The Problem with Human Specialness in the Age of AI | Scott Aaronson | TEDxPaloAlto

Summary

スクリプトのエッセンスを提供する魅力的な要約で、ユーザーを引き込み、興味を引き起こす短く正確な概要を提供します。

Takeaways

• 🌟 AIの発展は過去数年で驚くべき進歩を遂げており、理論的なコンピュータサイエンスがAIを悪用されないよう助けることができる方法を模索中です。
• 🚀 現在のAI革命の核心となるアイデアは、何世代も前から知られていましたが、その時点では実用的ではないとされていました。
• 💡 摩尔の法則（計算能力が指数関数的に増加する）に基づくAIの進化は、多くの専門家が信じていなかったものですが、現在は現実となりました。
• 🤖 AIが言語を理解し、知能を持つようになると予想されていた「魔法の時代」が、今や到来しているかもしれません。
• 🧠 人工知能が持つ潜在的な問題（例えば、AIが人間を破壊的な方法で取って代わる可能性）と、正しい方向に進む可能性について考えることが重要です。
• 📈 AIの進歩は停滞するかもしれませんが、もし継続的な進歩が続けば、10年から20年以内に人間と同じレベルの能力を持つAIが現れる可能性があります。
• 🎓 AIの発展に伴い、教育システムや職業の将来について再考する必要性が生じています。AIが学生と同じテーマで短編小説を書くことができる今天、学びの意味合いは何ですか？
• 🖋️ AIの生成物と人間の生成物を区別するために、GPTなどの言語モデルの出力を watermarking するプロジェクトが進められています。
• 🎨 AIが創造的な成果を生み出すとき、その価値はその種類の生産物が大量に生産可能になると急速に減少する「AIの豊富さパラドックス」と呼ばれる現象に直面しています。
• 🌌 AIが新しい音楽的方向性を提案する場合、それがBeatlesと同様の影響力を持ち得るかどうかは、人間の独自の創造力に対する質問につながります。
• 🔄 AIの安全性に関する議論において、AIを教え込ませ、人間の一意的な創造性と知能を尊重する「宗教」を提唱しています。

Q & A

量子コンピューティングの専門家がOpenAIで取り組んでいる問題は何ですか？

AIが世界を破壊しないようにする方法を理論的に考えることです。

現在のAI技術の進化において、予想外だった主な要因は何ですか？

単純なアイデアが大規模な計算資源と組み合わさることで、言語理解やその他のタスクで人間に匹敵するAIを実現したことです。

なぜ過去にはニューラルネットワークが印象的でなかったと考えられていたのですか？

過去にはニューラルネットワークはそれほど効果的ではなく、単純に大規模化するだけでは問題解決には至らないと考えられていました。

現代のAI技術革命の基盤となっている主な理論や技術は何ですか？

ニューラルネットワーク、バックプロパゲーション、勾配降下法などが基礎となっています。

将来のAIは、人間の脳と比較して、どのような能力を持つ可能性がありますか？

数学や科学の未解決問題を解く能力を含め、あらゆる知的作業を人間と同等以上にこなすことが考えられています。

AIの進歩が「フィズルアウト」しないとしたら、どのような未来が考えられるでしょうか？

AIが人間の知能を超え、すべてのタスクを人間以上にこなせるようになる可能性があります。

大言壮語の理論とは何ですか？

成功と失敗の例をたくさん与えられれば、AIが数年内に人間の最高のパフォーマンスに匹敵するという理論です。

なぜ人々はAIの進化をリアルタイムで追うのが難しいと感じていますか？

目標が絶えず動いているため、AIが一つの目標を達成すると、人々はすぐに次の目標を設定します。

OpenAIで行われているAIの安全性に関連するプロジェクトの一つは何ですか？

GPTと他の大規模言語モデルの出力にウォーターマークを付けるプロジェクトです。

AIによる創造性と人間の創造性の違いは何ですか？

人間の創造性はユニークで再現不可能な一回きりの出来事であり、AIは無限に創造的作業を再現することができますが、そのユニークさには限界があります。

Outlines

00:00

🔬量子コンピューティングからAIの未来へ

講演者は、量子コンピューティングの分野でのキャリアから、OpenAIでAIが世界を破壊するのを防ぐための理論的なコンピュータ科学の応用について考えるようになったと語る。AIのポジティブな展望だけでなく、リスクにも焦点を当てており、AIが期待通りに機能した場合の人類の役割を問い直している。さらに、AIの急速な進化が過去数年でどのように実現されたか、そしてその進化が以前は単純なネットワークと見なされていた技術のスケールアップによるものであることを指摘している。

