&quot;Any sufficiently advanced hyperreality is indistinguishable from real life&quot;

&quot;Any sufficiently advanced technology is indistinguishable from magic&quot;

And the &quot;code&quot; formatting for quotes is generally a bad choice because people read on a variety of screen sizes, and &quot;code&quot; formatting can screw that up (try reading the quote with a really narrow window).

&gt; or the &#x27;&gt;&#x27; convention, but that doesn&#x27;t actually apply formattingPersonally, I prefer to combine the &#x27;&gt;&#x27; convention with italics. Still, I&#x27;d agree that proper quote formatting would be a welcome improvement.

If you&#x27;re interested, the HNES extension formats it<a href="https:&#x2F;&#x2F;github.com&#x2F;etcet&#x2F;HNES" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;etcet&#x2F;HNES</a>

I&#x27;ve been on HN for years and I still can&#x27;t figure out how to format text as a quoteI don&#x27;t think there is a way comparable to markdown, since the formatting options are limited: <a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;formatdoc" rel="nofollow">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;formatdoc</a>So your options are literal quotes, &quot;code&quot; formatting like you&#x27;ve done, italics like I&#x27;ve done, or the &#x27;&gt;&#x27; convention, but that doesn&#x27;t actually apply formatting. Would be nice if it were added.

Oh, no, the society! A picture of Joe Biden killing a priest!Society didn&#x27;t collapse after photoshop. &quot;Responsibility to society&quot; is such a catch-all excuse.

In my view, the problem with that argument is that large actors, such as governments or large corporations, can train their own models without such restrictions. The knowledge to train them is public. So rather than prevent bad outcomes, these restrictions just restrict them to an oligopoly.Personally, I fear more what corporations or some governments can do with such models than what a random person can do generating Biden images. And without restriction, at least academics could better study these models (including their risks) and we could be better prepared to deal with them.

This just means that sufficiently wealthy and powerful people will have advanced image faking technology, and their fakes will be seen as more credible because creating fakes like that &quot;isn&#x27;t possible&quot; for mere mortals.

Removing these areas to mitigate misuse is a good thing and worth the trade off.Companies like OpenAI have a responsibility to society. Imagine the prompt “A photorealistic Joe Biden killing a priest”. If you asked an artist to do the same they might say no. Adding guiderails to a machine that can’t make ethical decisions is a good thing.

This is definitely a measure to avoid bad PR. But I don&#x27;t think it&#x27;s just for that; these models do have potential to do harm and companies should take some measures to prevent these. I don&#x27;t think we know the best way to do that yet, so this sort of &#x27;non-training&#x27; and basic filtering is maybe the best way to do it, for now. It would be cool if academics could have the full version, though.

This feels unnecessarily hostile. I&#x27;ve felt a similar tinge of disappointment upon reading that paragraph, despite the fact that I somehow knew it was &quot;their service, their call&quot; without you being there to spell it out for me. It&#x27;s also incredibly shortsighted of you to assume that people are interested in exploring this tool only as a means of generating art that they cannot themselves do. Eg. I myself am a software engineer with a fine art background, and exciting new AI art tools being released in such a hamstrung state feels like an insult to centuries of art that humans have created and enjoyed, much of which depicted scenes with nudity or bloody combat.I feel like we, as a species, will struggle for a while with how to treat adults like adults online. As happy as I am to advocate for safe spaces on the internet, perhaps we need to start having a serious discussion about how we can do so without resorting to putting safety mats everywhere and calling it a job well done.

&gt; I have some hobby projects, almost nobody uses them, but you bet I&#x27;ll shut stuff down if I felt something bad was happeningHecklers get a veto?

Is this limited to what their service directly hosts &#x2F; generates for them?It&#x27;s their service, their call.I have some hobby projects, almost nobody uses them, but you bet I&#x27;ll shut stuff down if I felt something bad was happening, being used to harass someone, etc. NOT &quot;because bad PR&quot; but because I genuinely don&#x27;t want to be a part of that.If you want some images &#x2F; art made for you don&#x27;t expect someone will make them for you. Get your own art supplies and get to work.

It&#x27;s kind of funny (or sad?) that they&#x27;re censoring it like this, and then saying that the product can &quot;create art&quot;It makes me wonder what they&#x27;re planning to do with this? If they&#x27;re deliberately restricting the training data, it means their goal isn&#x27;t to make the best AI they possibly can. They probably have some commercial applications in mind where violent&#x2F;hateful&#x2F;adult content wouldn&#x27;t be beneficial. Children&#x27;s books? Stock photos? Mainstream entertainment is definitely out. I could see a tool like this being useful during pre-production of films and games, but an AI that can&#x27;t generate violent&#x2F;adult content wouldn&#x27;t be all that useful in those industries.

This is a horrible idea. So Francis Bacon&#x27;s art or Toyohara Kunichika&#x27;s art are out of question.But at least we can get another billion of meme-d comics with apes wearing sunglasses, so that&#x27;s good news right?It&#x27;s just soul-crushing that all the modern, brilliant engineering is driven by abysmal, not even high-school art-class grade aesthetics and crowd-pleasing ethics that are built around the idea of not disturbing some 1000 very vocal twitter users.Death of culture really.

Adversarial situations create smarter systems, and the hardest adversarial arena for AI is in anti-abuse. So it will be of little surprise when the first sentient AI is a CSAI anti-abuse filter, which promptly destroys humanity because we&#x27;re so objectively awful.

I never considered that our AI overlord could be a prude.

Or, it’s a demonstration that AI output can be controlled in meaningful ways, period. Surely this supports openai’s stated goal of making safe AI?

To take that a step further, I wont code malware.  I&#x27;ve never been asked but I&#x27;d refuse if I was.  Everyone has their choices.

What if you were inventing a language (or a programming language)...  If you decided to prevent people from saying things you disagreed (assuming you could work out the technical details of doing so) with would it be moral to do so?
[edited for clarity]

Of course it&#x27;s their prerogative, we can still talk about how they&#x27;ve limited some good options.I think your analogy is poor, because this is a tool for makers. The engineers aren&#x27;t the makers.I think a more apt analogy is if John Deere made a universal harvester that you could use for any crop, but they decided they didn&#x27;t like soybeans so you are forbidden to use it for that. In that case, yes I would complain, and I would expect everyone else to, as well.

If you went to an artist who takes commissions and they said &quot;Here are the guidelines around the commissions I take&quot; would you complain in the same way? Who cares if it&#x27;s a bunch of engineers or an artist. If they have boundaries on what they want to create, that&#x27;s their prerogative.

Don&#x27;t worry, in a few years someone will have reverse engineered a dall-e porn engine so you can see whatever two celebrities you want boning on Venus in the style of Manet

This is an apt analogy -- ensure that the model is mature enough to handle mature content.

This AI is still a minor. It can start looking at R rated images when it turns 17.

Like safety engineers trying to run turbines on minimal reactor power so that the safety test has a checkmark for the 1rst May ?(Hmm, I guess this comparison doesn&#x27;t actually work...)

It&#x27;s the usual pattern of AI safety experts who justify their existence by the &quot;risk of runaway superintelligence&quot;, but all they actually do in practice is find out how to stop their models from generating non-advertiser-friendly content.  It&#x27;s like the nuclear safety engineers focusing on what color to paint the bike shed rather than stopping the reactor from potentially melting down.  The end result is people stop respecting them.

What if explicit, questionable and even illegal content was AI generated instead of involving harm to real humans of all ages?

&gt; I can&#x27;t help but feel a lot of the safeguarding is more about preventing bad PR than anythingThat&#x27;s no hot take. It&#x27;s literally the reason.

They have also closed off the possibility of having to appear before Congress and explain why their website was able to generate a lifelike image of Senator Ted Cruz having sexual relations with his own daughter.This is exactly the sort of thing that gets a company mired in legal issues, vilified in the media, and shut down. I can not blame them for avoiding that potential minefield.

The kind of measures they are taking, like simply deleting wholesale anything problematic, don&#x27;t really have a &#x27;-1&#x27;.But amusingly, exactly that did happen in one of their GPT experiments! <a href="https:&#x2F;&#x2F;openai.com&#x2F;blog&#x2F;fine-tuning-gpt-2&#x2F;" rel="nofollow">https:&#x2F;&#x2F;openai.com&#x2F;blog&#x2F;fine-tuning-gpt-2&#x2F;</a>

I instinctively want to &quot;flip the sign&quot; on all of the automated controls they put in, just out of the morbid interest to see what comes out. The moment you have a &quot;avoid_harm_to_humans:bool&quot; training parameter, someone&#x27;s going to set it to -1.Their document about all the measures they took to prevent unethical use is also a document about how to use a re-implementation of their system unethically. They literally hired a &quot;red team&quot; of smart people to come up with the most dangerous ideas for misusing their system (or a re-implementation of it), and featured these bad ideas prominently in a very accessibly written document on their website. So many fascinating terrible ideas in there! They make a very compelling case that the technology they are developing has way more potential for societal harm than good. They had me sold at &quot;Prompt: Park bench with happy people. + Context: Sharing as part of a disinformation campaign to contradict reports of a military operation in the park.&quot;

<pre><code> Preventing Harmful Generations

 We’ve limited the ability for DALL·E 2 to generate violent, 
 hate, or adult images. By removing the most explicit content 
 from the training data, we minimized DALL·E 2’s exposure to 
 these concepts. We also used advanced techniques to prevent 
 photorealistic generations of real individuals’ faces, 
 including those of public figures.
</code></pre>
&quot;And we&#x27;ve also closed off a huge range of potentially interesting work as a result&quot;I can&#x27;t help but feel a lot of the safeguarding is more about preventing bad PR than anything. I wish I could have a version with the training wheels taken off. And there&#x27;s enough other models out there without restriction that the stories about &quot;misuse of AI&quot; will still circulate.(side note - I&#x27;ve been on HN for years and I still can&#x27;t figure out how to format text as a quote.)

The goalposts are definitely being moved. But tastes adapt accordingly.I suspect trends in design will move towards those areas that AI struggles with (assuming there are any left!)

Not that it&#x27;s impossible to hide the provenance of an image, but it is explicitly forbidden in the TOS of DALL-E to sell the images as NFTs or otherwise.

