Highlights of the Fireside Chat with Ilya Sutskever & Jensen Huang: AI Today & Vision of the Future

Summary

在这段访谈中，受访者分享了他对深度学习的直觉和成功背后的思考。他从人工智能的潜在影响和对人类意识的好奇心出发，探讨了机器学习的早期挑战和神经网络的潜力。他强调了无监督学习和压缩的重要性，并介绍了OpenAI的创立初衷和早期项目，如实时策略游戏DotA 2的AI研究。他还讨论了GPT模型的发展，特别是GPT-4在理解、推理和多模态学习方面的显著进步，以及这些技术未来可能的发展方向。

Takeaways

• 🤖 深度学习的强大力量改变了很多东西，包括对人工智能的兴趣和对其影响的认识。
• 🧠 人工智能的进步有助于理解意识和人类经验。
• 💡 2002-2003年，学习是计算机无法做到的，而人工学习被认为是AI进步的关键。
• 🌟 神经网络因其自动编程的特性和与大脑的相似性而被视为具有巨大潜力。
• 🚀 2015-2016年，OpenAI成立时，人工智能领域还处于起步阶段，研究者数量有限。
• 🔍 OpenAI最初的两大理念是无监督学习和通过压缩实现学习，这在当时是一个未解决的问题。
• 📈 通过预测下一个字符或token，神经网络能够学习并压缩数据，从而发现数据中的秘密。
• 🎯 扩大模型规模、增加训练数据可以提高性能，这是OpenAI从一开始就坚持的信念。
• 🤔 GPT-3和GPT-4之间的主要区别在于GPT-4在预测下一个单词方面更加精确，这表明了其更深的理解。
• 🌐 多模态学习使神经网络能够从文本和图像中学习，并提高对世界的理解。
• 🔮 未来，语言模型的发展将集中在提高可靠性和信任度上，使系统更加有用。

Q & A

深度学习的成功背后的直觉是什么？

深度学习成功的直觉源于对人工智能影响的直观理解和对意识、人类经验的好奇心。深度学习被视为人工智能进步的关键，因为它涉及到机器的学习能力，这是早期计算机所不具备的。

Jeff Hinton在深度学习发展中起到了什么作用？

Jeff Hinton在深度学习发展中起到了关键作用。他是当时在讲述者所在大学的教授，专注于神经网络研究。他的工作使讲述者意识到神经网络具有自动编程并行计算机的特性，这与大脑的工作方式相似，为深度学习的发展提供了巨大的长期潜力。

OpenAI成立初期的工作重点是什么？

OpenAI成立初期的工作重点包括无监督学习和强化学习。他们相信通过数据压缩可以实现无监督学习，并通过在实时策略游戏（如DotA 2）中的强化学习研究，探索人工智能的应用。

GPT模型的预训练阶段是如何工作的？

GPT模型的预训练阶段通过训练大型神经网络准确预测不同文本中的下一个单词。这一过程不仅仅是学习文本的统计相关性，而是通过压缩数据来学习世界模型，从而获得对人类条件、希望、梦想、动机以及所处情境的压缩抽象可用表示。

如何提高神经网络的可靠性？

提高神经网络的可靠性可以通过进一步研究和开发更精确的训练算法来实现。这包括让网络在不理解时请求澄清、在不确定时请求更多信息，以及在不知道某事时能够明确表示出来。这些改进将使神经网络在实际应用中更加值得信赖。

GPT-4与ChatGPT相比有哪些改进？

GPT-4在多个维度上对ChatGPT进行了显著改进。GPT-4的基础模型能够以更高的准确性预测下一个单词，这表明它对文本的理解更深。此外，GPT-4还展示了在数学问题解决、遵循指令以及视觉理解方面的能力提升。

多模态学习的重要性是什么？

多模态学习的重要性在于它扩展了神经网络的理解能力，不仅包括文本，还包括图像。这样做可以增强对世界的理解，并且通过视觉信息学习，可能会发现文本中难以捕捉的信息。

GPT-4在视觉理解方面有哪些能力？

GPT-4能够理解和解释图像内容，包括解释笑话、解释复杂的图表和图解。这种能力使得GPT-4不仅能够处理文本输入，还能处理和理解图像输入，从而提供更全面的解答。

深度学习的发展对讲述者来说最令人惊讶的是什么？

对讲述者来说，最令人惊讶的是深度学习实际上真的有效。经过多年的发展，即使是简单的神经网络概念，只要扩大规模、改进数据集和训练方式，就能取得如此显著的成果。

未来一两年，语言模型领域可能会有哪些进展？

未来一两年，语言模型领域可能会在可靠性方面取得显著进展，包括提高系统的信任度、确保系统能够理解并准确地遵循用户意图，以及在不理解时能够请求澄清或承认不了解的内容。

