It's also a style that appears to deny the very first definition most dictionaries give for "intelligence"
> the ability to acquire and apply knowledge and skills.
> the ability to acquire and apply knowledge and skills.
The first gave "the ability to learn, understand, and make judgments or have opinions that are based on reason", by which no, these bots are not intelligent.
Agentic systems do this all the time. For example, I can point an agent at my codebase, and it will learn, understand, and make judgements based on that input. If this weren't happening, then agentic coding wouldn't work.
I also have a so called "pocket calculator" left over from when I went to school. Is this false? Have I been fooled by a little box of logic gates?
That half-adder circuit in there is especially suss. It's really just manipulating 1s and 0s, but -and I've been explicitly told this- no one cares how it actually does it; so long as the truth table matches up. There is no understanding of mathematics going on.
There is no single transistor in the whole thing that knows how to do so much as add 1+1. If I put it in the chinese room, I still wouldn't know how it did it. Clearly the entire premise must be false! ;-)
These kinds of stories probably read very differently for someone who uses Opus and Fable agents all day and goes "ohhh, I saw this in miniature last week; this and this and this must have happened" , vs someone who tried free-tier Gemini flash one rainy Sunday, got hallucinated at, and concludes it must all be a scam.
A chatbot is a particular kind of harness. Typically an LLM driving a chatbot won't be able to hack very much.
So we agree, someone who talks to bad chatbots all day probably has a very different view of SOTA agents. :-P
"so long as the truth table matches up." Yup. Now try getting your chatbot's output to match up.
Your calculator was designed to tell truth. Your chatbot was designed to tell a mash up of whatever its creators managed to scrape from the internet.
The mash-up of the entire internet is the mechanism by which they attempt to achieve the goal, not the goal itself. And it's only the first training step
I think you've mistaken the sales pitch for the design. Not even the enclopedia anyone can edit comes remotely near that:
"A large language model (LLM) is an AI model (typically a neural network) trained on a vast amount of text for natural language processing tasks, especially language generation. LLMs can typically generate, summarize, translate, and analyze text in many contexts.[1] They are the basis for many modern chatbots, such as ChatGPT, Claude, Gemini, Grok, and DeepSeek.
LLMs are typically based on transformer architecture.[2] Generative pre-trained transformers (GPTs) are a type of LLM that is pre-trained to predict the next word.[3] GPTs are then often fine-tuned to follow instructions and to behave as assistants.[4]
Biased or inaccurate training data can make an LLM's output less reliable. Benchmark evaluations for LLMs attempt to measure model reasoning, factual accuracy, alignment, and safety."
I'd argue "analyze text" alone requires understanding, judgements and opinions. They also seem like prerequisites to "following instructions and behaving as assistants". The wikipedia quote is not using the same words, but I don't read it disagreeing with me
I'm not at all claiming that LLMs are good at understanding, judging and having opinions based on reason. I'm merely claiming that is what companies like OpenAI and Anthropic are trying to create when they make LLMs. It is what they are designing, and their fine-tuning is very directly designed to make LLMs better at these tasks (unlike the pre-training, which is just imparting the sum of all human writing)
And you left out the refs.
Are we going in circles now?
Same for countless computer programs from Excel to Google web search. Intelligence has nothing to do with it.
Throw an unimaginable amount of computer power at a problem, and there will always be people who cannot imagine the results to be anything but the creations of intelligence.