starlust2

One could theoretically use a prompt injection attack to exploit a privilege escalation vulnerability on the kernel.

Fixing the hole is easy. Few more steps than you described, but still easy. Texturing less so.

The joke about someone using chatGPT to write a lengthy email that the recipient will summarize with ChatGPT is the perfect example of how pretend much work is.

We also need that for brightness. The brightness on Amazon Prime TV ads is painful. I literally hide under a blanket until they are done.

Even if you're married, you might not be married forever, could be in an open relationship, or a partner may cheat. None of which is the pharmacist's business.

Maybe not immediately but gathering the data on those areas will eventually lead to their ability to drive there.

Sounds like it's a person actively monitoring but not driving. The point is minimizing risk until safety can be proven.

It's not that they can't build a mental model, it's that they don't attempt to build one. LLMs jump straight from text to code with little to no time spent trying to architect the system.

> through brute force

The same is true of humanity in aggregate. We attribute discoveries to an individual or group of researchers but to claim humans are efficient at novel research is a form of survivorship bias. We ignore the numerous researchers who failed to achieve the same discoveries.

A thing people miss is that there are many different right ways to solve a problem. A legacy system might need the compatibility or it might be a greenfield. If you leave a technical requirement out of the prompt you are letting the LLM decide. Maybe that will agree with your nuanced view of things, but maybe not.

We're not yet at a point where LLM coders will learn all your idiosyncrasies automatically, but those feedback loops are well within our technical ability. LLM's are roughly a knowledgeable but naïve junior dev; you must train them!

Hint: add that requirement to your system/app prompt and be done with it.

Well I'm certainly not saying that AI should generate more corporate spam. That's part of them problem! And also a strawman argument!

A big challenge is that programmers all have unique ever changing personal style and vision that they've never had to communicate before. As well they generally "bikeshed" and add undefined unrequested requirements, because you know someday we might need to support 10000x more users than we have. This is all well and good when the programmer implements something themselves but falls apart when it must be communicated to an LLM. Most projects/systems/orgs don't have the necessary level of detail in their documentation, documentation is fragmented across git/jira/confluence/etc/etc/etc., and it's a hodge podge of technologies without a semblance of consistency.

I think we'll find that over the next few years the first really big win will be AI tearing down the mountain of tech & documentation debt. Bringing efficiency to corporate knowledge is likely a key element to AI working within them.

Wouldn't the fact that another group researched ABCA1 validate that the assistant did find a reasonable topic to research?

Ultimately we want effective treatments but the goal of the assistant isn't to perfectly predict solutions. Rather it's to reduce the overall cost and time to a solution through automation.

The thing with vague requirements is that the real problem is that making decisions is hard. There are always tradeoffs and consequences. Rarely is there a truly clear and objective decision. In the end either you or the LLM are guessing what the best option is.

I'm not sure about that but in that scenario polar regions would be 75-85F and life would be very harsh just about everywhere else. The one period in time where temperatures were that high, the Paleocene–Eocene Thermal Maximum, diversity plummeted, mammals were smaller and migrated to higher latitudes.