GPT-4在数学问题解决方面有哪些令人印象深刻的表现？

GPT-4在数学问题解决方面的能力有显著提升，它能够执行复杂的数学推导、单位转换等。这表明GPT-4不仅能够理解数学概念，还能够应用这些概念解决问题。

Outlines

00:00

🤖 人工智能的直觉与深度学习的起源

这段对话从对深度学习的兴趣和直觉开始，讲述了人工智能发展的个人历程。起初，对人工智能的兴趣源于对其潜在影响的认识和对人类意识的好奇。2002-2003年间，计算机尚无法进行有效的学习，这成为了人工智能发展的一大障碍。然而，随着Jeff Hinton在神经网络方面的工作，深度学习开始展现出巨大的潜力。尽管当时并不清楚如何实现这一目标，但神经网络与大脑的相似性以及对并行计算机的编程能力，使得这一领域充满了长期的研究价值。

05:03

🌟 无监督学习的突破与OpenAI的初步探索

这段对话继续探讨了无监督学习的概念，特别是在2016年，这一领域还远未解决。通过压缩数据实现无监督学习的想法逐渐变得清晰，尤其是GPT模型的出现，证明了数据压缩与学习的关系。此外，还提到了情感神经元的工作，这在机器学习领域产生了深远的影响。OpenAI的创立初衷是探索如何利用规模来推动人工智能的发展，特别是在深度学习和强化学习方面。

10:05

🎮 强化学习在实时策略游戏中的应用

这段对话讨论了OpenAI在强化学习方面的首个重大项目，即训练一个能够玩实时策略游戏的智能代理。以DotA 2为例，这个项目展示了人工智能在复杂任务中的潜力。此外，对话还提到了GPT模型的发展，以及如何通过人类反馈进行强化学习，这是Chat GPT诞生的关键。

15:06

🚀 GPT模型的发展与Chat GPT的复杂系统

这段对话深入探讨了GPT模型的发展，特别是Chat GPT的复杂性。Chat GPT不仅仅是一个大型语言模型，它还包括了一系列复杂的系统，以确保其行为符合预期。对话中提到了预训练阶段的重要性，以及如何通过人类反馈进行微调，以提高模型的可靠性和有用性。此外，还讨论了GPT-3和GPT-4之间的主要差异，以及这些改进如何体现在各种测试中。

20:06

🧠 神经网络的推理能力与GPT-4的局限性

这段对话讨论了神经网络的推理能力，以及GPT-4在这一领域的进步。尽管神经网络在推理方面仍有局限，但通过要求网络“大声思考”等方法，可以提高其推理能力。对话还提到了神经网络在可靠性方面的挑战，以及如何通过进一步的研究来提高其性能。此外，还探讨了多模态学习的重要性，以及GPT-4如何通过结合文本和图像来增强对世界的理解。

25:07

📈 语言模型的未来发展趋势

这段对话对未来语言模型的发展趋势进行了预测。虽然具体的细节难以确定，但可以肯定的是，随着研究的深入，我们将看到更多令人惊叹的系统。当前的研究重点在于提高系统的可靠性，确保用户可以信任其产出的内容。此外，对话还提到了GPT-4在数学问题解决、诗歌创作和视觉解释方面展现出的惊人能力，以及这些进步如何超出了预期。

30:08

🌈 深度学习的成功与未来的惊喜

这段对话总结了深度学习的成功，以及作者在这一领域近20年的经历。作者对深度学习的有效性感到惊讶，尤其是基础的神经网络概念和训练算法的长期正确性。对话强调了从小型神经网络到大型、复杂模型的发展，以及这些模型在解决实际问题方面的潜力。

Keywords

💡深度学习

深度学习是一种人工智能技术，通过模拟人脑神经网络的工作机制，使计算机能够从数据中学习并做出决策。在视频中，深度学习被视为人工智能发展的关键，因为它能够处理和学习人类所擅长的任务，如理解和预测。