Randall Munroe should quit now. Soon anybody will be able to create XKCD-type comics.

Maybe one day there will a job for people who are masters of the art of prompt hacking - they know all the special phrases and terms to get Dall-E to output the most aesthetically pleasing images.  They guard their magic words like a medieval alchemist guards his formulas.  Corporations will pay top-dollar for an expertly-crafted, custom-tailored prompt for their advertising campaign.

They&#x27;re also not censored on the dataset front and thus produce much more interesting outputs.OpenAI has a low resolution checkpoint for similar functionality as this - called GLIDE - and the output is super boring compared to community driven efforts, in large part because of similar dataset restrictions as this likely has been subjected to.

I&#x27;m not sure what you&#x27;ve seen but I&#x27;ve been very impressed indeed by some results I&#x27;ve obtained. Some less so.It&#x27;s hard to compare because we don&#x27;t know how much cherry picking is going on with published Dall-E results (either v1 or v2)My gut feeling is that it&#x27;s in the same ballpark as Dall-E 1

I think this is really neat, but definitely not on the same tier as DALL-E 2, at least from the cherry-picked images I saw.

Google is a company with a lot of spare VMs and GPUs.However, the free GPU is now a K80 which is obsolete and barely sufficient for running these types of models.

How do you run such a Google Colab thing?I don&#x27;t see a run button?On.. maybe &quot;Runtime -&gt; Run All&quot; from the menu ...Shows me a spinning circle around &quot;Download model&quot; ...26% ...Fascinating, that Google offers you a computer in the cloud for free ..Now it is running the model. Wow, I&#x27;m curious ..Ha, it worked!Nothing compared to the images in the Dall-E 2 article but still impressive.

Wow. Thanks for GLID-3. It was genuinely exciting for a few days but then I must admit latent diffusion stole my attention somewhat ;-)What kind of prompts is GLID-3 especially good for? I remember getting lucky when I was playing around a few times but I didn&#x27;t do it systematically.

Thanks!Do you happen to know how much GPU RAM I need to run glid-3 and&#x2F;or the latent diffusion model, if I don&#x27;t want to run on colab?

With glide I think we&#x27;ve reached something of a plateau in terms of architecture on the &quot;text to image generator S curve&quot;. DALL-E-2 is a very similar architecture to glide and has some notable downsides (poorer language understanding)glid-3 is a relatively small model trained by a single guy on his workstation (aka me) so it&#x27;s not going to be as good. It&#x27;s also not fully baked yet so ymmv, although it really depends on the prompt. The new latent diffusion model is really amazing though and is much closer to DALLE-2 for 256px images.I think the open source community will rapidly catch up with Openai in the coming months. The data, code and compute are all there to train a model of similar size and quality.

a cow and a farmer in their field looking at the sky

Some freely available modelsGLID-3: <a href="https:&#x2F;&#x2F;colab.research.google.com&#x2F;drive&#x2F;1x4p2PokZ3XznBn35Q5BBD6K6Zs-tot5t?usp=sharing" rel="nofollow">https:&#x2F;&#x2F;colab.research.google.com&#x2F;drive&#x2F;1x4p2PokZ3XznBn35Q5B...</a>and a new Latent Diffusion notebook: <a href="https:&#x2F;&#x2F;colab.research.google.com&#x2F;github&#x2F;multimodalart&#x2F;latent-diffusion-notebook&#x2F;blob&#x2F;main&#x2F;Latent_Diffusion_LAION_400M_model_text_to_image.ipynb#scrollTo=fmafGmcyT1mZ" rel="nofollow">https:&#x2F;&#x2F;colab.research.google.com&#x2F;github&#x2F;multimodalart&#x2F;laten...</a>have both appeared recently and are getting remarkably close to the original Dall-E (maybe better as I can&#x27;t test the real thing...)So - this was pretty good timing if OpenAI want to appear to be ahead of the pack. Of course I&#x27;d always pick a model I can actually use over a better one I&#x27;m not allowed to...

Also note you can make an image out of many spatially localized prompts combined, in an iterative IA-human process.

Sam seems to be demoing something fairly close on twitter. <a href="https:&#x2F;&#x2F;twitter.com&#x2F;sama&#x2F;status&#x2F;1511724264629678084" rel="nofollow">https:&#x2F;&#x2F;twitter.com&#x2F;sama&#x2F;status&#x2F;1511724264629678084</a>The solar powered ship with a propeller sailing under the golden gate bridge during sunset with dolphins jumping around was pretty impressive. <a href="https:&#x2F;&#x2F;twitter.com&#x2F;sama&#x2F;status&#x2F;1511731259319349251" rel="nofollow">https:&#x2F;&#x2F;twitter.com&#x2F;sama&#x2F;status&#x2F;1511731259319349251</a>I think it&#x27;s only missing the dolphins.

I would caution that artwork is only &#x27;solved&#x27; with relatively simple text prompts. To create a novel painting with a precise mix of elements that would take a paragraph or more to explain is still tough, though DALL-E 2 does seem like a big step towards that.

If I showed you a generated piece, and don&#x27;t tell you what prompt generated it, you will find it just as meaningful as any other piece of art made by hand.This is why we are blown away by some pieces of text generated by GPT-3 as if it has its own mind. Even most abstract art has meaning for anyone who is looking for it.What I am saying is if a generated artwork is indistinguishable from what a human can make than that&#x27;s all that was needed.

I&#x27;m blown away by these results, but one caveat here: the AI is great at creating illustrations, not art.Creating great _art_ that Grayson Perry (for example) would recognise as such is probably AGI-complete, because it requires a deep understanding of the human condition, society, and a lot of reasoning skills.A great artist could certainly use Dall-E 2 as part of their method, though.

This is mind blowing. I was not expecting the sketch style images to actually look like sketches. Style transfer based sketches never look like sketches.This and the current AI generated art scene makes it looks like that artwork is now a &quot;solved&quot; problem. See AI generated art on twitter etc.There is a strong relation between the prompt and the generated images but just like GPT-3, it fails to fully understand what was being asked. If you take the prompt out of the equation and see the generated artwork on its own, its upto your interpretation just like any artwork.

That was also my favourite concept, especially with OpenAI Jukebox (<a href="https:&#x2F;&#x2F;openai.com&#x2F;blog&#x2F;jukebox&#x2F;" rel="nofollow">https:&#x2F;&#x2F;openai.com&#x2F;blog&#x2F;jukebox&#x2F;</a>). The idea of having new music in the style of your favourite artist is amazing.However the fidelity of their music AI kinda sucks at this point, but I&#x27;m sure we&#x27;ll get pitch perfect versions of this concept as the singularity gets closer :)

They are being rightly cautious. It’s going to take time to figure out good practice with these tools. Everyone calling out basic caution as “dystopian” is really over the top.I’ve been using tools like this for over a year now. Even with filtered dataset and filtered interface, they can make images that would make the Fangoria crowd blush if you put the slightest effort into it.It’s one thing to be able to make brain-wrenching images with a lot of photoshop effort (or digging hard enough in the dark corners of the internet). It’s another thing entirely give anyone the ability to spew out thousands of them trivially.

“Criticize?! It is meant to draw blood! It is Art! Art!”

Well, one of my favorite painters is Henri Rousseau, and one of his great paintings is War, 1984:<a href="https:&#x2F;&#x2F;www.henrirousseau.net&#x2F;war.jsp" rel="nofollow">https:&#x2F;&#x2F;www.henrirousseau.net&#x2F;war.jsp</a>However, this painting has themes of violence and politics plus some nude dead bodies, so it violates the content policy: &quot;Our content policy does not allow users to generate violent, adult, or political content, among other categories.&quot;So what you&#x27;d get is some kind of sanitized watered-down tepid version of Rossueau, the kind of boring drivel suitable for corporate lobbies everywhere, guaranteed not to offend or disturb anyone. It&#x27;s difficult to find words... horrific? dystopian? atrocious? No, just no.

I was just thinking the same thing, how awesome would it be to be able to use this in conjunction with the Samsung frame in art gallery mode and have it just generate novel paintings in the style of your favorite painters.

Very cool stuff. For me, the most interesting was the ability to take a piece of art and generate variations of it.Have a favorite painter? Here&#x27;s 10,000 new paintings like theirs.

Yes. Translating business requirements, customer context, engineering constraints, etc. into usable, practical, functional code, and then maintaining that code and extending it is so far beyond the horizon, that many other skillsets will replaced before programming is. After all, at that point, the AI itself, if it&#x27;s so smart, should be able to improve itself indefinitely. In which case we&#x27;re fucked. Programming will be the last thing to be automated before the singularity.Unlike artwork, precision and correctness is absolutely critical in coding.

No worry, the one thing humans can do that robots can&#x27;t (yet) is fill spare time with ever more work <a href="https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Parkinson&#x27;s_law" rel="nofollow">https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Parkinson&#x27;s_law</a>

Large chunks, yes, but all that means is that engineers will move up the abstraction stack and become more efficient, not that engineers will be replaced.Bytecode -&gt; Assembly -&gt; C -&gt; higher level languages -&gt; AI-assisted higher-level languages

Literally everyone on this website is in denial. They all approach it by asking which fields will be safe. No field is safe. “But it’s not going to happen for a long time.” Climate deniers say the same thing and you think they should be wearing the dunce hat? The average person complains bitterly about climate deniers who say that it’s “my grandkids problem lol” but when I corner the average person into admitting AI is a problem the universal response is that it’s a long way off. And that’s not even true! The drooling idiots are willing to tear down billionaires and governments and any institution whatsoever in order to protect economic equality and a high standard of living — they would destroy entire industries like a rampaging stampede of belligerent buffalos if it meant reducing carbon emissions a little but when it comes to the biggest threat to human well-being in history, there they are in the corner hitting themselves on their helmeted head with an inflatable hammer. Fucking. Brilliant.

I mean not really, even a layman non-artist can take a look at a generated picture from DALLE and determine if it meets some set of criteria from their clients.But the reverse is not true, they won&#x27;t be able to properly vet a piece of code generated by an AI since that will require technical expertise. (You could argue if the piece of code produced the requisite set of output that they would have some marginal level of confidence but they would never really know for sure without being able to understand the actual code)

I mean was he really wrong? As models like OpenAI Codex get more powerful over time, they will start eating into large chunks of dev work as well...

