Ilya Sutskever | The future of AGI will be like what you see in the movies

Summary

这段视频剧本探讨了神经网络的复杂性和其在人工智能领域的应用。讨论了神经网络难以精确解释的特性，以及这种特性如何成为构建真正智能的潜在优势。剧本还回顾了神经网络的发展历程，包括早期的挑战和近年来的快速发展。此外，还讨论了OpenAI的成立初衷，以及对未来人工智能技术，特别是在编程、法律和其他白领职业中应用的展望。

Takeaways

• 🧠 神经网络的编写与传统编程不同，它涉及在循环内编写复杂方程，运行后难以精确理解其功能，这与神经网络的不可解释性相关。
• 🤖 神经网络的不可解释性可能是其特性而非缺陷，因为真正的智能本身就难以理解，正如人类自身的认知功能一样。
• 📈 神经网络的成功部分归因于它们在数学上的难以推理，这与早期AI领域的其他方法形成对比。
• 🕵️‍♂️ 早期对神经网络的研究者需要长期坚持，因为当时AI领域并不看好神经网络，直到2010年左右才开始受到关注。
• 🚀 神经网络的潜力被重新认识，随着计算能力的提升，人们对其潜力的期待也随之增加。
• 🌐 OpenAI的成立是出于将科学与工程紧密结合的愿景，以及对AI技术潜在影响的深刻理解。
• 🛡️ OpenAI的目标之一是确保AI技术的安全和有益使用，同时关注政策和伦理问题。
• 🔮 神经网络的未来发展不仅关乎技术进步，还涉及解决由AI带来的挑战和问题。
• 🔢 神经网络模型的扩展性表明，随着计算能力的增加和数据集的扩大，模型的性能也会随之提高。
• 💡 神经网络的泛化能力是其核心优势之一，但当前仍需大量数据来补偿其泛化能力的不足。
• 🎨 神经网络在创造性任务上的表现出乎意料，这可能与它们的生成模型特性有关，这些模型在艺术创作方面显示出巨大潜力。

Q & A

为什么作者认为神经网络的难以理解并不是一个缺陷，而是一个特性？

作者认为，如果我们想要构建智能，那么它不应该简单易懂。正如我们无法解释自己如何进行认知功能，如视觉、听觉和语言理解一样，如果计算机能够产生同样难以理解的对象，这可能意味着我们正走在正确的道路上。

作者在什么时期开始对神经网络产生兴趣，并与Jeff Hinton合作？

作者在2000年代初开始对神经网络感兴趣，并在2003年开始与Jeff Hinton合作。

为什么作者认为神经网络的成功部分是因为它们难以用数学来推理？

作者认为，神经网络之所以成功，部分原因是因为它们难以用数学来推理，这使得它们能够避免陷入数学证明的陷阱，专注于实际的进展。

作者对于人工智能未来的希望是什么？

作者希望在未来，我们不仅能构建强大且有用的AI，而且能够实现通用人工智能（AGI），并确保它能够被用来解决大量问题并创造出许多令人惊叹的应用。

为什么作者决定离开Google并创立OpenAI？

作者离开Google并创立OpenAI的动机之一是他认为将科学和工程融合在一起是AI发展的正确方式。另一个动机是他对AI技术有了更成熟的认识，希望建立一个能够意识到AI带来的挑战并致力于解决这些问题的公司。

OpenAI的初始愿景和目标是什么？

OpenAI的初始愿景是将科学和工程融合在一起，消除两者之间的界限，以科学的思想来指导工程实践，以工程的严谨性来推动科学发展。同时，作者希望OpenAI能够意识到AI的复杂性，并致力于使AI安全、有益。

为什么作者认为将科学和工程结合在一起是重要的？

作者认为，随着AI领域的成熟，小规模的调整已经不足以推动实质性的进步。将科学和工程结合在一起可以确保科学发现得到精心的工程实施，同时确保工程实践得到科学思想的指导。

为什么作者认为数据的可用性对于神经网络的性能至关重要？

作者指出，神经网络需要大量的数据来训练，以便更好地泛化。在数据丰富的领域，如语言处理，神经网络可以取得很好的效果。但在数据较少的领域，如法律，神经网络可能无法达到同样的性能水平。

