Over half of all tech industry workers view AI as overrated::undefined
Best assessment I’ve heard: Current AI is an aggressive autocomplete.
I’ve found that relying on it is a mistake anyhow, the amount of incorrect information I’ve seen from chatgpt has been crazy. It’s not a bad thing to get started with it but it’s like reading a grade school kids homework, you need to proofread the heck out of it.
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Because the average person hears “AI” and thinks Cortana/Terminator, not a bunch of if statements.
People are dumb when it comes to things they don’t understand. I’m dumb when it comes to mechanical engineering of any kind, but I’m competent with software. It’s all about where people’s strengths lie, but some people aren’t aware enough to know they don’t know something
People trust a squid predicting football matches.
I feel like the AI in self-driving cars is the same way. They’re like driving with a 15 year old that just got their learners permit.
Turns out that getting a computer to do 80% of a good job isn’t so great. It’s that extra 20% that makes all the difference.
That 80% also doesn’t take that much effort. Automation can still be helpful depending on how much effort it is to repeatedly do it, but that 20% is really where we need to see progress for a massive innovation to happen.
I actually disagree. Ai is great at doing the parts that are easy to do mentally but still take time to do. This “fancy autocomplete” is where it shines and can accelerate the work of a professional by an order of magnitude
I just reviewed a PR today and the code was… bad, like unusually bad for mycoworkers and left some comments.
Then my coworker said he used chatgpt without really thinking on what he was copypasting.
I have found that it’s like having a junior programmer assistant. It’s great for “write me python code for opening an in file from a command line argument, reading the contents into a key/value dict array, then closing the file.” It’s terrible for “write me a python code for pulling data into a redis database.”
I find it’s wrong 50% of the time for certain command line switches, Linux file structure, and aws cli.
I find it’s terrible for advanced stuff like, “using aws cli and jq, take all volumes in a vpc, and display the volume id, volume size in gb, instance id it’s attached to, private IP address of the instance, whether is a gp3 or gp2, and the vpc id in a comma separated format, sorted by volume size.”
Even worse at, “take all my gp2 volumes and make them gp3.”
I recently used it to update my resume with great success. But I also didn’t just blindly trust it.
Gave it my resume and then asked it to edit my resume to more closely align with a guide I found on Harvards website. Gave it the guide as well and it spit out a version of mine that much more closely resembled the provided guide.
Spent roughly 5 minutes editing the new version to correct for any problems it had and boom. Half an hour of worked parsed down to sub 10
I then had it use my new resume (I gave it a copy of the edited version) and asked it to write me a cover letter for a job (I provided the job description)
Boom. Cover letter. I spent about 10 minutes editing that piece. And then that new resume and cover letter lead to an interview and subsequent job offer.
AI is a tool not an all in one solution.
Nice one! I have heard it called a fuzzy JPG of the internet.
And that’s entirely correct
No. It’s not and hasn’t been for at least a year. Maybe the ai your dealing with is, but it’s shown understanding of concepts in ways that make no sense for how it was created. Gotta go.
it’s shown understanding of concepts
No it hasn’t.
It does a shockingly good analogue of “understanding” at the very least. Have you tried asking chatgpt to solve analogies? Those show up in all kinds of intelligence tests.
We don’t have agi, definitely, but this stuff has come a very long way and it’s quite close to being genuinely useful.
Even if we completely reject the “it’s ai,” we more or less have a natural language interface for computers that isn’t a shallow trick and that’s awesome.
Have you tried asking chatgpt to solve analogies? Those show up in all kinds of intelligence tests.
This two statements are causal to each other. And it actually gets them wrong with some frequency in ways that humans wouldn’t, forgets stuff it has already “learned”, or changes to opposite stances midways sentences. Because it is just an excel sheet on steroids.
It is, in my opinion, a shallow trick indeed.
“Excel sheet on steroids” isn’t oversimplification: it’s just incorrect. But it doesn’t really sound like you’re particularly open to honest discussion about this so whatever.
Well here’s the question. Is it solving them, or just regurgitating the answer? If it solves them it should be able to accurately solve completely novel analogies.
Novel analogies. Very easy to prove this independently for yourself.
yes, it’s has. the most famous example is the stacking of the laptop and the markers. you may not have access but it’s about to eclipse us imho. I’m no technological fanboy either. 20 years ago I argued that I wouldn’t be possible to understand human speech. now that is a everyday occurrence.
Depends on how you define understanding and how you test for it.
I assume we are talking LLM here?
It’s a tool. And like any tool it’s only as good as the person using it. I don’t think these people are very good at using it.
Too bad it’s bullshit.
