「アルゴリズム」って何？ハーバードの教授が教える最先端を目指すための基礎と道のり | 5 Levels | WIRED Japan

Summary

このスクリプトは、アルゴリズムの重要性と多様性について解説しています。コンピュータサイエンスの教授であるデイビッド・J・マ博士が、アルゴリズムが問題解決の機会を提供し、日常生活に潜むアルゴリズムの例を説明します。また、CPUやRAMなどのコンピュータの内部構成要素や、ソートアルゴリズム、検索アルゴリズム、機械学習、ディープラーニングなど、高度なトピックも触れています。さらに、アルゴリズムの研究や発展、そしてそれに伴う倫理的な問題についても議論しています。

Takeaways

• 😀 デビッド・J・マ教授は、アルゴリズムの重要性について説明しました。彼はハーバード大学のコンピューターサイエンス教授です。
• 😀 アルゴリズムは、物理世界だけでなく仮想世界でも問題解決の手段として広く使われています。
• 😀 コンピュータの基本的なハードウェア要素（CPU、RAM、ハードドライブ）について説明しました。
• 😀 アルゴリズムとは、特定の問題を解決するためのステップバイステップの指示のリストであると定義されました。
• 😀 デビッド教授と子供がピーナッツバターサンドイッチを作るアルゴリズムを一緒に作成しました。
• 😀 アルゴリズムの精度が重要であり、正確な指示を出す必要があることを強調しました。
• 😀 二分探索アルゴリズムを使って電話帳から名前を検索する方法を説明しました。
• 😀 アルゴリズムの「分割統治法」について、問題を小さく分割して解決する方法を説明しました。
• 😀 学生や研究者が、アルゴリズムを発明したり研究したりする方法について議論しました。
• 😀 現代のAIや機械学習アルゴリズムがどのようにして私たちの日常生活に浸透しているかについて述べました。

Q & A

アルゴリズムとは何かを説明してください。

アルゴリズムとは、問題を解決するための一連のステップや手順を意味します。例えば、就寝のルーティンやサンドイッチ作りのプロセスなどがアルゴリズムの一例です。

コンピュータのCPUとは何を意味していますか？

CPU、つまり中央処理ユニットはコンピュータの「脳」であり、命令に応じて動作するハードウェアの部品です。算術演算や方向の移動などの基本的な操作を行います。

コンピュータのメモリとRAMとはどのような関係がありますか？

メモリやRAMはコンピュータの「記憶力」であり、使用中のプログラムやゲームが保存される場所です。電源が切れてもハードドライブやソリッドステートドライブに保存されたデータは失われません。

「バブルソート」アルゴリズムの基本的な考え方はどのようなものですか？

バブルソートはローカルな小さい問題に焦点を当て、一番小さい値から順番に並べ替えるアルゴリズムです。隣接する要素同士を比較し、順序が逆であれば交換することで、徐々に配列を整序します。

「二分探索」アルゴリズムの利点は何ですか？

二分探索アルゴリズムは、大きな問題を半分に分割し、その半分を捨てて問題を解決する「分治法」を利用しています。これにより、検索時間を大幅に短縮できます。

「再帰アルゴリズム」とは何を意味していますか？

再帰アルゴリズムは、同じ問題を繰り返し小さくしていくプロセスで、アルゴリズム自体が自分自身を呼び出します。これは問題をより小さな部分問題に分割し、解決することで効率性を高める方法です。

ソーシャルメディアにおけるアルゴリズムの例として挙げられた「TikTokの“For You”ページ」の仕組みを説明してください。

TikTokの“For You”ページは、ユーザーが過去に好評だった投稿や興味を示した内容に基づいて、新しい投稿を推薦するアルゴリズムを利用しています。これにより、ユーザーのエンゲージメントを高めることができます。

機械学習アルゴリズムと統計的アルゴリズムの違いは何ですか？

機械学習アルゴリズムは、データから学習しパフォーマンスを向上させる一方で、統計的アルゴリズムは特定のデータセットに関する最良のモデルを見つけるために最適化されます。機械学習はより広い範囲でデータからの学習を意味するのに対し、統計的アルゴリズムは特定のデータセットに焦点を当てます。

アルゴリズムの「分治法」とはどのような手法ですか？

分治法は大きな問題を小さく分割し、それぞれの小問題を解決した後、結果を組み合わせて元の問題の解決策を得るアルゴリズムです。この手法は効率性を高めるために広く使われています。

アルゴリズムの研究や開発において、最も重要な要素は何ですか？

アルゴリズムの研究や開発では、効率性や問題解決の最適な方法を見つけることが最も重要です。また、アルゴリズムの理論的基礎を理解し、それがどのように機能し効果を発揮するかを理解することも重要です。

