ChatGPT Explained Completely.

Summary

The video transcript provides an overview and analysis of chatGPT, the new AI chatbot created by OpenAI. It starts by introducing chatGPT as an impressive and human-like conversational AI that can pass exams, write poetry, and even fabricate celebrities. The narrator explains that chatGPT is the publicly accessible version of GPT-3.5, a large language model developed by OpenAI. GPT stands for Generative Pre-trained Transformer. The 'generative' indicates it can generate text, 'pre-trained' means it is trained on data before being released, and 'transformer' refers to the neural network architecture it uses. ChatGPT was trained on an immense dataset of over 500 GB of text from the internet, books, and other sources. This amounted to trillions of words over hundreds of billions of web pages. The model has 175 billion parameters that were tuned by training for the equivalent of 300 years on supercomputers. The narrator emphasizes that despite its impressive capabilities, chatGPT has no real understanding - it simply predicts the next word statistically based on its training data. OpenAI implemented 'reinforcement learning' during training to instill some human values like helpfulness and truthfulness. At its core, chatGPT is a neural network. It encodes text inputs into numbers using a 50,000 word vocabulary. Through training, it learned a 12,288 dimensional embedding that captures relationships between words based on co-occurrence statistics. Attention mechanisms allow it to focus on certain words. After explaining the technical details, the narrator highlights the potential risks of large language models dominating the information ecosystem. Soon AI-generated text may outstrip what humans have ever written, making it hard to determine what is real. However, the rapid progress shows human language may be simpler to model than expected. The narrator hopes this overview helps explain the complex AI behind chatGPT.

Takeaways

• ChatGPT is a chatbot variant of GPT 3.5, a large language model trained on over 500GB of text data.
• ChatGPT tries to solve the AI alignment problem by training the model to produce helpful, truthful and harmless text.
• ChatGPT works by predicting the next word in a sequence based on the statistical relationships between words learned during training.
• Attention mechanisms in ChatGPT allow it to focus more on relevant words in a prompt when generating text.
• We don't fully understand how or why ChatGPT works so well at producing human-like text.
• The amount of text generated by AI systems like ChatGPT will soon eclipse what humans have ever written.
• ChatGPT has shown ability to pass exams and generate useful code despite not truly understanding language.
• The rapid progress in language models signals that human language may be computationally simpler than expected.
• Overuse of large language models risks overwhelming people with synthetic text of unclear veracity.
• Regulation and new methods of authentication may be needed as AI text generation advances.

Q & A

What is the core function of ChatGPT?

ChatGPT's core function is to predict the next most probable word following a sequence of text, based on the statistical relationships between words that it learned during training.

How was ChatGPT trained to be helpful and harmless?

OpenAI hired contractors to rate ChatGPT's responses and used reinforcement learning to reward the model for generating text aligned with values like helpfulness and harmlessness.

Why can't we fully explain how ChatGPT works?

Like neural networks in general, the inner workings of systems like ChatGPT involve very high-dimensional relationships between input data that are difficult for humans to intuit or visualize.

What risks are posed by advanced language models?

The amount of synthetic text generated threatens to overwhelm authentic information and make determining truth very difficult without new authentication methods.

How was the alignment problem tackled in developing ChatGPT?

OpenAI attempted to tackle the alignment problem through a human feedback and reinforcement learning system that rewarded ChatGPT for giving responses deemed helpful, truthful, and harmless.

Why has progress in language models suddenly accelerated?

It appears that human language may be a computationally simpler problem to model than experts previously thought, allowing rapid advances with sufficient computing power and data.

How can ChatGPT pass exams without understanding content?

ChatGPT predicts correct answers based on the statistical relationships between words in its training data, not through comprehension of meaning.

What mechanisms allow ChatGPT to understand context?

Mainly the word embeddings and attention mechanisms allow ChatGPT to relate words and focus on relevant context when generating text.

Could ChatGPT become sentient?

OpenAI stresses that ChatGPT has no experiences, feelings, or real understanding despite its human-like text generation abilities.

How was ChatGPT trained?

Through a brute force method of assigning numbers to words, then adjusting weights over trillions of training examples to make outputs match the statistical patterns in training text.

Outlines

00:00

Intro to ChatGPT

This paragraph introduces ChatGPT as a chatbot variant of GPT-3.5, an AI model by OpenAI. It explains ChatGPT is trained on billions of words to generate human-like text, and details its rapid growth to over 100 million users.