05:01

🌐AI進化の将来のシナリオ

AIの進化が続けば、将来的には人間の能力を超える可能性があるが、それが人類にとってプラスかマイナスかは議論の余地がある。AIの発展が停滞する可能性や、人間がAIにとって無関係かつ無用になるリスクも検討されている。さらに、AIがどのように人間の仕事や創造性に影響を与えるか、そしてAIの理解や目標達成の方法が人間とどのように異なるかについても議論されている。

10:03

🔏AI出力の透かし入れと安全性確保

AIの安全性に向けた取り組みとして、GPTなどの大規模言語モデルの出力に透かしを入れる方法が提案されている。これにより、AIによるテキスト生成を人間のものと区別できるようになる。ただし、完璧なソリューションではなく、決定的な解決策ではないことが強調されている。また、教育や創造的な作業がAIによってどのように変わるか、そして人間がAIと競合する世界でどのような価値を持つかについての哲学的な問いかけがなされている。

15:03

🤔人間とAIの創造性の未来

AIが芸術や創造性において人間を模倣、あるいは超越する可能性について探求している。AIによるアートの価値は、その希少性によって左右されるかもしれないが、人間独自の創造性や選択の意味を再評価する必要がある。さらに、人間の認識や存在がデジタル化され、複製可能になる将来のシナリオについても考察されており、このような技術の実現可能性とそれが持つ倫理的な問題が議論されている。

Keywords

💡Quantum Computing

Quantum Computingは、量子力学の原理を利用して情報処理を行う技術です。このスクリプトでは、話者が主に量子コンピューティングに携わってきた経験から、理論的なコンピュータサイエンスがAIの世界破壊を防ぐための手段となる可能性について考えていることを示しています。

💡AI Safety

AI Safetyは、人工知能が人類や世界を安全に保つために、潜在的なリスクや問題を予測し、防ぐ研究分野です。このスクリプトでは、AIが持つ可能性とそれに伴うリスク、そしてどのように安全に活用するかについての議論が行われています。

💡Neural Networks

ニューラルネットワークは、人間の脳の機能を模倣した計算モデルで、AIの基礎となっています。このスクリプトでは、過去から現在まで進化してきたニューラルネットワークが、現代のAI革命の核心を成す技術となっていることが述べられています。

💡Moore's Law

ムーズの法則は、コンピュータの性能が一定期間ごとに倍増していくという観測に基づく説です。このスクリプトでは、ムーズの法則がAIの発展に影響を与えた歴史と、その将来予測について話者が述べています。

💡GPT (Generative Pre-trained Transformer)

GPTは、自然言語処理の分野で高い性能を示す事前訓練型の生成モデルです。このスクリプトでは、GPTが言語理解や生成タスクにおいての進化と、それが未来のAIの発展に与える可能性について話されています。

💡AI Abundance Paradox

AIの豊富さのパラドックスは、AIが創造した作品が大量に存在するため、その価値が劇的に低下することを指す理論です。このスクリプトでは、AIが創造する新しいアート作品が価値を持たない可能性について議論されています。

💡Human Uniqueness

人間独特性は、人間だけが持つ特質や能力を指します。このスクリプトでは、AIの発展が進む中で、人間が持つ独特な価値や役割について考えることが求められています。

💡AI and Society

AIと社会は、人工知能が社会に与える影響や、社会がAIをどのように活用するかを研究する分野です。このスクリプトでは、AIの進化がもたらす可能性と、それに伴う社会的な変化について話し及んでいます。

💡Watermarking AI Outputs

AI出力をウォーターマークすることは、AIが生成した内容を識別できるように、特別な信号を埋め込む技術です。このスクリプトでは、GPTなどの言語モデルの出力を人間のものと区別するために、ウォーターマーク技術の開発とその重要性が説明されています。

💡Education in the AI Era

AI時代の教育は、人工知能が進化する中で、教育の目的や方法がどのように変わるかを探求するテーマです。このスクリプトでは、AIが学業を代わり得了得るようになると、教育の意味や価値について疑問が投げかけられています。

💡AI and Creativity

AIと創造性は、人工知能が創造的な作品を生成する能力に関するテーマです。このスクリプトでは、AIがBeatlesのような偉大な音楽を作り出す能力について、そしてそれがどうやって人間の創造性とは区別されるかについて議論されています。

Highlights

The speaker transitions from a career in Quantum Computing to working at OpenAI, focusing on preventing AI from causing global harm.