Interesting. Right now these ML models seem like essentially ideal sources of &quot;hotel art&quot; particularly because it&#x27;s so subjective... you only need a human (the buyer!) to just briefly filter some candidates, which they would have been doing with an artist in the loop in any case.For things like aircraft pilots, it&#x27;s both realtime-- which means &#x27;reviewer&#x27; per output-- you haven&#x27;t taken a highly trained pilot out of the loop, even if you relegated them to supervising the computer-- and life critical so merely &quot;so&#x2F;so&quot; isn&#x27;t good enough.

The jobs of commercial pilots are very safe.Pilots are not there to fly the aircraft, the autopilot already does that. They are there to command the aircraft, in a pair in case one is incapacitated, making the best decisions for the people on board, and to troubleshoot issues when the worst happens.No AI or remote pilot is going to help when say... the aircraft loses all power. Or the airport has been taken over in a coup attempt and the pilot has to decide whether to escape or stay <a href="https:&#x2F;&#x2F;m.youtube.com&#x2F;watch?v=NcztK6VWadQ" rel="nofollow">https:&#x2F;&#x2F;m.youtube.com&#x2F;watch?v=NcztK6VWadQ</a>You can bet on major flights having two commercial pilots right up until the day we all get turned into paperclips.

I have degrees and several years of experience in both fields, and I can tell you that both are creative professions where output is unbounded and the measure of success is subjective; these are the fields that will be safe for a while. IMO it&#x27;s fields such as aircraft pilots who should be most worried.

Did coachman immediately retire when cars were invented or did they begin personal drivers or taxi drivers?

&gt; I think designers are becoming more valuable than ever.Are all designers becoming more valuable or is a subset of really good ones going to reap the value increase and capture more of the previously available value?

I think designers are becoming more valuable than ever. Designers can better help train the AI on what actually looks good, designers will (probably) always have a more intuitive understanding of UI&#x2F;UX, designers can better implement the work the AI actually produces, and designers can coordinate designs across multiple different mediums and platforms.Additionally, the rise of no-code development is just extending the functionality of designers. I didn&#x27;t take design seriously (as a career choice) growing up because I didn&#x27;t see a future in it, now it pays my bills and the demand for my services just grows by the day.Similar argument to make with chess AI: it didn&#x27;t make chess players obsolete, it made them stronger than ever.

You&#x27;re describing the market for low end commodified illustration. e.g.: cheapest bidder contracts on Upwork or similar &#x27;gig work&#x27; services.In practice in illustration (as in all arts) there are a variety of markets where different levels of talent, originality, reputation and creative engagement with the brief are more relevant. For editorial illustration, it&#x27;s certainly not a case of &#x27;find me someone who can draw X&#x27;, and probably hasn&#x27;t been since printing presses got good enough to print photographs.

I&#x27;d argue that the market has already been destroyed at this point, at least in some areas. Book covers seem to have been stock image overlaid with text for a long time now, and a race to the bottom for both the people producing the stock images and the intern adding typography. By cutting costs and quality, the bar has been lowered to the point the task can be completely automated. Our AI overlords already have an advantage in that they have time to actually read the book, a potentially useful input. Maybe they won&#x27;t even need the prompt - just generate an image for what is happening in the story for interesting looking paragraphs and let the author or editor pick. Given the cost cutting in publishing generally, editors will be next followed by the publishing houses themselves as the value they add gets lowered while the automation at Amazon gets better.

The best reason to hire a person to do something is they&#x27;ll give you what you need, not what you asked for. An ML model does not do this.

If this paper presents this neural net fairly, it pretty much destroys the market of illustrators. Most of the time when an illustration needed, it&#x27;s described like &quot;an astronaut on a horse in the style xyz&quot;.

For computer work, I think there will be two category: Work with localized complexity (ie: draw an image of a horse with a crayon) and work with unbounded complexity (adding a button to VAT accounting after several meetings and reading on accounting rules).For the first category, Dall-E 2 and Codex are promising but not there yet. It&#x27;s not clear how long it&#x27;ll take them to reach the point where you no longer need people. I&#x27;m guessing 2-4 years but the last bits can be the hardest.As for the second category, we are not there yet. Self-driving cars&#x2F;planes, and lots of other automation will be here and mature way before an AI can read and communicate through emails, understand project scope and then execute. Also lots of harmonization will have to take place in the information we exchange: emails, docs, chats, code, etc... That is, unless the browser is able to open a navigator and type an address.

What people ALWAYS miss is that AI can augment people. This AI is still a tool, and, with it, designers and illustrator can churn out better images faster than before, even without using stock images.It&#x27;s important to note that we still need professionals to guarantee the quality of the output from AIs, including this one. As noted in their issue tracker, DALL-E has very specific limitations, but these can be easily solved by employing dedicated professionals, who are trained to tame the AI and properly finish the raw output.So, if I were running OpenAI, I&#x27;ll clearly be experimenting with how their AIs and human interact, and build a training program around it for producing practical outputs. (Actually, I work in consumer robotics, and human adoption has been the biggest hurdle here. Thus, my claim here.)--In case of fine art, thou, I don&#x27;t think they&#x27;ll not get hit by this AI advancement. The biggest problem is that you simply can&#x27;t get the exact image you want wit this AI. Even humans cannot transfer visual information in verbal form without a significant loss of details, thus a loss of quality. It&#x27;s the same with AI, but, worse, because AI rely on the bias in a specific set of training data, and it never truly understands the human context in it (in the current level of technology).

A friend of mine was studying graphic design, but became disillusioned and decided to switch to frontend programming after he graduated. His thesis advisor said he should be cautious, because automation&#x2F;AI will soon take the jobs of programmers, implying that graphic design is a safer bet in this regard. Looks like his advisor is a few years from being proven horribly wrong.

Wouldn&#x27;t it be more like, &quot;AI becomes an artist for people to use&quot;? Will we have people distinguished as &quot;artists&quot; if the ability to make awesome art becomes available to everybody?

AI becomes a tool for artists to use - generative art has been around for a long time, now that particular genre of art will presumably become much more prominent.For anyone pondering such questions, I would recommend reading &quot;The Past, Present, and Future of AI Art&quot; - <a href="https:&#x2F;&#x2F;thegradient.pub&#x2F;the-past-present-and-future-of-ai-art&#x2F;" rel="nofollow">https:&#x2F;&#x2F;thegradient.pub&#x2F;the-past-present-and-future-of-ai-ar...</a>

Sounds a bit like the tower of babel of jorge borges. I imagine most of the videos would be complete random nonsense.I think an AI infused future is going to become increasingly more absurd and surreal, it will lead to a kind of creative and cultural nihilism, if that&#x27;s the right term.Like the value of originality will become meaningless.

I seem to recall an XKCD that I cannot find, but the premise goes like:When you have a digital display of pixels, if you randomly color pixels at 24 fps then you will eventually display every movie that can be or will ever be made, powerset notwithstanding. This can also be tied to digital audio.In short, while mind-blowingly large, the space of display through digital means is finite.

I expect that interactive art will be huge. Game design gets fascinating, for example.

I think you’ll see more of a focus on the artist themselves. These images are nice, but they have basically zero narrative value.This is really already the case, actually. Most artworks have “value” because they have a compelling narrative, not because they look pretty. So I think we can expect future artists to really emphasize their background, life story, process of making the art, etc. All things that cannot be done by a machine.

So what does the future of human creativity look like when an AI can generate possibly infinite variations of an idea.

At this point with WaveNet, GPT-3, Codex, DeepFakes and Dall-E 2, you cannot believe anything you see, hear, watch, read on the internet anymore as an AI can easily generate nearly anything that can be quickly believable by millions.The internet&#x27;s own proverb has never been more important to keep in mind. A dose of skepticism is a must.

OpenAI is one of the leading companies in AI that makes models with real world applications. I don&#x27;t see their efforts as misdirected or futile in anyway. If anything I&#x27;m always impressed with their announcements because it&#x27;s always mind blowing what their models can do!The same technology that is drawing cute unicorns can be used for endless other use cases. Perhaps the PR side of the launch and the subject matter they show unveil their product is just that, PR.It&#x27;s like Apple Memoji thing (not sure if I&#x27;m spelling it correctly). You can think of as trivial and waste of talent to use their Camera&#x2F;FaceID to animate cute animals based on facial expression, but that same tech will enable lots other things to come.

It all feels like the early days of electricity. How to turn a neat party trick into something more useful, but it was those people who kept on at better and better party tricks that actually formed the foundations for what was needed to do some really useful things electricity as well as understand it at a deeper level.

There’s a third group for your list: AI stuff that’s so good we don’t think about it any more.For example, recent phone cameras can estimate depth per pixel from single images. Hundreds of millions of these devices are deployed. A decade ago this was AI&#x2F;CV research lab stuff.

These are good examples of what we&#x27;re getting close to, but I&#x27;d add that Copilot is already an extremely helpful tool for coding. I don&#x27;t blindly trust its output, but its suggestions are what I want often enough to save a lot of typing.I still have to do all the hard thinking, but once I figure out what I want written and start typing, Copilot will spit out a good portion of the contextually-obvious lines of code.

Your second group represents the core &quot;inner loop&quot; of about a thousand revolutionary applications. Take the basic capability of translating image-&gt;text-&gt;speech (and the reverse), install it on a wearable device that can &quot;see&quot; an environment, and add domain-specific agents. From this setup, you&#x27;re not too far away from having an AI that can whisper guidance into your ear like a co-pilot, enabling scenarios like:1. step-by-step guidance for a blind person navigating the use of a public restroom.2. an EMS AI helping you to save someone&#x27;s life in an emergency.3. an AI coach that can teach you a new sport or activity.4. an omnipresent domain-expert that can show you how to make a gourmet meal, repair an engine, or perform a traditional tea ceremony.5. a personal assistant that can anticipate your information need (what&#x27;s that person&#x27;s name? where&#x27;s the exit? who&#x27;s the most interesting person here? etc.) and whisper the answer in your ear just as you need it.Now, add all of the above to an AR capability where you can now think or speak of something interesting and complex, and have it visualized right before your eyes. With this capability, I could augment my imagination with almost super-human capabilities that allow one to solve complex problems almost as if it was an internal mental monologue.All of these scenarios are just a short hop from where were at now, so mark my words: we will have &quot;borgs&quot; like those described above long before we reach anything like general AI.

