AI Boom Vs. Internet Boom
Summary
TLDR在这段视频中,讨论了当前人工智能(AI)与网络1.0时代之间的共同主题。与互联网时代相比,AI更像是微处理器或大型计算机时代的延续。互联网是一个连接众多现有计算机的网络,而AI则是一个信息处理系统,它处理数据并产生结果。AI的发展可能更类似于计算机行业的早期,从大型机到个人电脑再到智能手机,计算机的形态和规模经历了巨大的变化。AI的未来可能包含各种形状、大小和能力的模型,它们将基于不同的数据进行训练,并在不同的规模上运行,具有不同的隐私和安全政策。此外,AI的易用性可能导致与以往不同的用户锁定情况,因为AI可以像与人交谈一样简单。这段讨论提出了关于AI发展和行业格局的有趣见解。
Takeaways
- 🤖 **AI与互联网的类比**:AI更像是计算机或微处理器,而不是网络。AI处理数据,进行信息处理,与互联网的连接性质不同。
- 🌐 **网络效应**:互联网行业动态主要围绕建立网络或在网络之上构建应用程序,而AI则存在一些网络效应,但并不占主导地位。
- 💡 **AI的新特性**:AI和大型语言模型被视为一种新型计算机——基于概率的计算机,与以往确定性计算机(冯·诺依曼机)有本质区别。
- 🚀 **技术的进化**:从大型机到个人电脑再到智能手机,计算机的体积和成本不断减小,预示着AI模型也将呈现出多样化的形态和规模。
- 📈 **行业金字塔**:计算机行业形成了一个庞大的金字塔结构,从超级计算机到嵌入式系统,AI行业可能会发展出各种规模和能力的模型。
- 🔍 **AI的易用性**:AI因其高易用性而与众不同,用户可以轻松与之交互,这可能会影响其市场锁定效应和用户的选择自由度。
- 🌟 **AI的多样性**:AI模型将基于不同类型的数据进行训练,运行在不同的规模上,具有不同的隐私和安全政策。
- 📚 **历史经验**:从计算机行业的早期发展中汲取教训,而不仅仅是互联网的早期阶段。
- ⚙️ **技术发展周期**:技术发展通常会经历兴衰周期,包括过度兴奋和随后的萧条,AI也可能经历类似的周期。
- 🏭 **产业动态**:AI产业的展开可能会类似于计算机产业,从少数大型模型到广泛分布的各种规模的模型。
- 🧐 **开放性问题**:AI的锁定效应尚不明确,用户可能会根据特定任务的需求自由选择不同规模和价格的AI模型。
Q & A
为什么说将当前AI的发展与Web 1.0时代进行类比并不完全恰当?
-因为互联网是一个连接众多现有计算机的网络,而AI更像是一个计算机系统,特别是一个信息处理系统。AI的核心在于数据处理,与互联网时代的网络效应和行业动态有所不同。
在讨论AI时,为什么将AI比作微处理器而不是网络业务?
-AI更像是微处理器或原始计算机,因为它是一个系统,数据输入、处理和输出,与网络业务的网络效应和正反馈循环不同,AI更注重数据处理和信息处理。
AI与以前的计算机(如冯·诺依曼机)有何不同?
-AI和大型语言模型被视为一种新型计算机,基于概率的计算机,神经网络计算机,与以往确定性、严格按照程序运行的冯·诺依曼机不同,AI能够更好地与人类互动并理解世界。
为什么说AI的能力是可以组合的,并且可以构建出更复杂的东西?
-AI的能力是可以组合的,因为它基于模块化的设计,允许从小型、简单的组件构建出更大型、更复杂的系统,这与以往确定性计算机的局限性形成对比。
在AI领域,我们是否可能会看到类似于互联网初期的泡沫和泡沫破裂现象?