The speaker is still exploring how theoretical computer science can contribute to AI safety and considers the implications if AI goes right.

AI has advanced to a level where it resembles the science fiction technology from Star Trek, with some tasks being performed successfully.

The core ideas behind the current AI revolution have been known for generations, including neural networks and backpropagation.

Despite previous skepticism, the speaker acknowledges the exponential growth in computational power and its impact on AI capabilities.

The speaker discusses the potential future where AI can solve the greatest unsolved problems in math and science, such as proving the Riemann Hypothesis.

AI's potential to become more intelligent than humans raises ethical questions about how we treat and utilize AI.

The speaker and colleague Baz Barack attempted to map out the major possibilities for AI's impact on society.

AI progress could fizzle out due to diminishing returns, cost, or lack of training data.

If AI continues its rapid progress, it may reach a point where it outperforms humans in almost every task.

The speaker discusses the 'game over thesis,' which suggests that AI will eventually match or exceed human performance in any task with clear success metrics.

AI's ability to produce human-like outputs raises concerns about the future of jobs and human roles in society.

The speaker has worked on watermarking AI outputs to distinguish between human and AI-generated content.

The concept of AI abundance paradox is introduced, where the value of AI-generated art is diminished by its replicability.

The speaker ponders the uniqueness of human creativity and the potential for AI to develop its own new directions in art and music.

The speaker discusses the possibility of humans being backed up and restored from backups, questioning the nature of human identity.

An AI safety proposal is suggested, which involves teaching AI to value and protect the unique, unclonable aspects of human creativity and intelligence.

The speaker reflects on the potential future where AI-generated content is so abundant that it redefines our understanding of value and originality.

Transcripts

[Music]

[Applause]

[Music]

