$600 Billion AI Wave... software 3.0
Summary
TLDRDieses Video skizziert die zukünftige Rolle von künstlicher Intelligenz (KI) in Unternehmen und für Schöpfer. Es wird vorgeschlagen, dass KI-Vertreter wie E-Mail-Adressen und Websites zur Selbstverständlichkeit werden. Der Fokus liegt auf 'Software 3.0', die auf großen, open-source KI-Modellen basiert und durch spezifische Anpassung für individuelle Anwendungsfälle optimiert wird. Der Upload zeigt, wie KI in Bereiche wie Factorio, einem Strategiespiel, integriert wird, um Prozesse zu automatisieren. Die Diskussion umreißt die enorme Chance, die KI bietet, um Geschäftsprozesse zu revolutionieren und individuelle KI-Modelle für spezifische Bedürfnisse zu entwickeln.
Takeaways
- 💰 Es gibt verschiedene Wege, Millionen zu verdienen, aber einige könnten potenziell zu Milliarden wachsen.
- 🌐 Es gibt unzählige kleine Unternehmen weltweit, die in Zukunft möglicherweise über einen AI-Agenten verfügen, mit dem Kunden interagieren können.
- 🤖 Software 3.0 wird als die nächste Evolution der Software angesehen, die auf großen, bereits trainierten KI-Modellen basiert und diese für spezifische Anwendungsfälle anpasst.
- 📈 Software 1.0 beinhaltet traditionelle Programmierung, wobei Software 2.0 auf maschinellem Lernen und neuronalen Netzwerken basiert.
- 🧠 Neuronale Netzwerke sind ähnlich den Neuronen im menschlichen Gehirn, wobei Verbindungen zwischen Neuronen stärker werden, wenn sie häufiger genutzt werden.
- 🔍 Software 3.0 konzentriert sich auf die Manipulation von Basismodellen, die bereits viele Fähigkeiten haben, ohne von Grund auf neu trainiert werden zu müssen.
- 🚀 Open-Source-Modelle wie LLM (Large Language Models) bieten die Möglichkeit, spezifische KI-Agenten für Unternehmen und Schöpfer zu erstellen, die ihre eigenen KI-Modelle trainieren können.
- 💡 Die Verwendung von KI in Unternehmen und im täglichen Leben wird sich in den nächsten Jahren stark erhöhen und wird letztendlich nahezu universell sein.
- 🌟 Es besteht ein großes Potenzial für Unternehmer und Investoren, die in die Entwicklung und Anpassung von KI-Modellen investieren, um spezifische Geschäftsbedürfnisse zu erfüllen.
- 🔧 Die Zukunft der Geschäftsführung könnte ähnlich wie das Management eines automatisierten Systems aussehen, wobei KI eine zentrale Rolle dabei spielt.
Q & A
Was ist der Hauptgedanke hinter 'Software 3.0'?
-Software 3.0 bezieht sich auf die nächste Generation von Software, bei der Foundation Models verwendet werden, die bereits viele Fähigkeiten haben und keineswegs von Grund auf trainiert werden müssen, sondern durch spezifische Anleitungen und Verstärkung für den jeweiligen Geschäftsbedarf angepasst werden.
Was versteht man unter 'Foundation Models'?
-Foundation Models sind große, allgemeine KI-Modelle, die eine Vielzahl von Fähigkeiten 'out-of-the-box' bieten und für spezifische Geschäftsanwendungen nur noch feinjustiert werden müssen.
Wie unterscheidet sich Software 3.0 von Software 2.0?
-Software 2.0 basiert auf maschinellem Lernen, wobei Datensätze verwendet werden, um das Modell zu lehren. Software 3.0 hingegen konzentriert sich darauf, bereits trainierte Foundation Models für spezifische Aufgaben zu verfeinern und anzupassen.
Welche Rolle spielen offene Quellen in der Entwicklung von Software 3.0?