💡人工智能

人工智能是指使计算机和机器模拟人类智能行为的科学领域，包括学习、推理、感知、语言理解等能力。视频中，人工智能的发展是核心主题，讨论了其对人类经验的影响以及如何通过深度学习实现进步。

💡神经网络

神经网络是一种模仿人脑神经元连接方式的计算模型，用于实现机器学习。在视频中，神经网络是深度学习的基础，它们通过并行处理信息来学习，这与大脑的工作方式相似。

💡无监督学习

无监督学习是一种机器学习方法，它不依赖于标记的训练数据，而是通过分析数据本身的结构来学习。视频中提到，无监督学习是通过压缩数据来实现的，这种方法能够提取数据中的隐藏秘密，从而实现学习。

💡OpenAI

OpenAI是一个致力于人工智能研究的组织，旨在确保人工智能技术的发展能够对全人类产生积极影响。视频中提到OpenAI的成立和其在人工智能领域的贡献，特别是在开发GPT系列模型方面的工作。

💡强化学习

强化学习是一种机器学习范式，其中算法通过与环境的交互来学习行为策略，以最大化累积奖励。视频中提到OpenAI在强化学习领域的研究，特别是在实时策略游戏DotA 2中的应用。

💡ChatGPT

ChatGPT是OpenAI开发的一种基于GPT（生成预训练变换器）架构的语言模型，它能够生成连贯的文本回复，并与用户进行自然语言对话。视频中讨论了ChatGPT的开发背景和它如何结合了深度学习和强化学习的技术。

💡多模态学习

多模态学习是指机器学习模型能够处理并整合多种不同类型的数据输入，如文本、图像和声音。视频中提到GPT-4具备多模态学习能力，能够从文本和图像中学习并做出响应。

💡可扩展性

可扩展性是指系统、模型或算法能够适应增长或变化的能力，特别是在处理更大或更复杂数据集时仍能保持性能。视频中强调了可扩展性在深度学习中的重要性，特别是随着模型规模的增大，其性能也会提升。

💡可靠性

可靠性是指系统或模型在长时间内稳定、准确地执行其功能的能力。在视频中，作者提到提高神经网络的可靠性是使其变得真正有用的关键，包括正确理解用户意图和在不确定时请求澄清。

