DjangoCon US 2023: Don't Buy the "A.I." Hype
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- Опубліковано 20 жов 2023
- In my 2023 talk at DjangoCon US, I implore y'all not to buy into the "A.I." hype.
“Those who cannot remember the past are condemned to repeat it.”
- George Santayana, The Life of Reason, 1905.
Congratulations, technologists! We have reached a new record for the height of the peak of inflated expectations with the hype surrounding “A.I.” If you believe the recent press, “A.I.” is going to be capable of everything, with some even talking of immortality.
It is wonderful to be excited about new technology available to us, but this is at a level I have never seen in my career. There have been numerous lessons from the past that illustrate why we should avoid these levels of hype.
Is “A.I.” going to change everything? I don’t buy it, and in this talk, I’ll explain why. - Наука та технологія
I thought he was going to give a view that was going to say these things will have no impact. But he chose to say we need to be careful with how we use these things (LLMs). Which makes sense. It was a good talk
An incredibly mature talk on such a hot topic. Bravo sir! 👏
Fair point. Exactly happened on crypto hype a few years ago. Sometimes we need to step back and see what is really happening.
I was like "How did you get your talk up so fast!" As always, incredibly impressive, engaging and fun. Love you, friend!
Brilliant talk, thank you!
100th like! Great talk!
Hey Tim. Kyle from Penn. Good going.
When it comes to stock investing, you may be right. When it comes to the way "AI" (i would rather say Machine Learning) is and will be changing the world in near future - you are wrong. Chat-GPT may be overhyped, but remember that there are tons of ML applications that flourish and people don't know about.
ai is very useful to save time and easily increase productivity which in turn creates more profits, that said … you can’t stop this title wave. 🌊 it’s real. Pray to Jensen 💚🖤🇺🇸
As a person who fully believes the hype, i did enjoy this talk. Downplaying AI's current state is like downplaying a flu pandemic. Exponential Tech is difficult to comprehend, and comparing it to past tech after decades of Moores law is a mistake.
Except tech doesn’t advance exponentially anymore. That was only true in the 1900s.
@@phylocybe_
I'd like to see that graph.. what is your source for that idea?
Moores law is for hardware… and transistor gate width limits have been reached. You’re an overconfident tech bro, see the slide on overconfidence
@@siddardhab
Moore's law is about compute doubling while price goes down. That's still happening. Watch the latest Nvidia keynote. Physical chip size isn't stopping architecture and software optimization improvement continuing Moore's law.
@@zacharydaniels3186 As you say in your original post you fully believe the hype. Flipping your analogy around, the current AI hype train is like overplaying a flu pandemic so a small number of companies can make huge financial gains and exert control over the masses . Sound familiar?
A minor correction on the AI pie diagram. Current AI isn't just 1/6th of the approach. They currently use Machine Learning, NLP, Decision Making (to an extent), Object Recognition, and Robotics is being integrated with it now. This means that only 2.5 of the 6 pieces of the AI puzzle aren't being done currently, though they are working on it.
Additionally, while I agree that AI is currently over-hyped, I think the presenter is incorrect when he compares current AI to the 3D-TV, and other tech that went no where. There's a key difference here. Current AI, even in its limited state, improves the rate at which you can work, it reduces costs, and makes some mundane tasks easier. Anything that reduces costs, or makes life easier is not a fad that is going to go away. It may not live up to the hype, but it's not going to disappear either.
AI saves costs? Some hidden / overlooked costs are mentioned in this video: ua-cam.com/video/Nd7wrC62LEk/v-deo.htmlsi=4KCqvl_0dl0RD8VO
I’d love to understand how it saves costs, its code that it writes is copied and mediocre at best. I’ve used it to write some boiler plate python, but not that helpful after that.
@@Ynerson9003 As an author I use it for brainstorming. What used to take me several weeks or months to plan is now done over a weekend. Time = money.
Yes but the costs are still there, shifted to computing power and infrastructure. Currently you're not paying for it hence why they're hidden / overlooked costs.@@BruceWayne15325
@@BruceWayne15325 I agree. Even just for summarizing long texts - now I can read stuff way faster than before. If that's good or not is another issue ;)
Interesting. But it was def not just "4 people in a RU troll farm" 😂 we got alota smart folks all thinking the same things like that and that in itself proves dudes point
He said “foreign actors” not “four actors”
@@JustinKreule took the bait
@@Skunkhunt_42 i have been swindled
Can't help but notice the video yelling the truths on this has very few likes and views.
it's a goddamn Django conference
what have you expected
this was a very powerful video until you sidetracked into second life a bit too deeply, then picked up, but bogged down n FTX, MS, etc. sorry.