-是的,因为技术发展往往伴随着过度兴奋和随后的沮丧,AI领域也可能会有泡沫和泡沫破裂的现象,但具体情况会有所不同,因为AI和网络业务的行业动态不同。
计算机行业是如何从大型机时代演变到个人电脑和智能手机的?
-计算机行业经历了从大型、昂贵的IBM大型机,到成本更低的小型计算机,再到个人电脑和智能手机的过程。随着技术的进步,计算机变得更小、更便宜,最终普及到各个领域和消费者手中。
AI行业未来可能会如何发展,是只有几个大型模型还是会有多种不同规模的模型?
-AI行业很可能会发展成一个包含各种形状、大小和能力的模型的生态系统,类似于计算机行业的金字塔结构,从超级计算机集群到嵌入式系统,基于不同的数据、规模、隐私和安全政策。
为什么说AI是迄今为止最容易使用的计算机?
-AI能够理解和生成自然语言,使得与AI的交互就像与人交谈一样简单,这与以往需要专业知识和技能才能使用的计算机系统形成鲜明对比。
在AI时代,用户是否可能面临与以往计算机时代相同的锁定效应?
-AI的易用性可能会减少传统的锁定效应,但是否会出现新的锁定形式,例如对特定AI模型的依赖,仍然是一个开放的问题。
为什么说AI的发展可能不会遵循互联网初期的发展模式?
-AI作为一个信息处理系统,其发展模式更可能借鉴计算机行业早期的发展,特别是微处理器的发展,而不是互联网的网络效应和行业动态。
在AI领域,网络效应是否还像在互联网时代那样重要?
-虽然AI领域也存在网络效应,但它不像互联网业务那样占据主导地位。AI的核心在于其数据处理和学习能力,这些能力带来了新的价值和可能性。
AI的发展是否会像计算机行业那样,最终普及到几乎所有设备中?
-是的,随着技术的进步和成本的降低,AI技术很可能会像计算机芯片一样被集成到各种设备中,从而实现广泛的应用和普及。
Outlines
🤖 AI与互联网1.0的共同主题
在第一段中,Nathan和Ben讨论了当前人工智能(AI)与互联网1.0时代之间的最强大共同主题。他们提到,由于Nathan在Netscape的早期角色,以及Ben在互联网初期的经历,他们经常被问及AI和互联网之间的类比。Nathan认为,尽管互联网的兴起是一个重大的技术事件,但将AI的兴起与互联网的兴起进行比较并不完全恰当。他解释说,互联网是一个连接许多现有计算机的网络,而AI更像是一个信息处理系统,一个新型的计算机。AI的核心是处理数据,而不是建立网络。Nathan认为,AI更类似于微处理器的发展,而不是互联网的发展。他还强调了AI作为新型计算机的能力,特别是大型语言模型,它们与以往确定性计算机不同,具有概率性,并且能够更好地与人类互动和理解世界。
📈 计算机行业的发展历程
第二段中,讨论了计算机行业的发展历程,以及它对AI未来发展的启示。Nathan提到了早期计算机的巨大规模和成本,以及IBM创始人Thomas Watson的著名论断,即世界只需要五台计算机。随着时间的推移,计算机变得更小、更便宜,最终普及到个人和企业。Nathan预测,AI模型也将呈现出类似的多样性,从大型的“上帝模型”到小型的嵌入式系统,每种模型都有其特定的用途和用户。他还指出,与以往计算机不同,AI非常容易使用,因为它可以理解自然语言,这可能会影响用户对AI模型的依赖和选择。
🌐 AI的未来发展和用户选择
在第三段中,讨论了AI的未来发展,特别是用户将如何根据自己的需求选择不同规模和能力的AI模型。Nathan提出了一个观点,即AI的发展可能会导致一个包含各种规模和能力的模型的生态系统,而不是只有几个主导模型。他还提出了一个有趣的问题,即用户在使用AI时是否会像使用以前的计算机系统那样面临锁定效应,或者他们将能够自由地根据特定任务的需求选择最合适的模型。这段讨论强调了AI技术的易用性,以及它可能如何改变人们对技术的依赖和选择。
Mindmap
Keywords
💡人工智能(AI)
💡网络效应(Network Effects)
💡冯·诺依曼架构(Von Neumann architecture)
💡微处理器(Microprocessor)
💡确定性计算机(Deterministic computer)
💡神经网络(Neural Network)
💡主框架(Mainframe)
💡迷你计算机(Minicomputer)
💡个人电脑(PC)
💡智能手机(Smartphone)
💡嵌入式系统(Embedded Systems)
Highlights
AI的发展与网络1.0时代的相似之处被讨论,但最终认为两者之间存在根本性差异。
互联网是一个连接许多现有计算机的网络,而AI更像是一种信息处理系统,即一种新型计算机。
AI的行业动态、竞争动态和创业动态与互联网时代不同,更类似于微处理器的发展。
AI的网络效应与互联网时代相比较弱,AI更像是一个芯片或计算机,具有数据处理的功能。
传统计算机是确定性的,每次执行相同的任务,而AI计算机是概率性的,结果可能不同。
AI能够理解语言和图像,这是以往确定性计算机无法解决的问题。
AI的发展可能更接近于计算机行业的早期阶段,而不是互联网的早期阶段。
技术发展中不可避免的会有过度兴奋和随后的低迷,即所谓的“泡沫和破裂”。
计算机行业的发展从大型机到微型计算机,再到个人电脑和智能手机,呈现出多样化的趋势。
AI的未来可能包含各种形状、大小和能力的模型,形成整个生态系统。
AI模型的多样性将基于不同种类的数据训练,运行在不同的规模上。
AI的易用性是其一大特点,它可以通过自然语言与人类交流。
AI的“锁定效应”与传统计算机不同,用户可能更自由地选择所需的AI模型。
AI的发展可能带来与以往不同的行业格局,包括不同的公司和多样化的AI应用。
AI的普及将导致几乎所有设备都嵌入芯片,并最终连接到互联网。
计算机行业的发展历史对理解AI的未来发展提供了宝贵的视角。
AI的未来发展可能由多个因素决定,包括成本、选择速度和特定任务的需求。
Transcripts
[Music]
Nathan uh Nathan OD asks uh what are the
strongest common themes between the
current state of AI and web 1.0 and so
let me start there let me give you a
theory Ben and see what you think um so
I get this question you know because of
my role and you know Ben you you with me
at Netscape you know we we get this
question a lot because of our role early
on with the with the internet so there's
a you know the internet boom was like a
major major event in technology and it's
still within a lot of you know people's
memories um uh and so uh you know the
sort of you people you know people like
to reason from analogy so it's like okay
the AI boom must be like the internet
boom starting an AI company must be like
starting an internet company um and so
you know what what is this like and we
actually got a bunch of questions like
that you know that are kind of analogy
questions like that um I actually think
and you know and then Ben you know you
and I were there for the internet boom
so we you know we live through that and
the bust and the boom and the bust um so
um I actually think that the analogy
doesn't really work um for the most part
it works in certain ways but it doesn't
really work for the most part and the
reason is because the the
internet the internet was a network um