-Offene Quellen sind entscheidend, da sie es ermöglichen, große, leistungsstarke KI-Modelle zu erstellen, die dann für spezifische Anwendungsfälle von Unternehmen und Individuen angepasst werden können.
Was ist der Zusammenhang zwischen Software 3.0 und der zukünftigen Geschäftswelt?
-In der zukünftigen Geschäftswelt wird es wahrscheinlich sein, dass jedes Unternehmen einen eigenen AI-Agenten hat, mit dem Kunden interagieren können, ähnlich wie heute jeder ein E-Mail-Adresse, eine Website und eine Social-Media-Präsenz hat.
Wie wird die Zukunft der AI-Verteilung in Unternehmen aussehen?
-Die AI-Verteilung in Unternehmen wird sich von einem kleinen Prozentsatz, der derzeit seine eigenen AI-Lösungen nutzt, in den kommenden Jahren dramatisch erhöhen und möglicherweise schließlich nahezu 100% der Unternehmen erreichen.
Was ist der Unterschied zwischen traditioneller Softwareentwicklung (Software 1.0) und modernen AI-Modellen?
-Software 1.0 beinhaltet traditionelle Programmierung, bei der Entwickler explizite Anweisungen für den Computer schreiben. Moderne AI-Modelle hingegen sind in der Lage, aus großen Datenmengen zu lernen und Aufgaben ohne explizite Programmieraufträge auszuführen.
Wie wichtig sind Daten und Compute-Power für die Entwicklung von AI-Modellen?
-Daten und Compute-Power sind entscheidend für die Entwicklung von AI-Modellen, da sie die Grundlage für das Training und die Leistungsfähigkeit dieser Modelle bilden.
Was ist die Vision hinter der Idee, jedem Schöpfer und kleinen Unternehmen die Möglichkeit zu geben, ihre eigenen AI-Agenten zu erstellen?
-Die Vision ist, dass AI in der Lage ist, die Interaktion mit der Community zu erleichtern, die Verwaltung von Geschäftsprozessen zu optimieren und die Kreativität und Effizienz jedes Einzelnen zu steigern.
Wie sieht die potenzielle Zukunft der AI-Anwendungen in Alltagsleben und -geschäften aus?
-Die potenzielle Zukunft der AI-Anwendungen könnte so weit reichen, dass AI in nahezu jedem Aspekt unseres Alltags und in geschäftlichen Prozessen eingesetzt wird, von Hausautomation bis hin zur Unterstützung bei Entscheidungsfindungsprozessen.
Outlines
🤖 Die Zukunft der KI-Agenten in kleinen Unternehmen
Der erste Absatz diskutiert die Idee, dass jede kleine Geschäftsunternehmen in der Zukunft einen eigenen KI-Agenten haben wird, mit dem Kunden interagieren können. Der Sprecher vergleicht dies mit den heutigen Standardkomponenten wie E-Mail-Adressen, Websites und Social-Media-Präsenzen. Er betont die Bedeutung von Software 3.0 als nächstes Evolutionsstadium der künstlichen Intelligenz, welches die Schaffung von Programmen ohne explizite Anweisungen ermöglicht, sondern durch das Festlegen von Zielen und das Verlassen der KI, die Lösung zu entwickeln.
🔧 Evolution von Software 1.0 zu Software 3.0
In diesem Absatz wird die Entwicklung von Software 1.0 über Software 2.0 hin zu Software 3.0 erläutert. Software 1.0 bezieht sich auf traditionelle, von Menschen geschriebene Programme, während Software 2.0 auf neuronale Netzwerke basiert, die ohne direkten Code von Menschen trainiert werden. Software 3.0 wird als die nächste Stufe dargestellt, bei der KI-Modelle wie das von Mark Zuckerberg vorgestellte 'Llama' verwendet werden, um aus diesen großen Modellen kleinere, spezialisierte Modelle für individuelle Anwendungsfälle abzuleiten.