作者如何看待神经网络在编程领域的应用，例如Codex模型？

作者对Codex模型感到兴奋，因为它展示了神经网络能够从自然语言转换到代码，这是AI领域的一个重要进步。Codex模型能够理解自然语言描述的需求，并生成可运行的代码，这可能会改变编程职业的性质。

为什么作者认为神经网络在创造性任务上的表现可能优于简单机器人任务？

作者认为，神经网络在创造性任务上的表现可能优于简单机器人任务，因为创造性任务如图像和文本生成有大量的数字数据可供学习，而机器人任务受限于现实世界中的数据收集。

作者如何看待神经网络的泛化能力？

作者认为，神经网络的泛化能力是其核心优势之一。泛化能力使得神经网络能够在面对新的、不同于训练数据的情况时做出正确的反应。然而，这种能力尚未达到人类水平，因此需要大量的数据来补偿。

作者对神经网络在未来的发展有哪些期望？

作者期望神经网络能够继续提高其泛化能力，减少对大量数据的依赖。他还期望神经网络能够更好地理解和生成代码，从而改变编程和其他白领职业的性质。

为什么作者认为神经网络的泛化能力对于创造性应用特别重要？

作者认为，创造性应用特别适合神经网络，因为生成模型在机器学习中扮演着核心角色，它们生成新的、合理的数据的方式与艺术创作过程有相似之处。

Outlines

00:00

🤖 神经网络的不可解释性与智能发展

第一段主要讨论了神经网络与传统编程的区别，强调了神经网络的复杂性和不可解释性。作者认为，神经网络的这种特性并不是缺陷，而是其作为智能系统的一部分，反映了人类智能的复杂性。此外，提到了作者在2000年初对神经网络产生兴趣，并与Jeff Hinton合作，以及他对神经网络未来潜力的期待和对AI领域发展方向的早期看法。

05:02

🌐 AI技术的双刃剑特性及其全球影响

第二段中，讨论了AI技术潜在的积极影响和挑战，强调了AI的复杂性，以及它可能带来的问题和对人类生活的改善。作者表达了对AI技术未来发展的深刻认识，包括对政策和技术安全性的考虑，以及AI技术在全球范围内的分布和应用将如何定义未来几十年的世界格局。

10:05

🔬 科学与工程的融合以及AI的未来发展

第三段讲述了作者创立OpenAI的初衷，包括将科学与工程融合以推动AI发展，以及对AI技术潜在影响的深思熟虑。作者认为，科学与工程的紧密结合是AI领域成熟的标志，并且对于如何使用AI技术解决挑战和政策问题有着清晰的愿景。

15:08

📈 深度学习的扩展性与数据的重要性

第四段深入探讨了深度学习模型的扩展性，特别是随着计算能力和数据量的增加，模型性能的提升。作者指出，尽管有些人担心我们已经达到了深度学习的极限，但历史表明这种担忧往往是短视的。同时，作者也提出了数据量对于模型性能的影响，尤其是在数据较少的领域，如法律领域，AI的发展可能会面临挑战。

20:08

🛠️ Codex模型：从自然语言到代码的转变

第五段讨论了Codex模型，这是一个大型的GPT神经网络，它被训练来预测代码中的下一个词，而不是文本。作者强调了这种模型的实用性和创新性，以及它如何改变编程领域。Codex模型能够将自然语言转换为机器可执行的代码，这不仅展示了AI在编程领域的潜力，也预示着编程职业未来的变革。

25:08

🎨 AI在创意领域的应用及其对社会的影响

最后一段探讨了AI在创意领域，如艺术和写作中的应用，以及这些应用对社会的潜在影响。作者提到，尽管人们普遍认为简单的机器人任务将是自动化的首批目标，但实际上创意任务受到了较大的影响。AI在生成艺术作品和文本方面取得了显著进展，这可能会对社会经济和职业结构产生深远的影响。

Keywords

💡神经网络

神经网络是一种模拟人脑神经网络结构和功能的数学模型，用于处理复杂的数据模式。在视频中，提到神经网络的编写方式与传统编程不同，它们通过循环中的复杂方程式进行训练，其结果难以精确理解，这与神经网络的不透明性有关，但也是其强大之处。