whereas AI is a computer
yep okay yeah so so so people understand
what we're saying like the PC
boom or or the PC Boomer even I would
say the microprocessor like my best
analogy is to the micro processor yeah
or or even to like the original
computers like back to the main frame
era um and and the reason it's because
yeah look what the internet did was the
internet you know obviously was a nwor
nwor but the network connected together
many existing computers and then of
course people built many other new kinds
of computers to connect to the internet
but fundamentally the internet was a
network and then and and and that's
important because most of most of the
sort of Industry Dynamics competitive
Dynamics startup Dynamics around the
internet had to do with basically
building either building networks or
building applications that run on top of
networks and this you know the internet
generation of startups was very consumed
by Network effects and you know all all
these so positive feedback loops that
you get when you connect a lot of people
together and you know things like met
you know so so-called metast law which
is sort of the value of a network you
know expands you know kind of the way it
expands as you add more people to it um
and then you know there were all these
fights you know these fights you know
all the social networks or whatever
fighting to try to get network of facts
and try to steal each other's users uh
because of the network of facts and so
it was kind of you know it's dominated
by by by Network effects um which is
what you'd expect from from from a
network business um AI like there there
are some networks effects in AI that we
can talk about but um it's it's more
like a microprocessor it's more like a
chip it's more like a computer
um in that it's a system that basically
right it it data comes in data gets
processed data comes out things happen
um that's a computer it's an information
processing system it's a computer it's a
new it's a new kind of computer it's a
you know we like to say the the the sort
of computers up until now have been what
are called Von noyman machines which is
to say they're deterministic computers
which is they're like you know hyper
literal and they do exactly the same
thing every time and if they make a
mistake it's it's the programmer's fault
uh but they're very limited in their
ability to interact with people and
understand the world
um you know we we think of AI and large
language models as a new kind of
computer a probabilistic computer a
neural network based computer um that
you know by the way is not very accurate
and is you know doesn't give you the
same result every time and in fact might
actually argue with you and tell you
that it doesn't want to answer your
question yeah yeah which makes it very
different in nature than the old
computers um and it makes it get kind of
composability you know the ability to
build things big things out of little
things more
complex right but but the capabilities
are new and different and and valuable
and important because it can understand
language and images and you know all
these do all these things that you you
see when you use domains we could never
solve with deterministic computers we
can now go after right right right yeah
exactly and so I think I think Ben I
think the analogy and I think the
Lessons Learned are much more likely to
be drawn from the early days of the
computer industry or from the early days
of the microprocessor than the early
days of the internet does that does that
sound right I think so yeah I definitely
think so and that doesn't mean there's
no like um boom and bust and all that
because that's just the nature of
Technology you know people get too
excited and then they get too
depressed so there will be some of that
