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.
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.
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 a 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 over those teams. 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 is often an excuse for taking the short-term easy at the expense of a better long-term solution.
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.
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."
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.
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.
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.
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.
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...
If you don't like this tool, don't use it! If you like, it, use it!
I think a lot of people miss that due to being shown in search instances and companion apps.
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.
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?"
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?