OpenAI Insider Talks About the Future of AGI + Scaling Laws of Neural Nets

Summary

この動画は、人工知能(AI)の発展と、将来的に人工汎用知能(AGI)に到達する可能性について議論しています。特に、ニューラルネットワークのパラメーター数が人間の脳と同等になった時に、AGIが実現する可能性があることを示唆しています。また、OpenAIの研究員であるScott Aronsonが、AGIの安全性と制御の問題に取り組んでいることにも言及しています。さらに、AGIの実現が人類に与える影響や倫理的課題についても触れられています。

Takeaways

• 🧠 人間の脳には約1000兆のパラメーター(シナプス)があり、AIモデルの能力は一般にパラメーター数に比例する。
• 📈 GPT-3は1750億のパラメーターを持ち、猫の脳に匹敵する規模。完全なAGI(人工般人工知能)を実現するには、人間の脳と同等の1000兆パラメーターが必要とされている。
• ⚡ AIの進化は主にデータ量とコンピューティングパワーの増加によって促進されてきた。AGIの基本的な理論は1950年代から存在していた。
• 🔮 一部の専門家は、AGIを実現するための技術的詳細はすでに解決済みで、あとはハードウェアの進化を待つだけだと主張している。
• 🤖 遠隔労働者が行える作業はほとんどすべてAGIが代替可能になると予測されている。
• 🕵️‍♂️ スコット・アロンソン氏は量子コンピューター研究者からOpenAIのAI安全性・整合性の研究に携わるようになった。
• 🤔 GPT-4やOpenAIの最新モデルが1000兆パラメーターに達したという噂は、作者自身があまり信用していないようだ。
• 📝 スコット・アロンソンの研究「線形光学の計算複雑性」は、量子コンピューターが古典コンピューターで効率的にシミュレートできないことを示唆している。
• 🔑 AGIを実現する上で重要なポイントは10個程度で、それらはこれまでの様々な研究テキストの中に隠されている可能性がある。
• ✍️ 作者は今回の内容をやや散漫で分かりづらいと認めているが、AGIの話題はますます興味深くなってきている。

Q & A

人工知能はいつ人間レベルの能力に到達すると考えられていますか?

この記事によると、人工知能のパラメーター数が人間の脳のシナプス数に匹敵する約2*10^14個になると、人間レベルの能力に達すると予測されています。ただし、この予測には幅があり、最低でGPT-3レベル、最高で現在の1万倍のパラメーター数が必要となる可能性があると述べられています。

GPT-3とはどのようなモデルですか?

GPT-3は2020年にリリースされた大規模な言語モデルで、1750億個のパラメーターを持ち、ある程度の推論や文章生成が可能でした。この能力には多くの人が驚きを示しましたが、人間レベルの知能とはまだ程遠い段階でした。

ScottAronsonはどのような人物ですか?

ScottAronsonは量子コンピューターの研究者で、2022年半ばにOpenAIの人工知能の安全性と整合性に関する部門に入社しました。彼は人工知能の危険性について警鐘を鳴らす一方で、その進歩を阻止すべきではないと主張しています。

パラメーター数が多ければ多いほど、人工知能の性能は向上するのでしょうか?

はい、おおむねその傾向があると考えられています。パラメーター数が多ければ、ニューラルネットワークに組み込めるデータの量が増え、より複雑なパターンを認識できるようになります。しかし、単にパラメーター数を増やすだけでは不十分で、適切な学習データとアルゴリズムも重要になります。

人工知能がAGI(汎用人工知能)に到達するための技術的詳細は、すでに解決されていると考えられていますか?

はい、John Carmackら一部の専門家は、AGIを実現するための技術的詳細は過去数十年の研究で概ね解決されていると主張しています。彫琢が必要なのは、計算能力とデータの確保だけだと述べられています。ただし、この見解には異論もあります。

ニューラルネットワークにおけるパラメーターとは具体的に何を指しますか?

