Reminds me of the web3 crypto hype and this hype is the same thing that happened with GPT-3. Closed source black-box AI model hidden behind a SaaS confidently generating wrong answers as the truth.

Sounds like OpenAI (effectively Microsoft's new AI division) are selling snake-oil again.

> I think there are useful applications for this technology but I think we should leave that to the people who understand LLM's best and keep the charlatans out of it.

There are certainly charlatans, grifters and snake-oil sales people pretending and hyping about their so-called 'AI startup' when it is actually uses the GPT-3 or ChatGPT API. Another emperor has no clothes confidence trick generating garbage.

Given that ChatGPT cannot explain its own answers transparently when questioned especially when it confidently generates wrong answers means that you cannot trust its output and telling it to give you a sophisticated answer to an 'un-googleable' question is where you see it clearly trip over.

What would really 'revolutionize the industry' is an open-source LLM model which is a smaller model and more transparent than ChatGPT, like what Stable Diffusion did to DALLE-2.

The Bing openai integration is already dramatically different and better than chatgpt. I’d really advise trying it before forming too many opinions. Certainly don’t mistake any experience you have with chatgpt as indicative.

Fundamentally, GPT itself and LLMs are essentially still black-box neural networks and all have the same drawbacks as them:

* More data needed to train them. Which then...

* ...Increases the size of the model (Getting bigger on each revision)

* Opaqueness (Cannot transparently explain its own decisions when it confidently generates the wrong answer.)

* Massive cost involved in training, retraining and fine-tuning it.

The best part is that you can't even give an explanation on why it is generating nonsense or why it overfitted or got confused over an invalid character or input. Little to nothing fundamental about neural networks (which is what LLMs are based on) has changed. It is essentially more of: 'Just waste more money on training it on more data.'

> The Bing openai integration is already dramatically different and better than chatgpt. I’d really advise trying it before forming too many opinions.

What I have said about ChatGPT and GPT-3 still holds true and hyping about a chatbot confidently generating nonsense and that cannot cite its own sources makes it highly untrustworthy to use. It is always eternal to the shortcomings of neural networks fundamentally; which is what LLMs use. They are great sophists and bullshit generators.

At least what needs to exist is an open-source LLM that is more transparent and is smaller than GPT-3 or GPT-4. There is a basic reason why OpenAI won't do that.

And even in LLMs, it sounds like you're still seeing training/retraining as single monolithic events which are super expensive. Training is super expensive! New techniques (google LoRA; there are others) are changing that.

I hope you get into what has changed in the past few years, starting with Transformer and continuing pretty much daily. There really is a lot of innovation and improvement that is publicly documented and missing from your opinion here.

It is a GPT (Using OpenAI's one) and fundamentally it is a black box neural network that when asked to explain it's own decisions, it still cannot transparently do so. This have been an unsolved problem for decades and still overlooked by every AI hype cycle to date.

> And even in LLMs, it sounds like you're still seeing training/retraining as single monolithic events which are super expensive. Training is super expensive! New techniques (google LoRA; there are others) are changing that.

Perhaps that is why only Big Tech companies are the ones doing it for training LLMs and the so-called 'AI companies' aren't doing it themselves and still sit on their AI APIs hence why you could only name Google who can afford to foot the bill for training their LLM.

Everyone else has to sit on their APIs just like before.

> I hope you get into what has changed in the past few years...

I think you have struggled to address the other short-comings that both LLMs and fundamentally neural networks still face that I have already outlined.

Until I see a open-source LLM or some other open source solution that matches or surpasses OpenAI's GPT offerings in parameters, explainability, and is small enough in model size to fit on a smartphone then we can discuss about how this AI cycle is worth the hype. Having yet another AI API product with even larger and expensive models sitting behind a SaaS is an indication of another AI bubble.

Very few pieces of software today explain their decisions, and it doesn't seem to be a big problem for users.

Long-range Radio??

I do wish people would check if acronyms don't already have well-established meanings before adopting them.

Also OP (not so much you) is way too caught up in the “chat” aspect - that is the first exciting UX that got popular, but these are much, much more than chatbots. Pretending that they’re human/conscious/in a conversation is fun, but having an invisible partner that knows you and tailors your search results… that’s powerful.

For example, you’ll never have to add “Reddit” again, or at least you’ll only have to tell it once. An LLM can easily identify the kind of questions where you want forum posts, read thousands of posts in a second, summarize all their content, and label each link with other information that helps you decide which threads to read in full.

I can’t wait!

For one, you can't just "hook up" a language model to some other task, or to the web. ChatGPT is specifically built and trained to have good conversations. The fact that it can sort of appear to do other things is a happy coincidence.

To do any of what you want, new algorithms need to be built, and none of that is "easy". And finally, these models take A LOT of cpu time. They are not going to be reading thousands of posts in a second without serious and expensive compute hardware backing it, and that level of compute isn't remotely feasible to give out to individual users.