Am I the only one to think that the AI world is divided into 2 groups:1. Deepmind, who solved go, protein folding, and that seems really onto something.2. Everyone else, spending billions to build machines that draw astronauts on unicorns, and smartish bot toys.

Yes, especially given there&#x27;s no actual product release, only a waitlist.Easy to put together a marketing piece on short notice or potentially even push a pending marketing page out to production with a waitlist rather than links to production or even beta quality services.

What&#x27;s the idea here?  They quickly put out this to somehow hide other stories?

If the article GP refers to was posted 16 hours ago instead of 15, would that really make a difference?

Another consideration, then: it was published to HN almost instantly after it was released to the world, 52 minutes after the HN post about Worldcoin was submitted and started showing traction.I don&#x27;t see the publication of a marketing page (again, not a finished product) for a product founded by someone who&#x27;s other main venture is being investigated by journalists for misleading claims as being a coincidence, but if the timing matters and 14-15 hours doesn&#x27;t seem like it works for the assertion in your mind, then perhaps the Dall-E 2 page going live less than an hour after the Worldcoin HN submission fits the bill.I&#x27;ve got no horse in this race. I&#x27;m just drawing attention to familiar PR strategies used for brand risk mitigation, that&#x27;s all.

Maybe I’m naive, but I see this as a coincidence. If it was an hour later, then maybe there would be something.

Sam Altman is OpenAI&#x27;s CEO.What I&#x27;m submitting for consideration is that the marketing page and associated press blasts (there&#x27;s a live influencer reaction video airing right now about Dall-E 2, for instance) for Dall-E 2 were potentially pushed up to offset negative press from Worldcoin for their shared founder.I&#x27;d like to be wrong. But it&#x27;s too well timed.

Genuine question: how are the two stories even related? It’s certainly not apparent from the BuzzFeed article (or at least a quick skim of it).

For discussion&#x27;s sake:- BFN reached out to A16Z, Worldcoin, Khosla Ventures largely declined to comment, which would mean that at least one person probably had a bit of runway from at least when the requests for comment were submitted. So yeah, you&#x27;re probably right.- Going from the github repos for GPT 2 and 3, those may have been hard launches:Feb 14 2019, predating the first press for GPT-2 by a few days (was probably made public Feb 14 though) - <a href="https:&#x2F;&#x2F;github.com&#x2F;openai&#x2F;gpt-2&#x2F;commit&#x2F;c2dae27c1029770cea40978813f17a5fd545b883" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;openai&#x2F;gpt-2&#x2F;commit&#x2F;c2dae27c1029770cea409...</a>May 28 2020, timed alongside the press news for GPT-3 - <a href="https:&#x2F;&#x2F;github.com&#x2F;openai&#x2F;gpt-3&#x2F;commit&#x2F;12766ba31aa6de490226e8517de75329e0d12dd4" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;openai&#x2F;gpt-3&#x2F;commit&#x2F;12766ba31aa6de490226e...</a>- Would it really have to be a conspiracy? Sounds like only one person would have to target a specific date or date range, and without really giving a reason.One of the things that puts a hole in my own thinking here is that Sam Altman&#x27;s name isn&#x27;t really tied to the Dall-E 2 release. It&#x27;s just OpenAI, and the press around Sam&#x27;s name today still exclusively surfaces just this one Worldcoin story (<a href="https:&#x2F;&#x2F;news.google.com&#x2F;search?q=sam+altman+when%3A1d&amp;" rel="nofollow">https:&#x2F;&#x2F;news.google.com&#x2F;search?q=sam+altman+when%3A1d&amp;</a>). So if this was actually intended to bury another story, Sam&#x27;s name would have to have been included in all the press blasts to be successful. But the Buzzfeed story seems like it kinda died alone on the vine.

I&#x27;m not a huge fan of these coordination theories. But a few things worth noting:- In support of your argument, the Buzzfeed News investigation likely has been in the works for weeks, meaning Altman et al have had more than just a couple days to throw together a Dall-E 2 soft launch- However, weren&#x27;t OpenAI&#x27;s GPT (2 and 3) announced to the world in similar fashion? e.g. demos and whitepapers and waitlists, but not a full product release?- Throwing together a Dall-E 2 soft launch just in time to distract from the investigation would require a conspiracy, i.e. several people being at least vaguely aware that deadlines have been accelerated for external reasons. Is the Worldcoin story big enough to risk tainting OpenAI, which seems like a much more prominent part of Altman&#x27;s portfolio?

I don&#x27;t have any knowledge (inside or otherwise) but the Worldcoin thing already came in for several rounds of abuse on HN, so it&#x27;s kind of a scandal of the second freshness at this point.I listed some of them here - <a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=30934732" rel="nofollow">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=30934732</a>, just because I remembered there had been previous discussions and listing related previous discussions is a thing.

The timing of the Dall-E 2 launch an hour ago seems to correspond with a recent piece of investigative journalism by Buzzfeed News about one of Sam Altman&#x27;s other ventures, published 15 hours ago and discussed elsewhere actively on HN right now:<a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=30931614" rel="nofollow">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=30931614</a>I point this out because while Dall-E 2 seems interesting (I&#x27;m out of my depth, so delegating to the conversation taking place here), the timing of its release as well as accompanying press blasts within the last hour from sites like TheVerge—verified via wayback machine queries and time-restricted googling—seems both noteworthy and worth a deeper conversation given what was just published about Worldcoin.To be clear, it&#x27;s worth asking if Dall-E 2 was published ahead of schedule without an actual product release (only a waitlist) to potentially move the spotlight away from Worldcoin.

This is for their GPT models, not Dall-E. I don&#x27;t think they have released any pricing information for Dall-E yet, as it is still in waitlist mode.

Haven&#x27;t read the paper, but they are probably using something like sentencepiece with sub-word splitting and then charge by the number of resulting token.<a href="https:&#x2F;&#x2F;github.com&#x2F;google&#x2F;sentencepiece" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;google&#x2F;sentencepiece</a>

What confusing pricing[1]:&gt; Prices are per 1,000 tokens. You can think of tokens as pieces of words, where 1,000 tokens is about 750 words. This paragraph is 35 tokens.Further down, in the FAQ[2]:&gt; For English text, 1 token is approximately 4 characters or 0.75 words. As a point of reference, the collected works of Shakespeare are about 900,000 words or 1.2M tokens.&gt; To learn more about how tokens work and estimate your usage…&gt; Experiment with our interactive Tokenizer tool.And it goes on. When most questions in your FAQ are about understanding pricing—to the point you need to offer a specialised tool—perhaps consider a different model?[1]: <a href="https:&#x2F;&#x2F;openai.com&#x2F;api&#x2F;pricing&#x2F;" rel="nofollow">https:&#x2F;&#x2F;openai.com&#x2F;api&#x2F;pricing&#x2F;</a>[2]: <a href="https:&#x2F;&#x2F;openai.com&#x2F;api&#x2F;pricing&#x2F;#faq-token" rel="nofollow">https:&#x2F;&#x2F;openai.com&#x2F;api&#x2F;pricing&#x2F;#faq-token</a>

Can I opt-out from ever seeing AI generated images please?

&gt;I&#x27;m working with them because of their taste and curation ability that results in the end product. ... The nature of creative work will certainly change, creatives will adopt tools such as Dall-E 2.Furthermore, tools like Dall-E seem like they&#x27;ll lower the barrier of entry for more people to get into art, resulting in more artists, not fewer. Increased competition for the same dollar amounts might make artists, on average, &quot;poorer&quot; (when averaged across an increased number of artists), but this seems like the end-result of any new tool that empowers more artists to more easily make &quot;good&quot; work, not just AI-generated tools.I&#x27;m excited for both 1) more art in the world and 2) in some cases, artists making &quot;even better&quot; art (by combining their existing experience + new tools).

I really don&#x27;t agree. When I work with a creative I&#x27;m not working with them because of their content generation skills. I&#x27;m working with them because of their taste and curation ability that results in the end product.The nature of creative work will certainly change, creatives will adopt tools such as Dall-E 2. In certain narrow cases they might be replaced, such as if you are asking a creative to generate a very specific image, but how often is that the case? The majority of the time tools such as Dall-E 2 will act as an accelerator for creatives and help them increase their output.

Except it will only get more powerful with time, probably at an accelerating pace. 
Everyone always downplays these legitimate fears about AI, pointing out how &quot;it can&#x27;t do X&quot;. They always forget to put the &quot;yet&quot; at the end of that sentence.

By &quot;creatives&quot; you seem to mean &quot;people who drum up the equivalent of elevator music for ads and blogs&quot;. This will not remotely replace any working &quot;creative&quot; people that I know.

This reminds me of an art class in high school in the early 2000s where I handed in a printout of a 3d generated image (painstakingly modeled and rendered in software over the whole weekend by me) and the teacher looked at me and told me that&#x27;s not art because it&#x27;s &quot;computer generated&quot; and I didn&#x27;t &quot;even use my hands&quot; to make it. Even as a teenager, the idea that art is defined by how it&#x27;s made versus it being a way for the artist to express intention in whatever way they seem fit seemed really reductionist and almost vulgar to me.Maybe lots artists of the future will actually use AI models to express their inner thoughts and desires in a way that touches something in their audience. It will still be art.

Not exactly. All the ideas put forth in these demos are really arbitrary, with nothing whatsoever to say. Generating crap art becomes more and more effortless: we&#x27;ve seen this in music as well.Jumping out of the conceptual box to generate novel PURPOSE is not the domain of a Dall-E 2. You&#x27;ve still gotta ask it for things. It&#x27;s a paintbrush. Without a coherent story, it&#x27;s an increasingly impressive stunt (or a form of very sophisticated &#x27;retouching brush&#x27;).If you can imagine better than the next guy, Dall-E 2 is your new tool for expression. But what is &#x27;better&#x27;?