I'm sure uh there will be over buildout
you know potentially of eventually of
chips and power and that kind of thing
um you know we start with the shortage
but but I agree like I think networks
are fundamentally different in the
nature of how they evolved on computers
um and and the kind of just the adoption
curve and all those kinds of things will
be
different yeah so then and this kind of
goes to where how I think the industry
is going to unfold and so this is kind
of my best theory for kind of what
happens from here this kind of this you
know this this giant question of like
you know is the industry going to be a
few God models or you know a very large
number of of models of different sizes
and so forth so the computer like
famously the you know the the original
computers like the original IBM
mainframes you know the big computers um
you know they they were very very large
and expensive um and there were only a
few of them um and the prevailing view
actually for a long time was that's all
there would ever be um and there was
this famous statement by Thomas Watson
Senor who was the creator of IBM you
know which was the dominant company for
the first like you know 50 years of the
of the computer industry um and uh he
said he said he said I I believe this
actually true he said I don't I don't
know I don't know that the world will
ever need more than five computers um
and I think the reason for that it was
literally it was like the government's
going to have two and then there's like
three big insurance companies and then
that's it yeah um who else would need to
do all that maath exactly who yeah who
else would need to who else needs to
keep track of huge amounts of numbers
who who else needs that level of you
know calculation capability it's just
not a relevant you know it's just not
not not a relevant concept and by the
way they were like big and expensive and
so who else can afford them right and
who else can afford all the headcount
required to manage them and maintain
them I mean this in the days I mean
these things were big these things were
so big that You' have an entire building
that got built around a computer right
um and they'd have like they'd famously
have all these guys in white lab coats
literally like taking care of the
computer uh because everything had to be
kept super clean or the computer would
stop working um and so you know it was
this thing where you know today we have
the idea of an AI God model which is
like a big foundation model then you
know then we have the idea of like a god
Mainframe like there there would just be
a few a few of these things and by the
way if you watch old science fiction it
almost always has this sort of conceit
it's like okay there's a big
supercomputer and it either is like
doing the right thing or doing the wrong
thing and if it's doing the wrong thing
you know that's that's often the plot of
the of the science fiction movies is you
have to go in and try to you know figure
out how to fix it or defeat it so sort
of this this idea of like a single top
down thing of course and and that held
for a long time like that held for you
know the first few decades and then you
know even when computers computers
started to get smaller so then you had
so-called mini computers was the next
phase and so that was a computer that
you know didn't cost $50 million instead
it costs you know 500 $500,000 but even
still $500,000 is a lot of money people
aren't putting many computers in their
homes and so it's like midsize companies
can can buy many computers but certainly
people can't and then of course with the
PC they shrunk down to like $2,500 and
then with the smartphone they shrunk
down to
$500 um and then you know sitting here
today obviously you have computers of
every shape size description all the way