Highlights

深度学习的巨大变革，感谢深度学习带来的惊人力量。

个人对人工智能的兴趣源于对意识和人类经验的好奇，以及人工智能在这方面的潜在帮助。

2002-2003年，学习是人类可以做而计算机做不到的事情，这成为了人工智能发展的关键。

神经网络的自动并行编程特性，以及与大脑的相似性，使其成为人工智能发展的重要方向。

OpenAI的创立和初期工作重点，包括对无监督学习和压缩的兴趣。

通过压缩数据实现无监督学习的理论，以及GPT模型实际上对训练数据的压缩。

情感神经元的发现，展示了无监督学习的潜力。

OpenAI的两个初始理念：无监督学习和强化学习。

通过强化学习解决实时策略游戏的尝试，如DotA 2。

GPT模型的发展，以及它们如何成为OpenAI其他工作的基础。

大型神经网络预训练阶段的作用，以及如何通过预测下一个词来学习世界模型。

GPT-4相较于ChatGPT的显著改进，包括更准确的下一个词预测能力。

GPT-4在多模态学习方面的能力，包括理解和响应文本和图像输入。

多模态学习的重要性，以及它如何增强对世界的理解。

GPT-4在可靠性方面的提升，以及这如何影响其实用性。

GPT-4在解决数学问题、理解和生成诗歌、解释笑话等方面的能力。

对GPT-4的惊喜反应，包括其可靠性和解决问题的能力。

对未来一两年语言模型发展的预测，特别是在可靠性和系统信任方面的进步。

深度学习的成功和其对人工智能领域的影响，以及对这一领域长期信念的验证。

Transcripts

what was your intuition around deep

learning why did you know that it was

going to work did you have any intuition

that are going to lead to this kind of

success

okay well first of all thank you so much

for the quote for all the kind words

a lot has changed thanks to the

incredible power of deep learning

like I think this my personal starting

point I was interested in artificial

intelligence for a whole variety of

reasons

starting from

an intuitive understanding of

appreciation of its impact

and also I had a lot of curiosity about

what is consciousness what is The Human

Experience

and it felt like progress in artificial

intelligence will help with that

The Next Step was well back then I was

starting in 2002-2003

and it seemed like learning is the thing

that humans can do that people can do

that computers can't do at all

in 2003 2002 computers could not learn

anything

and it wasn't even clear that it was

possible in theory

and so I thought that making progress

in learning in artificial learning in

machine learning

that would lead to the greatest progress

in AI

and then I started to look around for

what was out there

and nothing seemed too promising

but to my great luck

Jeff Hinton was a professor at my

University

and I was able to find him and he was

working in neural networks and it

immediately made sense

because neural networks had the property

that we are learning we are

automatically programming parallel

computers back then the parallel

computers were small

but the promise was if you could somehow

figure out how learning in neural

networks work

then you can program small parallel

computers from data and it was also

similar enough to the brain and the

brain works so it's like you had these

several factors going for it now it

wasn't clear how to get it to work

but of all the things that existed that

seemed like it had by far the greatest

long-term promise Big Bang of AI fast

forward to now you came out to the

valley you started open AI with some

friends

um you're the chief scientist

now what was the first initial idea

about what to work on at open AI because

you guys worked on several things some

of the trails of of inventions and and

work uh you could you could see led up

to

the chat GPT moment

um but what were the initial inspiration

what were you how would you approach

intelligence from that moment and led to

this yeah so obviously when we started

it wasn't

100 clear how to proceed

and the field was also very different

compared to the way it is right now

so right now you already used we already

used to

you have these amazing artifacts these

amazing neural Nets who are doing

incredible things and everyone is so

excited but back in 2015 2016 early 2016

when you were starting out

the whole thing seemed pretty crazy

there were so many fewer researchers

like

like 100 maybe they were between a

hundred and a thousand times fewer

people in the field compared to now like

back then you had like

100 people most of them are working in

Google slash deepmind and that was that

and then there were people picking up

the skills but it was very very scarce

very rare still

and we had two

big initial ideas

at the start of open AI

that state that had a lot of staying

power and they stayed with us to this

day

and I'll describe them right now

the first big idea that we had

one which

I was especially excited about very

early on

is the idea

of

unsupervised learning through

compression

some context

today we take it for granted that on

supervised learning is this easy thing

you just pre-train on everything and it

all does exactly as you'd expect

in 2016

unsupervised learning was an unsolved

problem in machine learning that no one

had any insight exactly any clue as to