アルゴリズムの適用分野が拡大する中で、個人のプライバシーに対する影響はどのようになっていますか？

アルゴリズムの適用が拡大するにつれて、個人データの収集と分析が行われることが増え、プライバシーに対する懸念が高まっています。アルゴリズムはマーケターにとって有益である一方で、個人にとっては不要なターゲティングや侵入的な広告表示の原因になることがあります。

最近のAI技術の発展において、アルゴリズムの理解がどの程度重要ですか？

AI技術の発展の中でも、アルゴリズムの理解は依然として重要です。アルゴリズムはAIの基礎であり、機械学習やディープラーニングなどの高度な技術を理解するためには、基本的なアルゴリズムの知識が必要です。

アルゴリズムの「ブラックボックス」問題とは何ですか？

「ブラックボックス」問題は、アルゴリズムの内部動作やその決定プロセスが理解できない状態を指します。特にディープラーニングのような複雑なアルゴリズムでは、モデルがなぜ特定の結果を出すのかを説明することが難しいことがあります。

アルゴリズムの「分治法」を実践する際に、どのような注意点がありますか？

分治法を実践する際には、問題が適切に分割され、各サブ問題に対する解決策が独立して考慮され、最終的な結果が正確に組み合わせられることが重要です。また、分割の方法や再結合のアルゴリズムの選択にも注意が必要です。

Outlines

00:00

👨‍🏫 算法の基礎と重要性

デイビッド・J・マ教授はハーバード大学のコンピューターサイエンス教授として、アルゴリズムの5つのレベルを解説します。アルゴリズムは物理世界だけでなく、バーチャル世界でも問題解決の機会を提供しています。コンピュータは電子機器で、CPUとRAMが重要な役割を果たします。アルゴリズムはステップバイステップの指令で、例えばベッドタイムルーティンやランチのサンドイッチ作りを例に説明します。正確性は非常に重要で、ネット検索のアルゴリズムもその一例です。

05:00

🔍 検索アルゴリズムの進化

アルゴリズムの応用として、電話帳での検索方法が語られます。最初のアルゴリズムはページを1つずつ確認する線形探索ですが、2ページずつ飛ばす方法で高速化が図られます。さらに、二分探索法と呼ばれるアルゴリズムが紹介され、問題を半分に分割し効率的に解決するプロセスが説明されています。これは現代の携帯電話の連絡先検索にも応用されています。

10:02

🤖 再帰アルゴリズムとソート技術

再帰アルゴリズムは自分自身を用いて問題を小さく分割し、解決する高度なアルゴリズムです。バブルソートが例に挙げられ、ローカルな問題を小さくまとめることで全体を整えていく方法が説明されています。また、ソートアルゴリズムの他にも、機械学習やディープラーニングにおけるアルゴリズムの応用が触れられ、TikTokの「For You」ページの推薦システムなど、アルゴリズムが日常生活に潜入している例が示されています。

15:03

🧠 機械学習とアルゴリズムの研究

アルゴリズムの研究と開発は、非効率性を見つけ出して解決策を提供することにあります。機械学習は日常に溶け込んでおり、Googleの検索エンジンやNetflixの推薦システムなどがその一例です。ディープラーニングは、巨大なデータから学習し、ゲームや偽造動画の生成など、多様な応用が見られます。アルゴリズムの研究は、理論的基礎を追求する一方で、実際の問題解決にもつながっています。

20:04

📊 データサイエンスとアルゴリズムの実践

データサイエンスとアルゴリズムの実践が語られます。ニューヨークタイムズのデータサイエンティストとして、機械学習をニュースルームやビジネス問題に適用しています。アルゴリズムは最適なモデルを見つけるための最適化アルゴリズムとして機能し、統計的性能だけでなく、ソフトウェア工学やシステム統合も重要です。AIスタートアップとアカデミアの間にも、アルゴリズムの応用が広がっており、その間で繋がりが生まれています。

25:05

🤖 AIの進化とアルゴリズムの役割

AIの進化とアルゴリズムの役割が議論されています。大規模言語モデルは、アルゴリズムのトレーニングとファインチューニングに関与し、その設計や問題解決能力に対する理解が進化しています。しかし、アルゴリズムのブラックボックス化が進むことで、完全な制御が失われているとも言えます。アルゴリズムの理解は、技術の進歩と密接に関係しており、基本的なアルゴリズムから高度なものへと学びを深めていくことが求められます。