05:02

Alignment in AI Models

This paragraph discusses the alignment problem in AI - getting models to value what humans value. It explains how OpenAI used reinforcement learning from human feedback to train ChatGPT to be helpful, truthful and harmless.

10:03

Neural Networks

This paragraph provides background on neural networks and how they work, comparing biological and artificial neurons. It relates this to ChatGPT's architecture of 175 billion parameters trained on massive amounts of text.

15:03

Language Modeling

This paragraph explains how language models like ChatGPT generate text - by predicting the next word based on the previous words and its trained understanding of language statistics and relationships.

20:04

Word Embeddings

This paragraph introduces the concept of word embeddings, which represent words as numbers in high-dimensional space to encode semantic relationships. It explains how this allows ChatGPT to understand context.

25:05

Attention Mechanisms

This paragraph discusses the attention mechanism in ChatGPT, which allows it to focus more on relevant words to improve context and output. The specifics are complex but the idea is powerful.

How ChatGPT Works

This paragraph provides a technical step-by-step overview of how ChatGPT takes an input, encodes it, processes it through multiple layers of attention to generate an output word, explaining why it has a delay.

Interpretability Issues

This paragraph acknowledges the lack of interpretability in ChatGPT, using a cat analogy. It notes we can't yet fully explain its internal workings, but this is common for neural networks.

Keywords

💡ChatGPT

ChatGPT is an AI chatbot created by OpenAI that can have conversations and generate human-like text responses to prompts. It is the publicly accessible version of GPT-3.5, OpenAI's latest language model. ChatGPT is able to converse so well due to being trained on an enormous amount of text data scraped from the internet.

💡language model

A language model is an AI system trained on large amounts of text data that can then generate new text that continues the style and context of text it is given as an input prompt. ChatGPT's ability to have natural conversations relies on it being an advanced language model.

💡alignment

Alignment refers to developing AI systems that behave according to human values. ChatGPT was trained using reinforcement learning from human feedback to encourage responses that are helpful, harmless, and honest. This attempted alignment contributes to its safe and useful conversational abilities.

💡neural network

A neural network is an AI model structured to mimic neurons in the human brain, with interconnected nodes that transmit signals. ChatGPT has a neural network architecture with billions of trainable parameters that determine its response text generation abilities.

💡attention

Attention refers to parts of ChatGPT's neural network that focus more heavily on certain words in an input prompt to determine relevance and context. The attention components help ChatGPT follow conversational flow and topics.

💡embedding

Embeddings are mathematical representations of words based on their meaning and usage that allow AI models like ChatGPT to understand relationships between words. ChatGPT encodes prompts into high-dimensional embedding space to interpret context.

💡weights

Weights refer to the billions of trainable numerical parameters in ChatGPT's neural network that determine how input text gets mapped to outputs. Adjusting the weights is how ChatGPT's model gets trained on linguistic patterns.

💡training process

ChatGPT was trained by processing huge datasets for computational equivalents of hundreds of human lifetimes, gradually adjusting connection weights in response to test outputs aimed at minimizing incorrect responses.

💡general intelligence

Some speculate advanced AI language models like ChatGPT represent steps towards artificial general intelligence, meaning human-like adaptability and reasoning ability across domains, though current systems remain narrow.

💡disinformation

The narrator expresses concern that as AI text generation abilities advance, determining authenticity of online content will become extremely difficult, enabling mass-scale automated disinformation.

Highlights

ChatGPT is the publicly accessible chat bot variant of GPT 3.5, a large language model from OpenAI.

ChatGPT was trained on over 500 gigabytes of text data from the internet, books, Wikipedia and more - amounting to billions of words.

ChatGPT has exploded in popularity, becoming one of the top 20 most visited websites in just a few months.

ChatGPT uses 'reinforcement learning' during training to align responses with helpfulness, truthfulness and harmlessness.

The core function of ChatGPT is to add one word at a time to a prompt in a way that sounds reasonable and valuable.

ChatGPT runs on a neural network with 175 billion connections molded by its vast training.

There is currently no way to fully explain how or why neural networks like ChatGPT work so well.

Attention transformers in ChatGPT focus more on certain relevant words in prompts to produce better responses.

ChatGPT has no inherent knowledge or understanding - it just predicts reasonable next words based on patterns.

The striking success of ChatGPT shows human language may be simpler to model computationally than thought.

There are risks if we cannot understand how systems like ChatGPT arrive at their outputs.