down to you know computers that cost a
penny you know you've got a computer in
your thermostat that you know basically
controls the temperature in the room and
it you know probably cost a penny and
it's probably some embedded arm ship
with firmware on it um and there's you
know many billions of those all around
the world you buy a new car today it has
something new cars today have something
on the order of 200 computers in them
maybe maybe more at this point um and so
you you just basically assume with the
chip today sitting here today you just
kind of assume that everything has a
chip in it you assume that everything by
the way draws electricity or has a
battery because it needs to power the
chip and then increasingly you assume
that everything's on the internet
because basically all computers are
assumed to be on the Internet or or they
will be um and so so and so as a
consequence what you have is the
computer industry today is this massive
pyramid and you still have a small
number of like these supercomputer
clusters or these giant mainframes that
are like the god model you know the god
the god the god main frames and then
you've got you know a larger number of
minicomputers you've got a larger number
of PCS you've got a much larger number
of smartphones and then you've got a
giant number of embedded systems um and
it turns out like the computer industry
is all of those things um and you know
what what is a what you know what what
size of computer do you want is based on
what exactly are you trying to do and
who are you and what do you need and so
if if that analogy holds it basically
means actually we are going to have ai
models of every conceivable shape size
description capability um right based on
trained on lots of different kinds of
data at running at very different kinds
of scale very different privacy policies
different you know security policies you
know you're just you're just going to
have like enormous variability um and
variety um and it's going to be an
entire ecosystem and not just a couple
of companies yeah let me see what you
think of that well I think that's right
and I also think that the the other
thing that's interesting about this era
of computing if you look at prior areas
of computing from the Mainframe to the
smartphone um a huge source of lock in
was you know basically
the difficulty of using them so you know
nobody ever got fired for buying IBM
because like you know you had people
trained on them you know people knew how
to use uh the operating system like it
was you know it was just kind of like a
safe Choice due to the massive
complexity of like dealing with a
computer and then even with a smartphone
like the re you know why is the Apple
computer um smartphone so dominant um
you know what makes it so powerful it's
well because like switching off of it is
so expensive and complicated and so
forth it's an interesting question with
AI because AI is the easiest computer to
use by far it speaks English it's like
talking to a person um and so like what
is the lock in there and so are you
completely free to use the size price
Choice speed that you need for your
particular task or are you locked into
the god model
um
and you know I think it's still a bit of
an open question uh but it's it's pretty
interesting and that that that thing
could be very different than prior
Generations
[Music]
関連動画をさらに表示
Artificial Intelligence Explained Simply in 1 Minute! ✨
The Personal Computer Revolution: Crash Course Computer Science #25
Networking for GenAI Training and Inference Clusters | Jongsoo Park & Petr Lapukhov
You Won't Believe OpenAI JUST Said About GPT-5! Microsoft Secret AI, Hallucination Solved, GPT2
Natural Language Processing: Crash Course Computer Science #36
5. From Panic to Suffering
5.0 / 5 (0 votes)