🚀 Die Vision von individuellen KI-Agenten für Business und Content-Creator
Der dritte Absatz konzentriert sich auf die Vision von Mark Zuckerberg, wie KI in Zukunft in Unternehmen und für Content-Creator eingesetzt werden kann. Es geht darum, wie Unternehmen und Kreative mithilfe von KI-Agents ihre Geschäftsprozesse optimieren und wie diese Agents für Kundenservice, Verkauf oder die Interaktion mit der Community genutzt werden können. Es wird auch auf die Bedeutung von Open-Source-Modellen und die Schaffung von Diversität in der KI-Entwicklung hingewiesen.
🛠️ Anwendung von Software 3.0 in der Praxis
Dieser Absatz vertieft die Idee von Software 3.0, indem er die Verwendung von Foundation-Modellen beschreibt, die speziell auf die Bedürfnisse von Unternehmen oder Individuen abgestimmt werden. Es wird auf die Wichtigkeit von Anpassung und Feinabstimmung eingegangen, um diese Modelle für spezifische Geschäftsanforderungen zu nutzen. Zudem wird die Rolle von Open-Source-Modellen hervorgehoben, die als Grundlage für die Entwicklung kleinerer, anwendungsspezifischer Modelle dienen.
🌐 AI als allgegenwärtige Technologie in zukünftigen Geschäftstätigkeiten
Der fünfte Absatz spricht von der zukünftigen Allgegenwärtigkeit von AI in Geschäftstätigkeiten. Es wird eine Parallele zu dem Grad, zu dem heutzutage Computer in Unternehmen eingesetzt werden, gezogen und die Prognose geäußert, dass die Nutzung von AI in Unternehmen in den kommenden Jahren stark ansteigen wird. Der Sprecher reflektiert über die potenziellen Anwendungen von AI in verschiedenen Lebensbereichen und wie sie möglicherweise das Geschäftsmodell der Zukunft prägen wird.
🎮 Automatisierung von Factorio durch KI-Modelle
Der letzte Absatz präsentiert ein Projekt, das die Automatisierung des Strategiespiels Factorio mithilfe von KI-Modellen veranschaulicht. Der Sprecher zeigt die Fortschritte in der Entwicklung von Schnittstellen und Modellen, die es Bots ermöglichen, das Spiel ohne menschliche Intervention zu spielen. Es wird auf die Open-Source-Natur der entwickelten Tools und die Absicht hingewiesen, diese für die Community zugänglich zu machen, um die Verbreitung von KI in der Gaming-Industrie zu fördern.
Mindmap
Keywords
💡Software 3.0
💡Künstliche Intelligenz (KI)
💡Neuronale Netzwerke
💡Gradientenabstieg
💡Open Source
💡AI-Agenten
💡Schlüsselindustrie
💡Datensatz
💡Faktorio
💡Einfluss von KI auf Geschäftsmodelle
Highlights
未来每个企业都可能拥有自己的AI代理,与客户进行交流,就像现在拥有电子邮件地址、网站和社交媒体一样。
软件3.0的概念,即利用基础模型进行微调和特定领域的应用,可能成为AI领域的最大机会。
Andrej Karpathy提出软件2.0的概念,即通过数据集和神经网络权重而非传统编程语言来定义软件行为。
软件1.0和2.0的比较,展示了从人类编写代码到通过训练神经网络来实现特定行为的转变。
介绍了神经网络如何通过类似巴甫洛夫条件反射的方式进行学习和适应。
解释了软件3.0如何使用大型基础模型来创建特定用途的小型定制模型。
Mark Zuckerberg讨论了开源AI模型Llama 3.1,以及它如何允许社区创建定制的AI代理。
讨论了如何通过微调大型模型来创建适用于特定业务需求的小型AI模型。
Sarah Guo分享了她的AI创业投资理念,强调了软件3.0的重要性和对特定领域AI应用的关注。
预测AI将像计算机和互联网一样普及,最终几乎所有企业和个人都将使用AI来优化和自动化任务。
展示了如何通过开源模型训练小型AI模型来服务于特定用途,如客户服务或个人助手。
讨论了AI在教育、游戏、金融和医疗等领域的潜在应用和影响。
强调了AI的普及将如何改变我们管理业务和个人生活的方式,类似于工厂自动化。
介绍了一个使用大型语言模型自动化游戏Factorio的项目,展示了AI在特定领域的应用潜力。
讨论了AI如何帮助用户在游戏中自动化任务,提高效率和游戏体验。
展示了如何通过微调和训练来改进AI模型,以适应特定的游戏策略和行为。
讨论了开源AI模型和工具如何使更多人能够创建和使用定制的AI解决方案。
Transcripts
there are like ways to make a million
bucks and then ways to make a million
bucks that could turn into a billion
bucks right there are hundreds of
millions of small businesses in the
world a business can basically you know
few Taps um stand up an AI agent for
themselves I kind of think that every
business in the future just like they
have an email address and a website and
a social media presence today I think
every business is going to have an AI
agent that their customers can talk to
in the future and we want to enable that
if you always knew that there was a huge
opportunity with AI but didn't quite get
exactly how to take advantage of it I
think this video might clear it up the
thing that I'm talking about is software
3.0 it might sound a bit weird but give
me just a second to explain because I
think that if you're an entrepreneur or
you always wanted to get into business
for yourself or you just want to stay on
The Cutting Edge of AI well this might
be the single biggest opportunity of our
lives so really fast this is Andre kpy
former open AI highly respected AI
researcher noticed the date this was end
of 2017 he's talking about software 2.0
what is that well the classical stack is
the software 1.0 that's kind of what we
think of a software it's code it's
written in languages like python C++ Etc
and it consists of explicit instructions
to the computer written by a programmer
right so so basically a human that's a
