It's awful and a complete waste of time. I'm not sure if LLMs are getting good use yet / general chatbots are good or ready for business use.
You misery and wasted time is their improved stock price and bonus package.
I think the problem is you haven’t shifted your mindset to using AI correctly yet.
Edit: More everyday examples from just the last 3 days
- Use carbide bits to drill into rocks. Googling “best bits for drilling rocks” doesn’t bring up anything obvious about carbide but it was the main thing chatGPT suggested.
- gave it dimensions for a barn I’m building and asked it how many gallons of paint I would need of a particular type. I could probably work that out myself but it’s a bunch of lookups (what’s the total sq footage, how many sq ft per gallon, what type of paint stands up to a lot of scuffing etc.)
- coarse threaded inserts for softwood when I asked it for threaded insert recommendations. I would have probably ended up not caring and fine threaded slips right out of pine.
- lookup ingredients in a face cream and list out any harms (with citations) for any of them.
- speeds and feeds for acrylic cutting for my particular CNC. Don’t use a downcut bit because it might cause a fire, something I didn’t consider.
- an explanation of relevant NEMA outlets. Something that’s very hard to figure out if you’re just dropped into it via googling.
There is an indictment of AI "products" if I ever heard one
The point is ChatGPT's wild success doesn't automatically mean consumers want and possibly will never want a chatbot as their primary interface for your specific app or service.
It’s like people that kept going to the library even with Google around. You’re not playing to the strengths of AI and relying on whatever suboptimal previous method you used to find the answers. It does really, really well with very specific queries with a lot of looks ups and dependencies that nothing else can really answer without a lot of work on your end.
I had about 5-10 cinder blocks left over, not bad for an order of ~150
They left room for the idea that the technology could evolve to be useful. You're simply dismissing anyone who cannot use he technology as is as "using it wrong".
As someone who did a tad of UX, that's pretty much the worst thing you can say to a tester. it doesn't help them understand your POV, it builds animosity towards you and the tester, and you're ruining the idea of the test because you are not going to be there to say "you're doing it wrong" when the UX releases. There's 0 upsides to making such a response.
That doesn’t make them useless.
You can look at it in two ways, neither are particularly wrong unless your job is in fact to navigate file systems.
That's true, and they lack that capability. Many people seem to react as though this means they're missing all value, however. I find them incredibly useful; it just isn't possible to get much value out without investing effort myself.
That is partially marketing's fault, so I say that confusion is self inflicted. Because marketing isn't focusing on "make yourself more productive!"
clearly anyone trying to buy a car, which is already an ordeal with a human as is.
>I literally use ChatGPT 30 times a day
good for you? I use Google. mos of my queries aren't complex.
>Isn’t “this not good enough yet” line getting old?
as long as companies pretend 2024 AI can replace skilled labor, no. It's getting old how many more snake oil salesmen keep pretending that I can just use ChatGPT to refactor this very hot loop of performance sensitive code. And no ChatGPT, I do not have the time budget (real time) to hook into some distributed load for that function.
I'm sure in a decade it will wow me. But I prefer to for it to stay in its lane and I stay in mine for that decade.
>There nothing else that can estimate the number of cinder blocks I need to use for a project
is Calculus really this insurmontable feat to be defending big tech over? I'm not a great mathmatican, but give them excel/sheets and they can do the same in minutes.
>I can think of literally thousands of things I have asked that would have taken hours of googling that I can get an answer for in minutes.
I'm glad it works out for you. I'm more scrutinous in my searches and I see that about half the time its sources are a bit off at best, and dangerously wrong at worst. 50/50 isn't worth any potential time saved for what I research.
>I think the problem is you haven’t shifted your mindset to using AI correctly yet.
perhaps. But for my line of work that's probably for the best.
And do you? Every time someone tried to show me examples of “how amazing ChatGPT is at reasoning”, the answers had glaring mistakes. It would be funny if it weren’t so sad how it shows people turning off their critical thinking when using LLMs, to the point they won’t even verify answers when trying to make a point.
Here’s a small recent example of failure: I asked the “state of the art” ChatGPT model which Monty Python members have been knighted (it wasn’t a trick question, I really wanted to know). It answered Michael Palin and Terry Gilliam, and that they had been knighted for X, Y, and Z (I don’t recall the exact reasons). Then I verified the answer on the BBC, Wikipedia, and a few others, and determined only Michael Palin has been knighted, and those weren’t even the reasons.
Just for kicks, I then said I didn’t think Michael Palin had been knighted. It promptly apologised, told me I was right, and that only Terry Gilliam had been knighted. Worse than useless.
I also usually follow most prompts with “look it up I want accurate information”
That didn’t work so hot for two lawyers in the news a while back.
There are some more advanced ones using ChatGPT now. I'm guessing they simply pre-prompt it. Can lead to funny results like a customer making the Chevy bot implement an algo in Python.
So... certainly there's a space for new products.
...but perhaps for existing products, it's not as simple as 'slap some random AI on it and hope you ride the wave of AI'.
And post-ChatGPT, very few people want to have to deal with "a (more or less) general purpose chatbot."