If you are actually interested in the topic, here’s a few good reads:
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Do Large Language Models learn world models or just surface statistics? (Jan 2023)
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Actually, Othello-GPT Has A Linear Emergent World Representation (Mar 2023)
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Eight Things to Know about Large Language Models (April 2023)
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Playing chess with large language models (Aug 2023)
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Language Models Represent Space and Time (Oct 2023)
As you can see, the past year has shed a lot of light on the topic.
One of my favorite facts is that it takes on average 17 years before discoveries in research find their way to the average practitioner in the medical field. While tech as a discipline may be more quick to update itself, it’s still not sub-12 months, and as a result a lot of people are continuing to confidently parrot things that have recently been shown in research circles to be BS.
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Over half of tech industry workers have seen the “great demo -> overhyped bullshit” cycle before.
Largely because we understand that what they’re calling “AI” isn’t AI.
This is a growing pet peeve of mine. If and when actual AI becomes a thing, it’ll be a major turning point for humanity comparable to things like harnessing fire or electricity.
…and most people will be confused as fuck. “We’ve had this for years, what’s the big deal?” -_-
As in AGI?
No, INT.
What is that?
I’ve seen it refered to as AGI bit I think itns wrong. Chat GPT isnt intelligent in the slightest, it only makes guesses on what word is statistically more likely to come up next. There is no thikinking or problem solving involved.
A while ago I saw an article that with a tittle along the lines of “spark of AGI in ChatGPT 4” because it chose to use a calculator tool when facing a problme that required one. That would be AI (and not AGI). It has a problem, it learns and uses available tools to solve it.
AGI would be on a whole other level.
Edit: Grammar
The argument “it just predicts the most likely next word” while true massively under values what it even means to predict the next word or token. Largely these predictions are based on sentences and ideas the model has trained on from its data sets. It’s pretty intelligent if you think about it. You read a text book then when you apply the knowledge or take a test you use what you read to form a new sentence in relation to the context of the question or problem. For the models “text prediction” to be correct it has to understand certain relationships between complex ideas and objects to some capacity. Yes it absolutely is not as good as human intelligence. But what it’s doing is much more advanced then text to type on your phone keyboard. It’s a step in the right direction, over hyped right now but the hype is funneling cash into research. The models are already getting more advanced. Right now half of what it says is hot garbage but it can be pretty accurate.
Right? Like, I, too, predict the next word in my sentence to properly respond to inputs with desired output. Sure I have personality (usually) and interests, but that’s an emergent behavior of my intelligence, not a prerequisite.
It might not formulate thoughts the way we do, but it absolutely emulates some level of intelligence, artificially.
I think so many people overrate human intelligence, thus causing them to underrate AI. Don’t get me wrong, our brains are amazing, but they’re also so amazing that they can make crazy cool AI that is also really amazing.
People just hate the idea of being meat robots, I don’t blame em.
Given that AI isn’t purported to be AGI, how do you define AI such that multimodal transformers capable of developing abstract world models as linear representations and trained on unthinkable amounts of human content mirroring a wide array of capabilities which lead to the ability to do things thought to be impossible as recently as three years ago (such as explain jokes not in the training set or solve riddles not in the training set) isn’t “artificial intelligence”?
Yup. LLM RAG is just search 2.0 with a GPU.
For certain use cases it’s incredible, but those use cases shouldn’t be your first idea for a pipeline
THANK YOU! I’ve been saying this a long time, but have just kind of accepted that the definition of AI is no longer what it was.
It absolutely is AI. A lot of stuff is AI.
It’s just not that useful.
The decision tree my company uses to deny customer claims is not AI despite the business constantly referring to it as such.
There’s definitely a ton of “AI” that is nothing more than an If/Else statement.
for many years AI referred to that type of technology. It is not infact AGI but AI historically in the technical field refers more towards decision trees, and classification/ linear regression models.
That’s basically what video game AI is, and we’re happy enough to call it that
Well… it’s a video game. We also call them “CPU” which is also entirely inaccurate.
There are significant differences between statistical models and AI.
I work for an analytics department at a fortune 100 company. We have a very clear delineation between what constitutes a model and what constitutes an AI.
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I think it will be the next big thing in tech (or “disruptor” if you must buzzword). But I agree it’s being way over-hyped for where it is right now.
Clueless executives barely know what it is, they just know they want it get ahead of it in order to remain competitive. Marketing types reporting to those executives oversell it (because that’s their job).
One of my friends is an overpaid consultant for a huge corporation, and he says they are trying to force-retro-fit AI to things that barely make any sense…just so that they can say that it’s “powered by AI”.