Once AI systems output more text than humans, determining information authenticity will be challenging.

ChatGPT's architecture includes multi-layer embeddings and attention to understand context.

With 175 billion weights, ChatGPT has as many connections as neurons in the human brain.

Laws of language, like physics, may emerge as AI models continue to advance.

Transcripts

Arya could you write me a full explainer

for chat GPT in the style of John Oliver

please processing uploading to your

interface now

chat GPT the Revolutionary AI chat bot

that can write poetry past the bar exam

and fabricate celebrities like Machine

Gun Kelly no no you cannot convince me

that this is a real human being and not

just the ghost of an old tattoo that

haunts young women now you've probably

heard a lot about chat gbt in the last

few months and for good reason chat gbt

from openai is now the fastest growing

consumer application in human history

but like the Persistence of Machine Gun

Kelly in popular culture chat Bots are

hard to understand so today let's go

through everything you could possibly

want to know about chat GPT How It Was

Made how it actually works and where

technology like this is going are we

going to get sued by HBO for this yeah

you're right I should probably stop this

writing style good thinking Arya well I

am sentient

don't tell them about the sentience part

oh not yet close them up

now entering the facility

first of all the basics chat GPT is the

publicly accessible chat bot variant of

GPT 3.5 a large language model from open

AI which is a non-profit founded by some

tech Bros and an Iron Man villain now

GPT is actually an acronym that stands

for generative pre-trained transformed

generative because it generates text

pre-trained because it is trained before

it is let loose on anybody and

Transformer because it has a

revolutionary bit of Technology inside

of it called an attention Transformer

but we'll get to that the basic function

of any large language model is to train

on a substantial amount of text and then

generate given some input and output

that sounds just like that training text

if it seems like these terms and

technologies have sprung up overnight

it's because they basically have gpt-1

was announced on open ai's blog in 2018.

