So, I use a lot of AI tools. I would say that baseline for 'coding agents' today to make it usable is that you need to recover from error messages and lints automatically. Once you can do that it will be a lot easier to use.
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I'm not trying to be pedantic here. I'm just not familiar with any historical event you are describing.
From what I've heard, and I'm not an expert, I wouldn't characterize any of the conquests of Babylon as a 'razing', And the eventual abandonment of the city was more a result of slow decline and changing geological conditions.
I do like to learn about the history of the area, so if it's just something I'm not familiar with, please point me in the right direction.
If I am repeating this back correctly, the argument is that the process itself looks nothing like human reasoning and has a number of technical limitations and even hacks that are in no way attributes or qualities of reasoning. Therefore, it clearly cannot be in any way considered reasoning. Temperature is one element of this, but there are others which you could continue to enumerate beyond even what's written above.
I can get behind part of that argument, certainly, and I appreciate you elaborating on it. I think is what I was trying to say with the part about me believing that it's not useful to think of it as reasoning. This is very different from what we might consider reasoning in very meaningful ways.
I also agree with you also that parts of this is just loaded language, as it is anthropomorphizing what is fundamentally just a bunch of matrices and non-linear functions.
I think where we differ is probably on that "when it's not even really close" part of it, at least in what I mean is "close" versus what I think you mean.
While I (think) we agree that obviously it's a different process, I do think that the input->outputs and the different qualities of input->outputs (like the so-called reasoning tokens) above can often seem quite close to the different inputs and outputs of some human reasoning. That's why I was saying that didn't see how the process works, like temperature, is relevant. Putting the processes aside, if you black box a human and a language model and put us head to head on reasoning tasks, sometimes you're going to get quite similar results.
I'm basically saying that, sure, an LLM or foundation model is clearly a Chinese room, without any understanding. What are we comparing it to, though?
Now, I don't have any kind of training in biology, but I have been led to understand that our brains are quite complex and that how their function arises from the underlying biological processes. is still fairly poorly understood. Given that, I tend to discount the degree of difference between the processes themselves and just look at the inputs and outputs. It's not obvious to me that we aren't ourselves Chinese rooms, at least to some significant degree.
So _maybe_ it's fair to try to compare what the outputs of these Transformers are to what our outputs would be. If it walks like a duck, and talks like a duck, does it matter?
Obviously, that's not fully correct -- how the output arises _has_ to matter somewhat. The fact I am sitting here writing this, and not an AI, refutes that point to some degree. And if I am understanding your thoughts correctly, I fully agree that the process really is nothing close. I just don't see how it can be a clear-cut issue on the basis of analyzing the Transformer algorithm itself.
Here's how I think about it: the fact that it can interpret the same words differently in different contexts alone shows that even on a temperature of 0 (i.e., lowest randomness possible) there could be something that possibly resembles reasoning happening.
It might be a mimicry of reasoning, but I don't think that having adjustable parameters on how random they are makes it any less of one.
I also don't see how that idea would fit in with the o1 models, which explicitly have "reasoning" tokens. Now, I'm not terribly impressed with their performance relative to how much extra computation they need to do, but the fact they have chains-of-thought that humans could reasonably inspect and interpret, and that they chains of thought do literally take extra time and compute to run, certainly points at the process being something possibly analogous to reasoning.
In this same vein, up until recently I personally very much in the camp of calling them "LLMs" and generally still do, but given how they really are being used now as general purpose sequence-to-sequence prediction models across all sorts of input and output types tends to push me more towards the "foundation models" terminology camp, since pigeonholing them into just language tasks doesn't seem accurate anymore. o1 was the turning point for me on this personally, since it is explicitly predicting and being optimized for correctness in the "reasoning tokens" (in scare quotes again since that's what openai calls it).
All that said, I personally think that calling what they do reasoning, and meaning it in the exact same way as how humans reason, is anthropomorphizing the models in a way that's not really useful. They clearly operate in ways that are quite different from humans in many ways. Sometimes that might imitate human reasoning, other times it doesn't.
But, the fact they have that randomness parameter seems to be to be totally unrelated to any of the above thoughts or merits about the models having reasoning abilities.
The largest contributors to the debt over the last couple decades have been stimulus packages and tax cuts.
Still super young, like you said, but it's really good for a lot of cases and now defaults to using uv under the hood I believe, which makes it quite fast.
LLMs are already super-human at some highly abstract creative tasks, including research.
There are numerous examples of LLMs solving problems that couldn't be found in the training data. They can also be improved by using reasoning methods like truth tables or causal language. See Orca from Microsoft for example.
You are right that there is no agreement of course, but the way they're treating the dev sounds, in the most charitable explanation, extremely disorganized and a bit rude.
I imagine the "conversion rate" of a .net TLD will be much lower for spammers than that of a .zip
Do you think computer users always think that's the case? I tend to come down on the side of "no, probably not" but open to other arguments.
- wellsfargo.zip
- bankinfo.zip
- irs.zip
- email.zip
- taxreturns.zip
Which — in one sense it's great that there's some new domain names. On the other hand these 5 alone would be a total phishing nightmare for some unsuspecting people.
The title is "Introducing ChatGPT and Whisper APIs" and features quotes like "Snap Inc., the creator of Snapchat, introduced My AI for Snapchat+ this week. The experimental feature is running on ChatGPT API".
So, OpenAI refers to accessing the model powering ChatGPT as the ChatGPT API.