if you disagree with that then we'll have to agree to disagree.

Twitter is a far better user experience in that respect, to the point that I actively avoid clicking mastadon links now because they fail so often

Anyone that got in can share what it says?

You do know Mastodon is a federation of servers, right? I predict most people will move off this particular one to a faster one.

incredible what mental hoops people will jump through to disqualify AI!

the reality is that a lot of things have a terrible UI with unorganized data. that's why this tool is so amazing - because it doesn't matter anymore.

A woodworking example is that a planer is great tool that helps you make nice flat surfaces. But, to a certain extent, it's a downstream fix that wouldn't be necessary if a carpenter was using a better overall process. I.e., if their upstream process for cutting/ripping wood made nice flat surfaces to begin with, the awesomeness of the planer becomes moot. (Apologies to the legitimate woodworkers if this analogy is off).

Where tools like GPT becomes invaluable is when you have no control over those upstream processes but still need to get the job done. But leveraging a tool for a downstream fix when upstream fixes are possible is usually less-good approach to creating good systems.

To torture the woodworking analogy, your assumption is that the carpenter has no control over ripping the boards. In some instances that may very well be the case, but there will also the instances where the carpenter does have influence over creating the boards, or even wholesale control over ripping them. In those cases, using a planer to fix poorly ripped boards may not be the best approach.

Even in your examples, yes, you have to work with other teams. "Control" doesn't mean you have dictatorial control. But it does sometimes mean you have build relationships, leverage what you can, and explain the value to those that do have some modicum of control. The idea that we just throw our hands up and jump to workarounds can work, but it's often an excuse for taking the short-term easy at the expense of a better long-term solution. Even when you're using tools as a workaround, you're surrendering control to those tools/developers.

Cost of implementing better process for all carpenters is significantly higher, than all carpenters still using bad process + _one_ AI being able to clean it for carpenter, plumber, translator, developer (you name it, you got it).

Not even entering laziness/corposlowness gardens etc.

"Corposlowness" is just another name for "bad processes". It supports the claim rather than negates it. Using AI to overcome bad bureaucracy makes it a workaround, not an idealized process. What often happens when implementing workarounds rather than good processes is that the workaround can create bloat and waste of its own and overtime, not really fix the problem. Like hiring more administrators for a large organization, they can take on a life of their own, eventually becoming divorced from the problem they were intended to solve.

Again, I'm not saying that AI is misapplied in Tao's case. I'm just cautioning that it's not a panacea for bad processes. In many ways, it can be misused as a band-aid for bad processes, just like creating excess inventory is a band-aid for bad quality control.

There are a couple of reasons why it would be hard to change the upstream process to not necessitate planers. The main one is that logs are typically ripped into boards when the wood is still green, and in the process of drying, boards change shape and dimensions: they bow, cup, warp, and shrink, and you might still need a planer to bring them back to flatness and to desired final thickness.

If you are producing data then exposing it in a nice programmable format is an extra cost and generally provides you no benefit. It usually hurts you, if people stop visiting your site and see fewer of your ads!

This is "really" a problem of incentives. It is usually not possible to capture any of the positive externalities of exposing your data. So maybe we could convince everybody in the world to switch to using different browsers with a native micropayment system; that might incentivize everybody to release all data as clean machine-readable tuples.

What I'm saying is, the phrase "Better UX and data quality" ignores just how hard that solution really is. It turns out training an LLM over most of the internet is _easier_ than global coordination.

I have asked a langchain bot about wikidata ids for specific places, links to the page, to read it and then to answer facts about places and got very good results instead of made up numbers.

Wikidata links to FIPS codes, OSM ids, GeoNames and that gives us an opening to link against the cool datasets from Flickr, Foursquare and others who have created gazetteers.

To me, Semantic Web was dead on arrival because of its UX, but now a semi-smart agent can help us get past the UX problems and jump from plain text to json output.

I needed to gather various statistics on the speakers at the previous ICM

Why did this work need to be done? Are even the people who say they want this data actually going to use it for something productive? Is there something revelational in this data?

If somehow magically from the beginning of computing we had a natural language interface to a computer's operations, we would still have arrived at particular standards/specs for data formats. There would still be something like xml/json/csv. Indeed, I'd wager there would still at a certain point be some kind of high level programming (or otherwise formal) language adopted, to answer the particular clumsiness of natural language itself [1].