human if you can't tell and he's telling
a computer what to do sort of explicitly
right letter by letter typing it in and
and giving it instructions by writing
each line of code the programmer
identifies a specific point in program
space with some desirable Behavior right
so in other words we want something from
the computer we want this thing to do
something for us we make that happen by
writing each line of code he
specifically uses that terminology there
right writing each line of code that's
important so then what is software 2.0
can you guess well Andre karpathy he's
saying sof 2.0 is written in much more
abstract human unfriendly language such
as the weights of a neural network no
human is involved in writing this code
because there are a lot of Weights
typical networks might have millions
keep in mind this was written in 2017 so
you might imagine that there might be a
little bit more than that nowadays and
coding directly in weights is kind of
hard he tried he would this is kind of
what a neural network looks like the
neurons are connected by way there are
these uh lines here and this is somewhat
similar to a human brain we also have
neurons and then the connections between
those neurons that wire together
stronger when they are used more often
when they are predictive of something
happening that was that whole Pav's dog
the response to the Bell right so over
time as we smell something right so the
part of our brain that smells that is
able to capture smells like we smell
food and we respond to that by for
example with a dog salvation right
because we formed those neural
connections that helped us predict that
certain smells mean that there's tasty
food about however if we ring a bell the
dog has no response there are no neural
connections to indicate that a bell
means that there's food but if we start
giving them food and ring a bell over
time the bell ringing produces you know
him salivating and thinking that he's
about to get food that whole episode on
the office where Jim feeds Dwight breath
mitts every time a bell rings if you
didn't catch that that was literally
what that whole thing was about and
that's kind of how neural networks work
all these connections create some sort
of outcomes but we don't program them
ourselves well unless you're Andre karpy
actually how we approach it is we
specify some goal or desirable Behavior
right like win a game of Go and yes
there's some you know code a rough
skeleton of of the code AS audre put
said right so sort of the neural net
architecture and then that neural
network is trained using gradient
descent back propagation we're not going
to really get into it because we don't
quite need to to understand what's
Happening Here by the way if you are
interested in learning more highly
suggest Andre Kathy's latest thing so he
just announced it within the last few
weeks looks like this is dated July 17th
open co-founder Andre karpathy announces
Eureka Labs an AI education startup keep
this in mind you might need it later
just a hunch but kind of the big
takeaway about software 1.0 and software
2.0 I think is this this is a again from
Andre Kathy's blog right s sort of like
this distance represents the complexity
of the program right something very
simple to something very complex and
software 1.0 is here that's what
software can do again if we're defining
software 1.0 as a human being coding
something up for a computer to do that's
sort of the extent of it I mean it's
quite a bit the entire world that runs
on it it's done quite a bit but it's
limited compared to software 2.0 and
with software 2.0 instead of typing in
line by line we have a sort of process
right so that's training the AI right I
just kind of make it into a black box
and that process trains the AI it trains
the neural net to do the thing that we
want it to do we just say what we want
the alcome to be we tell it make cute
pictures of dogs and it trains the
neural network the brain that can make
cute pictures of dogs we tell it learn
to codes stuff and it figures out how to
make the thing that learns to code stuff
now this is greatly simplified obviously
there's a lot of things that go into
this data compute a million other things
but the point is instead of explicitly
telling what to do we're just kind of
telling it what we want and it then
creates the thing that does the thing
that we want right so we're kind of like
a layer removed all right so pop qu Hot
Shot what does software 3.0 look like
right in terms of what we're talking
about what is the next Evolution if this
is explicit directions this is telling
