Is the ChatGPT and Bing AI boom already over?
vox.com
vox.com
Saying it’s over is like saying that the internet is over after the dotcom bubble burst.
Nah. This is a revolutionizing foundational tech. Bigger than the internet even IMO. More like computerization of business, or the steam engine.
Look at what GPT-4 can achieve from only receiving text and a primitive form of image training.
There is no unlimited growth, and after the low hanging fruits each step in progress will be exponentially harder.
Most data has not been incorporated into a model yet. Most data is not in text format. We don't know what is possible once that changes.
Hopefully more and more tinkerers are going to fuel this fire even further, making it possible to run AI on-device with custom native chips.
That said, while it scores high, it's not as "generally" competent on most issues as GPT is, however, it's an exponentially smaller model and for that it does, it's really impressive.
Like most things, if you steer clear of all the hype there is a golden core of utility to be found. It sure is not just a party truck as the article daringly claims/questions it to be. The ones that make clever use of the foundational tool will stand to reap the benefits. Just like the ones that benefited from the spread of the internet.
To have real LLM comparison we would need access to pure chatgpt model's input/outputs. I wouldn't be surprised if certain open source LLMs like falcon-40b-instruct or llama2 have already surpassed chatgpt 3.5 models in quality.
But as far as I know no one so far has taken a bunch of open source models and wrapped them in a service like chatgpt 4. Why? Because the main cost of running such service using open source code (assuming the service would be open source too) would be the cost of renting hardware. Consequently the barrier to entry for competition would be very low so no one would sink their life savings in a company like that.
However, once it becomes possible to run multiple models like falcon-40B and llama2 (without quantization to 4bits) on a typical "high end pc" that will change. We will see open source projects that want to achieve a "chatgpt service" offline on consumer hardware. Why only then? Same reason why Linux was written once 386PC's became available. Could someone like Linus Torvalds write Linux 20 years before on their "university" or "company" computer? Sure, but except certain exceptions (gnu) most people engage in writing open source software they themselves want to run on their own hardware.
Big companies know this and they know very well such developments will out compete them shortly (like Linux out competed unix) so they are already doing all they can to stop or slow it down. By talking about "the dangers of AI", by not releasing powerfull ai accelerators on the market (google's tpu). And so on. But it will happen. Give it 10 years. I
I hang around a lot of solopreneurs and there is a serious cohort of founders/hustlers who are using AI tools to greatly improve their productivity.
Not sure how much of that can carry forth to a major corporation though.
Oh is it? Maybe you can tell us then how you'd get the models home without the internet? On a DVD? Or interact with a server that runs the model for you? Or how we'd even be having this discussion?
People said similar delusions about Second Life back in the day.
I don't actually agree with the point you're contesting with but your reasoning here is flawed. Also - you might want to check your tone for future posts. It's slighty combative.
How would you express your angle regarding the contested point?
That implies that nothing can be more important than all of the things that were neccesary for it to come to pass. And there are a huge number of neccesary preconditions for most things. Your definition would result in a very counterintuitive definition of importance.
GitHub copilot and sit.ilar will become defacto standard.
Image generation is already here.
School systems already have to act on this due to pupil using it for essays etc.
AI literally creates a new ara for us: it's the first time we structure or learn on data to have a knowledge agent.
Anyone who did data work in big, boring enterprises knows the staggering amount of dark, unstructured data they have. Free-form text input, chat/phone conversations, but also plain old documents (pdf scans, PowerPoint files, anything).
Have a LLM interpret what is there and you turn this unstructured mess into data with a clear schema that can go into a relational database for further processing or summarization.
LLMs are groundbreaking for this kind of automated data structuring. And many use cases don't need perfect accuracy; detecting trends or summarizing at a high level is good enough for inputs to downstream process mining tasks.
On the other hand, the economic and social impact of AI has only just begun. Now it will move more quietly to slowly integrate itself in society like a virus, making us more and more dependent on AI.
This integration will push us to becoming more like automatons, needing people less, and further concentrating the wealth of the world towards Silicon Valley, because it will create a worldwide addiction and dependency on something we never needed in the first place.
so you could say the same for electricity. And yet i dont see you calling it a virus!
> needing people less
no, needing people to do less isn't the outcome. The same amount of people can now produce _even_ more. Just like having tractors pushed out farmers that used to hire lots of manual labour.
It's a good outcome in the long term. It sucks for those who get displaced - but the train of technological progress runs over tracks made of the bones of those it displaces. this has always been the case in history, and will continue to be until the day we reach post-scarcity.
It is like a virus in a way.
> no, needing people to do less isn't the outcome. The same amount of people can now produce _even_ more. Just like having tractors pushed out farmers that used to hire lots of manual labour.
We have reached the point of diminishing returns when it comes to technological development. We do NOT need to produce more things more efficiently. That only leads to more resource usage, which this planet can no longer accommodate.
Instead, we need to concentrate on sustainability rather than productivity.
> It's a good outcome in the long term. It sucks for those who get displaced - but the train of technological progress runs over tracks made of the bones of those it displaces. this has always been the case in history, and will continue to be until the day we reach post-scarcity.
If we go down that route, we will use up too many resources. We should not aim for post-scarcity, but rather post-productivity. Technology is no longer making the world better, it's making it worse. If you live in a city, simply look out the window. We're destroying our planet all in the name of endless economic growth, which is only making us slaves to the technological machine.