ニューラルネットワークにおけるパラメーターとは、ノード間の結合の重みや、各ノードへの入力に対する閾値を表すバイアス値のことを指します。これらのパラメーターを調整することで、ネットワークの出力を最適化できます。生物の脳のシナプス結合に相当します。

AA (Anthropic AI)のTIIモデルとは何ですか?

TIIは、Anthropic AIによって開発されているAGIモデルの名称です。このモデルは、リモートワーカーが行える作業のほとんど全てを人間レベルで実行できることを目標としています。パラメーター数の予測値も示されています。

AIの性能予測において、パラメーター数以外に重要な要素はありますか?

パラメーター数だけでなく、学習データの質や量、使用されるアルゴリズム、モデルアーキテクチャなども、AIの性能に大きな影響を与えます。単にパラメーターを増やすだけでは、必ずしも性能が向上するわけではありません。適切な設計と学習が重要です。

AGI実現の鍵は、新しい技術ブレークスルーにあるのでしょうか?

この記事では、AGIを実現するための重要な技術的詳細はすでに過去の研究で明らかになっている可能性があると示唆されています。新しいブレークスルーよりも、既存の知識を組み合わせて適用することが鍵となるかもしれません。

この動画の主な主張は何ですか?

この動画の主な主張は、人工知能の進化はパラメーター数の増加と適切なデータの組み合わせによって加速する可能性があり、AGIに到達するための技術的詳細はすでに存在している可能性があるということです。ただし、具体的な時期予測には不確定要素が多いことも示唆されています。

Outlines

00:00

👾 AGIに対する懐疑的な見方とGPTの能力の進化

この段落では、GPTやChatGPTのような最新のAIシステムの能力は、単なる確率モデルや関数近似にすぎず、人間の脳や意識とは本質的に異なるものだという懐疑的な見方を紹介している。しかし同時に、GPT-3やChatGPTの登場により、AIが文章を理解し、学習し、道徳的判断ができるかのように見えるようになったことで、文明に大きな変化をもたらす可能性にも言及している。

05:00

🧠 ニューラルネットワークにおけるパラメータの役割

この段落では、ニューラルネットワークにおけるパラメータ(重み・バイアス)の役割について説明している。パラメータは、ニューロン同士の結合の強さを表し、より多くのパラメータを持つモデルほど、複雑なパターンを学習できる。また、パブロフの犬の実験を例に、条件付けによる学習プロセスとニューラルネットワークのパラメータ更新の類似性を示している。

10:02

🔢 ヒト脳とAIモデルのパラメータ数の比較

この段落では、ヒト脳のシナプス数(パラメータに相当)が1000億個程度であることを示し、AIモデルのパラメータ数とヒト脳のパラメータ数を比較している。GPT-3のパラメータ数は175億個であり、ネコの脳と同程度だが、ヒト脳に匹敵するには1000兆個のパラメータが必要であると指摘している。また、パラメータ数とAIの性能には相関関係があり、ヒト並みの能力を持つAIを実現するためには、ヒト脳と同等のパラメータ数が重要であると述べている。

15:02

📈 AGIを実現するためのパラメータ数の予測

この段落では、AGI(人工般化知能)を実現するために必要なパラメータ数の予測について述べている。あるレポートでは、AGIに必要なパラメータ数の中央値が1000万億個程度と推定されている。また、Quantum Computingの研究者でOpenAIのAI安全性の研究に従事しているScott Aronsonの見解に触れ、今後のAGIの進展に対する期待と懸念が示されている。

Keywords

💡ニューラルネットワーク

ニューラルネットワークとは、人間の脳の神経細胞のように情報を処理するコンピューターシステムのことです。この動画では、ニューラルネットワークの「パラメータ」と呼ばれる数値の重みが、人工知能の能力を決定する重要な要素として説明されています。ニューラルネットワークのパラメータ数が多ければ多いほど、より複雑な情報処理が可能になるといわれています。