what to do that's right iyanla Khan

would go around and give a talk give

talk saying that you have this Grand

Challenge in supervised learning

and I really believed

that really good compression

of the data will lead to unsupervised

learning

now

compression is not language that's

commonly used to describe what is really

being done until recently

when suddenly it became apparent to many

people that those gpts actually compress

the training data

you may recall that Ted Chiang New Times

article which also alluded to this

but there is a real mathematical sense

in which training these autoregressive

generative models compress the data and

intuitively you can see why that should

work

if you compress the data really well you

must extract all the hidden secrets

which exist in it

therefore that is the key

so that was the first idea that we're

really excited about and that led to

quite a few Works in openai

to the sentiment neuron

which I'll mention

very briefly it is not this work might

not be well known outside of the machine

learning field but it was very

influential especially in our thinking

this work

like the result there

was that when you train a neural network

back then it was not a Transformer it

was before the Transformer right small

recurrency

LST

some of the words that you've done

yourself you know

so the same lsdm with a few twists

trying to predict the next token in

Amazon reviews next character

and we discovered that if you predict

the next character well enough

it will be a neuron inside that lstm

that corresponds to its sentiment

so that was really cool because it

showed some traction for unsupervised

learning and it validated the idea that

really good next

character prediction next something

prediction compression yeah

has the probability that it discovers

the secrets in the data that's what we

see with these GPT models right you

train and people say just statistical

correlation I mean at this point it

should be so clear to anyone that

observation also

you know for me intuitively open up the

whole world of where do I get the data

for unsupervised learning

because I do have a whole lot of data if

I could just make you predict the next

character and I know what the ground

truth is I know what the answer is I

could be I could train a neural network

bothered with that so that that

observation and masking and other other

technology other approaches you know

open open my mind about where would the

world get all the data that's

unsupervised for unsupervised learning

well

you've always believed that scaling will

improve the performance of these models

yes larger larger networks deeper

networks more training data would scale

that

um there was a very important paper that

open AI wrote about the scaling laws and

the relationship between loss and the

size of the model and the amount of data

set the size of the data set when

Transformers came out it gave us the

opportunity to train very very large

models in very reasonable amount of time

um but what they in with the intuition

about about the scaling laws or the size

of of models and data

and your journey of gpt123 which came

first did you see the evidence of GPT

one through three first they were was

there the intuition about the scaling

law first

the intuition so I would say that the

way the way I'd phrase it is that I had

a very strong belief that bigger is

better

and that one of the goals that we had at

open AI is to figure out how to use the

scale correctly

there was a lot of belief about inopen

AI about scale from the very beginning

the question is

what to use it for precisely

because I'll mentioned right now we're

talking about the gpts but there is

another very important line of work

which I haven't mentioned the second big

idea but I think now is a good time to

make a detour and that's reinforcement

learning

that clearly seems important as well

what do you do with it

so the first really big project that was

done inside open AI

was our effort at solving a real-time

strategy game

and for context a real-time strategy

game is like it's a competitive sport

yeah right we need to be smart you need

to have faster you need to have a quick

reaction time you there's teamwork

and you're competing against another

team and it's pretty it's pretty it's

pretty involved

and there is a whole competitive league

for that game the game is called DotA 2.