Every generation says this, while at exactly the same time regarding previous ways of life completely unthinkable.
The world was MUCH better without all this junk.
The next step will be embedding these models in other products as practical tools (for example automatically summarising things) and they become part of day to day usage. That's already happening.
Computer just had their GUI moment again and we’re just getting started.
I use it as an extension of my mind towards getting answers for grey areas. A grey area such as "write a unit test skeleton for this method signature", where the method in my head is either too abstract or has complexity that increase my short term memory span.
I find it a helpful extra pair of eyes that seems to know a lot more than can be expected by a human.
I'm not sure how long this has been available. I noticed it last month, but it is pretty useful to search for documentation or if you don't know internal procedures. You can just just describe what you want to achieve and it is reasonably good in finding the right documents. The AI boom is far from over. It hasn't even started properly yet.
Then, like five years later, they still weren’t really driving themselves. (=
AI feels sort of like that.
ChatGPT is fun, but gets as much wrong as it gets right.
The only things I consistently use it for are rewriting my emails in different tones and to summarize large documents / emails that I don’t feel like reading.
ChatGPT is useful for generating text that look okay at a distance. I can see the use for creating low quality content to drive traffic to a website, something like medium.
For coding its not useful for any language you are proficient in. There is just so much hype around it but I just can't find the usecase for it.
Evidently: for coding in languages you are not proficient in! What could go wrong?
You're being facetious, but I agree unironically.
Anyway, all known AI techniques struggle with common sense reasoning. The majority of ambiguities in natural language are distinct and uncorrelated. Combining known techniques is not going to solve the problem. Either we discover new theories, or revise our natural languages to be less ambiguous (likely a deeply unpopular social change). Both will probably occur in this century.
As for ChatGPT and Bing? I expect these brands will be dead long before we get to the next wave of AI.
Additional reading for the curious: https://en.wikipedia.org/wiki/Winograd_schema_challenge
https://github.com/LudwigStumpp/llm-leaderboard/blob/main/RE...
The current top model, Vega v2, is already at 98.6 (compared to human’s 100.0). https://super.gluebenchmark.com/leaderboard
Several other LLMs score higher than 95.0 as well.
I suspect some upcoming multimodal LFMs will be very close to human level since their training data would include video, audio, and possibly even physical interaction input.
Moreover, top LLMs already surpass the human baseline on SuperGLUE, a suite of many language tasks.
This is my experience when using GPT-4 as well. It’s like an extremely well-read intern who follows instructions much better than an average person on the street. It is just often a little drunk or sleepy at work ¯\_(ツ)_/¯, ……for now.
Like the Turing test, Winograd is not a very good test and more of a philosophical question than a metric for engineering.
Sure there's nothing wrong with a model that at some point in time passes 100% and is even more consistent than humans, but it's not possible to make an exhaustive list of what humans can disambiguate that these models can't.
It's a moving target. We're just a cultural shift, scientific discovery, or political regime change away from the models needing to be updated all over again. For some applications these lagging inaccuracies are not acceptable. For yet still more applications, it's not possible to allow any amount of time to train the models ahead of seeing novel ambiguity.
There's a lot of work left for AI research and the threat of authoritarian-induced cultural stagnation just to serve the AI gods and the convenience of governance is absolutely terrifying. i.e. You can't speak that way because it confuses the AI, you can't look or act that way because it confuses the AI, etc. AI in its current form totally sucks and is unfit for much beyond low risk tasks.
> Anyway, all known AI techniques struggle with common sense reasoning. The majority of ambiguities in natural language are distinct and uncorrelated. Combining known techniques is not going to solve the problem.
> Statistical techniques underperform compared to symbolic rules no matter how much hardware you throw at the problem.
The best empirical machine learning techniques to measure a system's capabilities are good enough for many practical purposes. AI's commonsense and language understanding differs from that of humans but it's not necessarily inferior to the average human's understanding, even in its current state. AI's understanding is shallower but more interconnected to more diverse domains of knowledge, which is very useful for practical purposes.
We're not going to change our language to avoid confusing AI. If anything, it can already communicate, using our natural language, better than an average human in some practical contexts.
I do agree though that AI research in language understanding is far from completed.
To wrap AI technology into a chatbot is a deceiving, strategic move. It is a wolf in sheep's clothes aiming for people to believe the technology is there to help them. As such it is a solution looking for the problem, and now a year later there are not enough problems that a chat interface can solve.
In reality the genie is out of the box, but not to help people rather to replace them. Reduction of ChatGPT traffic does not tell us what is really ahead of us.
Ultimately I don't see those primary LLMaaS offerings being particularly profitable. Secondary LLM applications are a completely different matter. For example, LLMs acting as medical scribes, sitting in your doctor's office and filling out your chart, handling a job that formerly required a skilled human. That sort of thing will make tons of money. It just probably won't use Azure/Amazon/Google LLMs to do it.
Some use cases are enabled just the last few weeks by company-internal models.
This will accelerate and evolve. Generative AI is a new appliance. We just got started.
I have bought a perplexity pro subscription which is excellent - it creates useful summaries of search results across various sources but also has generative AI with chatgpt 3.5 FT, 4, Claude 2 along with llama 2.
I personally have difficulty trusting generative AI - too many hallucinations- so to see sources is helpful.
There is going to be a ton of losers with the incumbent big tech companies ending up as the winners.