Absolutely fair. I only had a few days to put together this talk as a fill-in for a speaker who dropped out, so the pacing could have been better in that section. I've given an updated version of this talk which is now on my channel, or click here: ua-cam.com/video/T7X7TW7Yz4A/v-deo.html
AI is a bigger deal than any of those tech fads you listed. Hype and irresponsible uses aside, the underlying machine learning tech is transformative in a way that the metaverse and blockchain never could have been even if they were as big a deal as the techbros made them out. Machine learning solved the protein folding problem. Machine learning found improved sort algorithms that are already merged upstream in the LLVM libc++ library. The previous code was hand tuned assembler and hadn't changed in ten years. Machine learning has come up with matrix multiply and hashing algorithms that beat the best humans had been able to do. While searching for new chemical synthesis paths for some existing pharmaceuticals, a machine learning algorithm produced 40,000 new potential chemical weapons, some predicted to be more toxic than VX (it also independenly invented VX).
None of the other tech hypefests in our lives involved technology powerful enough to do these things. AI/ML is different.
I do agree with you that turning loose these LLMs trained on god knows what random internet data was the height of irresponsibility. But any powerful new tech is going to be abused by the unscrupulous.
Machine Learning yes, but AI, no.
It didn't "solve the protein folding problem". It's just a new standard, even if it's orders of magnitude better. We still have to use experimental confirmation for those structures, and will keep having to do so for anything coming out of these systems, because LLMs aren't engines of reality but black boxes whose main purpose is making people believe that the string that comes next is reasonable.
It will surely help advance science and it's verifiable useful technology, but it's still being overhyped by techbros who, may I remind you, have a vested interest in inflating their portfolios by getting funding for AI startups.
@@ekki1993 It is indeed being overhyped by techbros. But the reality to hype ration is certainly far, far better than, say, blockchain
@@TracFone-xn7fj Oh, of course. That's a very low bar, though.
@@TracFone-xn7fj If you believe that, then you understand nothing about any of this or that. This is identical to crypto. Blockchain is beneficial, and bitcoin is at all time highs right now. But all the extra crap that people tried to do on top of that got overhyped. Bitcoin is still underhyped. AI is currently in the other crap over hype part. Devin was a complete hoax, and there are millions of similar companies getting millions of dollars providing literally nothing. It's all scams at this point.
Well, the people who jumped into the hype train of blockchain were wrong.
Many of them are staying away from the AI hype
Only the ones who lost all of their money. The rest are fully onboard with the next shiny thing through fear of missing out and a lack of understanding as was exactly the case with Crypto.
@@SW-by9ob exactly.
@@kh9242 Simply trying a new technology is different from hyping up something new.
I don't think it's all hype but we are way too soon in the game.
There certainly is hype over the current state of AI (machine learning, LLM's), but it definitely has real use cases, differently from metaverse, or Blockchain. I know, cause I use it daily. So, even if for some reason, it stopped improving today, it would already be a piece of disruptive technology. But it has not yet stopped improving, and we don't know were this will lead to.
17:00 I will give you three names. Tell me if any of their tunes have changed: Eliezer Yukowsky, Connor Leahy, Robert Miles. - This is not hype. This is reality.
"The voice of one crying in the wilderness". I mean, just compare the number of views to videos which praise AI and only "juggle" the buzzwords.
My question is what happened to AI from 1950s? (Then 1970s and 1990s.) Where are they?
It definitely feels that way sometimes! But it feels that way during the hype cycle. Soon enough, the media will turn on the "A.I." buzz - it is starting to happen already. The ELIZA program I mentioned is still used as a teaching example, to help people understand how a very basic LLM works. The "training set" here would be the data phrases, instead of whatever can be grabbed (copyright be damned) on the Internet: www.cs.cmu.edu/afs/cs/project/ai-repository/ai/areas/classics/eliza/basic/myeliza.bas
There is some solid points in here about ethical use and alignment but these comparisons just fall short for me... AI is already providing immense value in so many domains and is changing work life in the information age for good. AI ins't overhyped. It's overused as a term. AI has been used for decades for the dumbest things. State of the art neural nets of today are already doing things we didn't think were possible just a few years ago and there's a million more ways to teach neural nets useful concepts.
This is valid, "A.I." is a marketing term, not a technical one... the models we have now are neither artificial nor intelligent. There's no doubt there's something useful here, but I'm having trouble separating the wheat from the chaff. The hype is getting in the way of finding what is going to help improve the human condition. The people leading the way are the least qualified to provide the kind of moral, ethical-driven leadership we need so desperately. The environmental cost is also so incredibly massive for a glorified grammar check, code reviewer, and image generator.
@@kh9242 Thanks for taking the time to watch! I was short on time, but here's more detail. "A.I." is not artificial. The models' generated text and media responses are derivative from the work of real humans: artists, musicians, programmers, and writers whose creative and professional output is appropriated without consent.