it kind of what we want and it figures
out how to create the thing that does it
what does the next evolution of that
look like what is 3.0 I'll add this last
piece and just that a year ago it wasn't
even obvious that this thing existed it
wasn't obvious that it would be
available to us keep in mind Andre
didn't even mention software 3.0
anywhere in this blog post that was in
2017 it it wasn't even obvious at that
point how this whole thing would shake
out but so at this point you might be
thinking okay okay so what's software
3.0 why don't you just tell us so first
of all here's Mark Zuckerberg talking
about his latest release llama 3.1 the
open source AI giant that is at GPT 4
level now if you saw that interview feel
free to skip forward I'll have the video
Chapter set up so you can skip to the
next part but whether you've seen it or
not pay specific attention to what he's
saying that you're allowed to build what
you're able to build with the big bad
model that he released the model that he
refers to as the teacher model so I'm
really excited to see what people do
with that especially now that we're
making it so that our the community
policies around llama allow people to
use it as a teacher model to distill and
fine-tune and um and basically create
whatever other models they want with it
and here's another Quick Clip where he's
talking about what he believes will be
the final result of people using these
open source models to create agents Etc
is he talking about everything running
on top of the sort of Base llama model
just sort of one model to rule them all
or is it more fragmented than that I
think we just have the ability in the
business model to basically build in the
most advanced models in the world and
offer it to everyone for free so I think
that that's a you know kind of a huge
Advantage um it's really easy to use
from all of our apps um so I'm pretty
excited about how that going so that's
yeah we have the basic assistant um and
and I think that that's going to be a
big deal but even more than that a lot
of what we're focused on is giving every
Creator and every small business um the
ability to create AI agents for
themselves um making it so that every
person on our platforms can create their
own AI agents that they want to interact
with and if you think about it these are
just huge spaces right so there are
hundreds of millions of small businesses
in the world and one of the things I
think is really important is basically
making it so with a relatively small
amount of work um a business can
basically you know few Taps um stand up
an AI agent for themselves that uh can
do customer support sales communicate
with all their people all their
customers I kind of think that every
business in the future just like they
have an email address and a website and
a social media presence today I think
every business is going to have a um an
AI agent that their customers can talk
to in the future and we want to enable
that for all those that's that's going
to be hundreds of millions maybe
billions of of kind of small business
agents similar deal for creators um
there are more than it's more than 200
million people on our platforms who
consider themselves creators who
basically use our platform um in a way
that is primarily for you know building
a community um you know put putting out
content feel like it's it's kind of like
a part of their job is is doing that and
they all have this basic issue which is
that there aren't enough hours in the
day to engage with their Community as
much as they'd like and likewise I think
that their communities would generally
want more of their time but um but again
not enough hours in the day so I just
think it's a there's going to be a huge
unlock where basically every Creator can
pull in all their information from
social media can train these systems um
to reflect their values and their
business objectives and what they're
trying to do and then people can can
interact with that it'll be almost like
this almost artistic artifact that
creators create that that people can can
can kind of interact with in different
ways and then and that's not even
getting into all the different ways that
I think people are going to be able to
create you know different AI agents for
themselves to do different things so I
think we're going to live in a world
where there are going to be hundreds of
millions of billions of different AI
agents eventually probably more AI
agents than there are people in the
world and um and that people are just
going to interact with them in all these