🛠️ 技術の善悪とアルゴリズムの未来

技術の善悪について議論し、アルゴリズムの可能性と潜在的なリスクについて考察しています。技術は中立ではなく、新たな技術には良い面と悪い面の両方があると指摘されています。アルゴリズムの学習は、基本から高度なレベルへと進化し、最終的には理解し、適用することができるようになります。アルゴリズムの研究は、技術の進歩と共に進化し続けるでしょう。

Keywords

💡アルゴリズム

アルゴリズムとは、問題を解決するための一連のステップや手順のことで、コンピューターサイエンスの基礎概念です。このビデオでは、アルゴリズムが物理世界だけでなく、仮想世界でも広く存在し、問題解決の機会を提供していると強調されています。例えば、ベッドタイムルーティンやランチのサンドイッチ作りの過程が、アルゴリズムの一種であると説明されています。

💡コンピュータ

コンピュータは、電子的なデバイスで、矩形の形状でタイピングが可能な機器です。ビデオでは、コンピュータの内部にCPU（Central Processing Unit）やRAM（Random Access Memory）などの部品があると説明されています。CPUは命令に応答し、数学的な演算を行うハードウェアであり、RAMはプログラムやゲームが使用されている間を保存するメモリです。

💡CPU

CPUは、コンピューターの「脳」であり、技術用語で「Central Processing Unit」の略です。ビデオでは、CPUが命令に応答し、方向を移動したり、数学の加減算を行う役割を持っていると説明されています。

💡RAM

RAMは「Random Access Memory」の略で、コンピューターのメモリの一形態です。ビデオでは、RAMがプログラムが使用中的时候にデータを一時的に保存する場所であると説明されています。

💡ハードドライブ

ハードドライブやソリッドステートドライブは、コンピューターのデータが永続的に保存される場所です。ビデオでは、電源が切れても情報は保持されるため、データの安全性が確保されると説明されています。

💡バイナリサーチ

バイナリサーチは、データがアルファベット順に並べ替えられた配列やリスト内で効率的に検索を行うアルゴリズムです。ビデオでは、電話帳でジョンを検索する例として、バイナリサーチの概念が紹介されており、問題を半分に分けながら解決する「分治法」として位置づけられています。

💡バブルソート

バブルソートは、隣接する要素を比較し、順序が逆であれば交換する反復的プロセスです。ビデオでは、数字の並び替えの例として説明されており、ローカルな小規模な問題に焦点を当てながら全体を整える方法とされています。

💡ディープラーニング

ディープラーニングは、アルゴリズムの1つのタイプで、人工知能の分野で使われます。ビデオでは、ディープラーニングが学習アルゴリズムの一形態であり、大量のデータからパターンを学習し、新しい予測や判断を行う能力を持っていると説明されています。

💡機械学習

機械学習は、コンピューターがデータから学習し、判断や予測を行う能力を獲得するプロセスです。ビデオでは、機械学習がアルゴリズムの応用であり、例えばニューヨークタイムズのコンテンツのパーソナライゼーションやソーシャルメディアの「あなたに適したページ」の推薦システムに使われていると紹介されています。

💡最適化アルゴリズム

最適化アルゴリズムは、特定の問題に対する最適な解決策を見つけるために設計されたアルゴリズムです。ビデオでは、データサイエンスで最適化アルゴリズムがモデルの最適な説明を見つけるために使用されると説明されており、アルゴリズムの理論的な理解と実践的な応用が結びついていると強調されています。

💡ラーニングエージェント

ラーニングエージェントは、経験を通じて学習し、ゲームやタスクの戦略を改善するアルゴリズムです。ビデオでは、AlphaZeroやAlphaStarなどの例として提唱されており、これらのエージェントはゲームをプレイしながら学習し、データに基づいて戦略を洗練化することができると説明されています。

💡データサイエンティスト

データサイエンティストは、統計学、コンピューターサイエンス、ドメイン知識を組み合わせてデータから洞察を得る専門家です。ビデオでは、データサイエンティストがアルゴリズムを開発し、ビジネスやニュースルームの問題に適用する役割を持っていると紹介されています。

💡ラージ言語モデル

ラージ言語モデルは、大量のテキストデータで事前学習され、特定のタスクやテキストのサブセットに対してファインチューニングされる機械学習モデルです。ビデオでは、最新の技術としてラージ言語モデルが話題されており、そのトレーニングとファインチューニングのプロセスがアルゴリズムの応用例として説明されています。

