# Is OpenAI’s AI As Smart As A University Student? 🤖

## Метаданные

- **Канал:** Two Minute Papers
- **YouTube:** https://www.youtube.com/watch?v=9JZdAq8poww
- **Дата:** 03.03.2022
- **Длительность:** 9:30
- **Просмотры:** 125,251
- **Источник:** https://ekstraktznaniy.ru/video/13640

## Описание

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📝 The three papers are available here:
Grade school math: https://openai.com/blog/grade-school-math/
University level math: https://arxiv.org/abs/2112.15594
Olympiad: https://openai.com/blog/formal-math/

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## Транскрипт

### Segment 1 (00:00 - 05:00) []

Dear Fellow Scholars, this is Two Minute  Papers with Dr. Károly Zsolnai-Fehér. Today we are going to have a little taste of how  smart an AI can be these days. And it turns out,   these new AIs are not only smart enough to solve  some grade-school math problems, but get this,   a new development can perhaps even take a crack at  university-level problems. Is this even possible,   or is this science fiction? Well, the  answer is yes, it is possible…kind of.    So, why kind of? Let me try to explain. This is OpenAI’s work from Oct 2021. The goal  is to have their AI understand these questions,   understand the mathematics, and reason about a  possible solution for grade-school problems. Hmm,   alright. So, this means that the GPT-3 AI might  be suitable for the substrate of the solution.    What is that? GPT-3 is a technique that can  understand text, try to finish your sentences,   even build websites, and more. So, can  it even deal with these test questions? Let’s see together. Hold on to your  papers, because in goes a grade-school   level question. A little math brain  teaser if you will. And out comes,   my goodness. Is that right? Here, out comes  not only the correct solution to the question,   but even the thought process that led to this  solution. Imagine someone claiming that they   had developed an AI this capable ten years ago.   This person would have been locked into an asylum.    And now, it is all there, right in  front of our eyes. Absolutely amazing. Okay, but, how amazing? Well, it can’t  get everything right all the time. Not   even close. If we do everything right,  we can expect it to be correct about 35%   of the time. Not perfect, not even  close, but it is an amazing step forward. So what is the key here? Well, yes, you guessed  it right. The usual suspects. A big neural network   and lots of training data, the key  numbers are 175 billion model parameters,   and it needs to read a few thousand  problems and their solutions   as training samples. That is a big rocket,  and lots of rocket fuel if you will. But, this is nothing compared to what is to come.   Now, believe it or not, here is a followup paper   from just a few months later, January 2022  that claims to do something even better.    This is not from OpenAI, but it piggybacks on  OpenAI technology as you will see in a moment.    And this work promises that it can solve  university-level problems. And when I saw   this reading the paper, I thought…really? Now,  grade school materials, okay, that is a great   leap forward, but solving university-level  math exams? Now we’re talking! That’s where   the gloves come off. I am really curious to see  what this can do! Let’s have a look together. Some of these brain teasers smell very  much like MIT to me. Surprisingly short   and elegant questions, that often seem much  easier than they are. However, all of these   require a solid understanding of fundamentals,  and sometimes even a hint of creativity.    Let’s see. Yes! That is indeed right. These are  MIT introductory course questions. I love it. So,   can it answer them? Now, if you have been holding  on to your papers, now, squeeze that paper, and   let’s see the results together…my goodness. These  are all correct. Flying colors! Perfect accuracy,   at least on these questions. This is swift  progress in just a few months. Absolutely amazing. So, how is this black magic done? Yes,  I know that’s what you’re waiting for,   let’s pop the hood, and look inside together.    Um-hm. Alright! Two key differences  from OpenAI’s GPT3-based solution. Difference number one. It gets additional  guidance. For instance, it is told what topic are   we talking about, what code library to reach out  for, and what is the definition of mathematical   concepts, for instance, what is a singular value  decomposition. I would argue that this is not   too bad, students typically get taught these  things before the exam too. In my opinion, the

### Segment 2 (05:00 - 09:00) [5:00]

key is that this additional guidance is done in an  automated manner. The more automated, the better. Difference number two. The  substrate here is not GPT-3,   at least, not directly, but Codex. What is  that? Codex is OpenAI’s GPT language model   that was fine-tuned to be excellent at one  thing. And that is, writing computer programs,   or, finishing your code. And as we’ve seen in  a previous episode, it really is excellent. For   instance, it can not only be asked to explain a  piece of code, even if it is written in assembly.    Or, create a pong game in 30 seconds. But,  we can also give it plain text descriptions   about a space game, and it will write it.   Codex is super powerful. And now, it can be   used to solve previously unseen university-level  math problems. Now that is really something. And, it can even generate a bunch of  new questions, and these are bona fide,   real questions. Not just exchanging the  numbers, the new questions often require   completely different insights to solve  these problems. A little creativity I see!    Well done little AI! So, how good are these? Well,  according to human evaluators, they are almost   as good as the ones written by other humans. And  thus, these can even be used to provide more and   more training data for such an AI. More fuel for  that rocket. And, good kind of fuel. Excellent. And, it doesn’t end there, in the meantime,  as of February 2022, scientists at OpenAI   are already working on a followup paper that  solves no less than high-school mathematical   olympiad problems. These problems require a solid  understanding of fundamentals, proper reasoning,   and often even that is not enough. Many of these  tasks put up a seemingly impenetrable wall,   and climbing the wall typically requires a real  creative spark. Yes, this means that these can   get quite tough. And their new method is doing  really well at these. Once again, not perfect,   not even close, but it can solve about 30 to 40%  of these tasks, a that is a remarkable hit rate. Now we see that all of these works are  amazing, and they have their own tradeoffs.    They are good and bad at different things and  have different requirements. And most of all,   they all have their own limitations. Thus, none  of these works should be thought of as an AI that   just automatically does human-level math. However,  what we now see is that there is swift progress   in this area, and amazing new papers are popping  up not every year, pretty much every month.    And, this is an excellent place to apply The  First Law Of Papers, which says that research is   a process. Do not look at where we are, look at  where we will be two more papers down the line. So, what would you use this for? Please let me  know in the comments below, I’d love to hear   your ideas. And also, if you are excited by this  kind of incredible progress in AI research, make   sure to subscribe and hit the bell icon to not  miss it when we cover these amazing new papers. Thanks for watching and for your generous  support, and I'll see you next time!