Investment giant Goldman Sachs published a research paper
Goldman Sachs researchers also say that
It’s not a research paper; it’s a report. They’re not researchers; they’re analysts at a bank. This may seem like a nit-pick, but journalists need to (re-)learn to carefully distinguish between the thing that scientists do and corporate R&D, even though we sometimes use the word “research” for both. The AI hype in particular has been absolutely terrible for this. Companies have learned that putting out AI “research” that’s just them poking at their own product but dressed up in a science-lookin’ paper leads to an avalanche of free press from lazy credulous morons gorging themselves on the hype. I’ve written about this problem a lot. For example, in this post, which is about how Google wrote a so-called paper about how their LLM does compared to doctors, only for the press to uncritically repeat (and embellish on) the results all over the internet. Had anyone in the press actually fucking bothered to read the paper critically, they would’ve noticed that it’s actually junk science.
A big part of the problem – and this is not a new issue, goes back decades – is that a lot of terms in AI-land don’t correspond to concrete capabilities, so it’s easy to claim that you do X when X is generally-perceived to be a much-more-sophisticated thing than what you’re actually doing, even if your thing technically qualifies as X by some definition.
None of this in any way conflicts with my position that AI has tremendous potential. But if people are investing money without having a solid understanding of what they’re investing in, there are going to be people out there misrepresenting their product.
saying the quiet part out loud… big tech won’t like that.
I’ve found like, 4 tasks that are really helped with by AI, and I don’t have the faintest idea how you could monetize any of them beyond “Subscribe to chatgpt”
At my previous job their was a role where you just called insurance companies and asked them incredibly basic questions about what they planned to do for a patient with diagnosis X and plan Y. This information should be searchable in a document with a single correct answer, but insurance companies are too scummy for that to be reliable.
In 2021 we started using a robot that sounded like a human to call instead. It could handle the ~80%+ of calls that don't use any critical thinking. At a guess, that's maybe 5-10% of our division's workforce that wasn't needed anymore.
With the amount of jobs like this that are 100% bullshit, I'm sure there are plenty of other cases where businesses can save money by buying an automated bullshit generator, instead of hiring a breathing bullshit generator.
The problem is that 20% failure rate has no validation and you are 100% liable for the failures of an AI you’re using as a customer support agent, which can end up costing you a ton and killing your reputation. The unfixable problem is that an AI solution takes a ton of effort to validate, way more than just double checking a human answer.
It wont know it doesn’t know. At the current state of AI, it doesn’t seem to have almost any sense of what is right and wrong or a way to validate that - even when you tell it, it is wrong. Maybe there are systems that can but I am not aware of them.
The current state of AI chatbots, assigns a “confidence level” to every piece of output. It signals perfectly well when and where they should look for more information… but humans have been pushing them to “output something, anything”, instead of excusing itself for not knowing something, or running some additional processes in order to look for the missing information.
As of this year, Copilot has been running web searches to complement its lack of information, and Gemini is running both web searches, and iteratively self-checking its own answer in order to refine it (see “drafts”). It also seems like Gemini might be learning from humanity’s reactions to its wrong answers.
From my understanding, AI is a essentially a statistical method so naturally it will use a confidence level. Its hard for me to take the leap of faith to confidence level will correlate to accuracy. Seems to me it would be more dependent on its data set. If its data contains a commonly held belief, that is incorrect, would it not have a high confidence level on an answer with that incorrect info? If we use a highly authoritative data set, that will be very limited and we’d be back to more of a keyword system than a LLM. I am sure with time, we’ll be in more of a middle ground where accuracy will be better but what will that be? 5% 3% 10%?
I’ll freely admit I am not an expert in this at all.
It’s not a statistical method anymore. One of the breakthroughs of large model neural networks, has been that during training an emergent process, assigns neurons to both relatively high level and specific traits, which at the same time “cluster up” with other neurons assigned to related traits. Adding just a bit of randomness (“temperature”) allows the AI to jump from activating one trait to a close one, but not to one too far away. Confidence becomes a measure of how close is the output, to a consistent set of traits trained into the network. Interestingly, a temperature of 0 gives a confidence of 100%… but produces gibberish.
