> hallmark of civilization is right in the etymology -- the existence of cities.
That is not really the etymology of "civilisation" though. City and civilisation share etymological roots, but city is not the etymological origin of the world civilisation.
And of course then we just ask the question: what are cities? Do gopher, or prairie dog colonies count? (or are those just towns? :)) How about ant colonies or bee hives?
Clearly all of the above share some similarities with some human settlements. They also have important differences of course. So if we want to decide if there are other "civilisations" on Earth parallel with us, we have to be more precise with our definitions.
> city is not the etymological origin of the world civilisation
Civilisations are a subset of societies [1]. Urbanisation is commonly held as a divider between complex societies and full-blown civilisations.
> then we just ask the question: what are cities?
This is valid. I’d say the defining attribute is economies of scale. Ant colonies and bee hives demonstrate elements of this; the sum is greater than the whole.
Whether ants and bees form complex societies is less debatable, unless we reduce the terms to mean intricate where we begin enveloping colonies of trees and every social animal, potentially even just multicellular life, which while poetically pleasing isn’t useful.
Or a beehive. Bees moved into the basement window of my house and watching them go in and out it seemed to me that this single beehive had more departures and arrivals than all the commercial airports in the world put together.
If you look at the problem of "bee decline" from the viewpoint of the beekeeper where you are responsible for it you are responsible for a "city" of 50,000 insects that faces all kinds of threats from the inside and outside.
I don't entirely disagree with you, but "what products do people want" is overly conservative. Pre-ChatGPT, very few people wanted a (more or less) general purpose chatbot.
But this is like companies forcing self-checkout at retail. Companies get to cheap out on their customer support by paying a monthly fee instead of paying a person to do it. It doesn't matter if it's worse for you, as every business is getting sold on using this technology.
You misery and wasted time is their improved stock price and bonus package.
This works if you have a dominant market position, like Meta or Amazon, but a business like a local car dealership might be very ill served with this model since their customers can just go elsewhere.
Some of these are just text matchers with hardcoded options, no ML involved. Essentially phone trees, except worse because they don't tell you your options.
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.
Who cares. I literally use ChatGPT 30 times a day. It answers incredibly complex queries along with citations I can verify. Isn’t “this not good enough yet” line getting old? There nothing else that can estimate the number of cinder blocks I need to use for a project and account for the volume of concrete required for it (while taking into consideration the actual available volume available in a cinder block and settling) with a few quick sentences I speak to it. 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 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.
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.
That’s a glib, low effort dismissal but it makes sense if you consider it.
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 mean if my dentist adds a Helpful Super GenAI Infused Chatbot that can't book appointments or answer any questions about their services no amount of "you're holding it wrong" insistence about LLMs in general will actually make it useful to me.
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.
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 do. It’s not complex to click on the citation, skim the abstract and results and check the reputation of the publication. It’s built into how I have always searched for information.
I also usually follow most prompts with “look it up I want accurate information”
Please don't take this as a glib, low effort answer, but... I am glad you're not an engineer. Taking advice from an LLM on things like outlets, ingredient safety, and construction measurements seems like a mistake.
Yes, I finished the footings for the barn. I had to get two extra bags on an estimate of 68 bags. Not bad in my opinion considering the wastage from mixing, spilling etc. Also I would have had to do a bunch of tedious math that I didn’t have to.
I had about 5-10 cinder blocks left over, not bad for an order of ~150
>I'm not sure if LLMs are getting good use yet / general chatbots are good or ready for business use.
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.
depends on the tool and purpose. There was skill in navigating a file system, and now the next generation (obbscured from folder systems by mobile) seem to be losing that ability.
You can look at it in two ways, neither are particularly wrong unless your job is in fact to navigate file systems.
Of course, LLMs would be more useful to many more people if they could be used without skill, and were as "useful" as a human mentor.
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.
I don't think so, people have been wanting a general chatbot for a long time. It's useful for plenty of things, just not useful when embedded in random places.
I kind of remember the Turing Test was a big deal for some 70+ years.
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.
Stanford researchers said that ChatGPT passes the Turing Test. Honestly I don't understand how, since it's pretty easy to tell that you're talking to it, but yeah I don't think it really matters.
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.
To be fair, the overwhelming feedback appears to be that people dont want a general purpose chatbot in every product and website, especially when it's labelled 'AI'.
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 they hugely sucked, and basically were a sign you were dealing with either a fly by night company or a corporation so faceless and shitty you'd never willingly do business with them.
It was literally replacing a hierarchical link tree and that almost always was easier to use.
Or you were told this one chatbot was “haunted” by a creepypasta character and was amazed that it would actually reference that story (not knowing it was just a silly program trained on its interactions, leading to a feedback loop).
>very few people wanted a (more or less) general purpose chatbot.
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.
It's branding (see: TensorFlow); also, pretty much anything (linear) you would do with an arbitrarily ranked tensor can be expressed in terms of vector ops and matmuls
It’s a mistake to think of vectors as coordinates of objects in space, though. You can visualize them like that, but that’s not what they are. The vectors are the objects.