Highlights

David J Ma教授强调算法在现实世界和虚拟世界中无处不在，并代表解决问题的机会。

计算机被定义为一种电子设备，具有中央处理器（CPU）和随机存取存储器（RAM）等硬件部件。

算法是一系列指令，用于解决问题，例如睡前常规或制作三明治的步骤。

精确性在算法中至关重要，错误的指令可能导致错误的结果。

搜索算法，如二分查找，展示了如何通过分治法快速定位信息。

递归算法通过自我调用解决相同问题的更小版本。

排序算法，如冒泡排序，通过逐步解决局部问题来优化整体情况。

社交媒体算法，如TikTok的推荐系统，基于用户行为和偏好来提供个性化内容。

学习算法在日常生活中的应用，如搜索引擎和推荐系统，正在不断增长。

深度伪造技术展示了学习算法如何模拟人类的语音和外观。

机器学习与经典算法的结合，如AlphaZero在围棋中的应用。

算法研究涉及寻找效率和连接性，以及优化问题的新方法。

数据科学家使用算法来优化模型并解决新闻编辑室和商业问题。

大型语言模型（LLMs）是预测下一个词的架构，而算法涉及训练和微调这些模型。

尽管大型语言模型的工作原理不完全清楚，但它们在实际应用中表现出色。

算法的理解和进步不一定需要彼此，但它们是松散耦合的。

随着技术的发展，对算法基础知识的需求可能会减少，但它们仍然是技术进步的基础。

算法提供了从基础到高级的广泛范围，即使最先进的算法现在可能难以理解，但随着学习的深入，它们将变得更加可访问。

Transcripts

hello world my name is David J Ma and

I'm a professor of computer science at

Harvard University today I've been asked

to explain algorithms in five levels of

increasing difficulty algorithms are

important because they really are

everywhere not only in the physical

world but certainly in the virtual world

as well and in fact what excites me

about algorithms is that they really

represent an opportunity to solve

problems and I dare say no matter what

you do in life all of us have problems

to

solve so I'm a computer science

professor so I spend a lot of time with

computers how would you define a

computer for them well a computer is

electronic like a phone but it's um a

rectangle and you like could type like

tick tick tick and you work on it nice

do you know any of the parts that are

inside of a computer um no can I explain

a couple of them to you yeah so like

inside of every computer is some kind of

brain and the technical term for that is

CPU or Central processing unit and those

are the pieces of Hardware that know how

to respond to those instructions like

moving up or down or left or right knows

how to do math like addition and

subtraction and then there's at least

one other type of Hardware inside of a

computer called memory or Ram if you've

heard of this I know memory because you

have to memorize stuff yeah exactly and

computers have even different types of

memory they have what's called Ram

random access memory which is where your

games where your programs are stored

while they're being used but then it

also has a a hard drive or a solid state

drive which is where your data your high

scores your documents once you start

writing essays and and stories in the

future stays stays permanently so even

if the power goes out the computer can

still remember that information it's

still there because the computer can't

just like delete all the words itself

because your fingers can only do that

like you have to use your finger to

delete all the stuff exactly have you

heard of an algorithm before um yes

algorithm is a list of instructions to

tell people what to do or like a robot

what to do yeah exactly it's so it's

just stepbystep instructions for doing

something for solving a problem for yeah

so like if you have a bedtime routine

then first you say I get dressed I brush

my teeth I read a little story and then

I go to bed all right well how about

another algorithm like um what do you

tend to eat for for lunch any types of

sandwiches you like uh I eat peanut

butter let me get some supplies from the

cupboard here so should we make an

algorithm together for why don't we do

it this way why don't we pretend like

I'm a computer or maybe I'm a robot so I

only understand your instructions and so

I want you to feed me Noe pun intended

in algorithm so step-by-step

instructions for solving this problem

but remember algorithms you have to be

precise you have to give the right

instructions the right instructions just

do it for me so step step one was what

open the bag okay opening the bag of

bread stop grab the bread and put it on

the plate grab the bread and put it on

the

plate take all the bread back and put it

back in there so that's like an undo

command little control Z okay take one

bread and put it on the plate take the

lid off the peanut butter okay take the

lid off the peanut butter put the lid

down okay take the knife take the knife

put put the blade inside the peanut

butter and spread the peanut butter on

the bread I'm going to take out some

peanut butter and I'm going to spread

the peanut butter on the bread I put a

lot of peanut butter on because I love

peanut butter apparently I thought I was

messing with you here but I think you're

happy with this Put The Knife down and

then grab one bread and put it on top of

the second bread

sideways

sideways like put it flat on oh flat

ways okay and now done you're done with

your sandwich should we take a delicious

bite yep let's take a bite okay here we

go what would be the next step for you

here clean all this mess up clean all

this mess up right we made an algorithm

step by-step instructions for solving

some problem and if you think about now

how we made peanut butter and jelly

sandwiches sometimes we were imprecise

you didn't give me quite enough

information to do the algorithm

correctly and that's why I took out so

much bread Precision being very very

very correct with your instructions is

so important in the real world because

for instance when you're using the

worldwide web and you're searching for

something on Google or B you want to do

the right thing so like if you type in

just Google then you won't find the

answer to your question pretty much

everything we do in life is an algorithm

even if we don't use that fancy word to

describe it because you and I are sort

of following instructions either that we

came up with ourselves or maybe our

parents told us how to do these things

and so those are just algorithms but

when you start using algorithms in uh

computers that's when you start writing

code what do you know about algorithms

nothing really um at all honestly I

think it's just probably a way to store

information um in computers and I dare

say even though you might not have put