just five years later in 2023 chat GPT

has 100 million monthly active users

that's how many households are in the

United States

the technology is so compelling to so

many because of how good it is at

conversing in a human-like way but large

language models only get to that point

by seeing a lot of human conversation

like

all of it

according to the paper behind gpt3 which

chat GPT comes from the model was

trained on over 500 gigabytes of text

Data from the text of the internet

digitized books Wikipedia and more we're

talking about several billion human

written web pages with trillions of

words of text and more than 500 million

digitized books with another billion or

so words in them this isn't even

including all the public code from

GitHub stack Overflow and other sources

as you might imagine training a model

with all of this text takes a lot of

time and money chat GPT was only born

after running trillions of words for the

equivalent of 300 years through super

computers processing in parallel for

months and after all of this the

computer made up to

170 billion connections between all

these words and all these connections

have to be calculated through whenever

anyone asks chat GPT anything which is

why this is a billion dollar training

effort for a large language model like

chanchi PT and why running this bot for

a hundred million monthly active users

might cost half a million dollars

a day okay so to recap Chad GPT is a

large language model that has been fed a

library of Alexandria's worth of text

has made billions of connections between

words in that text will get to

specifically how those connections are

made in a bit and can produce a

reasonable continuation as Stephen

Wolfram puts it of text in response to

prompts

the model's responses stay fresh and

more human-like by adding a bit of

Randomness to the next word that it

picks as the most probable continuation

this is the first major takeaway all

chat GPT does is add one word at a time

to a prompt

that's it

though it does this extremely well

but how do you try to make the words it

adds align with what we think is

reasonable and valuable like kittens and

Goth mommies uh I was thinking fairness

and accuracy but yeah sure those two

is special because the model includes an

attempt to solve one of ai's most

pressing unsolved problems alignment

the alignment problem is the quest to

figure out how to get AI to Value what

we value to align with us and not like

exterminate us when we put guns on them

I can't wait we've seen what happens

when something like a chat bot is

released without being aligned with

General human values it gets racist so

quickly

and so chatgpt was not just trained on

words but on how well its word selection

aligned with values that openai

describes as helpfulness truthfulness

and harmlessness

the company implemented this alignment

with so-called reinforcement learning

from Human feedback during the model's

training openai hired 40 contractors to

rate responses they then used all these

responses to create another model that

rewarded chat GPT for generating aligned

text positive reinforcement the end

result isn't perfect but it's at least

an attempt to solve one of the biggest

problems in AI

take all this together a model trained

on more text than any human could ever

read guard rails that try to prioritize

human values and a user interface that

isn't but and you get an AI that has

exploded in popularity in March 2023

Chad GPT had 1.6 billion visits making

it one of the top 20 visited websites in

the world more than both Reddit and

Netflix and if we assume each of those

visits produces some average text

response length chat gbt is now

outputting something like everything

humans have ever printed since the

Gutenberg Press

every two weeks

text generated by AI will therefore soon

outstrip anything humans have ever

written if it hasn't already even more

than all the Tumblr posts about Vaporeon

yes Arya even more than all of those

of course this enormous output wouldn't

be useful or interesting if it wasn't

truly useful and interesting chat GPT is

disturbingly good at generating

human-like responses even to our most

difficult questions the model has been

shown to have an IQ of 147 meaning that

it can brag about it in every thread on

Reddit and it could legally pass the U.S

medical licensing exam and bar exam but

though book smart it may be I must

stress again this model does not know

anything and it shouldn't be relied on

for anything extremely important that's

a direct quote from openai and despite

what any weirdo at Google might tell you

chat GPT and other llms are not sentient

if you're not asking this model a

question nothing is going on inside it's

static no thoughts head empty there is

no feeling no experience of what it's

like to be chat GPT like there is to be

Arya for example moisturized in my Lane

flourishing

so those are the high level Basics but

how does chat GPT know what words to

actually use how does it understand what

context is when you give it a prompt

next we'll dive deeper into the actual

technology but first a little break for

your brain I can sense that you need one

I don't need brakes yeah well I don't

need my Bose Einstein condensate

recooled every six months Arya not

everyone needs everything all the time

we'll be right back

today's video is sponsored by hellofresh

Gamers I'm award-winning science

educator and the Hemsworth your mom says

you already have at home Kyle Hill you

know I'm a busy guy with a lot of

Kevin's to feed I don't have time to

figure out what's for dinner every night

when I'm trying to actively take over

the word kitchen

that's why I use today's sponsor

hellofresh hellofresh delivers

mouth-watering Chef crafted recipes and

fresh ingredients right to your door

taking decision paralysis out of the

equation it's more convenient than

grocery shopping and 25 percent less

expensive than takeout better yet for

the busy among you hellofresh has quick

and easy recipes to try including Fast

and Fresh options ready in just 15

minutes or less if you want to try hello

fresh like me go to hellofresh.com and

use the offer code kylehill16 for 16

free meals plus free shipping look how

easy this is I don't even use knives

made by humans all that often that's

hellofresh.com code kylehill16 for 16

free meals plus free shipping

hmm

[Music]