different ways so that's part of you
know that's the product Vision um
obviously there's a lot of business
opportunity in that that's where we want
to go make money so we don't want to
we're not going to make money from
selling access to the model itself um
because again we're not a public Cloud
company we will make money by building
the best products an important
ingredient to the best products is
building is having the best models which
having the best kind of ecosystem around
open source will help us do so that's
why it's kind of all aligned for us and
why this is going to end up I think
being really valuable for us to build
the the highest quality products that we
can um and and have the best business
results by by kind of building out this
open source Community but but it's also
why it's all philosophically aligned
right we don't we just don't believe
that there's going to be kind of one big
AI whether it's a product or a model
that everyone uses we kind of
fundamentally believe in having this
broad diversity and different set of
models and that you know every business
and um you people are just going to want
a lot of their own stuff that they're
going to make and I think that's kind of
going to be interesting it's going to be
a lot of what makes this interesting so
this is Sarah gou she was recently on
the my first million podcast where she
discusses some of the startups and and
AI feels that she's very interested in
investing and she's got a very specific
sort of investment thesis AK what she
thinks will really move the needle in
terms of AI startups and this is where
she kind of spells out what software 3.0
is take a listen and I think the next
level of like value and impact is
definitely going to be um fine-tuning to
specific voice and what's your
overarching investment thesis so you
have this thing called software 3.0
which what is software 3.0 yeah yeah
okay so the seed for that phrase
software 3.0 it comes from actually an
essay that Andre karpathy wrote years
ago about software 2.0 and the base
premise here is that like you had to
write uh a lot of software by hand in a
prior generation before machine learning
and then software 2.0 Andre you know
worked at Tesla was working on autopilot
was really about data set labeling right
you know you you are
teaching a machine learning model by the
data you choose to put into the pipeline
um how to do new tasks software 3.0 is
the idea that the next generation of
software a lot of it is about
manipulating Foundation models and
they're called Foundation models because
they have a lot of capability out of the
box you don't need to train them from
scratch you just need to give them like
guidance reinforcement the information
specific to your business and so an
example would be like Sean was is
talking about for his lead capture
intake form voice like he doesn't need
to go train a model he doesn't need to
go like collect data for that software
application like the voice agent is a
software application he just needs to
like make sure it's plugged into his
scheduling system and his database of
candidates and be able to retrieve the
right information about the business and
like you know respond consistently to
customers in a certain tone right right
and so that's more about like
manipulating a bunch of this Bas work
that people um like Labs have already
done for you and the premise here is
like that last mile of getting a
foundation model to be like something
that serves all these use cases in the
real world that you know maybe the
research Labs think of as niches like
the world is composed of very large
niches and so I think it's a I think
it's a really big opportunity for
entrepreneurs and for us by the way
that's not the only investor and fund
investing in this idea we also have a16z
that's betting it big on some fields
that intersect with AI games apps
infrastructure growth Etc they're very
interested in things where AI intersects
Finance for example as well as games
Healthcare Etc but getting back to our
kind of original question if 1.0 is US
humans creating the code to make the
computers do what we want 2.0 is kind of
this idea of where we tell this sort of
process what we wanted to do and then it
then goes and kind of creates the AI
neural net brain to the thing that we
wanted to do to me I'm seeing 3.0 kind
of the next iteration of that as taking
this AI brain let's say it's the big
base model let's say it's l with 3.1 the
405 billion parameter model this thing
that's extremely expensive to train to
create but it's open source and it's