this word on it odds are you executed as

a human multiple algorithms today even

before you came here today like what

were a few things that you did I got

ready okay and get ready what does that

mean brushing my teeth brushing my hair

okay getting dressed okay so all of

those frankly if we really um Dove more

deeply could be broken down into

stepbystep instructions and presumably

your mom your dad someone in the past

sort of programmed you as a human to

know what to do and then after that as a

smart human you can sort of take it from

there and you don't need their help

anymore but that's kind of what we're

doing when we program computers

something may be even more familiar

nowadays like odds are you have a cell

phone your contacts or your address book

let me ask you why that is like why does

Apple or Google or anyone else bother

alphabetizing your contacts I just

assumed it would be easier to navigate

what if your friend happened to be at

the very bottom of this randomly

organized list like why is that a

problem like he or she is still there I

guess it would take a while to get to

while you're scrolling that in of itself

is kind of a problem or it's an

inefficient solution to the problem so

it turns out that back in my day before

there were cell phones like everyone's

numbers from my schools like were

literally printed in a book and everyone

in my town and my city my state was

printed in an actual phone book even if

you've never seen this technology before

how would you propose verbally to find

John in this phone book or I would just

slip through and just look for the J I

guess yeah so let me propose that we

start that way I could just start at the

beginning and step by step I could just

look at each page looking for John

looking for John now even if you've

never seen this here technology before

it turns out this is exactly what your

phone could be doing in software like

someone from Google or Apple or the like

they could write software that uses a

technique and programming known as a

loop and a loop as the word implies is

just sort of do something again and

again what if instead of starting from

the beginning and going one page at a

time what if I or what if your phone

goes like two pages or two names at a

time would this be correct do you think

well you could skip over John I think in

what sense if he's in one of the middle

pages that you skipped over yeah so sort

of accidentally and frankly with like

50/50 probability John could get

sandwiched in between two pages but does

that mean I have to throw that algorithm

out Al together maybe you could use that

strategy and until you get close to the

section and then switch to going one by

one okay that's nice so you could kind

of like go twice as fast but then kind

of pump the brakes as you near your exit

on the highway or in this case near the

J section of the book exactly and maybe

alternatively if I get to like a BC d e

f g h i j k if I get to the K section

then I could just double back like one

page just to make sure John didn't get

sandwiched between those pages so the

nice thing about that second algorithm

is that I'm flying through the phone

book like two pages at a time so 2 4 6 8

10 12 it's not perfect it's not NE neily

correct but it is if I just take like

one extra step so I think it's fixable

but what your phone is probably doing

and frankly what I and like my parents

and grandparents used to do back in the

day is we'd probably go roughly to the

middle of the phone book here and just

intuitively if this is an alphabetized

phone book in English what section am I

probably going to find myself in roughly

okay okay so I'm in the K section is

John going to be to the left or to the

right to the left yeah so John is going

to be to the left or the right and what

we can do here though your phone does

something smarter is tear the problem in

half throw half of the problem away

being left with just 500 pages now but

what might I next do I could sort of

naively just start at the beginning

again but we've learned to do better I

can go roughly to the middle here do it

again yeah exactly so now maybe I'm in

the E section which is a little to the

left so John is clearly going to be to

the right so I can again tear the

problem portly in half throw this half

of the problem away and I claim now that

if we started with 1,000 Pages now we've

gone to 500 250 now we're really moving

quickly

eventually I'm hopefully dramatically

with just one single page at point is

either on that page or not on that page

and I can call him roughly how many

steps might this third algorithm take if

I started with a th000 Pages then went

to 500 250 125 like how many times can

you divide 1,000 and half maybe 10

that's roughly 10 because in the first

algorithm looking again for someone like

Zoe in the worst case might have to go

all the way through thousand pages but

the second algorithm you said was 500

maybe 5001 essentially the same thing so

twice as fast but this third and final

algorithm is sort of fundamentally

faster because you're you're sort of

dividing and conquering it in half and

half and half not just taking one or two

bites out of it at of a time so this of

course is not how we used to use phone

books back in the day since otherwise

they'd be single use only but it is how

your phone is actually searching for Zoe

for John for anyone else but it's doing

it in software oh that's cool so here

we've happened to focus on searching

algorithms looking for John in the phone

book but the technique we just Ed can

indeed be called divide and conquer

where you take a big problem and you

divide and conquer that is you try to

chop it up into smaller smaller smaller

pieces a more sophisticated type of

algorithm at least depending on how you

implement it something known as a

recursive algorithm recursive algorithm

is essentially an algorithm that uses

