The only thing LLM does really well is statistical prediction.

As should be expected, sometimes it predicts correctly and sometimes it doesn't.

It's kinda like FSD mode in a Tesla. If you're not willing to bet your life on it (and why would you?), it's really not all that useful.

I very much appreciate all the ways we're improving our ideas of what "smart" means.

I wouldn't call LLMs "smart" either, but with a different definition than the one you use here: to me, at the moment, "smart" means being able to learn efficiently, with few examples needed to master a new challenge.

This may not be sufficient, but it does avoid any circular arguments about if any given model would have any "understanding" at all.

> If you think AI is “smart” or “PhD level” or that it “has an IQ of 120”...

It's not there yet, it's still learning™, but a lot of progress in AI has happened recently, which I would give them that.

However, as you point out in your newsletter already, there are also lots of misleading and dubious claims alongside too much hype in the hopes to raise VC capital which comes with the overpromising in AI as well.

One of them is the true meaning of "AGI" (right now it is starting to look like a scam), since there are several conflicting definitions directly from those who benefit.

What do you think it truly means given your observations?

No arguing with anything, but the (link below) doesn't exist.

This is a really surface level investigation that just happens to exclusively use a part of the current multimodal model that is really bad at the task presented, that is demanding precise images, graphs, charts, etc. Try asking for tables of data or matplotlib code to generate the same visualizations and it will typically do far better. That said if you actually use even the latest models day to day you'll inevitably run into even stupider mistakes/hallucinations than this, but the point you're trying to make is undermined by appearing to have picked up chatgpt with the exclusive goal of making a substack post dunking on it.

I don't believe ChatGPT has an IQ of 120, and after reading the linked article, I don't think the author does either.

Last time I checked, we are still at the stage of AI apologetics where an NBA player "throwing bricks" gets misinterpreted as an act of vandalism.

In other words, there is limited capacity for real "understanding" --- which McDonalds and IBM discovered after 3 years of trying to get AI to take orders at drive-thru windows.

https://www.cio.com/article/190888/5-famous-analytics-and-ai...

I dunno if the title got changed, but it's now "ChatGPT Blows Mapmaking 101"… and this certainly isn't PhD-level mistakes happening. It's stuff you'd be docked points for in middle school.

You clearly didn’t read the article if you think that’s where the goalposts were. The title is understatement.

How about “ChatGPT can’t even keep its own maps consistent from one prompt to the next, much less get a PhD in geography”?

I think the reason the goalposts keep shifting is really that these AI labs are illustrating Goodhart's law in action: https://en.wikipedia.org/wiki/Goodhart%27s_law

We're at a point where AI labs are fairly reliably able to create AI systems that can achieve good performance on given benchmarks, via targeted training efforts. You argue that it's not reasonable to expect that performance to generalize across other domains. But given that these same companies are trumpeting that AGI is around the corner, I think that's a fair expectation for us to have, and AI has disappointed in that regard.

It's worth noting that "these posts" these days also seem to always demand image generated graphs rather than tables of data.

You don't want to ask AI to directly manipulate 360 distinct data points for the same reason you wouldn't ask a human to do that.

Get it to write a script to process the data instead.

The comparisons between AI systems and "PhD-level" intelligence don't really make sense as a meaningful benchmark, and I agree with your assessment of what a PhD actually represents.

A PhD isn't simply a marker of raw intelligence or knowledge retention, it represents years of specialised training, critical thinking development, original research, and the creation of new knowledge. The process involves learning to identify meaningful questions, design appropriate methodologies, interpret results with nuance, and contribute novel insights to a field.

I've been thinking about this from another angle though: what if we considered "PhD-level" more narrowly in terms of context window capacity? Since an average PhD dissertation is around 70K words (translates to roughly 90-100K tokens in most LLM tokenization schemes), perhaps one benchmark could be whether an AI system can maintain the equivalent context. By this definition, several current models would technically qualify:

- Claude 3.5 Sonnet: 200K tokens - GPT-4o: 128K tokens - Claude 3 Opus: 200K tokens - Anthropic's experimental systems: ~1M tokens - Google Gemini Ultra: 1M tokens

But this framing has significant limitations. Context window is just raw capacity, like saying a hard drive with enough storage for a dissertation is "PhD-level." The ability to simply retain 70,000 words of context doesn't mean a system can identify significant gaps in existing knowledge, formulate original research questions, or synthesize new insights that advance human understanding.

Current AI systems, regardless of context window size, don't truly "understand" information the way humans do. They recognise patterns in data and generate outputs based on statistical relationships, but lack the deeper conceptual understanding, intentionality, and the other characteristics of human intelligence.

A more meaningful comparison might focus on specific capabilities rather than vague intelligence comparisons or simple context metrics. The goal shouldn't be simply to declare AI systems as "PhD-level smart" but to develop tools that complement human intelligence and extend our collective problem-solving capabilities.