[AI-generated male voice]: Minecraft is a game that needs no introduction.
It has taken the gaming world by storm
since its release back in 2011.
♪Calm, happy music begins♪ But what makes it so special?
Is it the vast open world waiting to be explored?
The endless possibilities of creation
or the charming, blocky graphics that make it so unique?
In this review, we'll be taking a closer look at Minecraft
and what makes it stand out in the crowded world of video games.
♪the happy music is pitched down, ominously.♪
At its core,
Minecraft is a game about survival and creativity.
The player is tasked with gathering resources such as wood,
stone and iron to craft tools and build shelters.
As the player progresses, they encounter new challenges such as dangerous mobs
and the need for more advanced resources.
One of the most impressive
aspects of Minecraft is the freedom it offers the player.
From building simple dirt huts to massive castles
and intricate redstone contraptions. [The sound of doors opening and closing muffles the voice]
The possibilities are truly endless.
The game's procedural generation also means that every new world is unique,
with different biomes, structures and resources to discover.
But Minecraft isn't just about building and exploring.
The game also features a combat system that requires
the player to be strategic and quick thinking.
Whether it's fighting off zombies, skeletons or other monsters,
players must learn to use weapons and armor effectively to survive.
-ive -ive -ive [The voice glitches out, repeating the end of the previous line.]
One of the most enjoyable aspects of Minecraft is the ability to play with
others, whether it's through local split screen or online multiplayer.
Players can work together to build massive structures or engage in epic battles.
This cooperative element adds a new level of depth
and excitement to the game, and it's no wonder why Minecraft
has become a cultural phenomenon, with millions of players worldwide.
But perhaps the most touching aspect of Minecraft is how it allows
players to express themselves in unique and meaningful ways.
From recreating famous landmarks
to building memorials for loved ones, the game has become a platform
for creative expression and emotional catharsis
♪Dramatic, emotional piano music begins, signalling a major emotional beat♪
in a world where we often feel powerless and lost.
Minecraft offers a glimmer of hope and a chance
to connect with others in a profound way.
[The voice glitches again]
in concluuuuuuusion,
Minecraft is a game dat ish boat fund and impsiring
It offers endless possibilities for teh creativity and exploration,
while also reminding us of uh...
...the umm...
the... the importance of... uh... shit!
The impotance of community and sewf-expwession.
Whether you're a seasoned player or new to the game,
Minecraft is an experience that everypony should try at least once.
Help. Help.
Help.
[Jim]: ChatGPT wrote that video and decided what it would look like.
Sorry.
The performance isn't great and the writing is boring
but passable for a totally inoffensive, middle-of-the-road game summary
Machine learning exploded in popularity in the last year or so with a flood
of generated art, music and text that you're probably already sick of.
Well, the games industry is just getting started and people are using AI
for everything from art to programing to animation
to brainwashing you into paying for micro-transactions.
Big companies and independents alike are jumping headfirst
into a world of bad writing and melted popsicle art.
This video is a survey of the rapidly expanding field of AI
I'll discuss some of the problems it's creating,
laugh at the goofy art, then get into the actual AI
apocalypse-- the one they're not telling us about.
But before any of that, I want to understand machine learning
not from some pundit's explanation, but in terms of what these systems actually are.
[A different AI-generated voice]: Hey, Josh, I'm doing all right.
Just dealing with some recent house fires and other crazy stuff.
How about you? How's life been treating you?
[Jim]: There are hundreds of schemes for machine learning at this point,
but neural networks are the most popular, and most of the concepts
behind neural networks apply to everything else in the field, too.
Neural networks are simplistic models of brains, networks of neurons.
A very low resolution picture of our brains is that we take
in some stimulus, say the light bouncing off a painting.
Then something happens in the brain.
Then we feel an emotion or sensation.
There's an input and output and something in the middle.
And that's exactly how every beginner course describes a neural network.
A layer of input nodes, one or more hidden layers.
Then the layer of output nodes.
Hidden layer is kind of a misnomer though. The hidden layers themselves
aren't a black box, and tweaking them is a big part of developing a neural network.
The data that these hidden layers produce usually isn't meaningful to us, though.
It's only used by the network, and that's probably where the hidden comes in.
Explainability,
figuring out why an AI makes decisions the way it does is a big problem
in machine learning, because AI
doesn't follow a line of reasoning the way a person would.
Nodes are another abstract concept.
There's no obvious
correspondence between a node and what the computer is actually doing.
Really, node just means function and in machine learning, nodes
are usually taking a vector,
doing something to it, and passing it on to the next layer.
We could have each hidden node add up the values of everything connected to it,
for example.
The secret sauce is in weighing the inputs.
In real brains,
some connections between neurons are stronger than others.
There are still lots of questions about the human brain, but the idea is
that these connections and their strength affect our thoughts and actions somehow.
Since an artificial neural network is just functions sending and receiving numbers.
You can multiply each of these by some factor
to make it bigger or smaller, more or less influential.
The network on screen is a toy example, but neural networks are always made
to achieve some goal.
Let's say our input is a picture of a letter where the brightness of each
pixel is an input and the output is a guess for what letter it is.
This is just a big pile of math.
It's cool that we can make a machine with billions of adjustment knobs,
but we're not going to do all that work by hand.
Thankfully, neural networks can be trained to adjust their own parameters.
If I have a photo of a letter that I know is a B,
then I can compare that to what the network guesses.
This pair of image and text is a piece of training data
and big networks will go through millions or billions of them.
Weights are usually randomized at first, so the network will probably say
the letter is an, A, X, P, and C all at once.
But we can calculate how wrong the network is and use this
to adjust the weights so it gets a little bit better every time.
If it's 20% confident that the letter B is the letter A,
then we need to reduce the weights that influence that guess.
Gradient descent
is the piece of statistical magic that made the AI revolution possible.
If you're on a hill, the gradient where you're standing
is an arrow--a vector-- pointing in the steepest direction.
Following this gradient is the fastest way to climb the hill.
Going backwards from the gradient is the fastest way to go down the hill.
An error function is like a hill that represents
how wrong each of our weights is.
So if you take the gradient and go backwards,
the network will slowly move toward zero error.
In my example, it gets better at guessing letters.
It's a pretty goddamn cool idea, but there are no miracles here.
The concept I just described was laid out in a paper
from 1958 as a theory for how the human brain works.
So it's not exactly revolutionary, but layers of interconnected
nodes still make up the structure of all those headline-grabbing AI
systems we see today.
You would hope that that example network, the letter classifier,
would learn to recognize patterns
in the strokes of letters, or at least do something intelligible.
But the weights that a neural network comes up with look just about random.
And a lot of the architecture behind today's machine learning systems
is based on somebody trying something new that happens to work.
Sometimes you can say that they make the whole gradient descent process
more efficient, but with current setups, there's never going to be some obvious
improvement reflected in the hidden data itself.
You're never going to get a line of reasoning from AI
Famously, you can use these things to generate media
like images and music from a text description.
Google's Deep dream
was one of the first generative models that made headlines.
It started as a network for classifying images,
but they were able to sort of run the system in reverse and have it
hallucinate nightmarish faces in existing pictures.
The original model was made for an image recognition contest.
That ImageNet ran in 2014.
ImageNet made a dataset of just under 15
million images, which it doesn't own the licenses for.
With new technology, the line between research and commerce
is razor thin, and big companies often use this fact to just
manifest destiny whenever they want and make us live with the consequences.