itself to solve the exact same problem

again and again but chops it smaller and

smaller and smaller

ultimately hi my name is Patricia

Patricia nice to meet you where are you

a student at I'm starting my senior year

now at NYU oh nice and what have you

been studying the past few years I study

computer science and data science if you

were chatting with a non-cs non-data

science friend of yours like how would

you explain to them what an algorithm is

some kind of like systematic way of like

solving a problem or like a set of like

steps to kind of solve a certain like

problem you have so you probably recall

learning topics like binary search

versus linear search and like so I've

come here uh complete with a chalkboard

with some magnetic numbers on it here

like how would you tell a friend to sort

these I think one of the first things we

learned was something called Bubble

swort it was kind of like focusing on

like smaller like bubbles I guess I

would say like of the problem like

looking at like smaller segments rather

than like the whole thing at once what

is I think very true about what you're

hinting at is that bubble sort really

focuses on like local small problems

rather than taking a step back trying to

fix the whole thing let's just fix the

obvious problems in of us so for

instance when we're trying to get from

smallest to largest and the first two

things we see are eight followed by one

this looks like a problem cuz it's out

of order so what would be the simplest

fix the least amount of work we can do

to at least fix one problem just like

switch those two numbers cuz one is

obviously smaller than eight perfect so

we just swap those two then you would

switch those again yeah so that further

improves the situation and you can kind

of see it that the one and the two are

now in place how about 8 and six switch

it again switch those again 8 and three

switch it again

and conversely now the one and the two

are closer to and coincidentally are

exactly where we want them to be so are

we done no okay so obviously not but

what could we do now to further improve

the situation go through it again but

like you don't need to check the last

one anymore because we know like that

number is bubbled up to the top Yeah

because it has indeed bubbled all the

way to the top so one and two yeah keep

it as is okay two and six keep it as is

okay and three then you switch it okay

we switch or swap those six and four

swap it again okay so four and uh six

and seven uh keep it okay s and five

swap it okay and then I think per your

point we're pretty darn close let's go

through once more one and two keep it

two three keep it 3 four keep it 4 six

keep it 6 five and then switch it all

right we'll switch this and now to your

point we don't need to bother with the

ones that already bubbled their way up

now we're 100% sure it's sorted yeah and

certainly the search engines of the

world Google and and so forth they

probably don't keep web pages in sorted

order cuz that would be a crazy long

list when you're just trying to search

the data but there's probably some

algorithm underlying what they do and

they probably similarly just like we do

a bit of work upfront to get things

organized even if it's not strictly

sorted in the same way so that people

like you and me and others can find that

same information so how about social

media can you envision where the

algorithms are in that world like maybe

for example like Tik Tok like the for

you page it's kind of like cuz those are

like Rec like recommendations right it's

like sort of like Netflix

recommendations except more constant

because it's just like every video you

scroll it's like that's a new

recommendation basically and it's like

based on like what you've liked

previously what you've like saved

previously what you search up so I would

assume there's some kind of algorithm

there kind of figuring out like what to

put on your for you page absolutely just

trying to keep you presumably more

engaged so the better the algorithm is

the better your engagement is maybe the

more money the company then makes on the

platform and so forth so it all sort of

feeds together but what you're

describing really is more artificially

intelligent if I may because presumably

there's not someone at Tik Tok or any of

these social media companies saying if

Patricia likes this post then show her

this post if she likes this post then

show her this other post because the

code would sort of grow infinitely long

and there's just way too much content

for a programmer to be having those

kinds of conditionals those those

decisions being made behind the scenes

so it's probably a little more

artificially intelligent and in that

sense You have topics like neural

network works and machine learning which

really describe taking as input things

like what you watch what you click on

what your friends watch what they click

on and sort of trying to infer from that

instead what should we show Patricia or

her friends next okay yeah yeah that

makes like the distinction more makes

more sense now

yeah I am currently a fourth year PhD

student at NYU I do robot learning so

that's half and half Robotics and

Mission learning sounds like you've

dabbled with quite a few algorithms so

how does one actually research

algorithms or invent algorithms the most

important was just trying to think about

inefficiencies and also think about

Connecting Threads the way I think about

it is that algorithm for me is not just

about the way of doing something but

it's about doing something efficiently

learning algorithms are practically

everywhere now CU Google I would say for

example is learning every day about like

oh what what articles what links might

be better than others and reranking them

um there are recommender systems all

around us right like content feeds and

social media or you know like YouTube or