If its data contains a commonly held belief, that is incorrect
This is where things start to get weird. An AI system based on an LLM, can iterate over its own answers looking for the optimal one (Q*), and even detect inconsistencies in them. What it does after that, depends on whoever programmed it:
Maybe it casts any doubt aside, and outputs the first answer anyway (original ChatGPT did that, didn’t even bother self-checking too much)
Or it could ask an authoritative source (ChatGPT plugins work like that)
Or it could search the web for additional info (Copilot and Gemini do that)
Or it could alert the user to both the low confidence and the inconsistencies (…but people want omniscient AIs, not “err… I’m not sure, Dave” AIs)
…or, sometime in the future (or present?) they could re-train themselves, maybe via generating a LoRa, that would bring in corrected biases, or even additional concepts.
Over time, I think different AI systems will evolve to target accuracy, consistency, creativity, etc. Current systems are kind of rudimentary compared to what’s yet to come, and too many are used in very rudimentary ways by anyone who can slap an “AI” label and sell them.
That is pretty interesting and thanks for posting it. I hear the words and its intriguing but to be honest, I don’t really understand it. I’d have to give it some thought and read more about it. Do you have a place you suggest going to learn more?
I use chatgpt-4o currently for learning python and helping with grammar. I find it does great with grammar but even with relatively simple python questions it can produce some “creative” answers. Like its in the ball park but its not perfect and for a learner, that’s learning the hard way. To be fair I don’t use the assistant/code interpreter, which I have no idea about but based on its name I assume it might be better. So that’s what I based my somewhat skeptical opinion of ai on.
You may want to also check an intro to neural networks, and Q* is a somewhat new concept. Other than that… “the internet”. There are plenty of places with info, not sure if there is a more centralized and structured one.
Learning to code with just ChatGPT is not the best idea. You need to join three areas:
general principles (data structures, algorithms, etc)
language rules (best described in a language reference)
business logic (computer science, software engineering, development patterns, etc)
ChatGPT’s programming answers, give you an intersection of all those, often with some quirks, with the nice but only benefit of explaining what it thinks it is doing. You still need to have some basic understanding of those in order to understand what ChatGPT is talking about, how to double-check it, and how to look for more info. It can be a great timesaver as a way to generate drafts, though.
I feel like customer support is one place where AI may actually be used going forward because companies don’t really care if their customers get support. The only wrinkle is that if companies get held to promises the AI makes (there’s that Canada Air incident from last year where the AI offered a refund and the company tried to walk it back).
I’ve had this discussion come up in meetings recently.
CustomGPT is like $500/month for 5000 queries… that limitation and price (if you have a reasonable amount of customers), kind of just means you are better off hiring one employee. I’m not going to ping them for pricing for their enterprise plan beyond that, as going to cost an employee anyways.
With streaming services they’re proving it’s not viable to run a resource hog of a service with a measly monthly subscription.
With social media they’re proving it’s not viable to run a resource hog of a service for free, even with advertisement.
So naturally the best plan to monetize AI is to run a resource hog of a service with a measly monthly subscription and a free version without advertisements. /s
Sometimes that bear shits in my yard. And then the little asshole trashes my garden. I might buy a tag and shoot the son of a bitch this fall if he keeps it up…
Recently there was one in British Columbia that locked itself in a hot car, freaked out and tore up the interior completely, and then had to be rescued by the cops.
The stuff they’re calling ai now is just predictive text algorithms. I really can’t wait to stop hearing about this because it is all artificial with no intelligence.
LLMs are predictive-associative token algorithms with a degree of randomness and some self-reflection. A key aspect is that anything can be a token, they can self-feed their own output, creating the basis for a thought cycle, as well as output control input for other algorithms. It remains to be seen whether the core of “(human) intelligence” is much more than that, and by how much.
Stable Diffusion is a random image generator that refines its output based on perceptual traits associated with a prompt. It’s like a “lite” version of human dreaming, only with a super-human training set. Kind of an “uncanny valley” version of dreaming.
It just so happens that both algorithms have been showcased at about the same time, and it’s the first time we can build a “set and forget” AI system that can both make decisions about its own next steps, and emulate human creativity… which has driven the hype into overdrive.
I don’t think we’ll stop hearing about it, but I do think there is much more to be done, and it’s pretty much impossible to feed any of the algorithms with human experience data, without registering at least one human learning cycle, as in over many years from inside a humanoid robot.
Not sure if you were unable or unwilling to understand anything of what I wrote, and I don’t like your tone. Feel free to come back with something more serious.