💡AGI(人工般化知能)

AGI(人工般化知能)とは、人間と同等の汎用的な知能を持つ人工知能のことを指します。この動画では、AGIを実現するために必要なニューラルネットワークのパラメータ数について議論されています。AGIが実現されれば、リモートワーカーが行うほとんどすべての作業を人工知能が行えるようになると考えられています。

💡パラメータ

ニューラルネットワークにおけるパラメータとは、ニューロン同士の結合の強さを表す数値のことです。人間の脳の中の神経細胞同士の結合が「シナプス」に相当します。動画の中で、人間の脳のシナプスの数が約1,000兆個あると推定されており、AGIを実現するためにはそれに匹敵する規模のパラメータ数が必要だと説明されています。

💡スケーリング則

スケーリング則とは、ニューラルネットワークの規模(パラメータ数)が大きくなれば、その性能も比例して向上するという法則のことです。この動画では、スケーリング則に基づいて、AGIを実現するためのパラメータ数を予測する研究が紹介されています。パラメータ数が人間の脳と同等の規模に達すれば、人間レベルの知能が実現できるという仮説が示されています。

💡GPT-3

GPT-3はOpenAIが開発した、膨大な量の文章データを学習した大規模な言語モデルです。この動画では、GPT-3がパラメータ数約1,750億個と、当時として最大規模のニューラルネットワークであったことが紹介されています。GPT-3の出現により、人工知能が人間並みの文章を生成できるようになり、大きな驚きをもたらしました。

💡量子コンピューター

量子コンピューターとは、量子力学の原理に基づいて動作する次世代のコンピューターです。この動画では、オープンAIのAI安全性研究者であるスコット・アロンソンが、もともと量子コンピューターの研究者であったことが紹介されています。彼の経歴から、量子コンピューターの高速計算能力がAGI実現に役立つ可能性があると示唆されています。

💡データ

ニューラルネットワークを学習させるために必要なのがデータです。この動画では、インターネットを通じて莫大な量のデータが入手可能になったことが、近年のAI能力の飛躍的な向上に寄与していると指摘されています。質の高いデータを多く与えることで、ニューラルネットワークの性能が大幅に向上するためです。

💡バックプロパゲーション

バックプロパゲーションとは、ニューラルネットワークの学習において、出力結果と正解の差を逆算して、ネットワーク内の結合の重み(パラメータ)を修正する手法のことです。動画の中で、「温かいかヒントに基づいて修正」というゲームの比喩が使われていました。バックプロパゲーションによってパラメータを徐々に調整し、望ましい出力が得られるようにチューニングしていくのです。

💡トランスフォーマーモデル

トランスフォーマーモデルは、注意機構(Attention Mechanism)を活用したニューラルネットワークの一種で、言語処理などの自然言語タスクに優れた性能を発揮します。有名な例としてGPT(Generative Pre-trained Transformer)がありますが、この動画でもパラメータ数の増加に伴うトランスフォーマーモデル性能の向上が示唆されています。

💡パブロフの実験

パブロフの実験とは、条件付け学習の代表例として有名な心理学実験です。この動画では、犬に鐘を鳴らすと同時に餌を与えると、次第に鐘の音だけで唾液が出るようになったという実験が紹介されています。これは、ニューラルネットワークのパラメータが、刺激と結果の関係を学習するメカニズムに例えられています。

Highlights

The author discusses the concept of 'deflationary claims' about AI systems, where each person believes they are the first to make such claims, but the author sees these claims as reductionistic and lacking a principle that separates AI from human intelligence.

The author mentions Scott Aaronson, who worked for OpenAI on AI alignment and safety, and his blog post titled 'Letter to His 11-Year-Old Self' where he discusses the creation of an AI that can converse like humans and the ethical implications surrounding it.

The author expresses skepticism about a leaked paper claiming that GPT-4 or another model built in 2022 has 100 trillion parameters, stating that they don't buy it.