Netflix what we see is in a large part

determined by this kind of learning

algorithms nowadays there's a lot of

concerns around some applications of

machine learning and like deep fakes

where it can kind of learn how I talk

and learn how you talk and even how we

look and generate videos of us we're

doing this for real but you could

imagine a computer synthesizing this

conversation eventually but how does it

even know what I sound like and what I

look like and how to replicate that all

of this learning algorithms that we talk

about right uh a lot like what goes in

there is just lots and lots of data so

data goes in something else comes out

what comes out is whatever objective

function that you optimize for like

where is the line between algorithms

that like play games with and without AI

I think when I started off my undergrad

the current AI machine learning was not

very much synonymous okay and even in my

undergraduate in the AI class they

learned a lot of classical algorithms

for game plays like for example the a

star search right that's a very simple

example of how you can play a game

without having anything learned this is

very much oh you are at a game State you

just search down see what are the

possibilities and then you pick the best

possibility that it can see versus what

you think about when you think about I

gameplay like the alpha zero for example

or Alpha star or there are a lot of you

know like fancy new machine learning

agents that are you know even like

learning very difficult games like go

and those are learned agents as in they

are getting better as they play more and

more games and as they get more games

they kind of refine their strategy based

on the data that they seen and once

again this high level abstraction is

still the same you see a lot of data and

you learn from that right but the

question is what is objective function

that you're optimizing for is it winning

this game is it forcing a tie or is it

you know like opening a door in a

kitchen so if the world is very focused

on supervised unsupervised reinforcement

learning now like what comes next 5 10

years where's the world going I think

that this is just U going to be more and

more I don't want to use the word

encroachment but that's what it feels

like of algorithms into our everyday

life like even when I was taking the

train here right the trains are being

routed with algorithms but this has

existed for you know like 50 years

probably but as I was coming here as I

was checking my phone those are

different algorithms and you know

they're they're kind of getting getting

all around us getting there're with us

all the time they're making our life

better most places most cases and I

think that's just going to be

continuation of all of those and it

feels like they're even in places you

wouldn't expect and there's just so much

data about you and me and everyone else

online and this data is being mined and

analyzed and influencing things we see

and here it would seem so there is sort

of a Counterpoint which might be good

for the marketers but not necessarily

good for you and me as individuals you

know like we're human beings but for

someone we might be just a pair of eyes

who are

you know carrying a wallet and are there

to buy things but there is so much more

potential for this algorithms to just

make our life better without you know

like changing much about our

life I'm Chris Wiggins from an associate

professor of Applied Mathematics at

Columbia I'm also the chief data

scientist of the New York Times the data

science team at the New York Times

develops and deploys machine learning

for Newsroom and business problems but I

would say the things that we do mostly

you don't see but it might be things

like personalization algorithms or

recommending different content and do

data scientists which is rather distinct

from the phrase computer scientist do

data scientists still think in terms of

algorithms as driving a lot of it oh

absolutely yeah in fact so in data

science and Academia often the role of

the algorithm is the optimization

algorithm that helps you find the best

model or the best description of a data

set okay in data science and industry

the goal often it's centered around an

algorithm which becomes a data product

okay so a data scientist in Industry

might be developing and deploying the

algorithm which means not only

understanding the algorithm and its

statistical performance but also all of

the software engineering around systems

integration making sure that that

algorithm receives input that's reliable

and has output that's useful as well as

I would say the organizational

integration which is how does a

community of people like the set of

people working at the New York Times

integrate that algorithm into their

process interesting and I feel like AI

based startups are all the rage and

certainly within Academia are there

connections between Ai and the world of

data science absolutely the algorithms

that they're in connect those dots for

you're right that AI as a field has

really exploded I would say particularly

many people experienced a chatbot that

was really really good today when people

say I AI they're often thinking about

large language models or they're

thinking about generative AI or they

might be thinking about a chatbot one

thing to keep in mind is a chatbot is a

special case of generative AI which is a

special case of using large language

models which is a special case of using

machine learning generally which is what

most people mean by AI you may have

moments that are um what John McCarthy

called look M No Hands results where you

do some fantastic trick and you're not

quite sure how it worked I think it's

still very much early days large

language models is still in the point of

what might be called alchemy that people

are building large language models

without a real clear a priori sense of