💡解释性

解释性指的是模型或系统的可理解性，即我们能够理解模型是如何做出特定决策的。视频中提到，神经网络的不解释性不是缺陷，而是其特性之一，类似于人类智能的复杂性，我们无法完全解释我们的认知功能。

💡深度学习

深度学习是机器学习的一个子领域，它使用多层神经网络来模拟复杂的认知过程。视频中提到深度学习在数学上的难以推理性，以及它在AI领域的成功，这与深度学习模型的复杂性和强大能力有关。

💡Transformer

Transformer是一种深度学习模型架构，特别适用于处理序列数据，如文本和语音。视频中提到了GPT模型和Transformer架构，强调了它们在语言模型和预测任务中的有效性。

💡预测

在机器学习中，预测是指模型根据输入数据预测未知结果的能力。视频中讨论了预测与理解之间的联系，以及如何通过预测来提高模型的理解能力。

💡理解

理解通常指对信息或概念的深入认识。视频中提到，如果模型能够对接下来的内容做出很好的预测，那么它就具有一定程度的理解，即使这种理解并不完美。

💡数据集

数据集是用于训练和评估机器学习模型的数据集合。视频中提到了数据集在机器学习中的重要性，以及在特定领域（如法律）中数据的稀缺性可能限制了模型的性能。

💡计算能力

计算能力指的是执行计算任务的硬件和软件的能力。视频中讨论了随着计算能力的提升，深度学习模型的性能也随之提高，这是推动AI发展的关键因素之一。

💡Codex

Codex是一种大型语言模型，专门训练用于生成代码。视频中提到Codex能够将自然语言转换为机器可执行的代码，这展示了AI在编程领域的潜力和应用。

💡AGI（通用人工智能）

通用人工智能指的是具有广泛认知能力的人工智能，能够像人类一样在多个领域内学习和应用知识。视频中提到了对AGI的追求，以及它在未来可能带来的广泛应用和影响。

💡创新

创新是指创造新的或改进现有的想法、方法或产品。视频中提到了在数据不足的情况下，需要通过创新的方法来提高模型的性能，这包括改进算法和提高数据生成的效率。

💡泛化能力

泛化能力是指模型在未见过的数据上的表现能力。视频中讨论了神经网络的泛化能力，以及如何通过大量数据训练来提高这种能力，使其在新情境中也能做出正确的预测或决策。

💡创造性任务

创造性任务涉及生成新颖和原创的内容，如艺术、写作或编程。视频中提到，尽管人们可能认为简单的机器人任务更容易自动化，但实际上创造性任务正受到AI的显著影响，这与AI在生成新内容方面的能力有关。