"A.I." is not intelligent: models don't think, don't have an I.Q., and follow instructions input by humans for specific tasks. The first real-world applications from neural networks, the core tech behind ChatGPT and friends, were military: spotting ships from satellite photos.
people are just starting to realize that many of the things humans do don't actually require much creativity or thought beyond the given instructions
western digital art over the last decade or so has notably become more and more limited to specific styles, with much of it being fan art of intellectual properties following premade design choices and compositions. what makes money in their society is a very narrow and limited subset of what is actually possible, and when such extreme repetition in their "expression" is the majority of their popular art it has been easy to replicate that with statistical learning software
This will age like milk . after sora ai destroy video stock industry and porn industry
I'd like to borrow your crystal ball which can see the future once you're done with it, my friend. In the meantime, I'll remember the bold predictions of the past. 25 years ago bold claims were made that technology would cause healthcare employment to be halved (it ended up doubling). And 20 years ago, after The Matrix trilogy, many insisted that CGI would mean no more actors in TV or movies. I'll talk to you in a year, and we'll see who was closer to right, because I don't think either of us will be spot on. But I think I'll be *closer* to right than this comment!
Sora looks crap and uses copyrighted content... It's doomed
Dude confidently espouses debunked conspiracies from 2016 while asking to be taken seriously
Bro Putin himself admitted to manipulating 2016 elections 😂😂
Blue anon runs rampant in tech
@@Robdobalina
Oo! I like that phrase! I'm stealing it.
Indeed but he is not wrong.
People make mistakes all the time. Does this mean they are useless? Sorry but I couldn't find meaningful arguments in this talk. AI is real and it is more intelligent than 90% of the humans. It optimizes your work and performance. No arguments can deny that.
GPT-4 is dumb as rocks. I think future tech is rapidly gonna be better... But if GPT-4 is smarter than any portion of humanity, we're in deep shit.
An LLM is not intelligent. It takes information from other sources and presents it in a more human like way. There is no actual intelligence involved and it has been proven to give a lot of false information as has no ability to tell the difference. Shit in shit out effectively.
And overhyped does not mean useless anyway.
It's literally not intelligent at all
AI is not a hype. Codex, chatgpt are mature enough products. Saying AI is a hype is the new hype 😂
Crediting them with intelligence and potential of self-conscience is fat hyped lies. Period.
Calling it AI is hype, and you fell for it.
LLMs making things up is not a bug, it's a feature, and it's the most important feature one could ever hope for. LLMs (in particular, small 7B ones) are exactly the missing piece of the puzzle to make everything else work. So this dismissal of such a massive achievement is really uncalled for. On the other hand, what else to expect from the web coders, right?
That's certainly an interesting ethical take, absolving model trainers from any responsibility for accuracy. It would probably be popular with the "alternative facts" crowd. Painting all "web coders" in one fell swoop in such a demeaning fashion is also a paradox, considering the "web coders" made it possible to have a vast sea of content for LLMs to train on (copyright issues asise).
@@pyphilly look, if you're expecting "accurate answers" from a generative model, no matter how well trained, you're the problem here. For accurate answers use RAG + critical loop. And for a lot of things to work (e.g., automation of engineering inventions) you *need* your model to make things up, the more insane - the better.
And I'd recommend you to read the paper "Textbooks is all you need" - all the web content is an utter trash, we have much more high quality material.
@@vitalyl1327So you're opposed to plugging generative models into search engines? Because that is *exactly* what is happening at the primary sources human being go to seek information. People aren't just looking for accurate answers from generative models, they've been having it shoved down their throats. Just look at the Windows 11 start menu changes rolled out a few weeks ago. The proliferation continues. At least we can agree that the web content is utter trash, but when you look at LLMs and usage, that's what 99.9% of the people are using. The average web surfer isn't a discerning Hugging Face consumer, and finding the diamonds in the rough there isn't exactly a walk in the park either.
@@pyphilly the raw inference of a generative model is similar to a stream of consciousness. It's meaningless. It works only if you restrain it in a proper mental discipline loop and provide with all the relevant information *in place*. It should never rely on its recollection of the facts from training.
It can work in theory if you plug it into a search - e.g., see how Stackoverflow or Flux AI did it, it's 100% RAG-based and it's citing its sources for every single fact it summarised. What definitely won't work is to just plug in the raw inference (which is what most people see when they talk to ChatGPT interface), but I doubt anyone is proposing to do so.
I'm quite confident now when I use the LLM-based search for datasheets - as they always cite the sources anyway, and the search accuracy is much better than any plain text search can be.
What's this "everything else" you're talking about? Because the hype being talked about here is people expecting LLMs to give back information about reality. You can't really have a system built for simulating the training data and expect it to be useful for applications that require models of reality that are as accurate as possible.