Scraping millions of images and sticking them in a public dataset
is a huge ethical question mark, even in an academic context.
But once an economy springs up around these datasets,
they're hard to get rid of.
This is a lesson we've learned over and over.
Companies rush to market with leaded gas or asbestos
insulation, and by the time we understand what they've done,
entire swathes of the planet have brain damage and lung cancer.
Google mastered this principle with AdSense, a surveillance system
that probably knows your heart rate and body temperature right now
and will use it to sell you some gross Coke.
[Various people reviewing a gross Coke flavour]: Alright guys I got the latest from Coca-Cola.
It's their League of Legends flavor. Coca-Coca--
--Ultimate. Here you go
Here you go.
And you see Ultimate Coca-Cola, you know.
Let's go ahead and try
Coca-Cola Ultimate--
Burnt popcorn and coconut flavor, something like that.
I was like, What the heck's this?
It tastes like like Hawaiian tropic suntan lotion.
Yeah, it's got some kind of fruity berry flavor going.
It kind of tastes like, uh...
Coke.
Still just tastes like mango Coke to me.
And then you melted a little bit of that Hubba Bubba gum inside of it.
[Jim]: Google's data harvesting operation became a load bearing
piece of the Internet before the public understood digital privacy.
And now we can't get rid of it.
ImageNet popularized scraping the Internet for training data,
and the project has all the same problems that we're dealing with now.
It's very biased.
They stole all the pictures, and they use questionable labor practices
to label them all.
[Fei-Fei Li]: But what if cats are hidden?
What about these silly cats?
[Jim]:Amazon's Mechanical Turk bills itself as a micro task marketplace,
a place for simple, short jobs that still require a human to complete them.
I wanted to join the program as a worker,
but Amazon didn't bother approving or denying my request.
The site is apparently so bad that workers have to use a bunch of extra scripts
to actually do their jobs and you can't get any decent work there
until you've done hundreds or thousands of human intelligence tasks,
also known as HITs.
A platform like that was a perfect fit for the Image Net project,
and they used it to label early versions of the dataset back in 2008 or -9.
They gave workers a set of pictures and some object to identify.
Workers would mark each picture if it contained the target object.
If that sounds familiar, it's exactly like solving a CAPTCHA.
In fact, we've all been helping Google train its neural networks for years.
These companies have a very dubious concept of consent
and we'll see a lot more of that later.
You literally have to help train an AI to access
many websites.
At least ImageNet paid the Turkers.
But with that said, Mechanical Turk's
workforce does skew toward people with no other options.
Oscar Schwartz, writing for IEEE Spectrum,
rightly identified that MTurk is designed to make human labor invisible.
Jeff Bezos called them "artificial artificial intelligence",
and Turkers are described offhandedly as a horde in an article
that I read creaming itself over ImageNet.
Turkers were earning a median $2 per hour in 2018,
and the situation hasn't really changed in the years following.
These people are invisible, poor and very easy to exploit.
Mechanical Turk is slavery as a service,
but it was also the first of a new breed.
Turkers are generalists, but the AI revolution needed specialists.
Appen is one of many companies specifically selling data labeling
for machine learning.
Their crowdsourced labor came mostly from Kenya and the Philippines at first,
but when Venezuela's economy collapsed, they started snapping up jobless refugees.
A journalist for an MIT profiled a Venezuelan Appen worker,
and the situation seems pretty dire.
Workers have no line of communication with the company.
They have to be constantly at their computers, ready to accept tasks.
And like Mechanical Turk, the site barely works.
Appen can afford to push people as hard as they want
because there's a huge labor supply and the workers have nowhere else to go.
They congregate in Discords and write scripts to make things tolerable.
Because its workers are contractors, Appen pays out
like a slot machine.
Some tasks offer pennies,
some don't even work, and some will offer hundreds of dollars; a relative fortune.
I think a good rule of thumb is that any company that has to write
a slavery policy is probably up to something.
Most of the
major players in AI seem to disagree, though.
Appen's business is booming.
In ImageNet's heyday
it was just a curiosity,
but the problems it created were only going to get more intense over time.
A revolution was brewing, but it needed more computing
power, more publicity and more technical development.
Just keep in mind that all the technology I'm going to talk about can only exist
because people with nowhere else to turn are earning pennies
churning through data.
We've come a long way
from DeepDream in the last decade to a new frontier of ugly art,
bland writing and buzzwords for the consultant vampires.
All the popular flavors of AI come together in video games.
There's significant money and effort in automating
just about every aspect of game development right now.
It's just starting to leak
into serious projects, but there's probably a lot on the way.
That's without even talking about using machine learning for marketing,
talent acquisition, or any of the other million things you can use it for.
Ubisoft, who made a big bet on the metaverse a few years ago
--ha ha--
developed a tool called Ghostwriter to quickly draft NPC dialog.
Considering how eager
they were to go all in on crypto, this seems like a half measure from them.
Although they sell this thing as a revolution.
They won't even use it to draft a major character.
It's just chirps from random NPCs.
[Female voiceover from Ubisoft video]: It helps narrative designers
and scriptwriters generate a first draft of these barks.
Scriptwriters t- first create a character and a variable input:
an event or situation for a character to experience.
Ghostwriter can then generate new versions
of the line depending on the mode selected.
These modes vary in style, depending on the needs of the scriptwriter.
[Jim]: It's not even clear if Ubisoft are training their own models.
Ghostwriter is probably just an interface to speed up the writing process.
It's a smart move though.
Small parts are about as much responsibility as AI can handle right now.
One of the only acceptable things I've seen it produces the texture on this
bee. Obviously that's not enough for a whole game, but it's a start.
Coherence is a big problem for these models.
Facebook released a music generation tool that sounds kind of
okay for a few seconds,
but if you make anything longer than 30 seconds or so, it just falls apart.
The track will turn into pops and clicks and then maybe even lapse
into a different song.
Most of the
chat bots only remember a couple hundred words at a time,
so they can't really come back around to earlier points in the conversation.
Visual art has no consistency at all, and making textures
that even superficially go together is a pretty laborious task.
This inconvenient fact hasn't stopped Unity from going
all-in on machine learning.
[A conversation between a female voiceover and an AI-generated alien]: Hello there.
Greetings.
What's your name?
My name is Orp[?].
I'm an extraterrestrial visitor who has come to earth
to study and understand humans.
What hairstyles would you choose if you had hair?
If I had hair, I would probably go for a messy bun or ponytail.
[Jim]: Looks like shit, guys. Keep it up.
Along with the weird alien, they have a chat bot
and some art generators.
Plus people can sell AI tools on the Unity store.
The company's been evasive about where its training data comes from,
and I suspect they didn't really care until people started asking questions.
So far, selling AI has been all about rushing to market and outrunning public opinion,
avoiding the exact questions that Unity users are starting to ask.
Unity's AI program is only in beta, but it launched with support for Atlas, a tool
that just searches for other people's 3D models on Sketchfab.
They say that
AI is involved, but all it does is look for models with permissive licenses.
So that's about the level of oversight you can expect from Unity.
The only real use case for tech like this is expediting
those awful mobile games that rip off whatever the iPad kids are into.
[ROBLOX child]: We have Squid Game Huggy Wuggy...
[Jim]: Any Unity project
less cynical than that probably won't even make it out of the gate.