Highlights

神经网络与传统编程方式不同，通过循环中的复杂方程式来实现，难以精确理解其功能。

神经网络的不透明性可能是其特性而非缺陷，类似于人类智能的复杂性。

作者在2000年代初对神经网络产生兴趣，并在2003年开始与Jeff Hinton合作。

AI领域曾长期处于探索阶段，需要大量坚持和毅力。

神经网络之所以成功，部分原因是它们在数学上的难以推理性。

OpenAI的创立初衷是将科学和工程融合，以推动AI发展。

AI技术的发展需要同时关注技术进步和安全性、政策等方面。

AI的发展将深刻影响国家间的力量对比和世界的未来走向。

跨学科融合是创新的重要途径，如苹果公司硬件与软件的结合。

GPT模型展示了深度学习模型在工程上的极致要求和科研上的创新需求。

预测能力与理解能力紧密相关，好预测需要一定程度的理解。

Transformer架构和模型规模的扩大是GPT模型成功的关键。

数据量的多少将影响深度学习模型在特定领域的应用效果。

深度学习历史上每年都有声音认为达到了极限，但每年都有新突破。

Codex模型能够将自然语言转换为代码，展示了AI在编程领域的潜力。

AI的发展可能首先影响创意产业，而非简单重复性劳动。

神经网络的泛化能力是AI进步的基础，但当前仍需大量数据支持。

生成模型在艺术创作中的应用，展示了AI在创意领域的潜力。

Transcripts

because it's a very different way of

writing code normally you write code and

you can kind of think it through and

understand yep whereas a neural network

it's this you write an equation a

complicated equation inside a loop and

then you run the loop and a good luck

figuring out what it does precisely and

that connects to neural Nets not being

interpretable but it could also argue

that the difficulty of understanding

what neural networks do is not a bug but

it's feature

like we want to build

intelligence intelligence is not simple

to understand we can't explain how we do

the cognitive functions that we do how

we see how we hear how we understand

language so therefore if we can get if

computers can produce objects that are

similarly difficult to understand not

impossible but similarly difficult it

means we're on the right track and so

all those things helped me uh converge

on neural networks fairly early on

yeah what year was it when you sort of

like remember initially getting excited

about neural networks and being pretty

convicted like early 2000s I started

working with Jeff Hinton in 2003 yeah so

quite a while ago now long before I mean

obviously the craze kind of started

around 2010 and so there was a there's a

good so like I think this is a common

theme whenever you look at sort of any

uh anybody who works like in in any sort

of field that becomes very big but

there's a long stretch of like you know

wandering in the desert maybe is one way

to put it yeah I mean definitely lots of

perseverance is required because you

don't know how long the how long you

want to stay in the desert you just got

to endure yeah and that's very helpful

and did you expect like I mean obviously

today neural networks do some pretty

incredible things like do did did you

expect back in 2003 or early 2000s that

like in your lifetime you would see sort

of the the things that we're seeing now

with AI machine

learning I was hoping but I did not

expect it back

then the field of AI was on the wrong

track it was in a mindset of rejection

of neural networks right and the reason

for that is that neural networks are

difficult to reason about mathematically

while other stuff you can prove theorems

about and there is something very

seductive and dangerous about proving

theorems about things y because it's a

way to to Showcase your skill but it's

not necessarily aligned with what makes

the most progress in the field but I

think that neural networks are as

successful as they are precisely because

they're difficult to reason about

mathematically and so anyway my earlier

hope was to Simply to convince the field

that they should work on neural networks

rather than the other stuff that they

were doing yeah but then when computers

started to get fast then my my level of

excitement and about their potential has

increased as well yeah and so what what

are your aspirations today like what do

you what is the like in your lifetime

what's the thing you I mean I think it's

obvious from the uh open ey Mission set

but yeah I mean exactly right so now now

thep now the hopes are much larger now I

think we can really try to build not

only really powerful and useful AI but

actually AGI make it useful make it

beneficial use it to solve and make it

so that it will be used to solve a large

number of problems and create lots of

amazing applications that's that's what

I'd like that's what I hope to see

happen yep

um and then you know obviously along the

way you had been doing a lot of uh a lot

of This research and doing a lot of

groundbreaking work at Google and then

you sort of left and started open AI

with Sam mman and and Greg Brockman and

a bunch of others um what was kind of

the what were your kind of goals with

starting opening eye at the outset what

was what was sort of like the initial um

conception or the initial vision and um

and and what did you hope to accomplish

by by starting sort of a new lab there

were multiple motivations on my for

starting open AI so the first motivation