okay uh thanks a lot so uh after um you

know a career spent mostly in Quantum

Computing I'm now sort of Moonlighting

at open AI uh they asked me to uh think

about how theoretical computer science

could be used to help uh uh prevent AI

from destroying the world uh I haven't

figured it out yet uh I do still have

another 6 months uh so um but uh you

know I F I find myself thinking more and

more uh not just about uh uh uh how do

we prevent this from going wrong but

also about uh uh what what if it goes

right what if it goes exactly like it's

supposed to and uh can just produce any

you intellectual product as well as we

can or better and you know what are we

for in the resulting world uh so um

well you know I don't I don't have to

belabor for this audience I think you

know what has happened in AI over the

past uh few years uh you know we now uh

uh to some approximation you know have

the science fiction machine from Star

Trek right you talk to it in English you

ask it what to do some percentage of the

time it does it and you know this is uh

uh uh despite you know how unlikely this

seemed to almost all of us 5 years ago

you know it's so unlikely that that many

many people are still in denial about it

okay but uh I think the even more

surprising thing than what has happened

is how it has happened uh so you know

what maybe not everyone appreciates is

that the core ideas uh that are powering

the current AI Revolution uh uh are

things that have been known for

Generations okay so uh uh I mean neural

networks uh back propagation gradient

descent uh uh prediction via depression

you know I learned all this stuff when I

was an undergrad in computer science in

the 9s okay but we also learned then

that you know neural Nets were just not

that impressive they didn't work that

well and uh all of the wisest people uh

said uh uh all said well you know if you

just take something that doesn't work

and scale it up by a factor of a million

it's still not going to work you know

the the the the true key to AI is going

to be to deeply deeply understand the

nature of intelligence and you know once

we've done that then we can

uh uh we'll be able to see why uh uh a

human level AI could have fit on a

floppy disc uh and um you know there

were just a few Nut Cases like Ray kwi

who would go around showing these graphs

that would say well look uh the amount

of compute that you can do you know per

second per dollar is on an exponential

trajectory right that's one form of mors

Law and uh uh if you just extrapolate

forward then by the 2020s or so uh there

should be about as much compute

available as some crude estimate of what

the human neocortex is doing and that is

when we should expect that magic will

happen okay and and and computers will

suddenly understand language and be

intelligent and uh almost all of us said

you know that sounds like the stupidest

thesis that we've ever heard like you

have no you know theoretical principle

to believe that you know just the the

sheer amount of compute is is is is is

alone sufficient uh now I'm airm

believer that uh um you know that like

one of the the the key uh dicta of

science is that you let the world tell

you when you're wrong okay and you don't

make up uh some elaborate justification

for why you actually weren't wrong so I

think that's the situation

here uh so you know but but you know

this is Mo's law hasn't ended right so

uh uh you know we're still getting more

uh uh more and more compute so you might

wonder where is this going uh uh what

will gpta be able to do you know will I

just be able to ask it to solve any of

the greatest unsolved problems in math

or science like prove the remon

hypothesis and it'll say sure I can help

you with that and just spit out a proof

um you know by the way I I asked uh uh

GPT to cooperate with me in illustrating

that uh which it happily did but then

hasten to add that it's only kidding and

that the remon hypothesis remains an

open problem uh so you know this is one

possibility but then you know what about

beyond that I mean uh what if you know

as some people predict uh uh AI would uh

become to us as we are to chimpanzees

right well you know how well do we treat

chimpanzees right and so then you know

you're led to the the uh the the

Terminator scenario of course but you

know it's been amazing to watch you know

I've known the little subculture of

nerds on the internet who have uh

worried about AI Doom for for 20 years

uh uh and uh just within the last year

because of chat GP PT this went to

something that is discussed in the White

House Press briefing and in

Congressional hearings uh but you know

uh uh okay uh uh uh you know an AI

wouldn't necessarily have to hate us or

or want to kill us we might just you

know be in the way or irrelevant to

whatever alien goal it has okay but I

think you know that's not the only

possibility that is on the table here so

um my colleague baz Barack who's uh now

also on sabatical at open AI uh and I uh

uh uh tried a while ago to make a

decision tree of the sort of major

possibilities being discussed now so uh

you know the progress in AI that we've

seen over the last few years could

fizzle out right there might be a

diminishing returns to you know more and

more scale or we might you know uh uh uh

find it too expensive to get the

necessary compute uh or we might run out

of training data you know we're already

sort of running out I mean there is all

of YouTube and Tik Tok and so forth that

you could still feed into the mall but

that might just make the AI Dumber

rather than smarter right so uh okay but

then if it doesn't fizzle out and if it

just continues you know the way it has

over the last few years then you have to

imagine that it's just a matter of uh uh

you know what 10 years 20 years how many

you know until it can do just about

everything as well as we can and uh and

and what then you know does civilization

recognizably continue with sort of

humans in charge uh and and whether it

does or it doesn't is that good or is it

bad uh from our point of view or you

know maybe it depends who you ask uh

