/GPTs are often grossly or subtly wrong when answering technical questions.

Long story short, LLMs are only tools, nothing more.

My take:

• Great for helping with coding tasks (especially the boring grunt work) - with proper context and training! You have to put in the work to get better, more relevant results
• Pretty useless for everything else. I’ve tested it out for everyday tasks and it didn’t offer enough for me to really lean into.

Propaganda seems still a business case for me.

Another article conflating LLMs and AI.

AI is unfortunately supercharging lots of systems, especially in the police/intelligence spaces. Surveillance driven by AI is absolutely skyrocketing both in capabilities and prevalence.

xAI and OpenAI aren't seeing good ROI, being LLM companies. Palantir and their ilk are another beast altogether.

I almost wonder if this misstated "underperformance" of "AI" is intentionally trying to make people less fearful about it being weaponized against them.

After all, the AI balloon is deflating, right?

I don't think AI is done improving, but companies need to find something other than throwing more compute at it. It seems to get exponentially more expensive for logarithmic gains in performance. I honestly can't even tell the difference between ChatGPT 4 and 5. I don't doubt that it is better but I can't see a difference in my own productivity.

Time savings vs time sinks depends a lot on exactly what you're doing. Treading well-worn ground in a new domain can be speedy. But fixing a non-standard or niche (or shitty) code base can be a nightmare because nothing is done the standard way.

So far, I've gained a bit of productivity through AI, but I've been down a few rabbit holes, too. Integration tests can be a real pain. It always wants to recommend custom test configurations but then you wind up with a different test environment and you can't necessarily trust your tests. Date parsing with Jackson in particular can be different between your configured ObjectMapper injected by Spring and new ObjectMapper() in test to give just a super basic example.

I have only found one good use so far and that is to quickly analyze, manipulate and visualize closed datasets. The moment you let it expand beyond your dataset, or ask it to "think" or "imagine", it promptly shits the bed.

The old adage always applies. "Garbage in. Garbage out." When you cannot strictly control the datasets being considered, you cannot trust that the results will not include garbage and noise.

LLMs are super cool. You provide text A, and text B, add a little cosine similarity or something, and you've got a distance between the two texts.

Right, they also generate text. I guess embeddings aren't really new.

Well the embeddings are nice anyway. Makes it easy to do semantic text searching (or even images or other kinds of inputs). Not sure what that has to do with the general public, but it's great if you're writing a search tool.