was that I felt that the way to make the

most progress in AI was by merging

science and engineering into a single

hole into a unit to make it so that

there is no distinction or as little

distinction as possible between science

and engineering so that all the science

is in is infused with engineering

discipline and careful execution and all

the engineering is infused with the

scientific ideas and the the reason for

that is because the field is becoming

mature and so it is hard to just do

small scale tinkering without having a

lot of engineering skill and effort to

really make something work so that was

one motivation I really wanted to have a

comp a company that will be operating on

this principle another motivation was

that I came to

see AI technology in a more sober way I

used to think that AI will just be this

endless good and now I see it in a more

complex way where I think there will be

a lot of truly incredible inconceivable

applications that will improve our lives

in dramatic ways right but I also think

that there will be challenges I think

that there will be lots of problems that

will be posed by the misapplication of

AI and by its peculiar properties that

may be difficult for people to

understand stand and I wanted a company

that will be operating with this

awareness in mind and that will be

trying to address those challenges by

you know as best as possible by not only

working on advancing the technology but

also working on making it safe and also

working on the policy side of things as

much as is rational and reasonable to

make the whole be as useful and as

beneficial as possible totally and I

think something we agree on I mean I

think one thing that is very obvious to

me is that AI is something that's going

to you know which countries have access

to AI technology and the ways in which

they use them are going to Define how

the world plays out over the course of

the next few decades like it's just I

think that's how uh that's the path

we're on as a as a world that's right

among many other things right right and

you know I and I this thing that you

mentioned around sort of bringing

together the science and engineering I

think it's quite it's uh it's quite

profound I think for a few reasons right

because like one is that um uh you know

first of all I think a lot of the the

best the most incredible Innovative

things happen often times from sort of

blurring the lines between disciplines

like apple is one of the best examples

where from the very beginning they were

always like Hey we're blending hardware

and software and that's that's our

special sauce and obviously it's uh

produced some incredible things and I

think a lot of other research Labs you

know they operate in very sort of

scientists tell the engineers what to do

mindset which is counterproductive

because you really need to understand

both very well to understand what the

what kind of the limits of the

technology are yeah that's right and on

that point you may even say isn't it

obvious that the science and the

engineering should be together and on

some level it is but it just so happens

that historically it hasn't been this

way there's a certain kind of taste like

empirically it has been the case in the

past less so now that people who

gravitate to research would have a

certain taste that would also make them

less drawn to engine engineering and

vice versa and I think now because

people are also seeing this reality on

the ground that to do any kind of good

science you need a good engineering then

people then you have more and more

people who are strong in both of these

axes totally yeah and I think that you

know uh Switching gears a little bit to

to kind of the GPT models I this is a

great illustration right because the GPT

models are impossible without incredible

engineering like the that's sort of um

uh and and but yet they still require

novel research they still require novel

science to be able accomplish and they

they've obviously been some of the

biggest breakthroughs in the field of AI

as of late and sort of um blown open

many people's imaginations about what AI

can accomplish or at least increase

people's confidence that AI can

accomplish incredible things you know

I'm kind of curious about uh originally

when when at opening ey when you guys

were you've been working on these

language models for some time what were

the original sort of research

Inspirations behind it and what were the

original sort of um things that led you

all to say hey this is something that's

worth working on worth scaling up worth

continuing to double down on so there'