LLMs have been shown to have emergent math capabilities (that are the opposite of what is trained) so you’re simplifying way too much. Yes a lot is just “predictive text” but there’s a ton of “this was not the training and we don’t know how it knows this” as well.
Game of Life has cool emergent properties that are a lot more interesting and fun to play with than LLMs. LLMs also have emergent properties like, for instance, failing classification due to the manipulation of individual image pixels.
You know it’s funny how many times I’ve heard that “it’s just predictive text algorithms!” As a dismissal that I’m beginning to think we’re just predictive text algorithms.
Once. They do not have the ability to learn or adapt on their own. They are created by humans through “deep learning”, but that is fundamentally different from continuously learning based on one’s own actions and experiences.
Funny you should mention that McKinsey published a paper a few months back concluding that GenAI will take over most of the jobs in America because it was good at doing what McKinsey Associates do. Missed by the authors is that the job of a McKinsey associate is to confidently spout nonsense all day long and that’s actually exactly what chatgpt is programmed to do.
chatgpt: “Artificial Intelligence (AI) represents a transformative investment opportunity, characterized by robust growth potential and broad applicability across industries. The AI market, projected to exceed $190 billion by 2025, offers substantial upside in sectors such as healthcare, finance, automotive, and e-commerce. As businesses increasingly adopt AI to enhance efficiency and innovation, associated firms are poised for significant returns. Key investment areas include machine learning, natural language processing, robotics, and AI-driven analytics. Despite risks like regulatory challenges and ethical concerns, the strategic deployment of capital in AI technologies holds promise for long-term value creation. Diversification within this space is advisable to mitigate volatility.”
“Today” AI is Over hyped, Wildy expensive and unreliable. This is like the quote about the Internet not catching on, or how nobody would ever need more than 640kb of ram. honestly y’all make me chuckle.
You’re right. Once it settles into its niches and the hype dies down, it won’t be overhyped anymore because everyone will have moved on.
I’ve been working with generative AI for years now and we still struggle to solve real world problems with it. It isn’t useless or anything. It’s way too unreliable, and this isn’t one of those things where time will solve it - it’s being used to solve problems that have no perfect solutions, like human interfacing and generating culturally-appropriate and visually-accurate images. I’d expect it to improve at those tasks over time, but the scope needs to drop from every problem humanity has ever faced to the problems that these models are good at solving.
You sound like a teacher from the 80s telling a student that they won’t always have a calculator with them. Your lack of imagination solving problems with AI is and will remain yours. Automation and AI is going to change the entire world much like the automobile devastated the Horse transportation industry. Just as the Amazon killed the Malls. You just fail to see how you fit into this new strange future, and surprise you just perhaps don’t.
I don’t know why you think these ideas were mine, but I do work for a rather large company that has invested a lot of resources looking for solutions using these models. These ideas came from people far smarter than I.
The rest of your comment has so little to do with what I said that I’m inclined to believe it’s AI generated.
I’m dyslexic and visually impaired, I make mistakes despite using a grammar checker. My teachers used to tell me I was careless and lazy. Your comment made me laugh though, thanks.
Sure pal whatever you wanna believe. This comment is exactly the type of zero imagination hillbilly American thinking that represents what I’m arguing about. Did I just join in July? Is it plausible I created a second nsfw account? Or perhaps I was logged out of my account and didn’t save my password? Just maybe I burn my account every 6 months because I value privacy over fake comments points I can stroke my ego and cock to? Stay tuned to fined out.
I agree with this. Its wildly misunderstood and it’s the name. AI is absolutely the most amazing marketing name for it but its only a thin veneer of our sci fi dreams. Over time that veneer might get a bit thicker but it wont be what people think it will be. It is good at certain things, like you know, being a large language model, but it is a (very) limited subset of what human intelligence is.
It’s not “widely misunderstood”, it’s been widely hyped by the people actively selling it. The tech bros are pumping and dumping it, just like with every other tech panacea.
That’s what I am saying. The buyers wildly misunderstand it. The seller presents it with a very effective and misleading pitch.
Look at the Intuit CEO who just fired 10% of their labor to pivot to AI to um, “give financial advise.” And then goes on to say any other company who doesn’t do the same will fall behind and fail. Time will tell but I am going to go with, people will laugh when Intuit is on fire.