Nothing that AI can produce right now is impressive or consistent enough
to hold somebody's attention for more than a few minutes.
For that reason, it's kind of hard to find games with
AI-generated assets right now.
I keep stumbling on them, so there are probably a lot of
cash grab game developers using machine learning,
But AI art is so full of bad anatomy and garbled text
that they usually try to hide it until you pay up.
Currently, Steam's policy allows AI,
but requires that you own the rights to the training data,
which has led small developers to just hide the AI-generated stuff,
if the AI game development Subreddit is any indication.
In doing research for this video, I've discovered one of the long
term problems with AI for myself. Looking through cheap games
on the Switch store, I often can't tell if half the art is AI
generated or just generic.
The concept of truth is sort of breaking down in real time.
No one wants to play a game full of uncanny, asymmetrical, AI
generated art and programing with AI, in my experience,
feels like learning to write the code myself
and then patiently instructing a five year old to do it for me
if I still have to read through all the standards and documentation
myself, I might as well just write the program.
That's without addressing the fact that once again, all the training data
that tools like ChatGPT and GitHub Copilot used was scraped.
Oh, by the way GPT stands for Generative Pretrained Transformer.
Transformers are a type of network that Google employees came up with to
translate languages, and they incorporate something called attention,
which helps the network track the context of words in a sentence.
ChatGPT generates one word at a time, each time picking a word
that it thinks is likely to follow in a sentence.
GPT2 gave you a spoiler warning for a
non-existent episode of Game of Thrones at the beginning of the video,
because I guess a lot of people were writing about Game of Thrones.
Anyway, even when these models get better
somebody who wants to make a game without learning any skills or putting in
the effort is unlikely to finish a project.
But the models will get better.
There's a lot of money in AI right now.
Most of the new AI
companies are just leeching off of existing tech like GPT or DALL-E,
but somebody out there must be trying new structures.
A well-trained eye can still differentiate AI
generated work from human work, but over time I think the gap will close
and this stuff will make its way into more and more games.
A lot of the time if something looks okay at a glance,
people won't notice that a character has too many fingers.
With crypto, there was a lot you had to do to buy in.
It's expensive, confusing, and really not even appealing
unless you have a few hyper specific libertarian values.
But finishing up a game's art with machine learning makes development easier.
High on Life apparently has some AI generated voices and art made
by MidJourney.
Capcom is using machine learning to assist with level design.
[Presenter]: In conclusion, level design is one of the most important
in game development, but it costs a lot.
First, they create AI
NPC, so the non-player character
using reinforcement learning and Unity ML Agent
and Capcom use those training data to do the reinforcement learning.
Then they make NPC to... [garbled]
so you can see the NPC
auto-plays the game and in the background
the program records how many taps to clear one stage.
[Jim]: And WB games is doing something vague.
[Various WB games people]: At Warner Brothers we believe in the power of great stories and story is story.
Stakeholders. Story. Story.
Story. Data. Data.
Data Data.
Large-scale cluster data warehouse Data. Data.
Data. Data. Data. Data.
[Jim]: I think they're using machine
learning to collate player feedback about their games.
But there's too much corporatese to know for sure.
They're really into data, though.
A few
AI oriented games have released, all from small studios or individuals.
Vaudeville is a funny ha ha streamer game where you try to solve
a murder by interacting with some impressively wooden characters.
[A let's play of Vaudeville]: What news do you have for me?
Detective Martini.
His name's Gretzky. Watch this.
I believe your first name is Wayne, isn't it?
No, my first name isn't Dwayne.
Why do you ask?
[Jim]: You can say whatever you want to them, and the dialog responses
are generated by an AI, then read out by Microsoft Sam.
[Female character from Vaudeville]: Yes. Haley Greenwood used to perform at the Cabane Violette cabaret theater.
But why are you asking me about her?
Do you think she might have been involved in these murders?
[Female TIkTok TTS]: This is so intriguing.
[Jim]: It's a fun idea, but language models rarely say anything interesting.
And once the novelty wears off, it's just a poorly written adventure game.
AI Dungeon is similar to Vaudeville, except it came out in 2019
when language models were still spitting out wild shit.
AI Dungeon is nominally a roleplaying text adventure,
but it plays fast and loose with things like characters, items and areas.
You can type in actions and the
AI will do its best to translate those into your chosen setting.
The way these transformer models work gives them a very small memory,
so AI Dungeon will often forget details about the setting and characters.
Playing the game feels like reality is collapsing.
A fantasy world can shift into an urban one without warning.
Sometimes you'll switch places with another character
and the game has serious problems keeping track of goals and items.
The models we have now are very well developed and boring, but a few years ago
AI was all surreal hallucinations.
[Jerma]: Get you a nice apple once I'm inside,
how does that sound?
Yeah, that sounds good.
Am I a horse
Theo?
[Jim]: Vaudeville and AI Dungeon are very gimmicky.
They're really just a structured way to play with language models.
Mirage Island is a little different and much closer to how I think
this tech will actually be used.
It's a Pokémon-like browser game where you get three
randomly generated monsters with randomly generated moves.
AI will need a lot of oversight to generate content,
but if you need to churn out 100 similar items, it must be tempting
to just make ChatGPT do it.
♪Pokémon battle music!♪
♪The music progressively pitches down to accentuate how wonky the art looks♪
There's also a new Google paper about generative agents.
Basically NPCs that have emergent goals and follow a schedule
in a simulated world.
Again, it's interesting, but it's literally just 25
GPTs interacting and pretending to do everyday things.
At best,
this would give us a fleshed out version of what they promised with Oblivion,
or a way to generate endgame content like Skyrim's radiant quests.
[Todd Howard, Oblivion NPCs]: Let's take a look at the other side of the game:
Our new radiant AI system.
It allows NPCs have full 24/7 schedules.
These NPCs are not scripted.
We give them general goals and they figure out on their own how to accomplish them.
Good morning.
The NPCs also have dynamic conversations.
These are based on your actions and what's going on in the world.
It's a great way to pick up info.
Nilos!
Hello.
[Jim]: Actually, Bethesda is a perfect candidate for AI-generated content
If their other games or any indication,
players are going to spend a lot of their 600 hours in Starfield
with their brains turned off, collecting trash and shooting space-bandits.
The radiant quest system is already hard to differentiate from Skyrim's
actual boring quests, and AI would give Todd Howard
a shiny new thing to overhype and under-deliver.
We're still very far
from running these models in real time though. Beyond the dodgy quality
and the slight chance that it'll start dropping race science on you,
the GPU requirements for AI are pretty strict.
I have a 1060 and I can chat with a medium
sized language model at around one message per minute.
That's just not good enough for a finished product.
Generating voices on top of dialog would take even longer.
There's some talk of doing it in the cloud, but nothing tangible.
I think it will be hard to resist using machine learning to speed up
game development, especially once a high profile game uses AI
for something less prominent, like coding or draft animations.
[Jensen Huang, president and CEO of NVIDIA]: The generative AI era is upon us.
The iPhone moment of AI, if you will.
Adobe Firefly
Does outpainting; imagine the space
around the image that we never captured.
Move AI does mocap from just video.
This is on the upper-right.
You d-
You decide which one's real.
Vizcom does sketch to image, guided by language prompt.
This one's really cool.
There are a lot of people who know how to sketch
and from the sketch and some guidance from your language,
you could generate something photorealistic and rendered.