have been

multiple lines of thinking that led us

to convergent language

models there has been an idea that we

believed in relatively early on that you

can somehow link understanding to

prediction and specifically to

prediction of

whatever data you give to the to the

model where the idea is well let's let's

let's work out an example so before

before diving into the example I'll I'll

start with the conclusion first the

conclusion is that if you can make

really good guesses as to what's going

to come next you can't make it perfectly

it's impossible but if you can make a

really good guess you need to have a

meaningful degree of understanding you

know in the example of of a book suppose

that you read a book and it's a mystery

novel and in the last chapter all the

pieces are coming together and there is

a critical sentence and you start to

read you read the first word and the

second word now you say okay the

identity of some person is going to be

relieved and your mind is honing you

know like it's either this person or

that person you don't know which one it

is now maybe someone who read the book

and thought about it very carefully says

you know I think it's probably this

person maybe that but probably this so

what this example goes to show that

really good

prediction is connected to understanding

and this kind of thinking has led us to

EXP experiment with all kinds of

approaches of hey can we predict things

really well can we predict the next word

can we predict the next pixel and study

their their properties and through this

line of work we were able to get to we

did some work before the gpts before

Transformers before the Transformers

were

invented and with um something that we

call the sentiment neuron which is a

neural net which was trying to predict

the next word the next sorry the next

character in reviews of Amazon

products and it was a small neural net

because it was maybe four years ago but

it did prove the principle that if you

predict the next character well enough

you will eventually start to discover

the semantic properties of the text and

then with the gpts we took it further we

said okay well we have the Transformer

it's a better architecture so we have a

stronger effect and then later there

realization that if you make it larger

it will be better so let's make it

larger and it will be better yeah I mean

there's there's a lot of uh there's a

lot of great nuggets and what you just

mentioned right I think first is the

Elegance of this concept which is like

hey if you get really good at predicting

the next whatever get really good at

prediction you get that obligates you to

be good at all these other things if if

you're really good at that and it's it's

you know I think it's probably like

under underrated how um that required

some some Vision because it's like early

on you know you try to get really good

at predicting things and you know you

got the sentiment NE on which is cool

but it's like that's like a it's like a

a blip relative to what we obviously

have seen with the large language models

and so that I think is significant and I

think the other significant piece is um

where you just mentioned which is kind

of um scaling it up right and uh I think

you know you guys had had uh released

this paper about this kind of like a

scaling laws of what you have found as

you scaled up um compute data model size

sort of in concert with one another but

I'm I'm kind of curious like what's the

um obviously there's there's some

intuition which just like hey scaling

things up um is good and you see you see

great behaviors what's kind of your

intuition behind um sort of if you think

from now over the next few the next few

years or even the next few decades like

what what does scaling up mean why is it

likely to continue resulting in in great

results and and um what do you think the

limits are if any I think two the two

stat two statements are true at the same

time on the one hand it does look like

our models are quite large can we keep

scaling them up even further can we keep

finding more data for the scale up and I

want to spend a little bit of time on

the data question because I think it's

not obvious at all yeah

traditionally because of the roots of

the field of machine learning because of

the roots of because the field has been

fundamentally academic and fundamentally

concerned with discovering new meth

methods and less with the development of

very big and powerful systems the

mindset has been someone

builds someone creates a fixed

Benchmark so a data set of a certain of

a of a certain of certain shape of

certain characteristics and then

different people can compare their

methods on this data set but what it

does is that it forces everyone to work

with a fixed data set yep the thing with

the the gpts have shown in particular is

that scaling requires that you increase

the compute and the data and tend them

at the same time and if you do this then

you keep getting better and better

results and in some domains like

language there is quite a bit of data

valuable in other maybe more specialized

subdomains the amount of data is a lot

smaller and that could be for example if

you want to have an automated lawyer so

I think

your big language model will know quite

a bit about language and it will be able

to converse very intelligently about

many topics but it may perhaps not be as

good at being a lawyer as we'd like it

will be quite formidable but will it be

good enough so this is unknown because

the amount of data there is smaller but

any time where data is abundant then

it's possible to apply the Deep the

magic deep learning formula and to

produce these

increasingly good and increasingly more

powerful model

and then in terms of what are the limits

of scaling so I think one thing

that's notable about the history of deep

learning over the past 10 years is that

every year people said okay we had a

good run but now we've hit the limits

and that happened year after year after

year and so I think that I think that we

absolutely may hit the limits at some

point but I also think that it would

be unwise to bet against deep learning

yeah you know there's a number of things

I want to dig in here um because they're

they're all pretty interesting one is is

this this um just I think this you you

certainly have this mental model um that

I think is is is quite good which is

kind of like hey uh Mo's law is this

incredible this an incredible accelerant

for everything that we do right and the

more that there's Mor's law for

everything you know Mor's law for

different inputs that go into the deep

into the machine learning life cycle you

know we're just going to like push all

these things to the map and we're going

to see just incredible performance and I

think is significant because as you

mentioned about this data point it's

like hey if we if we get more efficient

at at we get more efficient at compute

which is something that's happening we