A vector is just a list of n numbers. Embedded into a n dimensional space, a vector is a distance in a direction. It isn’t ’the point you get to by going that distance in that direction from the origin of that space’. You don’t need as space to have an origin for the embedding to make sense - for ‘cosine similarity’ to make sense.
Cosine similarity is just ‘how similar is the direction these vectors point in’.
The geometric intuition of ‘angle between’ actually does a disservice here when we are talking about high dimensional vectors. We’re talking about things that are much more similar to functions than spatial vectors, and while you can readily talk about the ‘normalized dot product’ of two functions it’s much less reasonable to talk about the ‘cosine similarity’ between them - it just turns out that mathematically those are equivalent.
From my naive perspective, there seems to be a plateau, that everyone is converging on, somewhere between ChatGPT 3.5 and 4 level of performance, with some suspecting that the implementation of 4 might involve several expert models, which would already be extra sauce, external to the LLM. This, combined with the observation that generative models converge to the same output, given the same training data, regardless of architecture (having trouble finding the link, it was posted here some weeks ago), external secret sauce, outside the model, might be where the near term gains are.
A ton of progress can be made climbing a tree, but if your goal is reaching the moon it becomes clear pretty quickly that climbing taller trees will never get you there.
Not true. Climbing trees for millions of years taught us nothing about orbits, or rockets, or literally incomprehensible to human distances, or the vacuum of space, or any possible way to get higher than a tree.
We eventually moved on to lighter than air flight, which once again did not teach us any of those things and also was a dead end from the "get to the sky/moon" perspective, so then we invented heavier than air flight, which once again could not teach us about orbits, rockets, distances, or the vacuum of space.
What got us to the moon was rigorous analysis of reality with math to discover Newton's laws of motion, from which you can derive rockets, orbits, the insane scale of space, etc. No amount of further progress in planes, airships, kites, birds, anything on earth would ever have taught us the techniques to get to the moon. We had to analyze the form and nature of reality itself and derive an internally consistent model of that physical reality in order to understand anything about doing space.
> Climbing trees for millions of years taught us nothing about
Considering the chasm in the number of neurons between the apes and most other animals, I think one could claim that climbing those trees had some contribution to the ability to understand those things. ;) Navigating trees, at weight and speed, has a minimum intelligence reqiurement.
We have made progress in efficiency, not functionality. Instead of searching google or stack overflow or any particular documentation, we just go to Chatgpt.
Information compression is cool, but I want actual AI.
The idea that there has been no progress in functionality is silly.
Your whole brain might just be doing "information compression" by that analogy. An LLM is sort of learning concepts. Even Word2Vec "learned" than king - male + female = queen and that's a small model that's really just one part (not exact, but similar) of a transformer.
One level deep information compression is cool, but I want actual AI.
Its true that our brains compress information, but we compress it in a much more complex manner, in the sense that we can not only recall stuff, but also execute a decision tree that often involves physical actions to find the answer we are looking for.
An LLM isn't just recalling stuff. Brand new stuff, which it never saw in it's training, can come out.
The minute you take a token and turn it into an embedding, then start changing the numbers in that embedding based on other embeddings and learned weights, you are playing around with concepts.
As for executing a decision tree, ReAct or Tree of Thought or Graph of Thought is doing that. It might not be doing it as well as a human does, on certain tasks, but it's pretty darn amazing.
>Brand new stuff, which it never saw in it's training, can come out.
Sort of. You can get LLMs to produce some new things, but these are statistical averages of existing information. Its kinda like a static "knowledge tree", where it can do some interpolation, but even then, its interpolation based on statistically occurring text.
The interpolation isn't really based on statistically occurring text. It's based on statistically occurring concepts. A single token can have many meanings depending on context and many tokens can represent a concept depending on context. A (good) LLM is capturing that.
Neither just text or just concepts, but text-concepts — LLMs can only manipulate concepts as they can be conveyed via text. But I think wordlessly, in pure concepts and sense-images, and serialize my thoughts to text. That I have thoughts that I am incapable of verbalizing is what makes me different from an LLM - and, I would argue, actually capable of conceptual synthesis. I have been told some people think “in words” though.
> [the higher faculty proper of humans is] the primary function of a natural body possessing organs in so far as it commits acts of rational choice and deduction through opinion; and in so far as it perceives universal matters
Or, "Intelligence is the ability to reason, determining concepts".
(And a proper artificial such thing is something that does it well.)
The training isn't the issue per se, it's the regurgitation of verbatim text (or close enough to be immediately identifiable) within a for-profit product. Worse still that the regurgitation is done without attribution.
The legal argument, which I'm sure you are very well aware of, is that training a model on data, reorganizing, and then presenting that data as your own is copyright infringement.
Agreed, it is unclear. It's also a very commonly discussed issue with generative AI and there's been a significant amount of buzz around this. Is the NYT testing the legal waters? Maybe. Will this case set precedent? Yes. Is this a silly, random, completely unhinged case to bring?
Can you elaborate a bit more? That’s actually just a claim, not a legal argument.
Copyright law allows for transformative uses that add something new, with a further purpose or different character, and do not substitute for the original use of the work. Are LLM’s not transformative?