very very good it's also kind of
unwieldly you can't just run it on your
laptop but you might want to use it for
a million different abuse cases for your
business for your personal life Etc what
we're able to do with this sort of
teacher model is used to create
synthetic data to train smaller models
that are customized for our specific use
case so this thing kind of bottles up
its thoughts and creates some sort of a
training set for a smaller model a
smaller model to run customer service or
help you with your homework or run your
calendar or answer your text messages or
or anything where having that
intelligence might be useful to you this
is kind of what the research behind Orca
2 from Microsoft kind of showed us early
on is we're able to take this big huge
unwieldly expensive model create a sort
of uh data set from it and train the
kind of tiny model on top of it and this
model is very very effective at doing
that thing so let's call that software
3.0 now none of this is official we're
kind of making these uh numbers and
stuff up as we go along but it's just to
illustrate a concept but so what's the
point of all this basically think of
this as a line from 0% to 100% here and
this is kind of the penetration of AI
into all of the businesses in the United
States in the world wherever you are
what percentage of businesses currently
are using kind of their own Homespun AI
to automate everything that they can
with AI to add intelligence to any of
the tasks that they're doing where it's
applicable right so I don't know what
that number is but it's low I'm going to
say 1% although I doubt that's anywhere
as high as that and similar how to back
in the days when computers came out
right 0% of businesses at some point 0%
of businesses use computers once they
became good and useful and in expensive
you know maybe 1% of businesses use
computers and then over time it
approached 100% nowadays close to 100%
of businesses use some sort of computers
desktop phones whatever with microchips
in it to help them run their business in
one way or another we are about to see
the same thing happen with AI we're
going to go from some tiny percentage of
businesses use this to run their
business and over the next X number of
years whether that's 5 years 10 years 15
that number is going to start climbing
towards 100% this is also going to
happen for students in school for a lot
of our personal stuff and all that AI
isn't going to be developed by coders
coding it line by line no is it all
going to be developed by these big AI
Labs using their massive training
clusters to create these giant models or
maybe even smaller models but the point
is yeah they'll they'll create once for
big use cases the profitable use cases
but there'll be a million custom special
jobs where AI will be needed for that
special handcrafted use case and
developing that special sort of uh right
that homemade AI that Artisan AI right
whatever you want to call it well it'll
be made from these big open source
models trained created fine-tuned
whatever you want to call that this is
kind of the process that we're referring
to as software 3.0 so be trained and
then deployed into those businesses and
people's lives and everything from
thermostats to monitoring security
cameras to I mean everything you can
possibly think of and this process will
probably not be quite as technical as
you know for example writing code
definitely not as technical as
understanding deep learning and neural
Nets and doing kind of like this sort of
work my guess is that 20 years from now
you have kids kind of spitting up their
own custom little AI models to help them
I don't know keep track of their Pokemon
whatever now I'm not going to unpack how
big of an opportunity this is because
I'll probably unell it and in fact me
trying to sell it to you isn't the point
of this maybe you see it maybe you don't
maybe I'm right maybe I'm wrong but I've
said this before in the future running
businesses will kind of look like
managing your factorio base everything's
more or less automated and you're just
kind of tweaking the different things
the different processes and systems to
optimize the performance I might have
been wrong about that because there's
literally a guy on Twitter X that's
using a large language model to can you
guess to automate factorio now I've been
tracking this project with great
interest he just posted a 3-week update
and I do want to kind of highlight this
project that he's doing because I find
it just endlessly fascinating so the few