and so we train a reinforcement learning

agent to play against itself

to produce

with the goal of the reaching a level so

that it could compete against the best

players in the world and that was a

major undertaking as well it was a very

different line it was reinforcement

learning yeah remember the data that you

guys announced that work and this is

this by the way when I was asking

earlier about about there's a there's a

large body of work that have come out of

open AI some of it seem like detours

um but but in fact as you were

explaining now they might might have

been detours this seemingly detours but

they they really led up to some of the

important work that we're now talking

about GPT yeah I mean there has been

real convergence where the gpts

produce the foundation and in the

reinforcement learning from DOTA morphed

into reinforcement learning from Human

feedback that's right and that

combination gave us chat GPT you know

there's a there's a there's a

misunderstanding that that uh chat GPT

is uh in itself just one giant large

language model there's a system around

it that's fairly complicated is it could

could you could you explain

um briefly for the audience the the uh

the fine-tuning of the the reinforcement

learning of the the the

um uh you know the various surrounding

systems that allows you to uh keep it on

Rails and and let it let it give it

knowledge and you know so on and so

forth yeah I can

so the way to think about it

is that when we train a large neural

network

to accurately predict the next word

in lots of different texts from the

internet

what we are doing is that we are

learning a world model

it looks like we are learning this it

may it may look on the surface that we

are just learning statistical

correlations in text

but it turns out that to just learn the

statistical correlations in text

to compress them really well

what the neural network learns is some

representation of the process that

produced the text

this text is actually a projection of

the world

there is a world out there

and it has a projection on this text

and so what the neural network is

learning is more and more aspects of the

world of people of the human conditions

their their their hopes dreams and

motivations their interactions and the

situations that we are in and the neural

network learns a compressed abstract

usable representation of that this is

what's being learned from accurately

predicting the next word

and furthermore the more accurate you

are is predicting the next word

the higher the Fidelity the more

resolution you get in this process

so that's what the pre-training stage

does

but what this does not do

is specify the desired behavior that you

wish our neural network to exhibit

you see a language model what it really

tries to do

is to answer the following question

if I had some random piece of text on

the internet which starts with some

prefix some prompt

what will it complete to if you just

randomly ended up on some text from the

internet but this is different from well

I want to have an assistant which will

be truthful that will be helpful that

will follow certain rules and not

violate them that requires additional

training this is where the fine tuning

and the reinforcement learning from

Human teachers

and other forms of AI assistance it's

not just reinforcement learning from

Human teachers it's also reinforcement

learning from human and AI collaboration

our teachers are working together with

an AI to teach our AI to behave but here

we are not teaching it new knowledge

this is not what's happening we are

teaching it

we are communicating with it we are

communicating to it what it is that we

want it to be and this process the

second stage is also extremely important

the better we do the second stage the

more useful the more reliable this

neural network will be so the second

stage is extremely important too in

addition to the first stage of the to

learn everything

learn everything learn as much as you

can about the world from the projection

of the world

came out just a few months ago

um fastest growing application in the

history

of humanity

uh lots of lots of uh uh

interpretations about why

um but some of the some of the things

that that is clear it is it is the

easiest application that anyone has ever

created for anyone to use it performs

tasks it performs things it does things

that are Beyond people's expectation

anyone can use it there are no

instruction sets there are no wrong ways

to use it you you just use it and uh if

it's if your instructions are our

prompts are ambiguous the conversation

refines the ambiguity until your intents

are understood by by the by the

application by the AI

the the impact of course uh clearly

remarkable now yesterday this is the day

after gpt4 just a few months later the

the performance of gpt4 in many areas

astounding SAT scores GRE scores bar

exams the number of the number of

tests that is able to perform at very

capable levels very capable human levels

astounding

what were the what were the major

differences between Chad gbt and gpt4

that led to its improvements in these in

these areas

so gpt4

is a

pretty substantial Improvement on top of

chat GPT across very many dimensions

we trained gpt4 I would say

between more than six months ago maybe

eight months ago I don't remember

exactly

GPT is the first build big difference

between shared GPT and gpd4

and that's perhaps is the more

the most important difference

is that the base on top of gpt4 is built

predicts the next word with crater

accuracy this is really important

because the better a neural network can

predict the next word in text the more

it understands it this claim is now

perhaps accepted by many at this point

but it might still not be intuitive or

not completely intuitive as to why that

is so I'd like to take a small detour

and to give an analogy that will

hopefully clarify why more accurate

prediction of the next word leads to

more understanding real understanding

let's consider an example

say you read a detective novel

it's like a complicated plot a storyline

different characters lots of events

Mysteries like Clues it's unclear then

let's say that at the last page of the

book the detective has got all the clues

gathered all the people and saying okay

I'm going to reveal the identity of

whoever committed the crime and that

person's name is predict that word

predict that word exactly my goodness

right yeah right now

there are many different words but by

predicting those words better and better

and better the understanding of the text

keeps on increasing gpt4 predicts the

next word better hell yeah people say

that the Deep learning won't lead to

reasoning that deep learning won't lead

to reasoning but in order to predict

that next word figure out from all of

the agents that were there and and all

of their you know strengths or

weaknesses or their intentions and uh

the context

um and to be able to predict that word

who who was the murderer that requires

some amount of reasoning a fair amount

of reasoning and so so how did that how