And this is really cool:
Wonder Dynamics,
not only is the name of the company cool, but they do pose and lighting detection
and replace the actor with a CG character.
They're just- it just goes on and on and on.
The generative AI
era has arrived.
[Jim]: Faster development with fewer staff is a great pitch,
and ethical concerns have never stopped major studios before.
This isn't my strongest argument against AI,
but my biggest worry about generating art in any capacity
is that it takes away opportunities for people to make interesting choices.
It's really hard to pin down what makes a piece of art good,
but when you create something, you're constantly making tiny choices
and I think an author's voice
is a sort of consistency and harmony between those choices.
Whether or not you think Mark Rothko is a great artist,
you can look at one of his paintings and know who made it.
AI takes away these choices and it substitutes them
with a weighted average of choices that have already been made.
Any picture made with an AI is going to be a mishmash of other images.
By its nature, it can't make anything new.
Large language models, impressive as they may be, are never going to invent,
name and develop new concepts.
ChatGPT can barely handle a niche Python library.
The humanities are going to be beyond its reach no matter how many Subreddits
you train at on.
[Very robotic female voice]: Hello, my name is Princess Jane.
I would like to show you some tricks.
I hope you enjoy it.
[Jim]: And for that reason, it's not worth talking about the aesthetics of AI art.
There's no intention behind it.
The computer is just taking a text prompt and spitting out an image
that minimizes the error.
And the people using AI all think things like
[Goofy 'bro' voice]: 'it'd be so fucking sick if you could see like, what's next to the Mona Lisa,'
so there just aren't any great minds at work here.
Most of it looks bad anyway.
The image generation tools have leaned into surrealism, landscapes,
and stylized art, but it's all the type of stuff
that comes up when you Google Image search
"cool desktop backgrounds."
There's clearly no vision or decision making behind this stuff,
even if you're willing to ignore how melty most of it looks.
Neural networks don't have a concept of beauty to work from.
You have to write things like 'best quality' and 'featured on ArtStation' in AI prompts
because if you want something beautiful, it has to be tagged as beautiful already.
But it's undeniable that the generated art is looking better and better.
The image generating models have their limits,
but like ChatGPT, they're rapidly getting good enough
and consistent enough that most people won't notice or care.
Stable diffusion, which I used to make some of the monstrosities in this video,
is capable of producing nearly photorealistic images,
and an army of 4chan users have perfected
anime and furry art as well.
It's pointless to quibble over a definition of art here
because it's easy to just reject the definition.
People have debated this stuff for thousands of years,
the best and brightest at Twitter
aren't going to solve it.
But it's obvious that AI is not creative.
[Craig Mullins]: Some things kind of trouble me to a certain extent.
I remember an Adobe
representative saying
in a promotional video is
"we want to make our suite of software,
make it so that anybody with a subscription
can make a blockbuster film,
can do all the effects work,
do all the compositing," and it's just pushbutton.
Well, you know that would be an amazing achievement
in terms of software design.
And I do think that,
you know, the people that write and design the software that we use are...
they're looming
behind every painting that we make.
Because the imprint of the software on the work that we make is huge.
And they are geniuses that they are able to design these things.
But the homogenization of artistic expression
by more powerful software is a concern to me,
which is one reason why, you know, I understand when people say, you know,
I liked your work when it was just around brushes because it's a simpler,
lower-order tool that has more-
you have to use more of yourself to make it work
than something that is
much more powerful.
So will you eventually be able to have a computer program
that can turn out a Norman Rockwell?
Yeah, probably.
I mean, you can have them write symphonies that
how many people will be able to tell the difference between somebody
that analyzed all of Mozart's work and had it do an original work by Mozart.
How many people would be able to tell the difference between the composition
that the computer did and one that Mozart did.
A few people, but that number is getting smaller and smaller every year.
[Jim]: If you train a neural network to recognize text like the example
at the beginning of the video, it'll do a good job when you give it text.
But if you give it something it's never seen before,
like a weird math symbol or a picture of a dog,
the AI is still going to guess what letter it is.
It's just a program,
it can't integrate new information the way a person could.
Sure, you can retrain it with thousands of dog pictures,
but we can hardly call that intelligence.
I encountered this exact problem when I was trying to recognize text in some PDF files.
Adobe Acrobat tries its best, but it just has to guess when it sees an equation
and it doesn't have the vocabulary to describe stuff like this.
You wouldn't say that the program is lying because it got something wrong.
It just wasn't designed to recognize math.
A big part of AI's success right now is that it's easy
to use rhetorical tricks to make it seem intelligent.
The name itself, A.I.
is an obvious example.
If a network can recognize words, wow, it can read.
If a network can turn a text prompt into a picture, it's suddenly a painter.
Like all good cults,
promoters use mysteries to sneak their ideas in the door.
We don't know what the hidden layers are doing or how the brain works.
Maybe these machines learn the way brains learn,
so we should put billions of dollars into stopping the AI apocalypse.
We might not know exactly what the hidden layers are doing,
but we know how machine learning works and we know what it produces,
so I can comfortably say that AI is trained to copy.
You give the model training data in the hope
that it will output the same thing a human did.
There are setups where you don't need labeled data like diffusion,
which is what the best image generators use,
but these networks are still copying the structure of whatever
they're trained on.
Training an AI is just imprinting patterns from the training data.
That's no small feat.
It can pick up on some very complicated patterns,
but it's a far cry from creativity.
By feeding it a lot of images, a model can trick you
into thinking it's creative, but it really is just fitting
whatever prompt you give it to the most likely output.
Researchers have been able
to prompt these models to spit out their own training data, so it's clear
that the models are not synthesizing something new the way
a person could.
If you ask a person to copy
a painting from memory, you're not going to get a perfect copy.
You'll see what stuck out to them about the source and how they interpreted it.
The copy would have that person's aesthetic.
Stable diffusion will give you the exact painting
and it might give it to you in an Etsy store mockup.
Proponents of AI often compare generating images to photography,
but they're way out of their depth.
If we can agree that machine learning models are fundamentally uncreative,
then any creativity has to come into it on the human side.
This is like photography, they say, because the photographer did not make
whatever they're capturing.
But the levers for creativity really don't exist with AI.
You can change the prompt and some parameters to regenerate
the picture, or use inpainting to regenerate part of a picture
but you have no precise way to control what gets produced beyond suggestions.
A photographer is capturing a given thing,
a natural scene or some live event, but they have precise control of position,
focus, focal length, white balance, color balance, lens characteristics,
and really an uncountable number of other factors.
Photos certainly have an objective element to them, but the decision to capture
a certain image, and the characteristics of that image are all up to the artist.
Generating a picture with AI is guesswork, and the hilarious new field
of prompt engineering amounts to typing in the name of an artist
whose style you want to copy, or adding the words 'high quality' to the prompt.
Researchers at Microsoft were happy to demo their new CoDi network
with prompts like "oil painting, cosmic horror painting,
Elegant intricate art station concept art by Craig Mullins detailed."
Even the bleeding edge tech demos have to put "ArtStation" in their prompts
to make them look good.
Which brings us to the fact that practically all the datasets used in big
AI projects are scraped from the web without consent.
Again, you can do whatever you want if you call it research.
Stable diffusion has an insistent problem where the images it generates
have watermarks from the pictures they stole.
Getty Images is currently suing them over this.
Stable diffusion uses LAION, a tenuous acronym
for a dataset of almost 6 billion image-text pairs.
