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As annoying as the constant goalpost moving is in AI, it's also true that how these models fail is much more interesting than how they succeed. That's where all the information is. Dalle-2 is better at some things than others. Why? What's a limitation of the model, and what's an accident of architecture? I'm reminded of GPT-3's difficulty rhyming and doing math, which seems to be a result of the tokens it was trained on, compared with trailing off and inconsistency which may be more foundational.

I completely agree. However, here are some things a more useful study would cover:

- Describe the task creation process, why they are appropriate for measuring X, etc. Preferably drawing from similar studies in people instead of ad hoc. Lots of work has already gone into measuring these things. They may have to be modified for DALL-E but it would be a better starting point.

- Show several variations of the same task and outputs. For example #11, maybe the issue is that DALL-E has a poor understanding of milk sizes as expressed but not size relationships in general. Example #13 with pizza sizes is ambiguous by the authors own interpretation yet they deem it a failure. It would be trivial to construct dozens of similar examples to give a more holistic understanding.

- Narrow the scope of the prompts if you want to see how the model understands a particular relationship. Many of the tasks include multiple ancillary statements.

- Discuss prompt engineering in more depth. We already know that these models are sensitive to the formulation of the prompt. What did the authors try / not try?

- Replace the authors' individual opinions of the outputs with crowdsourced opinions from mturk or even Twitter. As I noted above, example #10 is not as clear cut as the authors suggest.

- Measure how well people do at the same task as a baseline: draw something that aligns with a given text prompt and compare to DALL-E, maybe with a crowdsourced opinion of which is a more accurate interpretation. Even if they're just stick figures this would be interesting to see.

As-is, this article doesn't really add anything substatial about the model's capabilities to the conversation.

> Why? What's a limitation of the model, and what's an accident of architecture?

The limitation is caused by the CLIP model they used to encode text and images. It's a separate model only generating an embedding, it's not using attention and pairwise interactions on the whole sequence. This causes Dalle2 to be bad at handling multiple objects with multiple attributes. There is no reason the complete prompt could not be related to the generated image instead of an embed, thus correctly stacking the coloured cubes and assigning the right age to each person mentioned in the prompt.

I was actually gonna say something like that, but I believe that the full prompt is also made available to the GLIDE portion. Even still, GLIDE isn't a language model it simply can't understand the full relational content of the caption. With changes running from minor tweaks to major upsizes that's probably fixable.

what goalposts changed? i pointed out same limits in 2001, 2012, 2018, 2019, 2020.

please actually read my work and please don’t make stuff up.

I think you'll find if you reread my comment I was defending your paper here! We agree on little, but your insights are appreciated.

I didn’t get a negative tone from the paper, it seemed mostly positive to me , just calling out a handful of areas ( like relative positioning ) where Dall-e fails .

> ... it is reasonable to question whether DALL-E 2 constitutes progress toward solving the deep challenges of commonsense reasoning, comprehension, reliability, and so forth that would be needed for a truly general-purpose AI ...

I like how they pose this question as a bait to the abstract yet they do not even attempt to answer it. Instead they focus on general shortcomings of the model.

Moreover, there is no proper conclusion which would discuss the findings.

Given how outspoken Gary Marcus is on Twitter - criticising current advances in DL I would expected him to do a much better job publishing a document about it.

Many of the perceived failures of DALL-E 2 are subjective and ambiguous (by the author's own interpretation in some cases!) and speak to the author's negative biases which are well known outside of this publication.

To be clear, I'm not defending DALL-E 2. I'm criticizing a poorly written paper that was published to Arxiv to lend further credibility for a Twitter audience to substantiate a claim that the DALL-E 2 authors have not made but that Gary Marcus has a vested interest in perpetuating:

> How much does DALL-E have to do with AGI? Maybe not so much, after all… A lesson in caveat emptor: - https://twitter.com/GaryMarcus/status/1521120022298464256

This should have been a blog post or Twitter thread like the dozen or so other experimentations people have done with the system.

Marcus is only one of three. From what we know if Aaronson, I doubt that this was a Marcus-driven paper with the others just along for the ride. But I agree some of the intro has a Marcus ring to it.

Yikes, that Twitter feed: "Thoughts and prayers for the deep learning fanboys," as if ones admiration for DALL-E 2's achievement is something to be mocked... I feel like this sentiment is the way we're going to get I Have no Mouth, and I Must Scream. There's just something in there about negative AI minimalists or AI alarmists (same coin different side).

Heh, so many of these people's reaction to 'we have not created AGI' seems to be mocking and pessimism that we ever will.

My response to 'we have not created AGI' is

"Thank goodness".

I don't think we're ready for that yet.

> Many of the perceived failures of DALL-E 2 are subjective and ambiguous

Could you expand on this a bit? DALL-E provides graphical, interpretive, output meant for humans. Isn't that necessarily subjective? Any qualitative metric of DALL-E 2 will need to involve some aggregate of humans being subjective. The papers title is "A very preliminary analysis of DALL-E 2", so low data points/opinions/speculation/further questions should be expected.