The author discusses the concept of scaling laws and when digital neural networks might exceed the complexity of the human brain, defining AGI as the ability to perform any intellectual task that a smart human can.

The author explains the basics of neural networks, including neurons, connections (parameters/synapses), and how they are trained through forward and backward propagation to adjust the weights and biases to produce desired outputs.

The author highlights John Carmack's belief that we've had the technical details of AGI solved for many decades, but lacked the computing power, data, and internet infrastructure to achieve it.

The author cites a figure that the human brain has around 100 trillion to 200 trillion synaptic connections (parameters), which is used as a benchmark for comparing the parameter count of AI models.

The author discusses a paper that predicts AI performance by parameter count, showing that as models approach the parameter count of the human brain, they are expected to reach human-level abilities.

The author mentions a transformative model called 'AA' (possibly a codename for a specific AI model) and its ability to perform tasks that remote human workers can do, as a potential indicator of AGI.

The author presents estimates for the number of parameters required for a 'transformative model' (potentially AGI) to achieve human-level abilities, ranging from the size of GPT-3 to one quintillion (10^18) parameters, which is 10,000 times more than the human brain.

The author expresses uncertainty about how much of the information presented is believable but finds Scott Aaronson's work on the computational complexity of linear optics and his role at OpenAI's AI safety and alignment team particularly interesting.

The author acknowledges that the video might have been a bit disjointed but expresses that the topic of AI and AGI is becoming increasingly interesting, with more to come soon.