It was literally replacing a hierarchical link tree and that almost always was easier to use.
We should have known that once we pass the Turing Test it would almost instantly become as passe as Deep Blue beating Kasparov on the road to general intelligence.
I am taking a break from my LLM subscriptions right now for the first time to gain some perspective and all I miss it for is as a code assistant. I would also miss it for learning another human language. It seems unsurprising that large language models use cases are with automated language. What is really surprising is how very limited the use cases for automated language seems to be.
Far more useful than simulating a person, OpenAI managed to index so much information and train their models to present it in a compact way, making ChatGPT better than Google search for some purposes. Also, code generation.
I mean, I still don't. But from a cynical business point of view, cutting customer servce costs (something virtually every company of scale has) of 99% of customer calls is a very obvious application of a genera purpose chatbot.
expand that to "better search engine" and "better autocomplete" and you already have very efficient, practical, and valuable tools to sell. but of course companies took the angle of "this can replace all labor" instead of offering these as assistive productivity tools.
If you can't convince people that this is benefiting them, and instead focus on talking to investors about how much you can kill off the working class (aka, your "customers" and nowadays "product audience"), you will make it harder to properly sell your product nor audience. Companies have forgotten who the real customers are, no wonder their products aren't resonating.
However, a lot of those got a bunch of investment or made some decent money in the short term. Very few are still around. We will see the same pattern here.
Pull requests in github is actually very similar conceptually to a consensus mechanism used in crypto currencies. Everyone has an identical copy of the main branch with an identical history of every commit in order, a PR is saying "I think this commit goes next" and, if you use code reviews, the PR approval is consensus.
Have you ever seen a git graph? Does this look linear? https://tortoisegit.org/docs/tortoisegit/images/RevisionGrap...
All secondary branches are works in progress that may be proposed as new commits to main.
Sticking with the blockchain comparison, every side branch in got is akin to potential blocks that miners are working on.
My point is that git is a data store involving a genesis block (initial commit), blocks of changes/diff's, tracked in sequential order, and with a form of consensus (code reviews and merges to primary).
What is missing that makes it not a blockchain?
And my caveat here, I can't stand arguments for cryptocurrencies and have never purchased any. Block chain as a concept is fine, and git is a blockchain as best I can tell.
git is very much a blockchain
- sequential list of changes to a data source (commits)
- single, shared history of changes (main branch)
- users creating potential next change(s) to be added to the history (side branches and forks)
- consensus mechanism for new change blocks (merge requests and code reviews or approvals)
What's missing?
So just zooming out, we need people trying to figure out what can be built with this Lego set. We also need people like you're saying to work the other side so everyone can meet in the middle.
Also: https://www.reuters.com/world/europe/eiffel-tower-grows-six-...
Utility may have been an afterthought, but it's still there.
Every cycle, theres all types of people hop on board whatever the hype train is... it's the same mindset as pioneering for gold in the wild west.
I just hope we can move along more in the "wheat" direction with AI products. There's so much low-effort crap already out there.
Customers are also more interested in AI products. The tech industry has stagnated for years with incremental improvements on existing products. ChatGPT and generative AI are new capabilities that draw interest, and companies have been doing anything they can to stand out today.
When you’re truly bring novel new value to things, sometime you need to say “we can do this cool thing, but don’t know what that means”. Simply knowing that capability opens you up to better sets of solutions.
- Bezos saw the growth rate of the internet, spent a few months mulling over the question: "what business would make sense to start in the context of massive internet adoption" and came up with an online bookstore.
- OpenAI's ChatGPT effort really began when they saw Google's paper on transformers and decided to see how far they could push this technology (it's hard to imagine they forecasted all the chatbot usecases; in reality I'm sure they were just stoked to push the technology forward).
- Intel was founded on the discovery of the integrated circuit, and again I think the dominant motivation was to see how far they could push transistor density with a very hazy vision at best of how the CPUs would eventually be used.
I think the reason this strategy works is that the newness of a truly important technology counteracts much of the adverse selection of starting a new business. If you make a new To-Do iPhone app, it's unlikely that people have overlooked a great idea in that space over the last 10 years. But if lithium ion batteries only just barely started becoming energy dense enough to make a car, there's a much more plausible argument why you could be successful now.
Said another way: "why hasn't this been done before?" (both by resource-rich incumbents as well as new entrants) is a good filter (and often a limiting one) for starting a business. New technological capabilities are one good answer to this question. Therefore if you're trying to come up with an idea for a business, it seems reasonable to look at new technologies that you think are actually important and then reason backward to what new businesses they enable.
Lastly - I'd bet there is a helpful motivational force for many people if they get to feel that they are on the cutting edge of technology that interests them and building products that simply weren't possible two years ago.
The idea that a company is an AI company should be as ridiculous as a company being a Python company. "We are Python-first, have Python experts, and all of our products are made with Python. Our customers want their apps to have Python in them. We just have to 'productize Python' and find the right killer app for Python and we'll be successful!" Going at it from the wrong direction. Replace Python in that quote with AI, and you probably have something a real company has said in 2024.