I'd be curious to compare the results of Dall-E 2's output vs a group of human artists each individually given the exact same text prompt (with no follow up clarification allowed) and asked to produce 3-5 drafts.

For me, I read the introduction and how they discuss how impressive DALL-E 2 is, and then they show very simple mistakes it makes and I want to be protective of DALL-E 2. I want to say "you did a good job!"

That the paper provides "a clearer picture of what remains to be done" is very hard to accept, as all it does is show edge cases which are subjective at best. If anything the picture is less clear as they don't even try to form a hypothesis why DALL-E makes mistakes like these. One thing in particular I have noticed is that DALL-E has trouble producing action images. It may be because it has no sense of temporality and in those cases it could serve to run the parameters a bit longer using the same scene. But I am not an AI scientist so what do I know.

I think you are being disingenuous by omitting the preceding claim.

> Whether results of this kind should be considered as successes for the program – what is the proper measure to use in evaluating success – depends on the intended use of the program. If the goal is to generate candidate images that a graphic artist will choose from, or choose from and edit, then the system can reasonably be measured in terms of the quality of the best result out of ten or out of one hundred.

They basically admit your Fiverr use case is valid. But say, that it should not be used "in safety-critical applications" which is neither a grand claim nor controversial. It is probably the most blasé claim because, as you point out, no one is expecting this to be used in safety-critical applications. From an economics point of view, the Fiverr use case seems pretty strained to me. If you've ever watched street art, some dazzling things can be done in under 10 minutes. Unless the DALL-E gets it correct on the first shot +99% of the time, someone sifting through images is probably just as costly as paying for Fiverr. What this paper elucidates to me is that even historical figures are off-limits which, in my expectation, is a non-trivial use case.

If someone says, "Cars may be great at transportation to and from work but we should be cautious to use them as a counterbalance for a trebuchet." I don't think it's disingenuous to take issue with the second part of the claim and to not give the author credit for the first part of the claim.

Right, unless the creator of the car's mission is to use it as a counterbalance for a trebuchet. From [1]:

OpenAI is an AI research and deployment company. Our mission is to ensure that artificial general intelligence benefits all of humanity.

[1] - https://openai.com/about/

I think that's an unfair perspective.

> Our mission is to ensure that artificial general intelligence benefits all of humanity.

Saying that DALL-E isn't achieving their mission of beneficial AGI is like getting mad at NASA for pointing their test rockets sideways when trying to go to the moon.

All technology known, biological or man made, was created with incremental improvements, with the early iterations almost alway being unhelpful, and sometimes creating a burden. For example, the construction/invention of the first plow was completely unhelpful, and nothing but a distraction, from that years crop. The incremental improvements after now have it where the majority of humanity would perish within a year if you were to remove modern plows from existence.

There's no instantaneous way to achieve their mission. The incremental steps to achieve their mission don't have to individually achieve that mission. That's not realistic or a rational expectation.

Disregard the above if your perspective is that all technology is bad for man, which I've seen rationally discussed.

I don't see what's unfair. It's a pretty uncontroversial take and I feel like the people replying are being unfair by using tangentially related analogies. In your analogy, no one is getting mad at NASA here. It's like it is WWII and Germany is hitting London with rockets. The claim here is like "these things are great if we want to launch explosives from this side of the channel and we don't care what we hit in the city. But if we're aiming for the moon, we still have a long way to go." Like, it is just a such an indifferent claim that I'm honestly shocked people are taking issue. For the past month, all I've been hearing is how amazing DALLE-2. If you were a layman, you might believe that we're at AGI. This is simply a sobering reminder not to get caught up in the PR machine. The authors have 10x the text espousing the impressive result, what is wrong with a single sentence?

> someone sifting through images is probably just as costly as paying for Fiverr

The difference being that anyone can sift through images and identify good ones, whereas few people can create good images. The amount of time it takes to complete the task may be the same, but the number of people who can do it greatly increases.

How quickly they forget. 45 minutes after the DALL-E n release, Sam Altmann tweeted “AGI is gonna get wild”; Greg Brockman posted multiple tweets around the idea of AGI and walls broken. That invites a critical analysis.

Ad hominem against me won’t remedy the limitations that we and others have observed.

This doesn't seem like an ad hominem attack at all. It's a criticism of criteria you're using to analyze DALL-E in this paper. You're talking about safety-critical applications, but nobody is expecting DALL-E to be used for those. Even your examples are just people tweeting generally about the future of AGI (the obvious context being that this is a big demonstrable step forward, not that DALL-E is an AGI that society is going to rely on for critical tasks).

Sure, if you analyze it based on the criteria you've set out, it fails. The point is that nobody else cares if it fails based on that criteria, because nobody else thinks that criteria is relevant.