Putting aside any issues of reliability, its simply not sustainable (economically, environmentally) to put all our work into this stuff. Even if it does shine with one off stuff like this.

1. https://www.cs.utexas.edu/~EWD/transcriptions/EWD06xx/EWD667...

Rich pro-ai argument.

The idea that everything would work great if only all of our data was structured and easily parseable everywhere just leads me to ask "Do you not interact with humans on a regular basis?"

how naive. how do you know it's right? ah, you have to manually do the calculation anyway to confirm, this is what Tao ended up saying in a reply asking as much.

AI is great, but it's not a silver bullet, since its correctness can never be 100% under the current LLM framework.

You have to do those same confirmation calculations anyway when you use a spreadsheet. In my experience the utility of something like what ChatGPT can do is still unparalleled.

I don't understand how you can hold this position with AI considering it's only the beginning.

I suspect the satire whooshed over your head.

This AI future you're wanting is an Idiocracy-like world where nobody knows how anything works and everything is in decay.

Some of us are just burned out on the hype cycles and prefer not to count our chicks before they hatch.

You're making the same mistake you're accusing them of making - assuming to know the future at the beginning. You're assuming that fixing these issues will prove to be trivial or at least inevitable. Sure, recent progress has been swift, but if you recall, it was damn-near stagnant for decades. Some were even claiming we were in the middle of an "AI Winter" and could not see the spring!

Based on all currently-available evidence, the current techniques that we utilize for generative AI are unreliable, in terms of accuracy of derived facts. It will require either a different or complementary approach to iron that out, or we're going to have to start seeing some _very interesting_ emergent properties from scaling higher. This stuff could show up tomorrow, or it might never show up at all! But the _current_ LLM framework does not look like it can do what we're looking for here, not reliably, certainly not 100% reliably.

Do you imagine a future where machines output data that can be barely read by humans, but can only managed through the help of AI? Honest question.

There are high peaks and troughs in AI buzz right now.

Yes, on the one hand you've the but-can-it-dance crowd.

On the other hand, Terence Tao on GPT-4. I mean, I'm not weird for really expecting the story here either be about GPT4 helping Terence Tao on some difficult newfangled proof, or Tao talking about the math behind large language models. Instead this boils down to

GPT4 even does the work of some of the smartest mathematicians in the world[1]

1: by parsing some web pages and PDFs for their meetings

> 1: by parsing some web pages and PDFs for their meetings

Like that old joke about the guy who impressed people by claiming he had helped a brilliant mathematician solve a problem that had stumped him. And the punchline is something like "yeah, and it only took me a few minutes, all I had to do was replace his timing belt."

I think a lot of people miss that due to being shown in search instances and companion apps.

The hundredth time you do it you're going to be like "why is this so f'in annoying still."

Today's interface to language models is subpar for a lot of applications. Lots of room to improve that. A tool can be both amazing and still be just another step on the road to something truly seamless - just like how now it's "tedious" to use the computer for it instead of mailing/faxing paper forms around and filling out tables by pen and pencil.

If you don't like this tool, don't use it! If you like, it, use it!

Umm, and I want a pony? The world doesn't come in perfectly structured data formats. I was pretty amazed when I pasted in some of my medical lab test results and ChatGPT was accurately able to parse everything out and summarize my data. It worked extremely well.

I think an important question is how much faith we give in the answer (especially with medical data!). There are lots of examples of great uses but also a number of examples of hallucinations and just plain bad summaries. When the stakes are high, the conviction needs to be couched in the risk of it being wrong. We need to be cognizant of the automation-trust factor that sometimes makes us place unwarranted trust in these systems.

In Tao's case, the data was organized already, simply not given to him.

In any case, Tao, per the post, had to manually calculate to confirm that GPT-4 was correct in any case (he implies as much in a comment asking if he checked for correctness).

I'm sure the company that did my medical labs also has my data in a structured format somewhere. So what, should I call them up and demand they give me my data in a spreadsheet? I can go down that useless path, or I can paste my data on ChatGPT and get results in 15 seconds.

Yes, go ahead and send OpenAI all of your HIPAA data.