Transcripts

I'm going to call it the religion of

jism okay so so there's like the you

know there's this whole sequence of

deflationary claims right like each

person who makes them thinks that

they're like the first one right and

they you know there there's like I've

seen like like 500 different variants of

this now right chat gbt you know it

doesn't matter how impressive it looks

because it is just a stochastic paret it

is just a next token predictor it is

just a function approximator it is just

a gargantuan autocomplete right and what

these people never do what it never

occurs to them to do is to ask the next

question what are you Justa

right right aren't you just the bundle

of neurons and synapses right I mean

like we could take that deflationary

reductionistic stance about you also

right or or if not then we have to give

some principle that separates the one

from the other right you know it is our

burden to give that principle so the way

that someone was putting it on my blog

was okay you know they they gave this

giant litany you know look GPT does not

interpret sentences it seems to

interpret them it does not learn it

seems to learn it does not judge moral

questions it seems to judge moral

questions and so I just responded to

this I said you know that's great and it

won't change civilization it will seem

to change

it so the person that was talking his

name is Scott arenson and he recently

went to work for openi of the AI

alignment and safety his previous work

in research was into Quantum Computing

and so he started working for open AI in

2022 probably around the middle of the

year and by the end of the year he put

out a blog post titled letter to his

11-year-old self in it he says this

there's a company building an AI that

fills giant rooms eats a Town's worth of

electricity and has recently gained an

astounding ability to converse like

people we can write essays or poetry on

any topic it can Ace college level exams

it's daily gaining new capabilities that

the engineers who tend to the AI can't

even talk about in public yet those

Engineers do however sit in the company

cafeteria and debate the meaning of what

they're creating what will it learn to

do next week which jobs might it render

obsolete should they slow down or stop

so as not to tickle the Tail of the

Dragon but wouldn't that mean someone

else probably someone with less Scruples

would wake the Dragon first is there an

ethical obligation to tell the world

more about this is there an obligation

to tell it less and he's saying that his

job at the company is to develop a

mathematical theory of how to prevent

the AI and its successors from wreaking

havoc so that's Aron right there in 2011

this is from this paper that was leaked

uh and my take is I I think this is BS

after reading it and trying to verify

some of it I mean it's I just don't buy

it here's the thing it starts out really

good it it had me going but at some

point it kind of rapidly falls apart and

it's trying to push this idea that GPT 4

or some other model that they built in

2022 has 100 trillion parameters now

again I I don't buy it I'll post it down

below if you guys want to take a look at

it but anyways my take is is a lot of

this is is nonsense but in this PDF

there are three interesting links to

papers or things that other very

credible researchers have wrote and

specific spefically this guy talking

about AI is also really interesting so

in this video Let's briefly look at

scaling laws and when we can expect

digital neural Nets to exceed kind of

the complexity of the human brain and

basically the definition of a gii that

the author kind of states is it can do

any intellectual task that a smart human

can 2020 was the first time I was

shocked by an AI system that was gpt3 so

the world was catching up just something

that these people were interacting with

years before so people were surprised by

its ability to reason even as early as

gpt3 which gpt3 I feel like most people

haven't even interacted with this

because Chad GPT the big thing that most

people got their hands on that was GPT

3.5 kind of an updated version and he's

saying that somewhere in there there was

this massive leap because before that

Chad Bots had no ability to respond

coherently at all why was gpt3 such a

massive leap and so here we're getting

into parameter count so really fast

exactly what is a parameter so in neural

networks we're kind of replicating the

human brain so here's kind of a diagram

of a neural network these little round

things they're called neurons which is

the digital version of the neuron that's

in our brain basically these neurons

connect to each other and pass

information back and forth so let's say

there's a neuron in your brain that's

responsible for food I'm simplifying

obviously but let's say there's another

one that's it's the smell of cooking so

when you when you smell something

delicious cooking on the stove that

triggers this neuron now obviously the

actual brain is much more complicated

there's a whole it's not like one neuron

does this or that but just for

illustrative purposes like let's say

this neuron is food and this neuron is

smell of cooking each time you smell

something cooking and then you get food

the connection between these two neurons

gets a little bit stronger over time it

gets stronger and stronger and stronger

until the smell of cooking gets to be

kind of a predictive thing for you

getting food whenever you smell cooking

you know that there's food around you're

going to get food this is kind of how

your brain is able to predict the future

if you will and this is how brains work

in humans now so in dogs if a dog smells

something cooking or the smell of food

whatever smells trigger food for them

you know it might start salivating

because it knows food is coming so one

day this handsome fella decided to

decided to see if he can trick these

dogs into creating other neur neural

connections that aren't triggered by

smells but instead by something kind of

random like ringing a bell so this is

Ivan Pavlov uh if you've ever hear that