get more efficient at uh producing data

or finding data or or generating data we

get more effic obviously there's more

efficiency out of the algorithms you

know all these things are just going to

keep enabling us to do the next

incredible thing and the next incredible

thing the next incredible thing um so

first I guess like do like I we've

talked about this a little bit before so

I know you agree with that but like how

what do you think is

um where do you think like is there any

flaw are there any flaws to that logic

what would you be worried about in terms

of how everything will scale up over the

next few years I mean I think I think

over the next few years I don't have too

much concern about Contin continued

progress I think that we will we will

have faster computers we will find more

data and we'll train better models I

think that is I don't see to I don't see

particular risk there I think moving

forward we will need to start being more

creative about okay so what do you do

when you don't have a lot of data can

you somehow intelligently use the same

compute to compensate for that lack of

data and I think those are the questions

that we and the the field will need to

to Grapple these to continue our

progress yeah no and I think this point

about data the other thing I wanted to

touch on because this is this is

something obviously at scale that we're

we focus on and I think that the large

language models thankfully because you

can leverage the internet really like

all the fact that like all this dat has

you know existed and been accumulating

for a while you can show some pretty

incredible things in all new domains you

need efficient ways to to generate lots

of data right and I think that there's

there's this whole question was like how

do you make it so that you know each

ounce of human effort that goes into

generating some data produces as much

data as possible um and I think that

like something that we're passionate

about that I think we've talked a little

bit about is like how do you get like a

mors law for data right how do you get

you know more and more efficiency out of

like a human effort in producing data

and that might require novel new um

paradigms but uh is something that I

think is required for in this lawyer for

example uh that you mentioned like we

have a pretty finite set of lawyers how

do we get those lawyers to produce

enough data so you can create some great

legal uh legal AI the choices that we

have is either improve our methods so

that we can do more with the same data

or do the same with less data and the

second is like you say somehow increase

the efficiency of the

teachers yep and I think both will be

needed to make the most progress yeah

well it's kind of like you know I I

really think Mor's law is instructive

right like to get these chips performing

better people try all sorts of random

crap and then the end output is that you

have you have chips that have more

transistors right and I think this like

if we think about is like do we have

models that perform better with like

certain amounts of data or certain

amounts of of teaching um how do we how

do we make that go

yeah I mean I think I'm I'm sure that

there will be ways to do that I mean for

example if you ask the human teachers to

help you only in the hardest cases I

think that will allow you to move faster

uh I want to switch gears to uh one of

the offshoots of the large language

model efforts which is particularly

exciting especially to me as an engineer

probably most uh people who spend a lot

of time coding which is codex uh which

demonstrated some pretty incredible

capabilities of going from uh sort of

natural language and to code and sort of

being able to to uh interact with with a

program in in a very novel New Way um

you know I'm kind of curious for you

what what excites you about this effort

what do you think is the what do you

think are the reasonable expectations

for what codex and codex like systems

will enable in the next few years what

about far beyond that and and uh

ultimately why are you guys so excited

about

it for for some context codex is pretty

much a large GPT neural network that's

trained on code instead of training to

predict the next word in text it's

trying to predict the next word in code

the next I guess token in code and the

thing that's cool about it is that it

works at

all like I don't think it's self-evident

to most people

that it would be possible to train a

neural

net in such a way

so that if you just give it some

representation of text that describes

what you want and then the neural

network will just process this text and

produce code and this code will be

correct and it will run and it's

exciting for a variety of reasons so

first of all it is useful it is new it

shows

that I'd say when it code has been a

domain that hasn't really been touched

by AI too much

even though it's obviously very

important and it touches on aspects

where AI has been you know today's AI

deep learning has been perceived as weak

which is reasoning and

carefully laying

out plans

and not being

fuzzy and so so it turns out that in

fact they can do a quite quite a good

job here and like one analogy one

distinction between between codex and

language models is that the the Codex

models the code models they allow you to

they in effect they can control the

computer it's like they have the

computer as an actuator and so that

makes them much more it greatly expand

it it makes them much more useful you

can do so many more things with them and

of course we want to make them better

still I think they can improve in lots

of different ways those those are just

the preliminary code models

I expect them to be quite useful to

programmers and especially in areas

where you need to know random apis

because these neural networks they so

one thing that I think is small

digression the GPT neural networks they

don't learn quite like

people a person will often have somewhat

narrow knowledge in great depth while

these neural networks they want to know

everything that exists and they really

try to do that so their knowledge is

encyclopedic it's not as deep it's

pretty deep but not as deep as a

person and so because of that these

neural networks in their in the way they

work today they complement people with

their breaths so you might say I want to

do something with a library I don't

really know it could be some existing

library or maybe the neural network had

read all you know the code of all my of

all my colleagues and it knows it knows

what they've written and so I want to

use some Library I don't know how to use

the network will have a pretty good

guess of how to use it youd still need

to make sure that what it said is

correct because such is its level of

performance today you cannot trust it

blindly especially if the code is

important for some domains where it's

easy to undo anything that it writes any

