He probably makes quite good money as the go to guy for saying AI is rubbish? https://champions-speakers.co.uk/speaker-agent/gary-marcus

But that's certainly not a nuanced / trustworthy analysis of things unless you're a top tier researcher.

An example is citing Mr Sutskever's interview this way:

> in my 2022 “Deep learning is hitting a wall” evaluation of LLMs, which explicitly argued that the Kaplan scaling laws would eventually reach a point of diminishing returns (as Sutskever just did)

which is misleading, since Sutskever said it didn't hit a wall in 2022[0]:

> Up until 2020, from 2012 to 2020, it was the age of research. Now, from 2020 to 2025, it was the age of scaling

The larger point that Mr Marcus makes, though, is that the maze has no exit.

> there are many reasons to doubt that LLMs will ever deliver the rewards that many people expected.

That is something that most scientists disagree with. In fact the ongoing progress on LLMs has already accumulated tremendous utility which may already justify the investment.

[0]: https://garymarcus.substack.com/p/a-trillion-dollars-is-a-te...

I'm struggling to reconcile how these connect and he has been installed as Head of AI at Uber. Reeks of being a huckster

>...held the position briefly after Uber acquired his company, Geometric Intelligence, in late 2016. However, Marcus stepped down from the directorship in March 2017,

which maybe fits your hypothesis.

But maybe not contrarians/non-contrarians? They are just the agree/disagree commentators. And much of the most valuable commentary is nuanced with support for and against their own position. But generally for.

> 1997 - Professor of Psychology and Neural Science

Gary Marcus is a mindless talking head "contrarian" at this point. He should get a real job.

> Yet deep learning may well be approaching a wall, much as I anticipated earlier, at beginning of the resurgence (Marcus, 2012)

(From "Deep Learning: A Critical Appraisal")

https://arxiv.org/abs/1801.00631

Here are some of the points

Is deep learning approaching a wall? - He doesn't make a concrete prediction, which seems like a hedge to avoid looking silly later. Similarly, I noticed a hedge in this post:

Of course it ain’t over til it’s over. Maybe pure scaling ... will somehow magically yet solve ...

---

But the paper isn't wrong either:

Deep learning thus far is data hungry - yes, absolutely

Deep learning thus far is shallow and has limited capacity for transfer - yes, Sutskeyer is saying that deep learning doesn't generalize as well as humans

Deep learning thus far has no natural way to deal with hierarchical structure - I think this is technically true, but I would also say that a HUMAN can LEARN to use LLMs while taking these limitations into account. It's non-trivial to use them, but they are useful

Deep learning thus far has struggled with open-ended inference - same point as above -- all the limitations are of course open research questions, but it doesn't necessarily mean that scaling was "wrong". (The amount of money does seem crazy though, and if it screws up the US economy, I wouldn't be that surprised)

Deep learning thus far is not sufficiently transparent - absolutely, the scaling has greatly outpaced understanding/interpretability

Deep learning thus far has not been well integrated with prior knowledge - also seems like a valuable research direction

Deep learning thus far cannot inherently distinguish causation from correlation - ditto

Deep learning presumes a largely stable world, in ways that may be problematic - he uses the example of Google Flu Trends ... yes, deep learning cannot predict the future better than humans. That is a key point in the book "AI Snake Oil". I think this relates to the point about generalization -- deep learning is better at regurgitating and remixing the past, rather than generalizing and understanding the future.

Lots of people are saying otherwise, and then when you call them out on their predictions from 2 years ago, they have curiously short memories.

Deep learning thus far works well as an approximation, but its answers often cannot be fully trusted - absolutely, this is the main limitation. You have to verify its answers, and this can be very costly. Deep learning is only useful when verifying say 5 solutions is significantly cheaper than coming up with one yourself.

Deep learning thus far is difficult to engineer with - this is still true, e.g. deep learning failed to solve self-driving ~10 years ago

---

So Marcus is not wrong, and has nothing to apologize for. The scaling enthusiasts were not exactly wrong either, and we'll see what happens to their companies.

