Right now it's like we're in 2001-2005 for wikipedia. It's probably a good tool for basic, basic, basic beginnings of your work in the classroom, but you're going to get things wrong in surprising ways if you use it without verifying.
Right now it's like we're in 2001-2005 for wikipedia. It's probably a good tool for basic, basic, basic beginnings of your work in the classroom, but you're going to get things wrong in surprising ways if you use it without verifying.
One of the things AI's commonly allow is for people to ask questions without fear of judgement. I've not seen one scowl and call a kid an idiot yet (though I'm sure someone has a jailbreak for that). Having an AI trained to take these kids off the wall (and often based on historical inaccuracies) questions would be a really interesting tool.
Just not the only tool, and not really one that should be fully authoritative in itself.
https://www.rangevoting.org/FeynTexts.html
>The reason was that the books were so lousy. They were false. They were hurried. They would try to be rigorous, but they would use examples (like automobiles in the street for "sets") which were almost OK, but in which there were always some subtleties. The definitions weren't accurate. Everything was a little bit ambiguous – they weren't smart enough to understand what was meant by "rigor." They were faking it. They were teaching something they didn't understand, and which was, in fact, useless, at that time, for the child.
Not to be snarky, but education has come quite far since the 60's.
We are all clearly going mad.
We are all clearly going mad.
Maybe you should reflect on the abuses some humans have received from other humans. For example like your^H their own parents and teachers?
Not all of us have had great lives. People that have been abused tend to abuse others unless someone/something breaks that cycle (and it's almost never about pulling up your own bootstraps).
Search engines have been around for almost 30 years now and they do this job better than spicy autocomplete. I type stupid questions into Google all the time and get good answers. The "AI" version of this involves strapping a search engine onto a language model, ostensibly to summarize results, but in practice there are examples of the language model just lying instead of doing the actual search for you.
I type questions into Google and frequently get misleading or outright incorrect answers directly in their BS summaries.
This is a good point. If you treat a search engine as a language model and vice versa you will run into issues. These issues compound even further for users that decline to scroll down and click the links that search engines return
Some result or tasks are better fitted for LLM. Or you want a bullet point response that Google can’t do anymore.
It's similar with what I call "action AIs", where an AI tries to learn to walk, or race a car, optimally, through a track. It will often repeat mistakes, because short term it gains a higher score and takes time to learn that short term gains, in some cases, harm long term gains.
People are using a hammer to install screws. Technically it works, but that's not the droid they were really looking for.
Chain-of-Verification, Process Supervision, encoder/decoder and a plethora of other models are quickly maturing, AND it’s important to remember the current systems ARE NOT particularly optimized in any way for objective accuracy but instead to carry on conversation. It’s a conversation bot.
It’s also important to understand that most systems out there are building on top of the same AI APIs. There isn’t actually that much diversity in the ecosystem that’s broadly deployed yet, so problems with ChatGPT and Bard are “AI problems” and not limited in scope.
As the market matures, solution diversity will increase dramatically and systems that solve these major challenges will emerge and not necessarily from the incumbents. That’s been the pattern in tech waves of the past and why Apple always takes the wait and see and implement the winning solution in the product strategy so often.
These are early days and it’s important not to see the current generation as anything greatly exceeding the first demonstration level technology wave, hard as that is to imagine. It’s 2000, and we are looking at something like a Nokia 3210 (gpt 3.5) and 3310 (gpt 4) talking about how it has problems.
Yep. It’s not a iPhone yet. And we still think Nokia is going to dominate. And the current systems just have problems that are kinda holding them back… but this is the way technology waves break…
I was there [still am]. Recently, I provided an update to "transistor density," which was then cited by Perplexity.AI when I asked about a specific new processor type (eerie, having been an early adopter for both wiki and LLMs, from a user-perspective).
I'm left wondering "how much an old wiki handle" [account] might be worth, if it is so-readily cited as "leading authority" (when in reality I was just a curious teenager, trying to figure out what made encyclopedia "so special," when wikipedia provides all these linkages FOR FREE).
Half a lifetime ago, and I'm still curious how this whole "open source thing" is going to play out...
There isn't a single general source of truth that we can use without verifying. Human teachers especially aren't such sources.
If you were teaching fourth grade math, instead of assigning a workbook of math problems you'd prefer to tell the kids "Ask GPT to make up math problems" because it's the best tool, so if you could only pick one tool you'd go with that?
If you were teaching history and had the choice of sending kids to the university library to write research papers of having them ask GPT 4 about history, you'd have them just ask GPT 4 about history, because it's the best educational tool?
Bold claim.
Forcing an educator to choose it or a book demonstrates the book is in fact the best education tool unless the educator is going to double down and say they'd teach from a chatbot over a textbook.
But if I did sign up for a college course i would expect a more systematic presentation of the material than "whatever random thing I thought to ask an AI."
Not to mention just the other day I asked Chatgpt 4 accounting questions and it gave the wrong answer, and only interrogating it prompted it to correct itself.
The hardest thing about learning a new concept/idea is to get started. GPT lowers the barrier of entry, and you can use the knowledge you got to tackle other learning tools and fix the parts GPT got wrong.
One major value of the teacher in the classroom is to be able to sense when students are getting lost, and have ways to slow down, re-explain concepts that they missed.
A basic AI sounds like it could soon do a great job of providing the content of a physics textbook, with the adaptability of a one on one teacher.
At one end of the spectrum we have machines that are fast and efficient. Able to store data, search for data and retrieve data as accurately as it was originally stored.
On the other end of the spectrum we have machines that are so creative we can't control the creativity so it lies and is inaccurate.
What's missing is the machine in the center of these two extremes. A machine that can be creative and factually exact at the same time. We're slowly converging on that goal right now as chatGPT can now use bing to look things up.
If I wanted to google something (or bing, whatever), I would have done that. A major draw for me has been that chatgpt was providing a much better experience than search engines.
Now it sometimes feels like a fancy lmgtfy.
We're really speed running development cycles nowadays aren't we?
When did those tools start outputting truth?
It makes no sense to say something can't work when it clearly and obviously does. Most humans would do worse.
Anecdotes are so worthless, in fact, here's mine-- I asked Azure's GPT for Powershell help. After seven regens in which it tried to include a different fictional library, I gave up. So which of us had the "real" LLM experience?
These things are storytellers, not teachers. Sometimes it gets it right. Maybe most of the time. It's convincing enough that unless you're an expert, you'll never guess when it's wrong, and the lies are bespoke for every user so there's never going to be an errata page to document its failures. It will always appear reliable.
I really liked how a recent paper from DeepMind put it - LLMs are just role-playing: https://arxiv.org/abs/2305.16367. This explains so much.
Are you claiming you've always scored 100% on every multiple choice test because you've been "fed the answers"?
What kind of dumb response is that?
In giving it answers, you gave it context to infer the right one. You narrowed the search domain. It does have the same effect on humans, which is why multiple choice tests are easier than others-- when you show up to take the test wholly unprepared, you're looking for the most plausible answers based on the context you're given. Fortune tellers and Clever Hans work the same through interactive reactions.
You take to insult but I'll challenge you again to run your experiment and provide only wrong answers for all of the questions. Bullshit your fortune teller and see what answers it comes back with in the impossible situation you create.
I don't have any evidence of how humans perform on multiple choice tests where all the answers are incorrect and they are not given that option. Do you? Or are you just assuming that they would challenge the context?
https://www.youtube.com/watch?v=FUHkTs-Ipfg
Of all the 300,000 students who took the test, only three reported a problem with the question. So at least for this case, 1/100,000 were able to identify the problem with the question and report it.