term pavlovian response that's kind of

his doing he would ring a bell every

time before he served dogs food so he'd

ring a bell and give him some food and

this would go over a course of however

along ring a bell give him food ring a

bell give the dog food it was a whole

thing they really went all in on this

now obviously beforehand if you just

rang a bell the dog didn't really have

any response to it it didn't mean

anything to the dog but after doing this

for a Time the dogs started salivating

after hearing a bell the dogs were

conditioned to salivate and expect food

whenever that bell rang by the way this

is why the office was such a great show

cuz that whole prank that Jim plays on

Dwight with the breath mints was

literally him conditioning that

pavlovian response by giving him a

breath mint every time there was a the

Microsoft Office ding or whatever but

the point here is with the dogs and

Dwight I guess as this this thing kept

happening where a bell would ring and

then he and the dog would get a treat

the actual like physical wiring in the

brain these neural connections would get

stronger and stronger so the Bell became

a stronger and stronger signal for you

know there's food coming until the dog

was like okay anytime I hear a bell that

means I get food like I was convinced of

that so in the neural Nets in the AI

weights and biases they determine kind

of the strength of that connection how

often those connections get called how

strong they are so for example before

the pavlovian conditioning of the dog

you know a bell ringing might have a

very low connection to you know getting

food the dog doesn't connect a bell to

getting food but as he keeps hearing you

know being food being food this

connection gets stronger to where

there's a stronger there's a stronger

predictive ability between the being and

the food between the Bell and the food

and all these various connections are

referred to as parameters and so the

more parameters the more connections the

more possible I guess predictive

abilities and so when we refer to the

the size of the AI model the size of the

llm we refer to it as the number of

parameters the number of total

connections and then when we train the

model when we give it data you can think

of all these as little knobs and Dot FES

that we kind of twist and turn to try to

create these connections that make sense

then we then we have our input and the

output and we try to understand like how

good is this brain this series of

connections weights and biases how good

is it at producing the response if it's

way off then we have a process called

back propagation where we go back and

kind of like flip these dials into

different positions and we try again and

these back and forward passes over time

set all the little dials and knobs into

the correct position to get the outputs

that we're looking for so I kind of

think of this as that game where you say

if you're getting hotter or colder right

so you move in a certain direction

that's the forward pass and then the

person you're playing with goes you're

getting warmer and so you do the back

propagation so that's where you know

maybe you turn in a slightly different

direction and you head in that direction

right and then the person goes oh you're

almost there you're getting hot right so

basically the hotter you get the less

changes you make to what you're doing if

they're saying oh your ice cold then you

make a lot of changes and you flip all

these dials in in different directions I

mean slightly more complicated than that

but I feel like what I've described is a

pretty good analogy and so the paper

continues deep learning is a concept

that essentially goes back to the

beginning of AI research in the 1950s

the first neural network was created in

the ' 50s and modern neural networks are

just deeper meaning they contain more

layers these are the layers so there's

just more more layers across the network

and most of the major techniques in AI

today I rooted in the basic 1950s

research combined with a few minor

engineering Solutions like back

propagation and Transformer models yeah

it's not it's just a few minor tweaks I

think most people would say that these

are kind of I mean big deals but his

point is a lot of this the idea of

neural networks isn't exactly new so

he's saying there's only two reasons for

the recent explosion of AI capability

size and data uh so maybe a different

way of saying that is just we can have

massive progress massive improvements we

we've just improving the size and the

data like this alone will create massive

progress without necessarily other

breakthroughs I think that's fair to say

and a growing number of people in the

field are beginning to believe we've had

the technical details of AGI solved for

many decades we just didn't have the

computing power we didn't have the data

and we didn't have the internet for all

the data so this is John CarMax so he

was the guy that created the original

Doom him and John Romero and so he's

he's kind of a big deal like he's

wellknown highly respected here's him

with Elon Musk here's him with Notch

that's the creator of Minecraft who sold

it for billions I think to Microsoft I

think he like single-handedly coded

Minecraft way back in the day here's him

on stage with Steve Jobs I think he

worked for meta on the whole virtual

reality for for quite some time and on

the Lex freedom in podcast he talked

about kind of this very idea and he

actually founded recently announced that

he started his own AGI lab and he's

saying like for the first time in kind

of humor history just one or a handful

of people can have like an incredible

result on the world this leverage by

creating AGI and he kind of said

something similar that we probably have

the technical details of AGI like we

we've had it solved and I believe he

also said that if you had to write write

out like all the things that you needed

to know to solve AGI it would probably

fit on a napkin like there might be 10

things that we kind of needed to solve

that would allow for egi to happen and a