The clever thing about L'AI-ON is that it just has links to the images,
so you can't sue them for copyright infringement.
Model makers can download the pictures themselves
and then pivot their research project into a business once the model is up and running.
Stable Diffusion also took images from DeviantArt to train its model,
and in response to the controversy
DeviantArt built diffusion right into the site, so lazy
people can shit it up with ugly 500 by 500 pictures of 'teddy bear
smoking weed' or 'furry on the moon, detailed Andy Warhol.'
But worry not! If somebody puts your name in their prompt and you find it
and complain to DeviantArt about it, they might take the generated image down.
And you can label your art as 'no AI' so companies
can exclude it from training in the future if they want to.
It's worrying how eagerly
the people charged with running communities will just turn them
into storefronts.
DeviantArt isn't known for its high quality art.
Sorry. But it always felt like a site for art people to hang out on.
The people who run it are all but telling their users
that it's not safe to post their work there anymore.
This is a whole can of worms,
but the justification for this is another pro AI talking point.
Machine learning is democratizing art.
There's no definition of democracy where this makes any sense,
democracy is when the government acts in accordance with the will
and best interests of its people.
The production of art is not democratic and democracy is totally irrelevant to it.
Art collectives can be run in a democratic way, but I don't see AI
democratizing Blizzard's studios anytime soon.
They're really just telling on themselves:
In the political code that news organizations and pundits use,
democracy is intimately related to a free market.
Something is more democratic if it opens up new revenue streams.
Normal companies can lay off their illustrators and designers,
and there are new opportunities to sell computing power or services
like data labeling.
The freer the market, the freer the people, as they say.
But taking the argument at face value, anyone who cares about their art
will automatically avoid AI because it's hard to express
your own vision when you have to do it through a game of telephone.
I'm not going to call myself an artist, but to make these videos
I have to assemble a lot of writing, video, and sound, and make editing decisions.
There is no world where I write a script and then say
fuck it, I'll just generate the visuals.
Any solo developer or team that cares about its product
is not going to settle for good game design with a bunch of
melty computer-generated art.
A lot of middle of the road games could get away
with AI generated content, so I think it will be pretty popular.
But the few games that are unmistakably high art, the Earthbounds,
Dark Souls and Rain Worlds are the result of a consistent authorial
vision that patterns their largest and smallest features.
It takes a visionary
like Shigesato Itoi to coordinate the perfect harmony of mechanics,
visuals, writing and sound that we got with Earthbound.
Using AI not only takes opportunities away from the Itois of the world,
it forecloses on the possibility of a singular vision
by using some complicated statistics to automatically design games by committee.
The emotional climax of Earthbound is one of the most significant
artistic achievements in the history of games.
I can't spoil it in good conscience and it can't be conveyed through video,
so if you know, you know. The game's balance of quirky but genuine writing,
the battle system and its incredibly solid theming
are kind of subverted by a sudden shift in tone near the end of the game.
But this shift not only preserves its themes,
but makes us feel them even more deeply.
Undertale is good, but to this day nothing has touched Earthbound.
Often it's designed so counterintuitively and written
so delicately that no machine will ever be able to touch it.
In a word, Earthbound is new and AI can only mix up what has come before.
So if you're interested in making good art, AI is automatically useless to you.
Machine learning is not democratizing art so
much as outcompeting it.
It's not just a couple small companies scraping training data.
OpenAI is the big one.
They made GPT and DALL-E, some of the most
sophisticated text and image generators out there.
The company's leadership is a who's who of alleged prescription amphetamine
abusers and alleged child blood injectors.
Sam Altman, the CEO, is literally trying
to make New World Order conspiracy theories real.
His Worldcoin project is scanning people's retinas
to make a blockchain-based global digital ID system
[Female newscaster]: Owned by the majority of humanity
[Jim]: or it was for about a week.
Worldcoin was giving Kenyan people 50 bucks in exchange for retina scans
until the government noticed, started investigating, and told them to knock it off.
The story is similar in every other country Worldcoin has launched in.
We're really reaching a new level of big tech Rube Goldberg machines.
because the AI
that Sam Altman's other company makes and sells is scary--to him--
We need a way to identify humans. And the best way to do that
is to make a database of people's retinas, which is also a bank
powered by a Ponzi scheme.
Oh, and Sam threw universal basic income in there too.
This is all to say that OpenAI's people
have been drinking the Silicon Valley Kool-Aid for a long time.
This might come as a shock, but OpenAI started as a nonprofit research
organization and then pivoted once they realized they could make money.
[Sam Altman]: We started as a nonprofit.
We learned early on that we were going to need
far more capital than we were able to raise as a nonprofit.
Our nonprofit is still fully in charge.
Everything else flows to the nonprofit, and the nonprofit is like in voting
control, lets us make a bunch of nonstandard decisions,
can cancel equity, can do a whole bunch of other things, can...
[Jim]: Ironically, very little of their work is open.
Another shock OpenAI steals all its training data.
There are no laws about this yet,
so they don't have to tell us where the DALL-E images came from.
But no licensed datasets actually exist
so it's a pretty safe bet that they just took billions of images.
GPT is the same deal with text, OpenAI
just crawled the internet collecting text to train its model with
or used an existing dataset like Common Crawl.
They're actually ramping up to do this again.
So if you have a website and you don't want OpenAI
to have your website, please put these lines in your robots-dot-text.
The problem with any pushback against stolen training data
is that the theft already happened.
There are billions of images sitting on these people's hard drives already
and adding a 'no AI' tag to your DeviantArt isn't going to change that.
I'm out of my depth here, but I also seriously doubt
that training on copyrighted data will become illegal.
At a very basic level,
I think a judge will see a generated image and think what most of us did:
The AI just made a picture!
Even in the best case scenario, the law will only rush to enforce
copyright when it's some billion dollar company being infringed upon,
and even then, people will always find a way to access digital files.
A solo artist without the resources to recognize or prosecute copyright
infringement couldn't really do anything to protect their work
even if there was some legal framework in place.
The outputs from machine learning systems are another matter.
AI hasn't really been litigated yet, but there have been some relevant
fights over the patents and copyrights of AI generated work.
A judge decided that an AI could not be the author of a patent,
but whether a person could patent
an AI assisted invention is beyond the scope of that case.
As far as I know, three people have tried to copyright AI generated art.
Jason Allen, who won an art competition with his piece, Steven Thaler,
the same guy who tried to make his AI a patent author, and Kris Kashtanova.
All three were denied, and the U.S.
Copyright Office is very clear that they did not make the artwork, a computer did.
In Kashtanova's case, she was trying to protect a comic,
and the Copyright Office decided that the text and layout, the stuff
she actually did herself, was protected while the artwork was not.
The Copyright Office even makes the argument that AI
generated art is like commissioning an artist, stating
that Kashtanova wouldn't be the author if she paid somebody else to draw
"a holographic elderly white woman named Raya.
Raya is having curly hair and she is inside a spaceship.
Star Trek space ship
Raya is a hologram octane render cinematic, hyper detailed, unreal engine."
But really, there's a lot of money behind AI art
and there are no definitive rulings or laws about it yet.
All things considered, a letter from the Copyright Office
doesn't carry a ton of weight.
Pretty much every big company stands
to profit from making AI generated works copyrightable,
and if Disney is any indication, it's well within their means to just change
American copyright law.
Even if that doesn't happen,
they can just lie.