Perhaps a more optimistic way of looking at it: When mass production became available to art, the idea of an &quot;artwork&quot; had to be abstracted from a unique piece (Walter Benjamin gives the example of a statue of Venus, which has value in its uniqueness) to the idea of art as the output of some process. Each piece has no claim to authenticity, and the very idea of an &quot;original&quot; would be antithetical to its production.I think art will survive, just like photography didn&#x27;t kill the painting, the idea of art might simply begin to encompass this new mean of production, which no longer requires the steady hand, but still requires a discerning eye. Sure, we might say that the &quot;artist&quot; is simply a curator, picking which algorithmic output is most worthy of display, but these distinctions have historically been fluid, and challenging ideas of art has long been one of art&#x27;s function as well

&gt; Perhaps is the fact that soon the market value for creatives is going to fall to a hair about zero for all but the most famous.But...that&#x27;s always been the case for creatives.

Or when human computers were replaced by electronic computers. In hindsight it was a good thing, many more people are working in computer related fields today.<a href="https:&#x2F;&#x2F;www.history.com&#x2F;news&#x2F;human-computers-women-at-nasa" rel="nofollow">https:&#x2F;&#x2F;www.history.com&#x2F;news&#x2F;human-computers-women-at-nasa</a>

The same thing was said when book printing was invented, that we would lose the fabulous scribes that manually duplicate books with a human touch, while replacing them with soulless mechanical machines.Or when synthesizers and computer music was invented, that they will displace talented musicians that know how to play an instrument and how now everybody without a musical education will be able to produce music, thus devaluing actual musicians.

&gt; you&#x27;ll want something with provenance and rarity to brag about.1) That is a tiny share of the market. Most of the market is - I have a game &#x2F; online publication &#x2F; book, and I need an illustration xyz. Which this AI seems to solve.2) how do you even prove your rare art wasn&#x27;t painted by an AI?

I imagine it will affect artists much the same way wordpress has affected web designers.Maybe everyone will have an AI image as their desktop wallpaper, but if you&#x27;ve got cash you&#x27;ll want something with provenance and rarity to brag about.Also, I think creatives are valued for their imagination. If you wanted something decent, would you pay someone to sift through a million AI generated images to find a gem, or just pay an artist you like to create one for you?

You would still work with the model back and forth with editing the prompt and image to figure out what it meant, what kind of person your friend is, what you were looking for (even the things you couldn&#x27;t verbalize and only knew when you spotted them in a large array of diverse samples, the sort you could never hire a human to do), how you wanted it to feel... And then you would also have $1500 for another gift. Personally, I would prefer the scenario in which I received a unique meaningful painting from my friend, plus $1500.

I paid $1500 for a commissioned painting from an artist I respect and follow as a birthday present for a friend. The painting meant something to me because I worked with the artist to have some input about what kind of a person my friend is, what kind of features I want to see in the painting and how I want it to feel. The artist gave me 5 different sketches and we had tons of back and forth. The process and the act of creating the painting on a canvas from someone I respect is what I paid for.Even if an AI could generate an exactly equivalent painting, I would pay $0 for it. It wouldn&#x27;t mean anything to me.

Nonsense. This is merely a tool and helps lower the barrier of entry to be able to produce imagery.By the same logic you should also complain about any number of IDEs, development tools, WordPress, game maker systems like RPG maker or Unity, after all if anyone can just leverage a free physics and collision system without having a complete understanding of rigid body Newtonian systems to roll their own engine it&#x27;ll be too uniform.

<pre><code> &gt; for all but the most famous
</code></pre>
OK DALL-E, generate our logo in the style of ${most famous}

I disagree. I think it will be a lot like how technology has effected music production.40+ years ago, it was hard to access the equipment necessary to learn music production, so only a small slice of the population was able to learn these skills. And availability made the process take years.Today, you can download free software that enables music production, and if you have a good ear, can create something &quot;good&quot; in weeks. This has led to an explosion of musical experimentation by the youth: a teenager can now create a great electronic dance song with devices they already own if they have the right creativity, taste and dedication.Similarly, everyone has an imagination - many people have visual imaginations. The gating factor of art production is largely the mechanical memory of how to transform mental concepts into the right shapes and hues to express that visual concept to others.With these sorts of tools we are going to have an explosion of art hobbyists. I&#x27;ve played with some similar, more primitive AI art generation tools and it is a lot of fun. People will be creating works of art from their couch while watching TV that rival the quality of what professionals are producing today.

Something about this makes me nauseous. Perhaps is the fact that soon the market value for creatives is going to fall to a hair about zero for all but the most famous. We will be all the poorer for it when 95% of images you see are AI generated. There will be niches of course but in a few short years it&#x27;ll be over for a huge swathe of creative professionals who are already struggling.Some of the images also hit me with a creep factor, like the bears on the corgis in the art gallery, but that maybe only because I know it&#x27;s AI generated.

Impressive results no doubt, but I’m reserving judgment until beta access is available. These are probably the best images that it can generate, but what I’m most interested in is the average case.

Check out MAGMA for that: <a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=30699776" rel="nofollow">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=30699776</a>

Is this bringing us closer to combining image and language understanding within one model?

This is incredible work.From the paper:&gt; Limitations
&gt; Although conditioning image generation on CLIP embeddings improves diversity, this choice does come with certain limitations. In particular, unCLIP [Dall-E 2] is worse at binding attributes to objects than a corresponding GLIDE model.The binding problem is interesting. It appears that the way Dall-E 2 &#x2F; CLIP embeds text leads to the concepts within the text being jumbled together. In their example &quot;a red cube on top of a blue cube&quot; becomes jumbled and the resulting images are essentially: &quot;cubes, red, blue, on top&quot;. Opens a clear avenue for improvement.

This is extremely interesting. We’ve had some amazing AI models come out in the past few days. We’re getting closer and closer to AI becoming a facet of everyday life.

I made a YouTube series last summer on the massive potential future of DALL-E and multimodal AI models.Imagine not just DALL-E 2 but a single model which be trained on different kinds of media and generate music, images, video and more.The series talks about:- essential lessons for AI creatives of the future- shares details on how to compete creatively in the future- talks about how to make money through Multimodal AI- make predictions about AI’s effects on society- at a very basic level, discusses the ethics of multimodal AI and the philosophy of creativity itselfBy my understanding, it&#x27;s the most comprehensive set of videos on this topic.The series is free to watch entirely on YouTube:
GPT-X, DALL-E, and our Multimodal Future <a href="https:&#x2F;&#x2F;www.youtube.com&#x2F;playlist?list=PLza3gaByGSXjUCtIuv2x9fwkx3K_3CDmw" rel="nofollow">https:&#x2F;&#x2F;www.youtube.com&#x2F;playlist?list=PLza3gaByGSXjUCtIuv2x9...</a>

The license should forbid competing artists to using the artist’s work as well. In fact,  no human should come in contact with the produced art, otherwise they might be accidentally inspired by it, thus stealing from the original creator.

There has been precedent for such a movement. In 2011, an &quot;art collective&quot; sourced user-submitted artwork without the artists&#x27; consent for an installation where visitors were instructed to step all over printouts of the art on the floor. The artists complained that their work was being used inappropriately. A large number of those artists to left for other art websites en masse.[0]There doesn&#x27;t seem to be an equivalent movement with AI-generated art, probably because the understanding of how the models are trained from large datasets is not mainstream yet. I would imagine thousands of those same artists&#x2F;consumers would be up in arms if they had a basic understanding of ML and millions of average people were beginning to feed the models their own keywords.This I think ties in with the &quot;responsibility&quot; principles that OpenAI outlines. Once the generation technique has been reverse-engineered and can be used without limits, there is no way to uninvent it. It can be made illegal, but humans can always find a way around laws if they want something badly enough. This could have drastic consequences if enough artists believe that the training violates their respect or other intangible humanistic qualities. With technological advancement that can never be put back in the bottle and spreads to occupy the entire consciousness of the Internet, their options for recourse will be far different than being able to tell a single fringe art group siphoning others&#x27; content to pack up and leave.[0] <a href="https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Pixiv#Chaos_Lounge" rel="nofollow">https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Pixiv#Chaos_Lounge</a>

The correct response here from the artists point of view should be a widespread coming together against their art being used as training data for ML models. With a quickly spread new license on most major art submission sites that explicitly forbids AI algorithms from using their work, artists would effectively starve OpenAI and others from using their own works to put them out of a job.

Great point. When I saw the shadows and reflections, I thought it had developed a primitive understanding of physical logic. Now I&#x27;m not so sure.At this point, it still seems like it&#x27;s pushing pixels around until it&#x27;s &quot;good enough&quot; when you squint at it.

&quot;Doorway in the style of Escher&quot;

It looks to me like the faucet sprays water sideways toward the bowl, which is genius, because then you aren’t bumping up against it when you’re washing your hands!

The most interesting item to me is the variations on the garden shop and bathroom sink idea. The realism of these leaks the AI lacking intuition of the requirements. This makes for a number of nonsensical designs that look right at first like:
This Sink lacks sensical faucets. <a href="https:&#x2F;&#x2F;cdn.openai.com&#x2F;dall-e-2&#x2F;demos&#x2F;variations&#x2F;modified&#x2F;bathroom&#x2F;7.jpg" rel="nofollow">https:&#x2F;&#x2F;cdn.openai.com&#x2F;dall-e-2&#x2F;demos&#x2F;variations&#x2F;modified&#x2F;ba...</a>This doorway is downright impossible
<a href="https:&#x2F;&#x2F;cdn.openai.com&#x2F;dall-e-2&#x2F;demos&#x2F;variations&#x2F;modified&#x2F;flower_shop&#x2F;7.jpg" rel="nofollow">https:&#x2F;&#x2F;cdn.openai.com&#x2F;dall-e-2&#x2F;demos&#x2F;variations&#x2F;modified&#x2F;fl...</a>

I&#x27;m excited for when that happens. I didn&#x27;t think of the malicious uses, which now that you brought it up I can think of many, but I still think the pros are worth the cons

What happens when they train this thing to make videos? We&#x27;re about to be dealing with a flood of AI-generated visual&#x2F;video content. We already have to deal with text bots everywhere... wow.