Even chatGPT, which is doing a fraction of the tasks you are listing, costs millions of dollars worth of hardware a day. The only reason it exists for free is because Microsoft has donated all that time.

They had to pay someone to label data and filter the worst "content" humanity has to offer. Otherwise it would've ended up like numerous other attempts at exposing "AI" to the Internet.

So it also has a huge human cost that OpenAI is not properly accounting for (and another reason why dreaming up potential use-cases for the technology as if it will be miniaturized and become a commodity in the near future takes some wilful ignorance).

But chatgpt is already pretty darn good at summarizing and communicating. By “hook up” I literally just mean feed text from the internet into the prompt followed by whatever you want it to do with it - summarize, rank, whatever. Ignoring the economics for a moment (paid search engines?) and assuming that GPT 3.5 is the very best LLM we’ll ever get for simplicity: would you still say you don’t think tweaked versions of such models would MASSIVELY improve search?

There is also the business model to sort out. Right now search is primarily driven by ads, which I doubt will cover the costs of the sort of ultra-personalization that you're thinking about. Also, reducing the time you spend on a search engine or looking through results will further reduce ad revenue. However, I can see paid search engines perhaps leading to this.

So yes, eventually these models can help improve search, just not in the form that we have today. In a few years the story could well be different. I'm quite interested in seeing how Bing integrates chatgpt technology. They claim that they've created some new model on top of it that somehow links it to search results.

When you ask whether speaking English and using logic can improve the search experience, I wonder what you consider the most important parts of the search experience? I think many people, most of the time, might say that "accurate information" is their highest expectation, with "a pleasant conversation" somewhere below that. Delivering a plausible-sounding and pleasant answer that's completely wrong is... well, that's not a search engine I can depend on, is it?

You're hypothesizing a few things at the end that sound great! It's completely unclear whether any of those things will actually end up happening, so I think the focus on what is available today, with Chat-GPT and Bing, is more apt than a focus on what could be.

And I totally agree that a) a search engine that doesn’t cite its sources is useless, and b) you almost never want to chat with google like it’s a person. So you’re spot on. But the point I was trying to make is that the main use case is in stuff like automated summaries, specialized page rankings, expanding quick informal queries into longer formal-logic ones, etc.

The point is, "accurate information" is hard. Google's solution is snippets and while it might be fine for some cases, it fails terribly for others. There is zero guarantee an AI-based solution would be more precise, but for sure it will be way more confident - just like ChatGPT is.

I think the complaints are more about the "use logic" point than the sources, from my limited understanding, I would not say LLMs currently use logic.

The start of its lengthy response:

“ The use of probabilistic logic models in LLM can lead to more sophisticated and nuanced logical reasoning. These models represent knowledge about the world in a probabilistic form, which allows the LLM to take into account uncertainty and make inferences based on probabilities.

For example, an LLM system trained on a large knowledge base might encounter a question that it has not seen before. Using probabilistic reasoning, the LLM…”

There is a general issue where AIs fail in different ways than humans, and the failures look really dumb to a human. So humans tend to ascribe that dumbness from the human scale. Instead, I’d suggest they just have a dramatically different spider-graph of capabilities to a human, and are overall more capable than the “dumb spreadsheet / parrot” narrative admits. (Definitely not human-level IQ yet, to be clear.)

I question if this something that users need at the scale you're assuming. Wikipedia has existed for 20 years summarizing enormous breadth of human knowledge, with some articles having thousands of human editors. it's a boon for civilization in the way libraries are. But has it disrupted anything besides Microsoft's overpriced Encarta DVD market?

You're putting a lot of faith in computer models to provide accurate, both-sides'ed information on complex topics in a format that amounts to a soundbite.

Can you give me 3 example queries (questions and answers or typical searches) that are clear cut wins for a search engine application?

The nice way of saying grifters.

This is the most accurate description I've even read.

Those autocorrections are still frequently wrong.

That also happens with real people so... A web search also returns wrong answers, because it is not magic. It just searches through all the garbage out there. You just have to be aware of its flaws and limitations... as you do with you fellow humans.

It's not a person. It's software. It shouldn't be wrong just like humans.

Imagine opening your banking app and your total savings is wrong. Would you say, "It's okay, people make mistakes, too!" or would you be pissed that your bank's software is incorrect? Why are we pretending like this is any different?

AI machines and especially neural networks which LLMs are based on still cannot explain themselves and are as transparent as a black-box.

Search is absolutely like asking a random person right now. Whether I get raw hyperlinks back on a page or a chat window, the results are as good as their source data. Garbage in, garbage out.

Google is just as confidently wrong giving me garbage links as chat is as giving me garbage recommendations.

With ChatGPT, correct and incorrect information is presented identically. You have nothing to go on except falling back to web search to fact-check ChatGPT’s output. This can still be useful as a starting point, but a world with only ChatGPT and no web search would be horrible, would overall be a step back compared to a world with only web search and no ChatGPT.

I have seen no indication that they had anybody verify the factual content of answers in the training process, and many indications that they did not.