I’m usually the one saying “AI is already as good as it’s gonna get, for a long while.”
This article, in contrast, is quotes from folks making the next AI generation - saying the same.
I’m usually the one saying “AI is already as good as it’s gonna get, for a long while.”
This article, in contrast, is quotes from folks making the next AI generation - saying the same.
Lol, no they didn’t. The quotes this articles are using are talking about LLMs not chatbots. This is yet another stupid article from someone who doesn’t understand the technology. There is a lot of legitimate criticism for the way this technology is being implemented but FFS get the basics right at least.
Claiming that David Gerrard an Amy Castor “don’t understand the technology” is uh… Hoo boy… Well it sure is a take.
The title of the article is literally a lie which is easily fact checked. Follow the links to quotes in the article to see what the quoted individuals actually said about the topic.
Please learn the difference between “lying” and “presenting a conclusion.”
I know the difference. Neither OpenAI, Google, or Anthropic have admitted they can’t scale up their chat bots. That statement is not true.
Are you asserting that chatbots are so fundamentally different from LLMs that “oh shit we can’t just throw more CPU and data at this anymore” doesn’t apply to roughly the same degree?
I feel like people are using those terms pretty well interchangeably lately anyway
People that don’t understand those terms are using them interchangeably
LLM is the technology, Chatbot is an implementation of it. So yes a Chatbot as it’s talked about here is an LLM. Although obviously chatbots don’t have to be LLM, those that are not are irrelevant.
No, a chat bot as it’s talked about here is not an LLM. This article is discussing limitations of LLM training data and inferring that chat bots can not scale as a result. There are many techniques that can be used to continue to improve chat bots.
The chatbot is a front end to an LLM, you are being needlessly pedantic. What the chatbot serves you, is the result of LLM queries.
That may have been true for the early LLM chatbots but not anymore. ChatGPT for instance, now writes code to answer logical questions. The o1 models have background token usage because each response is actually the result of multiple background LLM responses.
Yes of course I’m asserting that. While the performance of LLMs may be plateauing, the cost, context window, and efficiency is still getting much better. When you chat with a modern chat bot it’s not just sending your input to an LLM like the first public version of ChatGPT. Nowadays a single chat bot response may require many LLM requests along with other techniques to mitigate the deficiencies of LLMs. Just ask the free version of ChatGPT a question that requires some calculation and you’ll have a better understanding of what’s going on and the direction of the industry.
I think you’re agreeing, just in a rude and condescending way.
There’s a lot of ways left to improve, but they’re not as simple as just throwing more data and CPU at the problem, anymore.
I’m sorry if I’m coming across as condescending, that’s not my intent. It’s never been “as simple as just throwing more data and CPU at the problem”. There were algorithmic challenges for every LLM evolution. There are still lots of potential improvements using the existing training data. But even if there wasn’t, we’ll still see loads of improvements in chat bots because of other techniques.
Edit: typo