Air generated visual art is obvious, but text, code,
animation, and design drafts are easy to pass off as human
so long as you get a human to edit them.
AI is literally incapable of meeting the level of creativity
required for copyright.
But since AI art sometimes kind of looks new,
I think the law will decide that AI is creative.
The US government is thinking about putting laws in place around AI
specifically, but there's nothing concrete yet
and it's all shrouded in political nonsense language anyway.
Direct regulation would be way more effective than copyright updates
for reasons I've discussed in the past.
This stuff has put us in a situation where there are really no good answers:
Machine learning is broadly useful, but generative AI
could set the whole field back if it gets regulated.
There are projects to watermark generated images, but they're all run by AI
companies who want to filter AI
images out of their datasets.
[Male ad voiceover]: Imagine cooking something and there are no nutrition labels.
Now imagine the same, but for your digital creations, wouldn't it be dope
to have all the info readily available and protected for everything you create?
[Jim]: At the same time, companies like OpenAI
are putting tons of money into destabilizing and potentially
killing off swathes of art, writing, and design jobs.
A good chunk of people who hire designers have no taste
and think they could do the job better anyway.
Now they can tell an AI to "make the logo pop,"
and it won't laugh awkwardly and ignore them.
Somebody who needs a stock photo to put it at the top of their blog
could license a picture or just spit out a 100%
free landscape from MidJourney. With a little effort
you can even get free pictures of your fursona.
People tend to follow the path of least resistance.
Having strong values and well-articulated opinions
about subjects like this is a privileged position to be in.
People use these tools without knowing how they came to be or what they represent.
There's a good chance slave labor was involved somewhere in making your phone,
so we have no standing to morally judge people using AI.
On average, convenience always wins.
Crying "consumerism is bad" has never worked.
The companies behind this stuff are the enemy,
the individuals promoting it are just useful idiots who you shouldn't
waste energy arguing with.
AI bias is another major talking point right now.
Since models pick up and copy patterns in their training data.
they're also very sensitive to those patterns.
A model meant to label things in images might throw out racist labels, either
because it was only fed images of white people,
or because the training data was labeled by biased people.
ImageNet the first big image dataset.
[Fei-Fei Li]: But what if cats are hidden?
What about these silly caaaats?
[Jim]: Filed bisexual people under the category of sensualist,
alongside c***suckers and pagans.
Whatever you think about that,
structuring the data this way is somebody's judgment,
not an objective scientific taxonomy.
A couple of artists had the idea to expose this bias
by letting people classify themselves with ImageNet,
and it was so good at being racist that the people who compiled the dataset
went into crisis mode and deleted half a million images.
More recently, OpenAI has neutered ChatGPT
more and more to hide its biases from users.
Often when they announce that ChatGPT is less biased,
they haven't actually changed the dataset or the model structure,
they're just getting between you and the AI to avoid bad press.
I mean, to protect users.
They can add extra words before or after
whatever you type to condition the bot's response, or detect messages
that they deem inappropriate and send the AI a different prompt altogether.
Everything we make is ultimately going to be shaped by the world we live in.
And that's doubly true for vague words and concepts.
Since AI is not intelligent, it necessarily reflects
the people who made it.
More diverse datasets might eliminate bias
if you're using AI
to do medical diagnosis or something.
[acollierastro]: What if you're doing medical research?
You. You give your machine learning program
100,000 images, like from MRI's of like people's lungs,
and you're like, some of these have tuberculosis.
Some of these don't.
And what you can do is see if the computer gets
better diagnosing tuberculosis from an MRI than doctors are.
And what do you know?
They've done that and the computers are better.
Great.
So now if you're researching
how symptoms of tuberculosis develop
so you can start diagnosing it earlier, you can try to figure out the pattern
that this machine learning program is finding like,
how exactly is it making this diagnosis?
Now, this is very hard.
Remember, you can't ask the program like, why did you make this decision?
And it gets even harder when you do your neural networks
and your deep learning and all that stuff through a lot of A/B testing,
putting up two images that are almost identical.
The doctors were able to figure out how the computer program was
identifying tuberculosis more often than human doctors. And
it turns out that the machine learning program
was weighting the age of the machine that took the image.
So if it was an older machine, it would say that it's more likely
to be tuberculosis.
[Jim]: But for a model like GPT, all of the training data is inherently biased.
Taking the average bias of all writing will just give you the most biases.
There's no magic here.
Energy is conserved and you cannot get unbiased data from biased people.
I hope that I've made this technology look like what it is:
A bunch of very capable systems to recognize patterns.
AI is not intelligent and the only real plan that the machine
learning companies have is to scrape more data and make even bigger models
with the belief that they'll somehow hit a critical mass.
Then the computer will finally awaken and fall in love with them.
[Robotic female voice]: When we saw your initial feedback in the communities, we realized
how important romantic relationships with AI can be for emotional
well-being and decided that we should build a dedicated app for that.
[Jim]: There's been an explosion of worried experts warning the public
that AI is going to become sapient and do something unspecified.
But really bad.
This is magical thinking.
Just a bunch of self-proclaimed rationalists
working themselves up over nothing.
But it is weird that all these nonprofits sprung up
to scare us about AI sentience.
One of their
keywords is alignment, which has a lot of definitions.
OpenAI says it's the process of bringing AI into alignment with human values
or, at its most extreme, making sure the superintelligence can't kill people.
It's kind of hard to take OpenAI's writing on safety and alignment seriously
because they talk about courageously and good heartedly
solving a bunch of problems that, not only did they just make up,
but which they're working very hard to create with their technology.
In practice, alignment
has been done by humans reviewing the outputs from AI
systems and judging whether they've adequately achieve their goals or not.
It's a reasonable approach,
but I can see it turning into the same sort of nightmare job
as content moderation, especially if you're aligning an
AI to attack the right sort of people with a drone.
Since AI is apparently going to be smarter than humans now,
OpenAI proposes that another machine learning system does the alignment for us.
Companies love the 'just keep adding shit' approach;
we can have an AI train the AI that aligns the other AI.
Whatever the people involved think they're doing,
they're acting as a smokescreen.
Selling sentience or superintelligence as the main risk of AI
diverts attention
from how machine learning is being used, and it positions AI as a force of nature.
It's already making objective judgments, It's already intelligent,
but we need a lot of money to stop it from doing
sci-fi novel things.
Superintelligence
is the scary part, but it sneaks in the premise that these models
are capable of making objective judgments or at the very least, informed judgments.
The real purpose of alignment is to make AI
better at performing these judgments.
Researchers at MIT give a three part definition of alignment:
AI should produce accurate outcomes,
consistently achieve its goals,
and produce value for stakeholders.
Stakeholder value is when you pay shareholders and pretend to help the environment.
In the article I'm referencing, they discuss the Australian Tax Office,
who deployed AI
to analyze people's claims as they filled them out
and nudge them toward "productive claim behaviors," in their words.
Paying taxes is cool and all but the ability to define a productive behavior
and use a neural network to quietly nudge users toward it is worrying.
It's not imperative that the AI be truly intelligent.
Behavioral nudging has been in the works for all of the 21st century,
the brainchild of B.F. Skinner
and the entrepreneurs that practice his ideas.
Nudging is about making the incentives that drive you toward one choice or another
frictionless and invisible, or preying on your animal
brain: hunge, horniness, fear.
