rdedev

If you have the ability, leave some parts of the codebase completely in your control. It's impossible to think and work deeply when using AI. So leave the most critical component to yourself and let AI handle the rest

How many steps till they become a full on revenue generating cult?

> Got a dud? Quality not as expected? Ordered two by mistake? Size is not fitting? Take it to nearest post office, scan a qr code and hand it over. Done.

I'd say this is probably a US privilege. Ive had some trouble returning items in india

This is probably a dumb question but did the model engage in hacking since their tasks were in that domain. Will they have engaged in hacking if their tasks was to solve some frontier math problem?

We don't have any information other than that they are capable enough of solving unsolved math problems. Are they good at writing a good movie script? Do they understand protein ligand interactions? Who knows. Let's wait till they release the model

> Do you think the current drug approval process is bullet proof enough that a completely novel AI-generated drug candidate with zero prior research literature can be considered safe if it makes it through the process?

I mean no? Even now we look at long term observational studies to see the effects of drugs. Any misalignment ai drug is just a side effect right?

The difference is that exxon knew the risks but spent time and effort to sow doubt. This is exactly like how tobacco companies tried to muddy the waters on health effects of tobacco use. The oil companies could have acknowledged the problems of burning fossil fuel but it took them almost 20 years to even admit climate change is real

> All of that to say that Llms have made people into over confident morons

My manager does not have a software background. Initially he used to come to me with an idea and we used to discuss on how we can technically implement it. He used to value what I had to say. Now he talks to copilot, come up with these grand ideas, dumps all that on our heads in a 30 min meeting and ask what do you think about it?

When comparing ant asian country to the US we should always keep physical activity and portion size in mind. A single cookie here in the US is massive and you always drive around just to get that cookie in the first place

When you are out of a job that's probably the last thing on someone's mind. Will I be able to pay rent for next month is probably their first thought

Is that how he himself felt when trying to figure out proofs by Ramanujan? Nevertheless what a shitty view

Tabular foundation models are one of those things where when you first look into it, it does not make sense as to why they would work so well but it does.

In drug property prediction domain, tabular foundation models coupled with another foundation model for molecules are pretty close to being the state of art.

Btw the article makes heavy use of AI or is written in that way A lot of unnecessary dramatic flair that gets very tiring

This worked for me. I was debugging a DL model and asking it a lot of questions. At the end I asked the llm to tell me what I lacked and it gave me a good explanation and pointed me to a bunch of resources i could learn from

This is probably my ignorance but why are we assuming that CXMT capacity will also not be gobbled up for datacenters?

I hope what happens in the original deus ex game happens here then, that the ASI does not want to merge with a human who wants it only for base desires

> AI will run and trample anything that stays in front of it.

Right now that entirely depends on the whims of a few people. We can try to make it not be that but I don't have much hope in that area. See climate change

> As a software engineer, I myself have only recently recovered from it

Can I ask you how you came here? Right now I am getting closer to "purpose death" as you call it. Software engineering is one of the few things I am good at and the only thing that I can rely on to put food on my table. Even if I end up being someone who gives minor suggestions to the AI, it always feels like C level execs can't wait to get rid of me

Reminds me of the METR blog post on the HF attach by OpenAI. At some point the agents believed a false fact (that the evaluator would try to figure out if they have cheated on a task) and spent a lot of time trying to find workarounds. At no point did any one of the agents try to verify that fact even though the information was available to them if they looked for it

> The problem is if it can do that when asked then how do we know when it's doing it when we didn't ask, like in model training.

It's really hard to definitely prove it's not doing it right? Hopefully the model does not do anything like this during training because its too much work

My specific contention is on trying to ascribe intention on what looks like gibberish.

> Well... no, it won't be nothing - it will be something. And I'm all ears for a plausible explanation, so fire away.

I'm just stating the null hypothesis that it's nothing. Especially since the agents were talking in clear english before and exchanging ideas

It could be that or it could be nothing. And we have no way to prove one way or the other without access to the agent logs right?

This reminds me of the plot of Hot Fuzz where the officer comes up with a grand narrative of what's was happening but the truth was such a mundane simple thing.

Short of OpenAI, or the agents themselves, telling the truth, we have no way of knowing what's real so let's not get carried away by grand narratives

Were the agents truly independent? Like it could have been the case that one agent spun up different sub agents with the task to write some messages in a public wiki. The subagents wouldn't know about each other and was surprised to discover each other.

The problem with such statements is that it's unfalsifiable.

It's not just about it feeling like a 3rd party library but it's a library that's at risk of changing significantly after every 'update' without warning.

Atleast with a well built library you know the contours and how it fits into your larger system

The problem is when humidity rises it makes it harder forbthe body to expel hear via evaporation. This is what is happening in places like UK this time around. An AC helps get rid of the humidity as it cools the air. I don't know about necessity now but 20 years down the line, who knows

> Reducing problems to that state and designing that environment remains a skill, and one that I expect we will be paid handsomely for.

This seems to be what AI these days seems almost super humanly good at. See coding or math I guess.

But it does beg the question, why would a programmer using AI as a tool be worse than a programmer building the harness and environment and asking AI to go hogwild? The latter is definitely faster but if it's the former, atleast I will have an understanding how the system works. Weather that is valuable is an open question as far as I am concerned

How exactly does one go about "tinkering" with an LLM? Any architectural change you introduce needs fine tuning. That needs data and compute

I tried to modify the embedding output of bert to make it generate box embeddings instead of point ones. At the time I had access to university provided A100 gpus but even with all that a training run took half a day. Models these days I don't think I can train it in any reasonable time with that much compute.

Here is an article by Pat Walters on the usefulness of ML in drug discovery. This article is a response to another one making the case that utility of ML models are very limited in drug discovery

https://patwalters.github.io/Response-to-Peter-Kenny/

> (4a) revert to traditional methods but keep the veneer of using ML to save face

I haven't worked in the industry side of things but in academia everyone kind of agrees that gradient boosting trees are some of the best models to do these things.

How much does rainmeter take? I used to load tons of widgets on it in my old laptop. Ahh those were the days

> their return on capex multiple, their CEO said they make a six-fold profit on their compute capex with 10 month recuperation

I am not familiar with chineese model companies as much as I am with US based ones so I don't have much to say beyond that the CEO is incentivced to pump up those numbers.

> By limiting the number of tokens you use per month? per week, per hour? And by limiting the inference time compute dedicated to each turn in each session.

If this was so simple I don't know why GitHub copilot went to token based billing at my company.

> This does not need to be solved, but rather only quantified. Innovation is needed to be able to reasonably bound this variance for a reasonable subset of tasks

It's much better to make a business case for them after finding this bound right? Currently I can't use copilot for anything serious since I cannot predict how many credits one request is going to consume.

Your point about non frontier tasks using less tokens makes sense. As you said, let's see if it holds up

These companies have spent billions of investor dollars and they will need to recoup that cost soon. And then show year over year growth on top of that.

Unless they can massively scale down training and inference cost or implement AGI I don't know what their plan is. Just provide a subsidized plan for the next 10 or 20 years? Their costs are directly proportional to the amount of tokens the LLM produces. How is a monthly subscription plan supposed to account for such costs?