On the other hand, AI is much better at some tasks than humans. That AI skill set is going to grow over time. And the accumulation of those skills will accelerate. I think we’ve all been distracted, entertained, and a little bit frightened by chat-focused and image-focused AIs. However, AI as a concept is broader and deeper than just chat and images. It’s going to do remarkable stuff in medicine, engineering, and design.
Personally, I think medicine will be the most impacted by AI. Medicine has already been increasingly implementing AI in many areas, and as the tech continues to mature, I am optimistic it will have tremendous effect. Already there are many studies confirming AI’s ability to outperform leading experts in early cancer and disease diagnoses. Just think what kind of impact that could have in developing countries once the tech is affordably scalable. Then you factor in how it can greatly speed up treatment research and it’s pretty exciting.
That being said, it’s always wise to remain cautiously skeptical.
The bad part is health insurance companies are also using AI.
Common US healthcare L
I’s ability to outperform leading experts in early cancer and disease diagnoses
It does, but it also has a black box problem.
A machine learning algorithm tells you that your patient has a 95% chance of developing skin cancer on his back within the next 2 years. Ok, cool, now what? What, specifically, is telling the algorithm that? What is actionable today? Do we start oncological treatment? According to what, attacking what? Do we just ask the patient to aggressively avoid the sun and use liberal amounts of sun screen? Do we start a monthly screening, bi-monthly, yearly, for how long do we keep it up? Should we only focus on the part that shows high risk or everywhere? Should we use the ML every single time? What is the most efficient and effective use of the tech? We know it’s accurate, but is it reliable?
There are a lot of moving parts to a general medical practice. And AI has to find a proper role that requires not just an abstract statistic from an ad-hoc study, but a systematic approach to healthcare. Right now, it doesn’t have that because the AI model can’t tell their handlers what it is seeing, what it means, and how it fits in the holistic view of human health. We can’t just blindly trust it when there’s human lives in the line.
As you can see, this seems to be relegating AI to a research role for the time being, and not on a diagnosing capacity yet.
There is a very complex algorithm for determining your risk of skin cancer: Take your age … then add a percent symbol after it. That is the probability that you have skin cancer.
Like you say, “AI” isn’t just LLMs and making images. We have previously seen, for example, expert systems, speech recognition, natural language processing, computer vision, machine learning, now LLM and generative art.
The earlier technologies have gone through their own hype cycles and come out the other end to be used in certain useful ways. AI has no doubt already done remarkable things in various industries. I can only imagine that will be true for LLMs some day.
I don’t think we are very close to AGI yet. Current AI like LLMs and machine vision require a lot of manual training and tuning. As far as I know, few AI technologies can learn entirely on their own and those that do are limited in scope. I’m not even sure AGI is really necessary to solve most problems. We may do AI “ala carte” for many years and one day someone will stitch a bunch of things together, et voila.
Thanks.
I’m glad you mentioned speech. Tortoise-TTS is an excellent text to speech AI tool that anyone can run on a GPU at home. I’ve been looking for a TTS tool that can generate a more natural -sounding voice for several years. Tortoise is somewhat labor intensive to use for now, but to my ear it sounds much better than the more expensive cloud-based solutions. It can clone voices convincingly, too. (Which is potentially problematic).
Ooh thanks for the heads up. Last time I played with TTS was years ago using Festival, which was good for the time. Looking forward to trying Tortoise TTS.
Honestly I believe AGI is currently a compute resource problem less than a software problem. A paper came out awhile ago showing that individual neurons in the human brain displayed behavior like decently sized deep learning models. If this is true the number of nodes required for artificial neural nets to even come close to human like intelligence maybe astronomically higher then predicted.
That’s my understanding as well, our brain is just an insane composition of incredibly simple mechanisms. Its compositions of compositions of compositions ad nauseam. We are manually simulating billions of years of evolution, using ourselves as a blueprint. We can get there… it’s hard to say when we’ll get there, but it’ll be interesting to watch.
Exactly, plus human consciousness might not be the most effective way to do it, might be easier less resource intensive ways.
It is overrated. It has a few uses, but it’s not a generalized AI. It’s like calling a basic calculator a computer. Sure it is an electronic computing device and makes a big difference in calculating speed for doing finances or retail cashiers or whatever. But it’s not a generalized computing system that can basically compute anything that it’s given instructions for which is what we think of when we hear something is a “computer”. It can only do basic math. It could never be used to display a photo , much less make a complex video game.
Similarly the current thing that’s called “AI”, can learn in a very narrow subject that it is designed for. It can’t learn just anything. It can’t make inferences beyond the training material or understand. It can’t create anything totally new, it just remixes things. It could never actually create a new genre of games with some kind of new interface that has never been thought of, or discover the exact mechanisms of how gravity works, since those things aren’t in its training material since they don’t yet exist.