It does seem similar to be dot com bubble - when the dust cleared, real value was created. But you can also see that the marketing was very self-serving.

Stuff like "AGI 2027" will come off poorly -- it's an attempt by people with little power to curry favor with powerful people. They are serving as the marketing arm, and oddly not realizing it.

"AI will write all the code" will also come off poorly. Or at least we will realize that software creation != writing code, and software creation is the valuable activity

>Deep learning thus far is shallow and has limited capacity for transfer - yes, Sutskeyer is saying that deep learning doesn't generalize as well as humans

But they do generalize to some extent, and my limited understanding is that they generalize way more than expected ("emergent abilities") from the pre-LLM era, when this prediction was made. Sutskever pretty much starts the podcast saying "Isn’t it straight out of science fiction?"

Now Gary Marcus says "limited capacity for transfer" so there is wiggle room there, but can this be quantified and compared to what is being seen today?

In the absence of concrete numbers, I would suspect he is wrong here. I mean, I still cannot mechanistically picture in my head how my intent, conveyed in high-level English, can get transformed into working code that fits just right into the rather bespoke surrounding code. Beyond coding, I've seen ChatGPT detect sarcasm in social media posts about truly absurd situations. In both cases, the test data is probably outside the distribution of the training data.

At some level, it is extracting abstract concepts from its training data, as well as my prompt and the unusual test data, even apply appropriate value judgements to those concepts where suitable, and combine everything properly to generate a correct response. These are much higher-level concepts than the ones Marcus says deep learning has no grasp of.

Absent quantifiable metrics, on a qualitative basis at least I would hold this point against him.

On a separate note:

> "AI will write all the code" will also come off poorly.

On the contrary, I think it is already true (cf agentic spec-driven development.) Sure, there are the hyper-boosters who were expecting software engineers to be replaced entirely, but looking back, claims from Dario, Satya, Pichai and their ilk were were all about "writing code" and not "creating software." They understand the difference and in retrospect were being deliberately careful in their wording while still aiming to create a splash.

Agreed on all points. Let's see some numerical support.

The public has now caught up with that view. Familiarity breeds contempt, in this case justifiably so.

EDIT: It is interesting that in a submission about Sutskever essentially citing Sutskever is downvoted. You can do it here, but the whole of YouTube will still hate "AI".

Oh please. What LLMs are doing now was complete and utter science fiction just 10 years ago (2015).

I’m under the impression that people who are still saying LLMs are unimpressive might just be not using them correctly/effectively.

Or as Primagean says: “skill issue”

Any fool could have anticipated the eventual result of transformer architecture if pursued to its maximum viable form.

What is impressive is the massive scale of data collection and compute resources rolled out, and the amount of money pouring into all this.

But 10 years ago, spammers were building simple little bots with markov chains to evade filters because their outputs sounded plausibly human enough. Not hard to see how a more advanced version of that could produce more useful outputs.

"Any fool could have seen this coming in 2012 if they were paying attention to vision model improvements"

Hindsight is 20/20.

Give me reliable and safe self driving for Interstate highways in moderate to good weather conditions and I would be very happy. Get better incrementally from there.

I live solidly in the snow belt.

Autopilot for planes works in this manner too. Theoretically a modern airliner could autofly takeoff to landing entirely autonomously at this point, but they do not. They decrease pilot workload.

If you want the full robotaxi panacea everywhere at all times in all conditions? Sure. None of us are likely to see that in our lifetime.

(That said, I disagree with them saying "In our lifetimes, neither you nor I", that's much too strong a claim)

LLMs screw up a lot, sure, but Watson couldn't do code reviews, or help me learn a foreign language by critiquing my use of articles and declination and idiom, nor could it create an SVG of a pelican riding a bicycle, nor help millions of bored kids cheat on their homework by writing entire essays for them.

He wasn't wrong though.