It's my data. The entire point of HIPAA is that I own the data and I can send it to whomever I want if I decide to. I get value out of it, others may not choose to do it, that's their right. But I'm pretty sure sharing my CBC results is not going to be the death of me.

Consider the time it would take to do manual entry for each of those examples compared with the time it takes to verify that the generated content is correct.

"Are these the correct state and zip codes?" is much faster than typing it by hand. You just ask yourself "is MA the correct state code for Massachusetts? Yep, next" rather than "Massachusetts that's... (lookup) MA, type MA; next that one is MI, already a state code ..." and down the list.

I would be willing to content that GPT will do the list faster and with better accuracy than a human doing the same work (that would also need to be checked for correctness).

Oh dear. Federation at its finest /s

If the instance is too small, it will easily fall over under heavy traffic. If it is too big, it is highly centralized and even then it cannot scale at a maximum of 300,000+ users at the same time.

Eventually, they would all be re-centralizing back to Cloudflare. Once that goes down, all of the biggest Mastodon instances go down at once.

As for GPT-4 in mathematics (LLMs in particular), in goes inline on what I said before. Fundamentally, these LLMs cannot reason transparently, nor can it directly bring up its own mathematical proofs with thorough explanations that allows mathematicians to work with. Only low hanging fruit of summarization of existing text.

If Mr. Tao can see its limitations, surely it puts the hype of unexplainable magical black-box LLMs to rest as being great sophists and bullshit generators.

"... automatically solving multiple challenging problems drawn from high school olympiads."

This combination of GPT and LEAN is the first thing that has finally got me really interested in the potential of LEAN. And LEAN very directly addresses the "bullshit" issue, since it formally verifies whether or not a proof is correct (it's like a compiler like Rust, but for mathematical proofs).

But the problem DID exist, and problems like that are likely to continue to exist for the foreseeable future.

I’m fairly convinced that for many problems (including this), AI is not the best solution but will become the preferred solution. “Best” doesnt get in the way of “good enough” when convenience is at stake (and making a good UX is often very very inconvenient)

I'm in the middle of reading Steve Jobs' very own words, Make Something Wonderful book. Apparently throughout his whole life he frequently mentioned and discussed a lot about providing and creating easy access to the computer because ultimately people do not want to program but want to use computer instead. He did mention about how Morse code was not popular as much of the later telephone technology because people refused to learn and use the unintuitive Morse code even though it only takes 40 hours for average person to learn the entire Morse code. As a trained communication engineers I can very much relate to this because even though we learnt much of the underlying technology of communication that enable the Morse code or the telephone to work across the Atlantic ocean most of us can't be bothered to learn the Morse code and use it if we can help it because the telephone make it redundant and it's counter intuitive to use.

Granted, now we know that telephone or circuit switching in general is a suboptimal in providing and solving the human communication problems and needs. As of now the Internet, packet switching and the multimedia approaches are probably the best solutions but telephone has served us a potent and still one of the best solutions for communication until very recently.

Because real life is messy.

> Why is Latex so hard to work with?

Because people that use latex want to be able to brag they use latex

(just joking for the second one.... unless ?)

This doesn't make sense - the data inherently is centralized. Tao implied as much.

> In this case GPT-4 is used to solve a problem that shouldn't have even been one to begin with.

Friction. These problems shouldn’t exist, but they do anyway, and they’re everywhere. Anything human is inevitably going to be imperfect and messy to some greater or lesser degree, introducing friction into dealing with it. Especially as we produce more and more unstructured or semi-structured data, which AI is particularly good at wrangling. If AI can help us cut through that friction significantly faster and more accurately, that’s a win.

Coworkers, associates, clients, family - everyone sends us funky data. Sometimes it's a PDF of text instead of a text file, or a picture of their computer screen with a paragraph in it, or copy-pasted text with unwanted formatting, etc, etc.

And the point of this article wasn't really about the tedious job of copy/pasting, it was that once the data was ready, instead of hunkering down and having to work through a problem with an unknown number of variables, GPT-4 can drop an answer in seconds flat. Maybe a few more if you have to tweak your prompt a few times.

Part 2 here is that we have a problem to solve, and that's how data is sent and received, and that it all needs to end up in a structural format that's usable by our AI tools.

I am thinking that writing a script that can dump textual data from literally anything I'm looking at so it's AI-ready makes a lot of sense, then we have step 1 taken care of as well for my own personal GPT-solvable situations.

