By definition, black swan events are unpredictable. This example isn't that.
If it can look at a picture, explain what’s in it, and hypothesize about physics inside the picture’s environment, is it “just pattern matching”?
That's extrapolation via approximation. Computers synthesize a specification. The difference is nuanced and entirely contextual.
I keep reading these opinions that LLMs are just doing some advanced form of copy paste. Actually, we don't know what they are doing. Are they actually doing some form of modelling and abstraction? Seems likely to me.
This is exactly the problem with AI. For business or government, the answer is as important as the methodology employed. A black box does not work for the majority of use cases.
Until it can show its work, it's a sideshow.
It's a straw man.
The need for transparency in process is known, documented, and undisputed. Your comment has no relevance. My brain might be a black box, but I can still communicate and/or document the specifics of a process.
Can [insert your preferred model] do that? Didn't think so.
If things keep going this way then pretty soon the black swans are going to outnumber the white ones.
A compressive copy of the internet brute-forcing its way through an exam (which it may even have digested already) is really not interpretable as performing well on the exam. It’s a meaningless measure because the tests were not designed with this use in mind.
I agree that LLMs are extremely likely to impact many areas of work, particularly bullshit work. But as it stands you absolutely cannot use them as fact machines, the results can be catastrophic.
What it does well, among others:
- Scaffolding text, breaking writers block etc.
- Compose basic texts from minimal input, for example for bullshit tasks -> I generated an internal "vision statement" during a Miro workshop for my team by inputting a bunch of bullet points gathered from the team members brain storming. It created a concise, fluid text that everybody liked. It's now the vision statement.
- Point you in good directions, give you ideas
What it does NOT well, among others:
- provide factual responses. all responses MUST be scrutinized because they are likely containing false information. This is very dangerous for society ("Can I take this medicine with this other medicine?")
- Compose creative texts that are coherent and novel. ChatGPT texts can be quite fun but they rarely make sense beyond very superficial screening and convey no deeper message.
However, ChatGPT-like tools are used with a lot of naivety and often blind acceptance instead of using them as tools to aid your work.
I am impressed by what we’ve seen from ChatGPT so far, but am especially excited to see what industry does with LLMs as new type of building block.
If 100,000 people ask critical questions then 10 people might run into potentially catastrophic consequences. ChatGPT is a powerful tool and will only become more so but it will probably not be perfectly reliable by any means due to the nature of the system.
I am excited for the generative AI future and whatever the hell is still coming. Only those who adapt will survive.