code that it writes then I think you can

trust it just fine but if you actually

want have real code you want to check

it but I expect that in the future those

models will continue to improve I expect

that the neural network that the code

neural networks will keep getting better

and I think the nature of the

programming of the programming

profession will change in response to

these models I think that in a like in a

sense it's a it's a natural

continuation

of how in in in the software engineering

World we've been using higher and higher

level programming languages at first

people wrote assembly then they had

foron then they had C now we have python

now we have all these amazing python

libraries that's a layer on top of that

and now we can be a little bit more imp

precise we can be a little bit more

ambitious and the model the neural

network will do a lot of the work for us

and I do think that I should say I

expect something similar to happen

across the board in lots of other white

color professions as well you know

there's if you think about the economic

impact of AI there's been an inversion I

think I think there's been a lot of

thinking that uh maybe simple robotics

tasks will be the first you know the

first ones to be hit by automation but

instead we are finding that the creative

tasks counterintuitively they seem to be

affected quite a bit if you look at the

generative neural networks in the way

you generate images now now it's you can

find it on Twitter all kinds of stunning

images being generated ated you know

generating cool text is happening as

well but the images are getting most of

the attention and then with things like

code things like a lot of writing tasks

this is the uh White Collar tasks they

are also being affected by these AIS and

I do expect that Society will change as

progress continues to make Society will

change and I think that it is important

for economists and people who think

about these questions to pay pay careful

attention to these Trends so that as

technology continues to improve there

are good ideas in place to like in

effect to to be ready for this

technology yeah there's a number of like

really again uh interesting nuggets in

there I think one is that um I think

like the one of the big Ideas behind

codex or codex like models right is that

you go from being able to go from human

language to machine language right and

and you kind of mention like oh all of a

sudden the machine is an actuator and if

you think about like I think many of us

when we think about AI we think about

like the Star Trek computer you know you

can just ask a computer and it'll do

things you know that's that this is this

is a key enabling step right because if

if all of a sudden you can go from how

we speak how human speak to things that

a machine can understand then you like

bridge this like key translation step so

I think that's super interesting you

know another thing that this inversion

that you just mentioned about is is

super interesting because I think that

you know one of the things that um my

beliefs on this is like hey this is the

reason that some things have become much

easier than others you know it's all a

product of availability of data right

there's some areas where we've had there

just exists lots and lots of Digital

Data that you can kind of suck up into

the algorithms and it can do quite well

and then in things like robotic tasks or

setting a table or you know all these

things that that are that we've had very

a lot of trouble um building machines to

do you're like fundamentally limited by

amount of data you have first just by

the amount of data that's been collected

so far but also like you know you can

only have so much stuff happening in the

real world to collect that data I'm

curious how like it how do you think

about that or do you think it's actually

something intrinsic to the sort of like

creative tasks that is uh that is

somehow more uh suited to current heral

networks I think it's both I think it is

unquestionably true that with the so we

can take a step

backwards at the base of all AI

progress that has happened at least in

in all of deep learning and arguably

more is the ability of neural networks

to generalize now generalization is a

technical term which

means that you understand something

correctly or take the right action in a

situation that's unlike any situations

that you've seen in the past in your

experience and you can see and so now a

system generalizes better if from the

same data it can do the right thing or

understand the right situation in a

broader set of

situations and so to make an analogy

suppose you have a student at a

university studying for an exam that

student might say this is a very

important exam for me let me memorize

this let me make sure I can solve every

single exercise in the textbook you know

such a student will be very well

prepared and could achieve a very very

high grade in the

exam now consider a different student

who might say you know what I don't need

to to learn to to figure to know how to

solve all the exercise in the textbooks

As Long As I Got the fundamentals right

I read the first 20 pages and I feel I

got the fundamentals if the if that

second student also achieves a high

grade in the exam that second student

did something harder than the first

student that second student exhibited a

greater degree of generalization they

were able to even though the questions

were the same the situation was less

familiar for the second student and the

first student and so our neural networks

are a lot like the first students they

they have an incredible ability to

generalize for a

computer but we could we could do more

and because they generalization is

not yet perfect definitely not yet at a

human level we need to compensate for it

by training on very large amounts of

data that's where the data comes in the

the better you generalize the less data

you need slash the further you can go

with the same data so maybe once we find

figure out how to make our neural

networks generalize a lot better then

all those small domains we don't have a

lot of data it actually won't matter the

neuro electric will say it's okay I know

what to do well enough even with this

limited amount of data but today we need

a lot of data but now when it comes to

the creative applications in particular

there is some way in which they are

especially well suited for neural

networks and that's because generative

models play a very Central role in

machine learning and the nature of the

generations of generative models are

somehow analogous to the artistic

process it's not perfect it doesn't

capture everything and very and there is

certain kinds of art which our models

cannot do yet but I think this second

connection the the the generative aspect

of Art

and the ability of generative models to

generate new plausible data is another

reason why art has been we've seen so

much progress in generative art