Some calculators can run DooM, though
Lol, those are different. I meant like a little solar powered addition, subtraction, multiplication, division and that’s it kind of calculator.
I remember when it first came out I asked it to help me write a MapperConfig custom strategy and the answer it gave me was so fantastically wrong - even with prompting - that I lost an afternoon. Honestly the only useful thing I’ve found for it is getting it to find potential syntax errors in terraform code that the plan might miss. It doesn’t even complement my programming skills like a traditional search engine can do; instead it assumes a solution that is usually wrong and you are left to try to build your house on the boilercode sand it spits out at you.
I also have tried to use it to help with programming problems, and it is confidently incorrect a high percentage (50%) of the time. It will fabricate package names, functions, and more. When you ask it to correct itself, it will give another confidently incorrect answer. Do this a few more times and you could end up with it suggesting the first incorrect answer it gave you and then you realize it is literally leading you in circles.
It’s definitely a nice option to check something quickly, and it has given me some good information, but you really can’t blindly trust its output.
At least with programming, you can validate fairly quickly that it is giving bad information. With other real-life applications, using it for cooking/baking, or trip planning, the consequences of bad information could be quite a bit worse.
Reality: most tech workers view it as fairly rated or slightly overrated according to the real data: https://www.techspot.com/images2/news/bigimage/2023/11/2023-11-20-image-3.png
Which is fair. AI at work is great but it only does fairly simple things. Nothing i can’t do myself but saves my sanity and time.
It’s all i want from it and it delivers.
Helps me write hacky scripts to solve one off problems. Honestly, it saves me a few work days.
But it’s far from replacing anybody.
You say it’s “far” but 70 years ago a simple calculator was the size of a house. The power of my desktop from 10 years ago is beat by my phone, hell maybe even my watch.
You know, you code, compute is improving rapidly, and even when it slows vertical scaling, it’s still horizontally scaling. All the while software is getting more efficient, developing new capabilities and techniques which only bring on even more innovation.
It compounds. At this point I think the only limiting factor is how much faith the rich and powerful put in AI’s ability to make them richer. The more they invest, the faster it’ll grow.
That’s because it is overrated and the people in the tech industry are actually qualified to make that determination. It’s a glorified assistant, nothing more. we’ve had these for years, they’re just getting a little bit better. it’s not gonna replace a network stack admin or a programmer anytime soon.
There is a lot of marketing about how it’s going to disrupt every possible industry, but I don’t think that’s reasonable. Generative AI has uses, but I’m not totally convinced it’s going to be this insane omni-tool just yet.
It is currently overhyped and so much of it just seems to be copying the same 3 generative AI tools into as many places as possible. This won’t work out because it is expensive to run the AI models. I can’t believe nobody talks about this cost.
Where AI shines is when something new is done with it, or there is a significant improvement in some way to an existing model (more powerful or runs on lower end chips, for example).
Of course, because hype didn’t come from tech people, but content writers, designers, PR people, etc. who all thought they didn’t need tech people anymore. The moment ChatGPT started being popular I started getting debugging requests from few designers. They went there and asked it to write a plugin or a script they needed. Only problem was it didn’t really work like it should. Debugging that code was a nightmare.
I’ve seen few clever uses. Couple of our clients made a “chat bot” whose reference was their poorly written documentation. So you’d ask a bot something technical related to that documentation and it would decipher the mess. I still claim making a better documentation was a smarter move, but what do I know.
I’ll join in on the cacophony in this thread and say it truly is way overrated right now. Is it cool and useful? Sure. Is it going to replace all of our jobs and do all of our thinking for us from now on? Not even close.
I, as a casual user, have already noticed some significant problems with the way that it operates such that I wouldn’t blindly trust any output that I get without some serious scrutiny. AI is mainly being pushed by upper management-types who don’t understand what it is or how it works, but they hear that it can generate stuff in a fraction of the time a person can and they start to see dollar signs.
It’s a fun toy, but it isn’t going to change the world overnight.
On one hard there’s the emergence of the best chat bot we’ve ever created. Neat, I guess.
On the other hand, there’s VC capital scurrying around for the next big thing to invest in, lazy journalism looking for a source of new content to write about, talentless middle management looking for something to latch on to so they can justify their existence through cost cutting, and FOMO from people who don’t understand that it’s just a fancy chat bot.
In a podcast I listen to where tech people discuss security topics they finally got to something related to AI, hesitated, snickered, said “Artificial Intelligence I guess is what I have to say now instead of Machine Learning” then both the host and the guest started just belting out laughs for a while before continuing.
I work in tech. AI is overrated.