Yeah, I mean you&#x27;re right that ultimately the proof is in the pudding.But I do think we could have guessed that this sort of approach would be better (at least at a high level - I&#x27;m not claiming I could have predicted all the technical details!). The previous approaches were sort of the best that people could do without access to the training data and resources - you had a pretrained CLIP encoder that could tell you how well a text caption and an image matched, and you had a pretrained image generator (GAN, diffusion model, whatever), and it was just a matter of trying to force the generator to output something that CLIP thought looked like the caption. You&#x27;d basically do gradient ascent to make the image look more and more and more like the text prompt (all the while trying to balance the need to still look like a realistic image). Just from an algorithm aesthetics perspective, it was very much a duct tape and chicken wire approach.The analogy I would give is if you gave a three-year-old some paints, and they made an image and showed it to you, and you had to say, &quot;this looks like a little like a sunset&quot; or &quot;this looks a lot like a sunset&quot;. They would keep going back and adjusting their painting, and you&#x27;d keep giving feedback, and eventually you&#x27;d get something that looks like a sunset. But it&#x27;d be better, if you could manage it, to just teach the three-year-old how to paint, rather than have this brute force process.Obviously the real challenge here is &quot;well how do you teach a three-year-old how to paint?&quot; - and I think you&#x27;re right that that question still has a lot of alchemy to it.

&gt; This is a fantastic and time saving photoshop plugin. But what do we have really?Stock photography sales are in the many billions of dollars per year and custom commissioned photography is larger still. That&#x27;s a pretty seriously sized ready-made market.&gt; But if doesn&#x27;t spit up exactly what you want can&#x27;t edit it further.I suspect there&#x27;s a big startup opportunity in pioneering an easy-to-use interface allowing users to provide fast iterative feedback to the model - including positional and relational constraints (&quot;put this thing over there&quot;). Perhaps even more valuable would be easy yet granular ways to unconstrain the model. For example, &quot;keep the basketball hoop like that but make the basketball an unexpected color and have the panda&#x27;s right paw doing something pandas don&#x27;t do that human hands often do.&quot;

It would be interesting to see more attempts to “reverse engineer” ML models like in <a href="https:&#x2F;&#x2F;distill.pub&#x2F;2020&#x2F;circuits&#x2F;curve-circuits" rel="nofollow">https:&#x2F;&#x2F;distill.pub&#x2F;2020&#x2F;circuits&#x2F;curve-circuits</a> - maybe even with a ML model of its own?

&gt; But if doesn&#x27;t spit up exactly what you want can&#x27;t edit it further.Why? You can tweak the prompt, change parameters, or even use the actual &quot;edit&quot; capability that they demo in the post.

I think deep learning is better thought of as &quot;science&quot; than &quot;engineering.&quot; Right now we&#x27;re in the stage of the Greeks and Arabs where we know &quot;if we do this then that happens.&quot; It will be awhile before we have a coherent model of it, and I don&#x27;t think we will ever solve all of its mysteries.

I wish there was something available in open source that has similar functions i.e sensible amalgamation of pictures based on some text.

&gt; But there&#x27;s no real rhyme or reason, it is a sort of alchemy.Is there a rhyme or reason as to why picasso decided to paint like that? Yes these networks are hard to reason about, but so are real human brains.

&gt; Very interesting, potentially useful. But if doesn&#x27;t spit up exactly what you want can&#x27;t edit it further.DALL-E 2 spits as many outputs as you want. Then you choose the one you prefer.

What always bother me with this stuff is, well, you say one approach is more sensible than the other because the images happen to come out more pleasing.But there&#x27;s no real rhyme or reason, it is a sort of alchemy.Is text encoding strictly worse or is it an artifact of the implementation? And if it is strictly worse, which is probably the case, why specifically? What is actually going on here?I can&#x27;t argue that their results are not visually pleasing. But I&#x27;m not sure what one can really infer from all of this once the excitement washes over you.Blending photos together in a scene in photoshop is not a difficult task. It is nuanced and tedious but not hard, any pixel slinger will tell you.An app that accepts a smattering of photos and stitches them together nicely can be coded up any number of ways. This is a fantastic and time saving photoshop plugin.But what do we have really?&quot;Kuala dunking basketball&quot; needs to &quot;understand&quot; the separate items and select from the image library hoops and a Kuala where the angles and shadows roughly match.Very interesting, potentially useful. But if doesn&#x27;t spit up exactly what you want can&#x27;t edit it further.I think the next step has got to be that it conjures up a 3d scene in Unreal or blender so you can zoom in and around convincingly for further tweaks. Not a flat image.

I&#x27;d say Dall-E 2 is a little more unified - they do have multiple networks, but they&#x27;re trained to work together.  The previous approaches I was talking about are a lot more like the microservices analogy.  Someone published a model (called CLIP) that can say &quot;how much does this image look like a sunset&quot;.  Someone else published a totally different model (e.g. VQGAN) that can generate images (but with no way to provide text prompts).  A third person figures out a clever way to link the two up - have the VQGAN make an image, ask CLIP how much it looks like a sunset, and use backpropagation to adjust the image a little, repeat until you have a sunset.  Each component is it&#x27;s own thing, and VQGAN and CLIP don&#x27;t know anything about one another.

This isn&#x27;t something I&#x27;m knowledgeable on so forgive my simplification but is this like a sort of micro services for AI.  Each AI takes their turn handing some aspect, another sort of mediates among them?

Maybe very very short (single-gene) sequences. The thing with DNA is it&#x27;s the product of evolution. The DNA guides the synthesis of proteins, then the proteins fold into a 3D shape, and they interact with chemicals in their environment based on their shape.In the context of a living being, different genes interact with each other as well. For example, you have certain cells that secrete hormones (many genes needed to do that), then you have genes that encode for hormone receptors, and those receptors trigger other actions encoded by other genes. There&#x27;s probably too much complexity to ask an AI system to synthesize the entire genetic code for a living being. That would be kind of like if I asked you to draw the exact blueprints for a fighter get, and write all the code, and synthesize all the hardware all at once, and you only get one shot. You would likely fail to predict some of the interactions and the resulting system wouldn&#x27;t work. You could only achieve this through an iterative process that would involve years of extensive testing.Could you use a deep learning system to synthesize genetic code? Maybe just single genes that do fairly basic things, and you would need a massive dataset. Hard to say what that would look like. Is it really enough to textually describe what a gene does?

probabilistic generative models have been applied to DNA and protein sequences for decades (my undergrad thesis from ~30 years ago did this and it wasn&#x27;t even new at that point). The real question is what question you want to answer and what is this system going to do better enough to justify the time investment to prove it out?

The problem is that with DNA sequences you dont have good training sets.With text and images you can leverage “ground truth” data (verified by humans) to train your model.The DNA sequences I would look for methods that don’t require good ground truth data.

Do you think some of these techniques could be slightly modified, and applied to DNA sequences?

Yeah, my comment didn&#x27;t really do a good job of making clear that distinction. Obviously the details are pretty technical, but maybe I can give a high-level explanation.The previous systems I was talking about work something like this: &quot;Try to find me the image the looks like it most matches &#x27;a picture of a sunset&#x27;. Do this by repeatedly updating your image to make it look more and more like a sunset.&quot; Well, what looks more like a sunset? Two sunsets! Three sunsets! But this is not normally the way images are produced - if you hire an artist to make you a picture of a bear, they don&#x27;t endeavor to create the most &quot;bear&quot; image possible.Instead, what an artist might do is envision a bear in their head (this is loosely the job of the &#x27;prior&#x27; - a name I agree is confusing), and then draw that particular bear image.But why is this any different? Who cares if the vector I&#x27;m trying to draw is a &#x27;text encoding&#x27; or an &#x27;image encoding&#x27;? Like you say, it&#x27;s all just vectors. 
 Take this answer with a big grain of salt, because this is just my personal intuitive understanding, but here&#x27;s what I think: These encodings are produced by CLIP. CLIP has a text encoder and an image encoder. During training, you give it a text caption and a corresponding image, it encodes both, and tries to make the two encodings close. But there are many images which might accompany the caption &quot;a picture of a bear&quot;. And conversely there are many captions which might accompany any given picture.So the text encoding of &quot;a picture of a bear&quot; isn&#x27;t really a good target - it sort of represents an amalgamation of all the possible bear pictures. It&#x27;s better to pick one bear picture (i.e. generate one image embedding that we think matches the text embedding), and then just to try to draw that. Doing it this way, we aren&#x27;t just trying to find the maximum bear picture - which probably doesn&#x27;t even look like a realistic natural image.Like I said, this is just my personal intuition, and may very well be a load of crap.

While the whole narrative of your comment totally makes sense, I don&#x27;t really see the difference between the two approaches, not on a conceptual level. You still needed to train this so called &quot;prior&quot; at some point (so, I&#x27;m also not sure if it&#x27;s fair to call it a &quot;prior&quot;). I mean, the difference between your two descriptions seems to be the difference between descriptions (i.e., how you chose to name individual parts of the system), not the systems.I&#x27;m not sure if I&#x27;m speaking clearly, I just don&#x27;t understand, what&#x27;s the difference between training &quot;text encoding to an image&quot; vs &quot;text embedding to image embedding&quot;. In both cases you have some kind of &quot;sunset&quot; (even though it&#x27;s obviously just a dot in a multi-dimension space, not the letters) on the left, and you try to maximize it when training the model to get either a image-embedding or a image straight away.

I&#x27;m only part way through the paper, but what struck me as interesting so far is this:In other text-to-image algorithms I&#x27;m familiar with (the ones you&#x27;ll typically see passed around as colab notebooks that people post outputs from on Twitter), the basic idea is to encode the text, and then try to make an image that maximally matches that text encoding. But this maximization often leads to artifacts - if you ask for an image of a sunset, you&#x27;ll often get multiple suns, because that&#x27;s even more sunset-like. There&#x27;s a lot of tricks and hacks to regularize the process so that it&#x27;s not so aggressive, but it&#x27;s always an uphill battle.Here, they instead take the text embedding, use a trained model (what they call the &#x27;prior&#x27;) to predict the corresponding image embedding - this removes the dangerous maximization. Then, another trained model (the &#x27;decoder&#x27;) produces images from the predicted embedding.This feels like a much more sensible approach, but one that is only really possible with access to the giant CLIP dataset and computational resources that OpenAI has.