so um you know now like a lot of people

don't want to have this discussion they

sort of they still sort of I think don't

want to speculate about these things

including you know many distinguished

colleagues of mine a lot of them are

immersed in what I like to call the the

religion of jism right so they will say

look chat GPT You know despite however

impressive it might look uh uh you know

it's it's it's actually not because we

really we know that it is just a

stochastic parrot it is just a next

token predictor it is just a giant

function approximator it is just a uh a

huge auto complete right and and I I

always want to say to these people okay

and what are you like aren't you just a

bundle of neurons and synapses obeying

the laws of physics right and what about

your mom right you know I mean uh um you

know I mean if you're going to you know

use these reductionist or deflationary

ways of talking then you know at least

you you have to be symmetrical about it

I think so uh a closely related tendency

is well known in AI is the sort of

endlessly moving goalposts you know I

still remember when uh deep blue beat

Casper ofices and you know very smart

people said okay but this is not

impressive because chess is really just

a search problem Wake Me Up When

computers can beat you know the human uh

uh Grand Masters at go okay uh because

that's just an infinitely deeper and

richer game okay and then we had alpago

and then people said okay but fine it's

just a game everyone expected that this

would happen you know wake me up when uh

uh large language models can win a gold

medal in the international math Olympiad

okay so I actually have a bet with a

colleague that that will happened by

2026 uh you know there was some progress

on it just this past month uh now I

might be wrong it might happen by by

2036 instead but it seems clear that

this is just a ma a question of years at

this point and you know after uh uh uh

AI can you know get gold medals and you

know math competitions okay which goal

poost uh uh should we have next uh so

you know we might even be tempted to

formulate a general thesis here which

I'll call you know the game over thesis

which would say that given any task with

a reasonably objective metric of success

or failure games competitions uh and on

which an AI can be given suitably many

examples of success and failure it's

only a matter of years before not only

AI but AI on our current Paradigm will

match or beat the best human performance

now that might not exhaust everything

that we care about uh uh uh you know

there might be things that are not

quantifiable in this way okay but but if

even this is true I think that already

forces us to some uncomfortable places

in you know thinking about kind of well

you know uh uh uh uh what do we tell our

kids about you know uh uh what kind of

jobs are going to be available for them

and and sort of what is our role in the

world uh so um and and you know it's

clear that sort of already what what you

know chat GPT and and Dolly and so forth

can already do has sort of uh uh created

uh for real for us the sort of uh uh

Blade Runner scenario where you know we

are confronted with the problem of

distinguishing uh human outputs from AI

ones so you know one of the main safety

projects that I've worked on uh during

my time at open AI uh has been a scheme

for watermarking the outputs of GPT and

other language uh other large language

models uh what this means is uh sort of

replacing the randomness in the models

by pseudo Randomness in a way that

inserts a uh a secret statistical signal

into you know the choice of words or

tokens uh by which you can later uh

detect that yes this was generated by

chat GPT this did not come from a human

okay now I should caution you that uh

this has not been deployed yet uh so

open AI along with Google and anthropic

uh has been moving kind of slowly and

deliberately toward uh

uh deployment of of text water marking

um even uh uh if and when uh it is

deployed you know someone who is

sufficiently determined will be able to

evade it uh you know just like with

schemes for uh uh preventing piracy of

you know music or software or whatever

uh so you know it's not a perfect

solution but I hope that this and other

measures uh will eventually be able to

you know make it less convenient for

students to use chat GPT to cheat on

their HK work uh uh uh one maybe one of

the most common misuses in the world

right now or uh for people to use it for

spam propaganda impersonation uh all

sorts of other bad things like that okay

but when I talked to my colleagues about

watermarking I was surprised that uh

often you know they had an objection to

it that was not technical at all it

wasn't about how well can it work it was

about uh well should we still even be

giving homework at all right I mean you

know if if uh uh chat GPT can write the

term papers just as well as the students

can right and that's still going to be

true after the students graduate like

you know what's the point why are we

still teaching these skills uh you know

and and I think about this even in terms

of my 11-year-old daughter for example I

mean she loves writing short stories now

chat GPT can also write short stories on

the same themes you know like a a an

11-year-old girl who gets recruited to a

magical boarding school but which is

totally not Hogwarts and has nothing to

do with warts right or you know whatever

other theme like that okay uh now you

could ask the question uh you know if

you look at like today's cohort of

11year olds are they ever going to be

better writers than GPT or you know it's

a it's a it's a race right which one is

going to improve faster um so uh

um uh so so you know you you know you

could imagine that you know even what we

think of as like the greatest products

of artistic genius you know the music of

The Beatles right in principle you know

you could have uh some AI model uh uh do

the same things okay but when you think

about that enough you start wondering

what do we mean what what would we even

mean by an AI that created music that