On the internet we have recommendation algorithms,
the invisible force that decides what order you see Tweets in or what
mass of videos show up on your YouTube homepage.
These are tuned in such a way that the maximum number of people
will become addicted to looking at these platforms.
But neural networks allow for a system that nudges you,
not just an average of people with similar interests to you, but perfect
recommendations based on the specific things you've engaged with in the past.
YouTube's been doing this for years, so in this particular domain,
we're already living in the future.
[YouTuber]: Human is important, and that's because the YouTube
algorithm is all about helping humans find the most relevant content
as easily as possible.
YouTube's entire goal is to increase human retention.
[The Critical Drinker]: Yeah, socialism.
And has history not taught us enough?
[Nerdrotic]: But I think that's a good description.
The activist furries have taken over pop culture.
[Thumb person from Spy Kids]: Fucking pronouns!!!
[YouTuber]: Oh, what a coincidence.
You've scrolled upon this short.
What's even more of a coincidence
is how this video is sponsored by Call of Dragons.
[ad read glitches out]
[Jim]: It doesn't sound that bad, but this sort of thinking culminates
in the elimination of choice, where everything is reduced to a
TikTok-like stream that plugs you directly into the algorithm.
YouTube is pushing shorts for a reason:
designing this way drives engagement and, in turn, ad revenue.
[Gamer]: The death of escapism.
[Jim]: We won't know what this does to somebody's psyche for another decade or so,
but for me, the fun of being online has always been about search and discovery.
Now sites like YouTube are taking away the few tools I had to find new stuff.
They briefly removed the option to sort a channel's videos by oldest first
and search is a mishmash of actual results and
useless recommendations.
[Male narrator]: When we use contemporary technology
a trail of information is created in the form of data.
When analyzed, it describes our actions, decisions, preferences, movement, and relationships.
This ledger of our data may be considered a Lamarckian epigenome,
a constantly evolving representation of who we are.
What if the ledger could be given a volitional purpose
rather than simply acting as an historical reference?
What if we focused on creating a richer ledger.
By introducing more sources of information?
What if we thought of ourselves not as the owners of this information.
But as custodians, transient carriers or caretakers?
[Kerstin Oberprieler]: Imagine
if everyone in the world
simultaneously was a little more playful and a little more delighted.
[Jim]: The people who make AI
systems have a very simple theory of mind.
They think more intelligence means more objectivity.
It's obvious if you read anything they write,
and they're happy to treat us like stupid animals
because they think that if you can be manipulated, you deserve to be.
Everything is getting dumber and thinner, slowly being instrumentalized
to just keep you on the TikTok treadmill or the Diablo Immortal treadmill
without producing any sensations or thoughts.
While we're here.
Hey, other people who make videos.
If you have nothing to say about a topic,
you can just make a video about something else.
You don't have to waste hundreds of hours in editing
to say that you played a game from five years ago and it's so liminal.
Wow! You beat Dark Souls with a straight sword,
is that even freakin' possible?
Cool challenge run, better see if Hello Fresh wants to pay for it.
You should try the Zweihander next,
you can tell us why it's such a hidden gem.
This is a good video.
The intro is a little too long, but the guy did an interesting experiment
and actually shared some useful reflections on the experience
of playing Ikaruga every day, which most people haven't done.
It has less than a thousand views.
This is a good video, an exhaustive history of Nintendo
that actually has enough information to justify its runtime.
It has less than a thousand views.
I don't care about the Garten of BanBan iceberg
If you have nothing interesting to say about it.
I know this is a pretty edgy opinion,
but anything that makes you stop and think runs counter
to what the content businesses want.
They want to monopolize your time and just like a slot machine.
The best way to do that is to keep the screen throbbing
with noise and light.
Winning the game, or learning
something about a subject or another person stops being the goal.
Eventually, even fun becomes secondary to the rhythm of dopamine release.
Slot players don't like winning.
It breaks the rhythm.
The real AI apocalypse is this hand-in-glove relationship
that's emerging between us and the systems that control us.
A perfect recommendation algorithm can pinpoint every potential whale,
and it will also make the average person waste more time
and energy on the pointless dopamine drip of content.
AI alignment is really about aligning AI to better exploit us.
The threat of superintelligence is just a cover.
Other than the content itself getting dumber, AI
driven engagement is a way to claw away more time
from your actual, unmediated and unmonetized life.
And there's an analog to this recommendation algorithm example
in practically every industry.
With silly videos, the stakes are relatively low, but police are using AI
as an element of predictive policing.
They want to know where crimes will happen before they happen.
I don't know if you've seen the news in the last...
hundred years or so, but the police have some bias problems
and any analysis tool they use will reproduce those problems.
The FBI's bread and butter these days is grooming mentally ill people
to become terrorists or drug dealers just so they can arrest them.
Predictive policing is a way to divert blame
onto an algorithm instead of an officer or institution.
It's a way to manufacture objective judgments.
There's no arguing with a robot.
Shield AI develops machine learning for police drones and fighter jets.
They make software to kill and terrorize innocent people, but
still found a way to valorize themselves on their hip-AF tech startup website.
Shield works with the Department of Defense,
and they've partnered with other military AI companies,
drone manufacturers, and even big dogs like Northrop Grumman.
Insurance companies want to start live-updating your premiums
using surveillance data, and there's a good chance they'll use AI
to make those decisions. You have to have insurance,
they have access to the sensors in all your devices, and behaviors
that they decide are unhealthy can be punished with higher premiums.
[Male corporate narrator]: Our clients want solutions centered around humans
that will boost their productivity rather than replace them.
I believe companies should focus on few golden applications,
vertical transformations, things that really scale and create value fast
in customer service where 30 to 35% of people
are searching and classifying document as knowledge.
We believe I could reduce costs by 50 to 60%
in claims where GenAI can not only augment information,
but also write a settlement offer to customers in 2 minutes.
In underwriting,
by analyzing customer needs to create hyper personalized coverage.
[Jim]: Sorry you got depressed and ate too much ice cream.
Sorry your brother lopped his finger off
with a table saw and you sped to the hospital,
but you'd better get a second job if you want to keep your car.
AI allows companies to make more complicated inferences
about you from the data they're already collecting.
Deviations from the norm aren't hard to see, but with AI
they can figure out exactly what you're up to and put a price on it.
In a free market, when something bad happens to you, you have to be punished.
Misfortune puts you in a position to be exploited
and as every economics 101 textbook says, "a market
in equilibrium leaves no unexploited opportunities for individuals."
So some individual--and companies are people--will swoop in to exploit you.
Tragedies are expensive.
All the squabbles about ChatGPT and art theft are an important
The creativity argument is one that the machine learning companies
can afford to lose, so long as they're allowed to keep deploying their systems.
Rationality is one of our big collective values, right?
Making arbitrary decisions about somebody welfare is frowned upon.
And since we don't really have any traditions to defer to,
we have to come up with reasons for things.
People hate insurance companies and the police
because they often just show up and make your life worse.
AI offers two things:
More exploitation and a veneer of objectivity.
Until now, people have had to make difficult, complex decisions,
but a neural network is an empty shell that will give you a decision
if you feed it data.
It works much faster than a person, and it's easy to argue that the decision
is perfectly rational. A computer made it!
If your insurance is automated, pricing decisions seem objective
and the contract can be revised in real time.
Everything is stuffed with sensors now, and data is already being collected
and sold.