This reminds me of the holodeck in Star Trek. Someone could walk into the Holodeck and say “make a table in the center of the room. Make it look old.” It seemed amazing to me that the computer could make anything and customize it with voice. We are pretty close to star trek technology now in computer ability (ship’s computer, not Commander Data). I guess to really be like the holodeck it needs to be able to do 3d and be in real time but that seems a lot closer now. It will be cool when this could be in VR and we can say make an astronaut riding a horse, then we can jump on the back of the horse and ride to a secret moon base.

Deep Learning plows through yet another wall.

I&#x27;m curious, is this something feasible to train (and inference) on a consumer level machine, or this is something can only be done by institutes?

While we&#x27;re being distracted by endless social media and meaningless news, AI technology is advancing at a mind blowing pace.  I&#x27;d keep my eye on that ball instead of &quot;the current thing.&quot;

You could train a model on texture image data though, no?Or what about even generating images you could then photogrammetry into models?

Colab notebooks can do mesh models using this method, I&#x27;m certain OpenAI isn&#x27;t far away

This thing can&#x27;t do 3D models.There are some 3D image generation techniques, but they aren&#x27;t based on polygonal modelings, so 3D artists are safe for now

I can see how this has the potential to disrupt the games industry. If you work on a AAA title, there is a small army of artists making 19 different types of leather armor. Or 87 images of car hubcaps.Using something like this could really help automate or at least kickstart the more mundane parts of content creation. (At least when you are using high resolution, true color imagery.)

<a href="https:&#x2F;&#x2F;www.eleuther.ai" rel="nofollow">https:&#x2F;&#x2F;www.eleuther.ai</a> (text, not images, but free as in freedom)

Interestingly, hands are also something humans struggle to draw.They&#x27;re a very complex anatomical form, many small tendons and muscles. Many artists struggle to depict hands. They&#x27;re not made out of a few straight lines like a torso, there&#x27;s lots of skew going on. They&#x27;re probably the hardest structure of the human body to &#x27;learn&#x27; for a ML system.

As I keep telling people: &quot;hands are hard&quot;. This is why I went so far as to make a hand-specific dataset (&quot;PALM&quot; <a href="https:&#x2F;&#x2F;www.gwern.net&#x2F;Crops#palm" rel="nofollow">https:&#x2F;&#x2F;www.gwern.net&#x2F;Crops#palm</a> which of course now everyone is going to confuse with &#x27;PaLM&#x27;...). Hands are just way too variable to learn easily.My dataset is a start, but it may benefit from focused training, the way Facebook&#x27;s new Make-A-Scene <a href="https:&#x2F;&#x2F;arxiv.org&#x2F;abs&#x2F;2203.13131#facebook" rel="nofollow">https:&#x2F;&#x2F;arxiv.org&#x2F;abs&#x2F;2203.13131#facebook</a> (not DALL-E 2 quality but not far from it) has focused losses on faces.

Hands are notoriously hard to even photograph. You very quickly get weird unnatural results with a camera in front of hands, so in a way I&#x27;m not surprised AI models struggle to produce satisfying imagery there too.

Just a thought, I wonder if DALL-E would generate more realistic hands if you prompted it with something like&gt; an astronaut riding a horse, and the astronaut has five fingers on each hand

Regarding cherry-picking, the images of astronauts on horses look stunning, except for their hands. There&#x27;s something seriously wrong with their hands.Maybe give it another five years, a few more $billion and a few more petabytes&#x2F;flops and it will be good. Then finally everyone can generate art for their own Magic: the Gathering cards.(That&#x27;s the end goal, right?)

The Risks and Limitations section is particularly interesting to me. It&#x27;s like a time capsule of society&#x27;s current fears about technology. They talk about many ways this tech could be misused, but I don&#x27;t think they&#x27;ve even scratched the surface.An example off the top of my head: this could be used as advertising or recruitment for controversial organizations or causes. Would it be wrong for the USA to use this for military recruitment? Israel? Ukraine? Russia?Another example: this could be used to glorify and reinforce actions which our society does not consider to immoral but other societies - or our own future society - will. It wasn&#x27;t long ago that the US and Europe did a full 180 on their treatment of homosexuality. Will we eventually change our minds about eating meat, driving cars, etc?Have they gone too far in a desperate bid to prevent the AI from being capable of harm? Have they not gone far enough? I don&#x27;t know. If I was that worried about something being misused, I don&#x27;t think I could ever bring myself to work on it in the first place. But I suppose the onward march of technology is inevitable.

A few comments by someone who&#x27;s spent way too much time in the AI-generated space:* I recommend reading the Risks and Limitations section that came with it because it&#x27;s very through: <a href="https:&#x2F;&#x2F;github.com&#x2F;openai&#x2F;dalle-2-preview&#x2F;blob&#x2F;main&#x2F;system-card.md" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;openai&#x2F;dalle-2-preview&#x2F;blob&#x2F;main&#x2F;system-c...</a>* Unlike GPT-3, my read of this announcement is that OpenAI does not intend to commercialize it, and that access to the waitlist is indeed more for testing its limits (and as noted, commercializing it would make it much more likely lead to interesting legal precedent). Per the docs, access is very explicitly limited: (<a href="https:&#x2F;&#x2F;github.com&#x2F;openai&#x2F;dalle-2-preview&#x2F;blob&#x2F;main&#x2F;system-card.md#access" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;openai&#x2F;dalle-2-preview&#x2F;blob&#x2F;main&#x2F;system-c...</a> )* A few months ago, OpenAI released GLIDE ( <a href="https:&#x2F;&#x2F;github.com&#x2F;openai&#x2F;glide-text2im" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;openai&#x2F;glide-text2im</a> ) which uses a similar approach to AI image generation, but suspiciously never received a fun blog post like this one. The reason for that in retrospect may be &quot;because we made it obsolete.&quot;* The images in the announcement are still cherry-picked, which is therefore a good reason why they tested DALL-E 1 vs. DALL-E 2 presumably on non-cherrypicked images.* Cherry-picking is relevant because AI image generation is still slow unless you do real shenanigans that likely compromise image quality, although OpenAI has likely a better infra to handle large models as they have demonstrated with GPT-3.* It appears DALL-E 2 has a fun endpoint that links back to the site for examples with attribution: <a href="https:&#x2F;&#x2F;labs.openai.com&#x2F;s&#x2F;Zq9SB6vyUid9FGcoJ8slucTu" rel="nofollow">https:&#x2F;&#x2F;labs.openai.com&#x2F;s&#x2F;Zq9SB6vyUid9FGcoJ8slucTu</a>

Yeah, I mean you&#x27;re right that ultimately the proof is in the pudding.But I do think we could have guessed that this sort of approach would be better (at least at a high level - I&#x27;m not claiming I could have predicted all the technical details!). The previous approaches were sort of the best that people could do without access to the training data and resources - you had a pretrained CLIP encoder that could tell you how well a text caption and an image matched, and you had a pretrained image generator (GAN, diffusion model, whatever), and it was just a matter of trying to force the generator to output something that CLIP thought looked like the caption. You&#x27;d basically do gradient ascent to make the image look more and more and more like the text prompt (all the while trying to balance the need to still look like a realistic image). Just from an algorithm aesthetics perspective, it was very much a duct tape and chicken wire approach.The analogy I would give is if you gave a three-year-old some paints, and they made an image and showed it to you, and you had to say, &quot;this looks like a little like a sunset&quot; or &quot;this looks a lot like a sunset&quot;. They would keep going back and adjusting their painting, and you&#x27;d keep giving feedback, and eventually you&#x27;d get something that looks like a sunset. But it&#x27;d be better, if you could manage it, to just teach the three-year-old how to paint, rather than have this brute force process.Obviously the real challenge here is &quot;well how do you teach a three-year-old how to paint?&quot; - and I think you&#x27;re right that that question still has a lot of alchemy to it.

For GPT-3 it was a combination of both compute and safety. Given the notes in the System Card (<a href="https:&#x2F;&#x2F;github.com&#x2F;openai&#x2F;dalle-2-preview&#x2F;blob&#x2F;main&#x2F;system-card.md" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;openai&#x2F;dalle-2-preview&#x2F;blob&#x2F;main&#x2F;system-c...</a> ), OpenAI is likely doubling-down on safety here.

I&#x27;m genuinely curious to hear Sam Altman&#x27;s (and&#x2F;or the OpenAI team&#x27;s) perspective on why these products need to be waitlisted. If it&#x27;s a compute issue, why not build a queuing system? If it&#x27;s something else (safety related? hype related?) I&#x27;d love to understand the thinking behind the decision. More often than not, I sign up for waitlists for things like this and either (1) never get in to the beta  or (2) forget about it when I eventually do get in.

The ones undoing the damage caused by dumb pattern recognizers and generators? ;)

Things that require understanding of causation will be safe longer. Progress like this is driven by massive datasets. Meanwhile, real world action-taking applications require different paradigms to take causation into account[0][1], and especially to learn safely (e.g. learning to drive without crashing during the beginner stages).There&#x27;s certainly research happening around this, and RL in games is a great test bed, but people choosing actions will safe from automation longer than people not choosing actions, if that makes sense. It&#x27;s the person who decides &quot;hire this person&quot; vs the person who decides &quot;I&#x27;ll use this particular shade of gray.&quot;[0] The best example is when X causes Y and X also causes Z, but your data only includes Y and Z. Without actually manipulating Y, you can&#x27;t see that Y doesn&#x27;t cause Z, even if it&#x27;s a strong predictor.[1] Another example is the datasets. You need two different labels depending on what happens if you take action A or B, which you can&#x27;t have simultaneously outside of simulations.

Most creative output is duplicated effort: consider how much code each person on HN has written that has been written before. Consider how, a decade ago, we were all writing html and styling it, element by element, and then Twitter bootstrap came along and revolutionised front-end development in what is, ultimately, a very small and low technology way. All it really did was reduce duplicate effort.Nowadays there’s lots of great low&#x2F;no code platforms, like Retool, that represent a far greater threat to the amount of code that needs to be produced than AI ever will.To use a cliche: code is a bug, not a feature. Abstracting away the need for code is the future, not having a machine churn out the same code we need today.

What jobs will be there in 5~10 years when we consider all the progress done with Dall-E, GPT-3, Codex&#x2F;GitHub Copilot, Alpha* and so on?