did the how is it that that um that it's

able to pre to learn reasoning and and

if if it learn reasoning

um you know one of the one of the things

that I was going to ask you is of all

the tests that were that were taken

um between Chad GPT and gpd4 there were

some tests

that gpt3 or chat GPT was already very

good at

there were some tests that gbt3 or chibi

was not as good at

um that gbt4 was much better at and

there were some tests that neither are

good at yet

I would love for it you know and some of

it has to do with reasoning it seems

that you know maybe in in calculus that

that it wasn't able to break maybe the

problem down

um into into its reasonable steps and

solve it is is it but yet in some areas

it seems to demonstrate reasoning skills

and so is that an area that that um uh

that in predicting the next word you're

you're learning reasoning and um uh what

are the limitations uh now of gpt4 that

would enhance his ability to reason even

even further

you know

the reasoning isn't this super

well-defined concept but we can try to

Define it anyway

which is when you maybe maybe when you

go further

where you're able to somehow think about

it a little bit and get a better answer

because of your reasoning

and I'd say I'd say that there are

neural Nets

you know maybe there is some kind of

limitation which could be addressed

by for example asking the neural network

to think out loud this has proven to be

extremely effective for reasoning but I

think it also remains to be seen just

how far the basic neural network will go

I think we have yet to uh

tap fully tap out its potential

but yeah I mean there is definitely some

sense where reasoning is still not

quiet at that level

as some of the other capabilities of the

neural network though we would like the

reasoning capabilities of the neural

network to be high higher

I think that it's fairly likely that

business as usual will keep will improve

the reasoning capabilities of the neural

network I wouldn't

I wouldn't necessarily confidently rule

out this possibility

yeah because one of the things that that

is really cool is you ask you as a

tragic a question that before it answers

the question tell me first first of what

you know and then to answer the question

um you know usually when somebody

answers a question if you give me the

the foundational knowledge that you have

or the foundational assumptions that

you're making before you answer the

question that really improves the my

believability of of the answer you're

also demonstrating some level of reason

when you're demonstrating reasoning and

so it seems to me that chat GPD has this

inherent capability embedded in it

yeah to some degree yeah this the the

the the the the way the one way to think

about what's happening now is that

these neural networks have a lot of

these capabilities they're just not

quite very reliable in fact you could

say that reliability is currently the

single biggest obstacle for these neural

networks being useful truly useful if

sometimes it is still the case

that these neural networks

hallucinate a little bit

or maybe make some mistakes which are

unexpected which you wouldn't expect the

person to make

it is this kind of unreliability that

makes them substantially less useful

but I think that perhaps with a little

bit more research with the current ideas

that you have and perhaps a few more of

the

ambitious research plans will be able to

achieve higher reliability as well and

that will be truly useful that will

allow us to have very accurate guard

rails which are very precise that's

right and it will make it ask for

clarification where it's unsure or maybe

say that it doesn't know something when

it does anything it doesn't know and do

so extremely reliably so I'd say that

these are

some of the bottlenecks really so it's

not about whether it exhibits some

particular capability but more how

reliable degree exactly yeah

multi-modality gpt4 has the ability to

learn from text and images

and respond to input from text and

images first of all

the foundation of multi-modality

learning

of course Transformers has made it

possible for us to learn from

multimodality tokenized text and images

but at the foundational level help us

understand how multimodality enhances

the understanding of the world

Beyond text by itself

and uh and my understanding is that that

that when you when you um uh do

multi-modality learning

learning that even when it is just a

text prompt the text prompt the text

understanding could actually be enhanced

tell us about multimodality at the

foundation why it's so important and and

what was what's the major breakthrough

in the the and the characteristic

differences as a result

so there are two Dimensions to

multimodality

two reasons why it is interesting

the first reason

is

a little bit humble the first reason is

that multimodality is useful

it is useful for a neural network to see

Vision in particular

because the world is very visual human

beings are very visual animals

I believe that a third of the visual

core of the human cortex is dedicated to

vision

and so

by not having vision

the usefulness of our neural networks

though still considerable

is not as big as it could be so it is a

very simple usefulness argument it is

simply

useful to see

and gpt4 can see quite well

the there is a second reason to division

which is that we learn more about the

world

by learning from images in addition to

learning from text

that is also a powerful argument though

it is not as clear-cut as it may seem

I'll give you an example

or rather before giving an example I'll

make the general comment

for a human being us human beings we get

to hear about one billion words

in our entire life

only only one billion words that's

amazing yeah that's not a lot yeah

that's not a lot

so we need to competent we need doesn't

include my own words in my own head

make it two billion but you see what I

mean yeah you know we can see that

because

um a billion seconds is 30 years so you

can kind of see like we don't get to see

more than a few words a second then if

you're asleep half the time so like a

couple billion words is the total we get

in our entire life so it becomes really

important for us to get as many sources

of information as we can and we

absolutely learn a lot more from vision

the same argument holds true for our

neural networks as well

except

except for the fact that the neural

network can learn from so many words

so

things which are hard to learn about the

world from text in a few billion words

may become easier

from trillions of words and I'll give

you an example

consider colors

surely one needs to see to understand

calories

and yet the text only neural networks

who've never seen a single Photon in

their entire life if you ask them which

colors are more similar to each other it

will know that red is more similar to

Orange than to Blue it will know that

blue is more similar to purple than to

Yellow

how does that happen

and one answer is that information about

the world even the visual information