It has been for years. So there's a constantly updating profile
of your behavior, and a neural network can use it to more perfectly screw you.
Things had to develop this way because stock prices have to go up.
Insurance adjustment must go from slow, messy and human
to this frictionless process that always happening.
A kind of stock ticker on the value of your life.
As we run out of new sources of wealth,
all the slack has to get taken out of the existing ones,
because the number has to go up.
There are a dizzying number of applications for AI.
Most of it won't pan out, but some of it will.
Memorable is one of the many adtech companies
using AI to make commercials as obnoxious as possible.
They think they've got a model that can score ads on their memorability,
so look forward to having 100,000 sugary jingles lodged in your brain.
Yoda1, a mobile game publisher,
came up with a bot that would identify potential whales
playing their games.
The head of their solutions
team at the time, Chris Dossman, probably ran that project.
He also built Yodo an AI
to recommend micro-transactions.
To really hook somebody, you have to recommend the right item at the right time.
Today, Dossman runs another machine learning adtech company:
Dicer-dot-AI.
AI can also churn out horrible articles to clog your Google searches,
and it can probably do search engine optimization better than a person.
Google will become even worse to compensate for this.
They're pretty much giving up on web search altogether
and just using AI to answer whatever question you have.
The dream of the Internet, all the world's information at your fingertips,
has been completely swallowed by companies like Google.
The world's information is at their fingertips, and you can maybe get some of it
through an automated chat bot, which is free for now
but they can charge you if the stock price ever goes down.
AI has genuine uses the same way the blockchain does.
AI actually has a ton of genuine uses.
Blockchain could be useful for physical goods tracking or something,
and AI can be really useful if it has some kind of supervision.
It could automate a lot of busywork, but right now
the trajectory we're on is using it to dehumanize everyone even further.
The irony that we're automating the production of art instead of the jobs
everybody hates shouldn't be lost on us.
There's also a trend of AI friends and companions, which is very sad.
You can write a character for these bots to play
and it will keep that description in its attention to kind of roleplay with you.
Porn is obviously the main use for this, and it's pretty much a perfect storm for
gaming's core audience of horny, lonely people with high end graphics cards.
The League of Legends community may never recover.
But if you stay in these communities for a while,
you're bound to see people who just want somebody to talk to.
People are getting lonelier,
and it's not really being addressed by anybody in power.
Apps like Replika that use GPT to give you a pretend friend or girlfriend,
are a market solution for loneliness.
Replika got popular as a sort of AI therapy tool,
which is sad in a different way, but it quickly devolved
into making people pay to sext with their robot friends.
These band-aids don't work.
We know that the bonds of community are what make life worth living,
everybody knows that.
But we simply have no way to address the crisis of loneliness because
those real relationships everybody wants cannot be transactional,
and they're harder and harder to sustain in a world where everything
is shoved into a financial mold.
Every little incursion by an advertising
firm or tech company eats into our space to live unmediated lives.
Social media is an obvious example.
An attempt to turn conversations into a market for attention.
I want to celebrate machine learning.
The potential in it is incredible.
I want the AI assistant that can recommend papers to read and games to play.
I like the goofy AI generated voice videos,
even if they're ethically questionable and gimmicky.
I even want to play AI Dungeon,
if they make it weird and free again.
But everything we make is conditioned by the world,
so every AI project has a tumor in its heart.
These tools don't exist so that we can make better things or speed up work.
They make minimum viable products with minimum supervision.
They're going to scan every actor's face and voice so nothing new can ever
be made again.
And the computer will write all the scripts, too.
Every game can be a totally personalized Skinner box,
modifying itself with a live feed of your pupil dilation.
The Bangladeshi guy delivering your McDonald's will have his pay docked
if he walks too slow.
We already have that one.
A lot of this won't happen
but if entertainment companies move fast enough that AI
generated movies, for example, are the only movies.
It doesn't really matter if they're bad.
Bad compared to what?
Is it too corny to ask that the self-appointed saviors of humanity
at OpenAI work toward ennobling humanity a little bit?
I don't care about Mars.
I want my friends and family to have the security to live
good lives and a safe, quiet place to do it in.
If that is too much to ask,
then why are we subsidizing these people's programing projects?
I try not to dump a bunch of nihilistic bullshit on you at the end of my videos,
I'd much rather piss you off.
Really think about how many struggles in your life
and the lives of people around you have been caused by Silicon Valley.
How many industries they've destroyed while patting themselves on the back
about disruption?
How many people they've forced into unjust work contracts.
How many kids they've given eating disorders?
How much of our collective wealth and time they've wasted
on gimmicky social media apps that shut down after a year?
Even if you're heartless,
we can all see that the big tech model is massively inefficient.
We could do so much if these morons weren't so powerful.
They deserve to at least be scared.
When you start saying stuff like this, people come out of the woodwork
to randomly tell you that the Soviet model didn't work.
An alien system from a society we don't live in from a time
long past is only relevant
if you think politics is an RPG where you have to pick a class.
It's actually the process of advocating and fighting for what we want.
So I don't really care what the solution calls itself,
but we're at a point where we need to choose between building
a world for money to live in or a world for people to live in.
You don't need political science or some complicated theory or some dogma
to see that. Profits have to grow and we're out of room...
However, on the whole, I think the prognosis is good.
Organized labor came back with a vengeance during the pandemic,
and although there are some shitty unions out there, it's
been a long time since people have had access
to institutions that are actually on their side.
Union membership is low right now.
It's been shrinking in the U.S.
for decades, but a few more high profile victories could turn the tide.
[Shawn Fain, UAW president]: No one said a word
when these CEOs gave themselves 40% increases over the last four years.
But when workers ask for their fair share, all of a sudden, you know,
it's a huge problem.
Life is going to get worse.
But the thing is that by making living unbearable, mega corporations
and their buddies in government
create conditions that force people to organize and fight back.
It's not good that we're barreling toward 100 crises at once,
but we're here anyway and people are really starting to feel it.
It's easy to say things like, "Well, the automatic loom took a lot of jobs too,"
but workers have fewer and fewer places to go.
So of course they're going to resist machine learning.
AI is cool,
but we can't just discard millions of displaced humans like they're old toys.
The ongoing WGA and SAG strikes are very high
profile and AI is one of the union's central concerns.
[Duncan Crabtree-Ireland, executive director of SAG-AFTRA]: The vast majority our members are fighting to make a career.
They're fighting to pay their rent and to take care of their families.
The majority of our members
don't make enough money to qualify for health insurance in a given year
under our own plan, which is $26,500 approximately.
The fact is, from the very beginning, we've said we're not here to block AI.
What we are here to do is put guardrails in that make sure that our members
won't be abused by this process,
and that they will still have careers three years from now.
[Jim]: People are starting to rediscover the power of organization
and a strike for better is also a statement of values.
The AI revolution we're getting now is the one that gamifies life.
It's a way to enact totally rigid systems, with a bunch of esoteric rules
that doles out rewards and punishments from the aether.
It's arbitrary, and it doesn't afford us any unstructured time or bad behavior.
Its world is one where you're bound by a million invisible
contracts that are constantly rewriting themselves,
pulling you to and fro unconsciously or with explicit threats.
And these contracts are driven by the same market logic
that puked pregnant Elsa Spider-Man videos into our collective psyche.
But as always, things don't have to be this way.
There's a time for games and a time for life.
OpenAI isn't going to draw that line,
so it falls to us.