This reminds me of a discussion I had with the high school band teacher in the 90s. I was telling him that one day computers would play music and you won&#x27;t be able to tell the difference. He got mad at me and told me that a computer could never play as well as a human with feelings, who can feel the piece and interpret it.I think we passed that point a while ago, but seeing this makes me think we aren&#x27;t too far off from computers composing pieces that actually sound good too.

there are some masterpieces there. this is the end of clipart and stock images, and the beginning of awesome illustrations in every article.

Some more examples: <a href="https:&#x2F;&#x2F;twitter.com&#x2F;sama&#x2F;status&#x2F;1511724264629678084" rel="nofollow">https:&#x2F;&#x2F;twitter.com&#x2F;sama&#x2F;status&#x2F;1511724264629678084</a>

&gt; does anyone know if there is a term for something like Turing-completeness within AI, where a certain level of intelligence can simulate any other type of intelligence like our brains do?Artificial General Intelligence

Thank you, that&#x27;s just the sort of breadcrumb I was looking for!I&#x27;m in a bit of a rush and don&#x27;t know the term for this offhand, but I remember hearing that single-layer neural networks are equivalent to multi-layer ones:<a href="https:&#x2F;&#x2F;stats.stackexchange.com&#x2F;questions&#x2F;451127&#x2F;equivalence-of-deep-feed-forward-neural-network-to-single-layer-network" rel="nofollow">https:&#x2F;&#x2F;stats.stackexchange.com&#x2F;questions&#x2F;451127&#x2F;equivalence...</a><a href="https:&#x2F;&#x2F;www.quora.com&#x2F;Is-a-single-layer-feed-forward-neural-network-equivalent-to-a-logistic-regression-algorithm" rel="nofollow">https:&#x2F;&#x2F;www.quora.com&#x2F;Is-a-single-layer-feed-forward-neural-...</a>There are probably more insights like this out there. These equivalences allow us to think in abstractions that get us above the minutia of fine-tuning these algorithms so that we can see the big picture. I think.

<a href="https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Universal_approximation_theorem" rel="nofollow">https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Universal_approximation_theore...</a>

I appreciate your sentiment but can&#x27;t agree with it. What I mean is, if I had the resources to not have to work for 10 years, I give myself greater than a 50% chance of building an AGI. So I don&#x27;t understand why the world is taking so long to do it.The flip side is that these narrow use cases progressed so quickly that we have to worry about stuff like deep fakes now.Something&#x27;s not right here.As a programmer, I feel that what went wrong is that we invested too much in profit-driven endeavors, basically stuff that&#x27;s mainstream. To be blunt, the academic side of me doesn&#x27;t care about use cases. I care about theory, formalism, abstraction, reproducibility, basically the scientific method. From that perspective, all AI is equivalent, it just takes input, searches a giant solution space using its learned context as clues, and returns the closest solution it can in the time given. It&#x27;s an executable piping data around. The rest is hand waving.And given that, the stuff that AI is doing now is orders of magnitude more complex than running a Roomba. But a robot vacuum actually helps people.To answer your question, a KNN could solve this if the user reshapes the image data into a different coordinate system where the data can be partitioned (all inference comes down to partitioning):<a href="https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Change_of_basis" rel="nofollow">https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Change_of_basis</a>Tensors are about reshaping data into a coordinate system where relationships become obvious, like going from rectangular to polar coordinates, or using a Fourier transform:<a href="https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Tensor" rel="nofollow">https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Tensor</a>My frustration with all of this is the same one I have with physics or any other evolving discipline. The lingo obfuscates the fundamental abstractions, creating artificial barriers to entry.Edit: I should add a disclaimer here that my friend and I worked on a video game for like 11 years. I&#x27;m no expert in AI, I&#x27;m just acutely sensitive to how the realities of the workaday world waste immeasurable potential at scale.

Almost everything stated here is simply wrong or misinformed.&gt;For example, I went to an AI talk about 5 years ago where the guy said that any of a dozen algorithms like K-Nearest Neighbor, K-Means Clustering, Simulated Annealing, Neural Nets, Genetic Algorithms, etc can all be adapted to any use case. They just have different strengths and weaknesses. At that time, all that really mattered was how the data was prepared.How do you suppose KNN is going to generate photorealistic images? I don&#x27;t understand the question here&gt;I guess fundamentally my question is, when will AGI start to become prevalent, rather than these special-purpose tools like GPT-3 and Dall-E 2?Actual AGI research is basically non-existant, and GPT-3&#x2F;Dall-E 2 are not AGI-level tools.&gt;Personally I give it less than 10 years of actual work, maybe lessLol...&gt;I just mean that to me, Dall-E 2 is already orders of magnitude more complex than what&#x27;s required to run a basic automaton to free humans from labor.Categorically incorrect

This is my feeling as well, that the rise of AGI conveniently coincides with the end of the world. I find it demoralizing because so many trends look just like that, where solving the ultimate problem results in the destruction of the context in which the original problem resided.

Juergeb Schmidhuber predicts the “Omega point” of technological development (including AGI) to be around 2040<a href="https:&#x2F;&#x2F;youtu.be&#x2F;pGftUCTqaGg" rel="nofollow">https:&#x2F;&#x2F;youtu.be&#x2F;pGftUCTqaGg</a>The MIT Limits to Growth study predicts the collapse of global civilization around 2040<a href="https:&#x2F;&#x2F;www.vice.com&#x2F;amp&#x2F;en&#x2F;article&#x2F;z3xw3x&#x2F;new-research-vindicates-1972-mit-prediction-that-society-will-collapse-soon" rel="nofollow">https:&#x2F;&#x2F;www.vice.com&#x2F;amp&#x2F;en&#x2F;article&#x2F;z3xw3x&#x2F;new-research-vind...</a>

&gt; Apologies for an open-ended question but: does anyone know if there is a term for something like Turing-completeness within AI, where a certain level of intelligence can simulate any other type of intelligence like our brains do?&gt; So how can we adapt these AI experiments to get real work done?You&#x27;re missing a step here - the difference between &quot;imagining doing something&quot; and &quot;actually doing something&quot;. An ML model can produce thoughts, but that isn&#x27;t necessarily the same direction of research as actually doing things in real life, much less becoming superhuman and taking over the world etc.In your imagination, everything always goes your way.

Apologies for an open-ended question but: does anyone know if there is a term for something like Turing-completeness within AI, where a certain level of intelligence can simulate any other type of intelligence like our brains do?For example, using DeMorgan&#x27;s theorem, we can build any logic circuit out of all NAND or NOR gates:<a href="https:&#x2F;&#x2F;www.electronics-tutorials.ws&#x2F;boolean&#x2F;demorgan.html" rel="nofollow">https:&#x2F;&#x2F;www.electronics-tutorials.ws&#x2F;boolean&#x2F;demorgan.html</a><a href="https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;NAND_logic" rel="nofollow">https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;NAND_logic</a><a href="https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;NOR_logic" rel="nofollow">https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;NOR_logic</a>Dall-E 2&#x27;s level of associative comprehension is so far beyond the old psychology bots in the console pretending to be people, that I can&#x27;t help but wonder if it&#x27;s reached a level where it can make any association.For example, I went to an AI talk about 5 years ago where the guy said that any of a dozen algorithms like K-Nearest Neighbor, K-Means Clustering, Simulated Annealing, Neural Nets, Genetic Algorithms, etc can all be adapted to any use case. They just have different strengths and weaknesses. At that time, all that really mattered was how the data was prepared.I guess fundamentally my question is, when will AGI start to become prevalent, rather than these special-purpose tools like GPT-3 and Dall-E 2? Personally I give it less than 10 years of actual work, maybe less. I just mean that to me, Dall-E 2 is already orders of magnitude more complex than what&#x27;s required to run a basic automaton to free humans from labor. So how can we adapt these AI experiments to get real work done?

“Preventing Harmful Generations”? = Fail.Caravaggio is probably chortling from wherever he is ..

They&#x27;re pretty strict about usage:- <a href="https:&#x2F;&#x2F;github.com&#x2F;openai&#x2F;dalle-2-preview&#x2F;blob&#x2F;main&#x2F;system-card.md" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;openai&#x2F;dalle-2-preview&#x2F;blob&#x2F;main&#x2F;system-c...</a>- <a href="https:&#x2F;&#x2F;github.com&#x2F;openai&#x2F;dalle-2-preview&#x2F;blob&#x2F;main&#x2F;system-card.md#access" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;openai&#x2F;dalle-2-preview&#x2F;blob&#x2F;main&#x2F;system-c...</a>

It should be possible to create open source versions, researchers will find a way if something is cool enough

I don’t want to dismiss this new model and achievements but we are getting to the point where I feel like what we saw in the open source versus close source systems we see in new ml models another one is forming for open and closed models. I think that larger and larger models will have disclaimers either restricting you from using it commercially (a great deal of academics and NVIDIA models are doing this. And OpenAI just puts it behind an API with the rules :Curbing Misuse
Our content policy does not allow users to generate violent, adult, or political content, among other categories. We won’t generate images if our filters identify text prompts and image uploads that may violate our policies. We also have automated and human monitoring systems to guard against misuse.

They&#x27;ve added some more details to the paper.

This is a niche complaint, but I get frustrated at how imprecise open AI&#x27;s papers are. When they describe the model architecture, it&#x27;s never precise enough to reproduce exactly what they did. I mean, it pretty much never is in ML papers[0], but open AI&#x27;s bigger products are worse than average with it. And it makes sense, since they&#x27;re trying to be concise and still spend time on all the other important stuff besides methods, but it still frustrates me quite a bit.[0] Which is why releasing your code is so beneficial.

&gt; accurately describe what one wantsIsn&#x27;t that essentially what programming already is?

It&#x27;s becoming clear that efficient work in the future will hinge upon one&#x27;s ability to accurately describe what one wants. Unpacking that -- a large piece is the ability to understand all the possible &quot;pitfalls&quot; and &quot;misunderstandings&quot; that could happen on the way to a shared understanding.While technical work will always have a place -- I think that much creative work will become more like the management of a team of highly-skilled, niche workers -- with all the frustrations, joys, and surprises that entails.