Because the world is not a database. His source were formats meant for Human consumption, not machines. This will never change, people are lazy, greedy, or fear "data leaks", so we will never have a machine-first-format that everyone will use.

I mean, that guy is using latex, others have markdown, or org-mode, or Microsoft Word, those all are not meant for easy scripting. This is why AI will be such a dealbreaker, because it will close this gap, make human-formats to machine-formats when neccessary.

> Out of curiosity, are you sure that GPT did it correctly? If yes, is it because you were able to "spot check" it in a few places? Or have you used GPT enough that you trust it with a task like this? Or is this for some kind of internal use where a few small errors is unimportant, and you only need the broad strokes to be correct?

> Yes, yes, and yes. (There were some obvious checksums, for instance by computing the total number of speakers several different ways.)

Still cool, but the circumstances above aren’t always true. In fact, in a lot of meaningful work, none of them probably are.

If GPT is like an assistant whose work you always have to double check in order to be sure of the validity of the results, that seems to be a pretty big caveat to me. In that case it’s probably better to just write a script whose output you know to be deterministic, even if it takes slightly longer (and the bigger your dataset to verify, the more likely it is that it’ll take less time to write a script than to validate results one by one).

The scary part is if/when companies/bureaucracies/governments/etc start using GPT for all sorts of Important Tasks and skip the validation part because they assume the machine will always get it right.

Sounds like every junior developer fresh out of college I've had to work with. Interns are even worse. Even senior devs mess up from time to time. That's why we do code review and other QA. Trust no one.

Another key difference is that a deterministic script that has determined to be valid will be valid for all subsequent runs, regardless of input size. That is not the case for ChatGPT, which might be correct on some runs and not others, or do well on small input sizes but start messing up when thousands (or more) of data items are involved.

Again, I still think ChatGPT is really cool, but thinking about all these nuances about the nature of the work that is actually being done when you use ChatGPT vs writing a script vs handing it off to an intern seems crucial to the debate.

In this case, AI was the tool used to have a better UX. People build something with use-case X in mind, and use the best tool for that job. Piping that into use-case Y requires some duct tape and plumbing. It turns out that AI is great at that sort of repurposing.

It's great if someone using the best tool for their job is nearly the best tool for the flexible infinity of other use cases, which is what flexible enough duct-tape gets us.

Do you mean build better, specific, UX and normalize data for only this task? Why spend that time, even as a developer of whatever improved app/system you're thinking of, when he can just turn to AI?

I'd rather lean on GPT4's ability to generalize highly specific technical work rather than ruminate on each individual app's lackings and how it could be better than using AI if we just... put more effort into it?

I think the reality is, there are cases where it matters, and cases where it doesn't matter (at least, not very much).

I work on aerospace software systems. I suspect that most software developers would be surprised at the process and rigor involved. That does not mean that humans are flawless; of course not. But there are going to be a lot more hurdles to replacing all of the humans with non-deterministic machines.

There are numerous software components on a modern airplane. How good is good enough? If the overall system works correctly 99% of the time, is that good enough? There are about 16 million flights annually in the United States alone. If 160,000 of them crash, is that good enough?

I mean, like, if Microsoft Word failed to save your document 99% of the time, you might be irked. If Professor Tao's data was only 99% accurate, he probably still has a reasonably good picture of the information he needs.

Regardless of if a human or a machine is writing the code, there are different levels of software criticality. As humans, we don't apply avionics software development process to iOS games because it's not worth the extra time and expense. Likewise, there will be a spectrum of where it makes good enough sense to automate tasks with AI and where it doesn't.

Which is not to dismiss AI! It's amazingly useful technology. But there really are places where above average accuracy is needed, and AI that works great for handling some tasks might not suffice for other tasks. If you truly need all of the results correct, then a nondeterministic neural network is probably not the best path to be on. There are other methods; even other automated methods.

I did this once, a fuel estimation program for 747's, the degree of understanding of the problem required to create something that would pass review was off the scale compared to any other program that I've ever written. It is also the only time that the customer wanted to get it right rather than that they were looking at what it cost (because the savings would pay for the development many times over this was probably less of an issue anyway). I'd love to be able to always work like that.

AI is just that. A UI/UX for any/your data.