what the right design is for a right

problem many people are trying different

things out often in large companies

where they can afford to have many

people trying things out seeing what

works publishing that instantiating it

as a product and that itself is part of

the scientific process I would think too

yeah very much well science and

engineering because often you're

building a thing and the thing does

something amazing to large extent we are

still looking for basic theoretical

results around why deep neural networks

generally work why are they able to

learn so well they're huge billions of

parameter models and it's difficult for

us to interpret how they are able to do

what they do and is this a good thing do

you think or an inevitable thing that we

the programmers we the computer

scientists the data science who are

inventing these things can't actually

explain how they work because I feel

like friends of mine in industry even

when it's something simple and

relatively familiar like autocomplete

they can't actually tell me like why

that name is appearing at the top of the

list where is years ago when these

algorithms were more deterministic and

more procedural you could even point to

the line that made that name bubble up

to the top so is this a good thing a bad

thing that we're sort of losing control

perhaps in some sense of the algorithms

it has risks I don't know that I would

say that it's good or bad but I would

say there's lots of scientific precedent

there are times when an algorithm works

really well and we have finite

understanding of why it works or a model

works really well and sometimes we have

very little understanding of why it

works the way it does in classes I teach

certainly spend a lot of time on

fundamentals algorithms that have been

taught in classes for decades now

whether it's binary search linear search

bubble swort selection sort or the like

but are if we're already at the point

where I can pull up chat GPT copy paste

a whole bunch of numbers or words and

say sort these for me does it really

matter how chat GPT is sorting it does

it really matter to me as the user how

the software is sorting it like do these

fundamentals become more dated and less

important do you think now you're

talking about the ways in which code and

computation is a special case of

Technology right so for driving a car

you may not necessarily need to know

much about organic chemistry even though

if if the organic chemistry is how the

car works right so you can drive the car

and use it in different ways without

understanding much about the

fundamentals so similarly with

computation we're at a point where the

computation is so high level right as

you you know you can import pyit learn

and you can go from zero to machine

learning in 30 seconds it's depending on

what level you want to understand the

technology where in the stack so to

speak um it's possible to understand it

and make wonderful things and Advance

the world without understanding it at

the particular level of somebody who

actually might have originally designed

the actual optimization algorithm I

should say though from any of the

optimization algorithms there are cases

where an algorithm works really well and

we publish a paper and there's a proof

in the paper and then years later people

realize actually that prove was wrong

and we're really still not sure why that

optimization works but it works really

well or it inspires people to make new

optimization algorithms so I I do think

that the the goal of understanding

algorithms is Loosely coupled to our

progress and advancing grade algorithms

but they don't always necessarily have

to require each other and for those

students especially or even adults who

are thinking of now steering into

computer science into programming who

were really jazzed about heading in that

direction up until for instance November

of 2022 when all of a sudden for many

people it looked like the world was now

changing and now maybe this isn't such a

promising path this isn't such a

lucrative path anymore are llms are

tools like chat GPT reason not to

perhaps steer into the field large

language models are a particular

architecture for predicting let's say

the next word or a set of tokens more

generally the algorithm comes in when

you think about how is that llm to be

trained or also how to be fine-tuned so

the P of GPT is a pre-trained algorithm

the idea is that you train a large

language model on some Corpus of text

could be encyclopedias or textbooks or

what have you and then you might want to

fine-tune that model around some

particular task or some particular

subset of texts so both of those are

examples of training algorithms so I

would say people's perception of

artificial intelligence has really

changed a lot in the last 6 months

particularly around November of 2022

when people experienced a really good

chatbot the technology though had been

around already before academics had

already been working with chat gpt3

before that and GPT 2 and gpt1 and and

for many people it sort of opened up

this conversation about what is

artificial intelligence and what could

we do with this and what are the

possible good and bad right like any

other piece of technology cransberg

first law of technology technology is

neither good nor bad nor is it neutral

every time we have some new technology

we should think about its capabilities

and the good and the possible bad as

with any area of study algorithms offer

a spectrum from the most basic to the

most advanced and even if right now the

most advanced of those algorithms feels

Out Of Reach because you just don't have

that background with each lesson you

learn with each algorithm you study that

endgame becomes closer and closer such

that it will before long be accessible

to you and you will be at the end of

that most advanced Spectrum