「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
TLDR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
👨🏫 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。
🔍 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。
🤖 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。
🧠 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。
📊 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。
🤖 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。
Mindmap
Keywords
💡アルゴリズム
💡コンピュータ
💡CPU
💡RAM
💡ハードドライブ
💡バイナリサーチ
💡バブルソート
💡ディープラーニング
💡機械学習
💡最適化アルゴリズム
💡ラーニングエージェント
💡データサイエンティスト
💡ラージ言語モデル
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
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