I suspect Intuit fired those workers for other reasons (free file) and are using AI as an excuse because to admit that free-file is an existential threat to their business is to admit that their company has no long term business prospects.
That seems entirely plausible for the staffing change. But Intuit is more than their tax software for example Quickbooks isn’t going anywhere. I am sure they do other stuff, probably payment processing and I don’t know what else. So they will survive at some level, it would be hard to kill Quickbooks.
Correct. Dress it up however you like, but LLM and ML programs are probability gamblers all the way down. We’re building a conversation tool, that doesn’t truly comprehend the language because it’s a calculator at its core - it’s like asking your eyeballs to see in UHF frequencies.
They’re called “computers” for a reason, and we are deep in the myopic tech tree of further and further complexity. The current wave of AI has solid potential, but not globally for all applications. It is a great at ‘digital assistant’ roles and is already killing it in CCTV monitoring software. Mindjourney can make incredible images, but it can’t make art. ChatGPT can write, but it’s a terrible author or speechwriter.
This is the same middlebrow dismissal that AI advocates have been using for years.
“It’s just a stochastic parrot.” “How do you know that you aren’t just a stochastic parrot?”
Well we do know. There are experts on human cognition. They have been studying it for decades. We may not know enough about it to know how to make a computer do it. But we certainly know enough about it to know when a computer chatbot is not doing it.
Sorry to break it to you but there is no defining art without disqualifying ai, the subject is so old it’s hardly an opinion at this point. Even the most imaginative mating rituals animals can do barely qualifies… And mind you, these have emotions and cognitive capabilities, so something as barebone as the kind of “ai” we make now… nothing more than a joke art wise.
AI has lots of potential for the future, and Goldman Sachs continues to invest in that sector.
They are specifically talking about the bubble of Generative AI startups, none of which have any long term viability as they either produce a novelty, or they produce something so inaccurate that nobody would trust it after using it.
They aren’t the people saying that the Internet won’t catch on. They’re the ones warning you that dot com is a bubble.
Hail to our Lord and Savior Goldman Sach. They are the word and the life I shall never doubt them again. I fall on my knees and beg forgiveness from the corporations. The profits have spoken and they have increased 5 percent every quarter. So let it be written so let it be done.
Holy mother of misinterpretation and misrepresentation. Did you not read their comment, did you not understand their comment, or did you choose to ignore and misrepresent it?
They deliberately misrepresented it. Just another person who thinks that if you oppose Goldman Sachs for their contributions to late stage capitalism that you are obligated to disagree with every single piece of messaging from them without exception.
If the CEO of Goldman Sachs shits in a toilet, and this guy finds out, he’s going to shit on the floor in protest.
I find comments like these on places like Beehaw almost amusing in a way. It’s like watching a drunk person stumble from a bar all the way to a courthouse and getting upset the clerk won’t sell them more liquor.
Seriously though, I’m not sure what you hope to accomplish here. Just about everybody here disagrees and isn’t keen on a take like this, and I’d figure you’d have been able to tell as much before posting. So… are you just here to argue?
There are studies that suggest that the information investment firms publish is not based on what they believe to be true, but on what they want others, including their competitors, believe to be true. And in many cases for serving their investment strategy, it benefits them to publish the opposite of what they believe to be true.
Intentions aside, it’s just some independent research that anyone can review and critique. If the research is bad then it should be pointed out and won’t be taken seriously, undermining any influence from Goldman Sachs now and in the future
Goldman Sachs would not publish it that prominantly if it didn’t help their internal goals. And their intention is certainly not to help the public or their competitors. There are independent studies of some topics that are all well made and get to opposite conclusions. Invedtment firms just do what serves them. I wouldn’t trust anything that they publish.
I mean, ask pretty much anyone familiar with the workings of AI who doesn’t have a vested interest, and they’ll say the same thing. Goldman is right.
I’d also say that it does have applications, but it’s going to take a moment for all the bullshit artists to move on to the next thing so the grown-ups can work. It’s a bit like graphene research circa-2011, although it’s way more proven than graphene ever was.
They might also say that the moment it does work reliably we should be scared, although it’s fair to say there’s many experts who take the obvious stance.
Oh no, you mean the big "smart" money investors that manage to crash the economy every decade or so and ruin every business they touch are gonna leave generative AI alone? Oh nooo. How will the science progress without Goldman Sachs's guiding hand?