slowly leaks in through text but slowly

not as quickly

but then you have a lot of text you can

still learn a lot of course once you

also add vision and learning about the

world from Vision you will learn

additional things which are not captured

in text

but it is no I would not say that it is

a binary there are things which are

impossible to learn from the from text

only I think this is more of an exchange

rate and in particular as you want to

learn if we are if you if you if you are

like a human being and you want to learn

from a billion words or a hundred

million words then of course the other

sources of information become far more

important

when you when you um I

on the on the context of the scores that

I saw

um the thing that was really interesting

was was uh the the data that you guys

published which which one of the tests

were were um uh performed well by gpt3

and which one of the tests performed

substantially better with gbt4

um

how did multi-modality contribute to

those tests you think oh I mean

in a pretty straightforward

straightforward way anytime there was a

test where a problem

would were to understand the problem you

need to look at a diagram like for

example in some math competitions like

there is a

cont math competition for high school

students called AMC 2012 right and there

presumably many of the problems have a

diagram

so GPT 3.5 does quite badly on that on

that X on that on the test gpt4 with

text only does

I think I don't remember but it's like

maybe from two percent to 20 accuracy of

success rate but then when you add

Vision it jumps to 40 success rate so

the vision is really doing a lot of work

the vision is extremely good and

I think being able to reason visually as

well and communicate visually will also

be very powerful and very nice things

which go beyond just learning about the

world you have several things you got to

learn you can learn about the world

you can reason about the world visually

and you can communicate visually where

now in the future perhaps in some future

version if you ask your neural net hey

like explain this to me rather than just

producing four paragraphs it will

produce hey like here's like a little

diagram which clearly conveys to you

exactly what you need to know and so

that's incredible tell tell us whatever

you can about about uh where we are now

and and what do you think will be and

and not not too distant future but you

know pick your your horizon a year or

two uh where do you think this whole

language Model area would be in some of

the areas that you're most excited about

you know predictions are hard

and um it's a bit although it's a little

difficult to do

say things which are too specific

I think it's safe to assume

that

progress will continue

and that we will keep on seeing systems

which Astound us

in their in the things that they can do

and the current Frontiers are will be

centered around reliability around the

system can be trusted really get into a

point where you can trust what it

produces

really get into a point where if he

doesn't understand something it asks for

a clarification

says that he doesn't know something says

it in his more information I think those

are perhaps the biggest the areas where

Improvement will lead to the biggest

impact on the usefulness of those

systems because right now that's really

what stands in the way you have an AF

asking your own network you're asking

neural net to maybe summarize some long

document and you get a summary

like are you sure that some important

detail wasn't omitted it's still a

useful summary

but it's a different story when you know

but all the important points have been

covered

at some point like and in particular

it's okay like if some even the reason

ambiguity it's fine but if a point is

clearly important such that anyone else

who saw that point would say this is

really important

when the neural network will also

recognize that reliably that's when you

know same for the guardrail say same for

its ability to clearly follow the intent

of the user of its operator

so I think we'll see a lot of that in

the next two years yeah because the

progress in those two areas will make

this technology trusted by people to use

and be able to apply for so many things

I was thinking that was going to be the

last question but I did have another one

sorry about it okay so so Chad uh chat

gbt to gpt4 gpt4 when when it first when

you first started using it what are some

of the skills that

it demonstrated that surprised even you

well

there were lots of really cool things

that it demonstrated which

which is which were quite cool and

surprising it was

it was quite good so I'll mention two

excess so let's see I'm just I'm just

trying to think

about the best way to go about it

the short answer

is that the level of its reliability was

surprising

where the previous neural networks if

you ask them a question sometimes they

might

misunderstand something in a kind of a

silly way whereas the gpt4 that stopped

happening

its ability to solve math problems

became far greater it's like you could

really like say sometimes you know

really do the derivation and like long

complicated derivation and you could

convert the units and so on and that was

really cool you know like many people

what's your proof it works through a

proof it's pretty amazing not all proofs

yeah naturally but but quite a few yeah

or another example would be like many

people noticed that it has the ability

to

produce poems with you know every word

starting with the same letters or every

word starting with some it follows

instructions really really clearly not

perfectly still but much better before

yeah really good and on the vision side

I really love how it can explain jokes

you can explain memes you show it a meme

and ask it why it's funny and it will

tell you and it will be correct the the

vision part I think is very

was also very it's like really actually

seeing it when you can ask questions

follow-up questions about some

complicated image with a complicated

diagram and get an explanation that's

really cool

but yeah overall I will say to take a

step back

you know I've been I've been in this

business for quite some time actually

like almost exactly 20 years

[Music]

and

the thing which most of which I find

most surprising is that it actually

works

yeah like it it's turned out to be the

same little thing all along which is no

longer little and it's a lot more

serious and much more intense

but it's the same neural network just

larger trained on maybe larger data sets

in different ways with the same

fundamental training algorithm yeah

so it's like wow

I would say this is what I find the most

surprising yeah whenever I take a step

back I go how is it possible that those

ideas those conceptual ideas about well

the brain has neurons so maybe

artificial neurons are just as good and

so maybe we just need to train them

somehow with some learning algorithm

that those arguments turned out to be so

incredibly correct

that would be

the biggest surprise it's