lot of them are probably hidden away in

various texts and textbooks over the

past you know power many decades so this

idea that it's probably not going to

come from some brand new thing that no

one has expected but rather from

something that has been already talked

about right like just like neural Nets

you know the first one was created in

the 50s right so it's been around for a

while and so they're saying what is this

parameter well it's kind of like a

sinapse synapse sinapse however you

pronounce that so it's like a syapse in

a biological brain connection between

neurons and each neuron in the

biological brain has roughly a thousand

connections to other neurons and of

course digital neural networks are

analogous to biological brains so this

is interesting how many parameters right

synapses or parameters are in a human

brain so the figure that is commonly

cited is 100 trillion so keep that

number in M 100 trillion 100 trillion

parameters in the human brain so with

AGI we're trying to achieve something

similar to a human brain or the human

brain's capabilities the general

intelligence so in nature that's 100

quote unquote parameters so Yale

Neuroscience 100 trillion synaptic

connections human brain there are more

neurons in a single brain than there are

stars in the Milky Way and a cat has 250

billion synapses a dog has 530 billion

sinaps says sinapse count generally

seems to predict higher intelligence

this guy is now just talking crap about

cats I'm not sure how I feel about that

and he know so yeah there's there's some

exceptions for example elephants have

higher count than humans yet display

lower intelligence and he kind of

explains that that the quality of data

might answer for those uh exceptions so

human brains evolved from higher quality

socialization and communication data

than elephants but the point is syapse

count is definitely important and so

gpt2 the syapse count is less than a

mouse's brain gpt3 is approaching a

cat's brain so it's intuitively obvious

that an AI system the size of a cat's

brain would be superior to an AI system

the size of a mouse's brain so all other

things equal certainly that's the case

predicting AI performance in 2020 after

the release of the 175 billion parameter

gpt3 many speculate about the potential

performance of a model that is 600 times

larger so that's the 100 trillion

parameters kind of where it's equivalent

to the human brain so gpt3 is like.

175% so it's like a tenth of 1% of what

the human brain is in terms of

parameters so is it possible to predict

AI performance by parameter count and as

it turns out the answer is yes and so

here's the paper so saying there are

roughly 2 2 * 10 14th power synapses in

the human brain

which that's that's 200 trillion so it's

double than that Yale quoted earlier

Yale NE science so here they're saying

200 you know double what double that

amount right and this line here looks

like it's this line on the chart that's

where the parameters equal synapses in

the brain so this is kind of where that

line when we cross it over that's when

neural networks match the parameters in

of the human brain according to this

article and the dark green line that's

the next line here so again this is

where it matches the human brain this is

the tii the transformative model so this

is AA cotra and so in this little speech

she gave the introducer said AA so I'm

just going to go with that AA so this is

a ja and this is from less wrong.com

I'll post this in the show notes we

probably it's a little bit older at this

point August 2022 but we might look into

it but she talks about tii which is I

mean you can think of it as AGI so

basically kind of a similar idea so like

at what point is it going to get to the

point of maybe replacing human workers

uh or or at least being as capable as

human workers but but this kind of

jumped out of me so she was saying when

writing my report I was imagining that a

transformative model would likely need

to be able to do almost all the tasks

that remote human workers can do and

again we might do a deep dive into this

article but I got to say in my mind I

think this is a much better sort of

conceptual way thinking about AGI like

at what point can it do anything that a

remote human worker can do so basically

if you have a job where a person doesn't

need to come into the office and you

communicate through emails and they do

whatever whatever it is that they do

whether that's Excel or writing or

coding design whatever like when will

tii or AGI when can AI kind of just do

all of that or in other words if you're

a remote worker and currently you spend

half of the time that you're supposed to

supposed to be working playing hell

divers 2 at what point can you spend all

of your time playing hell divers 2 but

the point is this green line that's the

median that's the average estimate for

the number of parameters in that

transformative model that tii and the

80% confidence interval so kind of is

between these two number of parameters

so meaning at what point are we fairly

certain that we've achieved that at how

many parameters have we achieved AGI

well it's as low as gpt3 and uh and as

high as so 10 to the 18th that's one

quintilian so if I'm doing my math right

so that's 10,000 times more than what

the human brain would be so this

extrapolation shows that AI performance

will reach human level abilities as it

reaches human level size parameter count

so what we just went over I don't know

how much of it I believe I went through

it I ended up cutting most of it but

this Scott Aronson guy really jumped out

to me here's a paper that he did the

computational complexity of linear

Optics talking about giving new evidence

that quantum computers cannot be

efficiently simulated by classical

computers it's interesting that a

quantum computer guy is working at open

ai's you know AI safety and Alignment

there's some more interesting stuff

ahead I apologize if today's video was a

little bit disjointed but this whole

thing is getting uh a lot more

interesting more to come very soon