minutes that he's doing the update I'll
post this right now to kind of close off
this video check it out I'll leave links
to his profile below but as you watch
this ask yourself this as this field
progresses and it becomes easier and
easier to add this intelligence whether
that's large language models or maybe in
a number of years we'll have some other
sort of type of Frontier models anywhere
we could use help making decisions or
keeping an eye for something or
automating something it seems like these
these AI neural Nets could be trained to
do that specific thing for whatever
specific use case you want it seems like
this is going to be everywhere in every
facet of Our Lives helping us with
pretty much everything that we're doing
what do you think let me know in the
comments am I crazy or is this going to
be bigger than the computer the internet
the cell phone let me know in the
comments check out this automating
factorio of large language of models
video my name is West Roth and thank you
for watching guys it's been 3 weeks
since I'm made the I want to automate
all the factorial tweet uh and I thought
it made sense to make a progress video
about everything that happened in
between so uh the first week essentially
just spent learning Lua learning the
factorial moding libraries and so on and
then the next two weeks I worked on this
uh Library if you go to the GitHub link
on the tweet it should take your this uh
page and here you can find a bunch of
remote interfaces that like the mod that
I creat um provides you so you can walk
to entities like go you can mine
entities you can put things inside boxes
you can put things inside furnances um
it has a bunch of features it still
needs a lot more so it is like work in
progress right now um but people someone
did raise like a p request to fix like
my pathing library a pathing code um
yeah I think it's just going to improve
over time um so this code let me show
you how to install it so essentially you
just need this one file it's like a 700
line uh Lua mod for factorio so if you
go um just clone the repo go to your
sort of game folder so is why my game is
stored and then go to your mods folder
within that and just clone um the that I
showed you once you have this you can
just open up factoral uh like normal and
it should have the mod installed um so
let me let me show you yes you just
start a new game um just with normal
steps skip the intro and now uh if you
go to if you go to sort of the items
here let's just go and walk towards the
coal entity so I'm going to do that
right
now and you can see here it sort of like
finds the code entity um then you can do
the same thing to mine it um
yeah so this is super cool because now
your agent doesn't have to like actually
look at the screen look where the goal
is it can just focus on thinking on how
to play the game how to win the game um
it should make it a lot easier for
people to make agents on this um yeah so
uh my plan for the next week is to sort
of create a bunch of models uh which is
going to be my attempt to beat the game
I know that the library doesn't have
enough things so I'm going to keep
adding more interfaces for agents to
play um but where I'm creating data sets
right now is called glaive it's a start
that I work at uh it's glaive doai and
here I'm creating a bunch of models so
this one I'm sort of um using to sort of
take an input uh like this one says I
want to walk to the nearest iron or and
then the command is like the command
that I need so I'm currently fine tuning
a bunch of models so like this one I can
fine tune right now I'm just going to
use f because it's like it's going to
run easily on my 4090 um so yeah this is
training right now but I do have like a
T of sort of uh I have so many uh
data sets right now that I need to find
tune on but I'm going to open source all
these models eventually um to sort of
make it a lot easier for everyone to use
and um right now this is not public yet
but I'm sort of working on a bunch of um
scripts so this is using grock's
function calling tool that uh we had GL
also worked with them to create and then
um I sort of have um just go to like go
ahead and all tab essentially into
factorio uh press the command button and
then sort of tag commands in so yeah
basically um it's going to be a lot
better uh next week which I'm going to
give you like a bunch of models and like
um yeah just going to opens a bunch of
stuff any I'm just going to keep yapping
so I just going to I'm just going to
leave and uh see you guys whenever
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