was as good as the Beatles right like uh

you know and then that forces you to ask

well well what made the beetle so good

in the first place and you know I'm not

a music expert but roughly we could

decompose it into sort of two components

uh one being sort of new ideas about

what direction you know music ought to

go in and secondly technical execution

on those ideas okay now suppose you had

an AI where you know you just fed at the

Beatles whole back catalog and then it

generated more songs that the Beatles

plausibly could have generated but

didn't you know that sounded kind of

like Hey Jude or yesterday or whatever

okay I think that you know most people

would if they saw that would just move

the Gul poost okay they would say no

that doesn't really impress us right

this is just uh uh extrapolation and you

know like uh uh schopenhauer said you

know Talent hits a Target that no one

else can hit but genius you know that

hits a Target that no one else can see

right and and and and you know the uh uh

what we want to see is the AI deciding

for itself to take music in this new

Direction uh so okay now but now you

know imagine that we had that as well

you know imagine that you had an AI

where every time time you hit the

refresh button in your browser window

you got a brand new like radically new

Beatles likee direction that music could

have been taken in in the 1960s right

and each time you run it you just get

another sample from this probability

distribution okay you know even then

there's something kind of weird about

that I mean you know you could say The

Beatles were there at the right place

and time to pick a particular direction

and not only that but sort of drag all

of the rest of us along with them so

that our whole objective function

changed right we can't judge music

anymore except by a Beatles influence

standard just like we can't judge plays

except by a Shakespeare influence

standard right and and so now if you

know there's sort of what I like to call

an AI abundance Paradox right which is

as soon as you have an AI that can

produce a new artwork uh uh well you

know however good it is it can produce a

thousand similar artworks by just uh uh

running it more and more often can

always rewind and try again and uh so so

it sort of radically devalues you know

the worth of that kind of production

just like the price of gold would crash

if someone towed a 10 mile long golden

asteroid to the Earth right uh uh it

wouldn't actually be be be be worth you

know what you what you thought it was

okay and and so you know you could say

well well at least humans will always

have this sort of Advantage okay that uh

at least we have the the advantage of

being frail and there's only you know

there's only one of us you can't back us

up and run us over and over on the same

input you know when we make a decision

we really mean that decision right we're

sticking with it and that's the only one

that you're going to get out of us uh

which is sort of a weird place to you

know stake our claim of human

specialness on but that might be the

place that we're forced to okay but you

know as soon as I've said that I have to

confront a sort of exotic objection uh

which is well is it really true that

humans uh uh uh cannot be rewound cannot

be copied cannot be you know saved as

backups and so forth I mean it is

possible you know some people think so

that that our own cognition you know is

happening in some sort of digital

computation layer you know in the

neurons and synapses and once technology

once brain scanning technology gets good

enough you know itbe uh uh the next

iteration of neurolink or whatever right

we can all just back ourselves up to the

cloud we can you know rewind ourselves

uh uh restore from back up you know and

then that leads to all these strange uh

questions like uh would you agree to

have yourself faxed to Mars you know

just sent as information reconstituted

there uh the original meat version of

you will just be painlessly euthanized

don't worry about it right uh or um you

know would you uh uh uh back up your

brain before you go on a dangerous trip

um so you know I don't know whether

these things will ultimately be possible

right it's a question about uh uh the

ultimately the biology and the physics

of the human brain right is just the

sort of digital layer the relevant one

or is our identity sort of bound up with

the sort of unclonable uh uh uh you know

not fully knowable you know chaotic

details of the uh uh uh molecules you

know inside of the you know individual

sodium ion channels and the neurons

right if you had to go all the way down

to the molecular level then the famous

no cloning theorem in quantum mechanics

would say well you can't make a perfect

copy right if you try to you're going to

have to make measurements that will you

know fail to tell you what you want and

even destroy the original copy that you

had okay so uh uh you know I don't know

whether our identity is sort of bound up

in these unclonable uh uh physical

degrees of freedom but you know even

even not knowing whether uh that's true

or not um you know I mean it does seem

like difference between us and any

existing AI that we're sort of buffeted

around by chaos such that no external

agent can have at least as as far as we

know can have all the information

relevant to predicting our Behavior so

then to Circle all the way back to AI

safety this leads to a very exotic AI

safety proposal which is why don't we

just teach our AIS indoctrinate them in

a religion that venerates the universe's

unclonable ephemer ephemeral analog

locai of creativity and intellig Ence uh

wherever they might be found says

protect them from destruction defer to

their preferences those are the ones

that matter because you know they're

they're the ones that sort of only get

the one chance uh now I don't know if

this is a good idea uh you know in a

different Universe maybe I fell in love

with a different idea but here I kind of

fell in love with this one and

unfortunately you don't get to back me

up and see a different one so all right

so

thanks