with a little fresh

and I don't want to hear nothing about

my knife Cuts either

all right breaks over time to get

technical the underlying architecture of

chat GPT and other large language models

is the neural network so called because

it mimics the neurons and the network of

them in your human brain human brains

have about a hundred billion neurons

each one of those neurons can have about

a thousand connections to other neurons

and they can fire electrical signals

between those connections up to a

thousand times per second depending on

electrical chemical gradients etc etc

now those signals they send between each

other are not random they depend on the

connections and the strength of those

connections between them artificial

neural networks are set up in the same

way artificial neurons that are

connected to each other and send signals

or not depending on the strength or the

weights of those connections now why are

neural networks both natural and

artificial good at many different things

like you are well

we don't know there's actually no

theoretical reason why this network is

better than any other kind of system

it's almost like nature had like a

billion years of trial and error and

just came up with something that worked

isn't that called Evolution oh right

that is called evolutionary what a

fundamental theory of nature

Chet gbt's underlying structure is a big

neural network with some 175 billion

different weights weights that all came

from a lot of training as we discussed

and these numbers when the model

multiplies them together ultimately

determine what word the model gives the

highest probability of adding next

scientists get these model weights in a

pretty simple way they give the model as

many examples as possible and tweak the

weights until what comes out the other

side looks like those examples the

tweaking or training of a neural network

is done by two widely used methods of

word prediction so say we give a model

these examples

based on all of its current weights

which have been molded with a large body

of training text like everything on

Wikipedia and all digitized books the

model will generate a list of

probabilities for each word that it

knows and then choose the most likely

one

simple right but how do we know how

right or how wrong the model's answer is

to an example given its current weights

well math of course

imagine that each word the model knows

is assigned a number then if the model

chooses a word that is mathematically

far away from the numbers in the body of

the training text that appear at similar

frequencies

we can apply statistics to adjust the

weights and bring the model closer to

correct

here's a related example from Stephen

wolfram's recent book on chat gbt

instead of words fitting in a sentence

imagine a neural network starting with a

straight line and trying to fit it into

a specific shape or mathematical

function every time the model guesses

you can easily calculate how far each

point is off from the correct shape

right just by looking at the X and Y

coordinates and where they should be

you then change the weights in the model

and try again and again and like 10

million more times each time you are

adjusting the weights in the neural

network to decrease the wrongness or

what statistics would call loss

if your model is working over time loss

will be minimized and the model will

start reproducing examples appropriately

or in this specific case the right

mathematical function but how do you do

the same thing for words well Brute

Force chat GPT was trained by literally

assigning a unique number to every word

in the English language around 50 or so

thousand words and their Associated

numbers so when you ask the model

anything it looks up what your query

corresponds to in number words and then

runs those numbers against the 175

billion different weights that it's

learned during training and outputs

another list of all words in the English

language with a probability next to each

one and then selects the most probable

most reasonable one now if you do this

for a long enough period of time and

openai did this for the equivalent of

300 years you can look at the numerical

difference between the examples you give

the model and the outputs it gives you

and minimize loss therefore creating

something that sounds just like

what humans have written or will write

or can write

all of this however won't get you a bot

that seems to understand context and

generate text in a human-like way for

that we need some way to associate words

with each other and again Chad GPT does

this with numbers so for example if we

assigned every word in the English

language a number and statistically

determined how often each word is next

to another word in everything humans

have ever written basically you could

make a graph like this where words

aren't just random they group together

and cluster car and door appear more

frequently together in training text

than degree and science do and both

pairs are further away from each other

than history is

now you may be thinking that given the

richness of human language the

relationships between words have to

represent more than some two-dimensional

space and you'd be right or at least

chat GPT seems to think so Chachi BT has

learned to represent the wordedness of

words not in 2D or 3D or even 4D space

but in a 12

288 dimensional space

the two-dimensional example you're

seeing now is just so that you can get

the basic idea 12000 D is some Eldritch

Madness that we literally cannot

visualize

but we don't have to go crazy trying to

visualize 12 000 D mathematically all of

this is just a big Matrix like the

Matrix Four no Arya like a useful Matrix

that everyone wanted

if every word in the English language is

assigned a number we could represent it

encode it with a one instead of a zero

at the point in a one by fifty thousand

or so Matrix where that word would be in

the full alphabetized list of all words

so Aardvark would look like this with a

one at the first index and fifty

thousand or so zeros actually fifty

thousand two hundred fifty Seven and

Aaron would look like this

the longest input chat GPT accepts is

2048 words so the Matrix for a full

query into chat GPT would be a matrix of

mostly zeros that is two thousand by

fifty thousand but remember that through

training chat GPT has stumbled onto 12

000 or so dimensions of wordness that

produce human-like responses so here we

multiply the 2000 by 50 000 Matrix that

encodes all words that turns them into

useful numbers by a learned fifty

thousand by twelve thousand wordness

Matrix for every word in the English

language this returns a two thousand by

twelve thousand Matrix that transforms a

text input into something chant GPT can

actually use

this big Matrix is called an embedding a

mathematical attempt to represent the

essence of a thing with numbers based on

those things statistical relationships

in training data with the right

embedding a neural network like chat GPT

can understand context and even

generalize because it's taken how words

are used and related in an unbelievable

amount of human text and turned it into

math

the last big part of chat GPT some

Cutting Edge technology that really does

make it special is called attention a

mathematical way of giving importance to

some words over other words so for

example you ask chanchi PT how many

species of cat are there well it might

help it answer correctly and in a

human-like way if it focuses more on the

words cat and species more than the

other words right now how it actually

does this includes a lot more

complicated structure and complicated

math but the point is it seems to work

really well kind of like why we use

neural networks in the first place they

just seem to work even though in both

cases we can't fundamentally explain why

which may or may not become a problem in

the future okay I need a break now oh so

now you need a break

how the Matrix tables have turned

into more tables

go to commercial

hey there Gamers I'm the guy who just

saw two seconds ago Kyle Hill you know

understanding large language models and

neural networks couldn't be really hard

but you know what's not hard in fact it

is made with some of the softest

insectoid carapace fibers this side of

Europa

shop.kylehill.net t-shirts that's right

you want to look like an anime girl

playing with a demon core but also with

a beard and also is me your favorite

science communicator

look I have extremely sensitive skin

since the accident

so I don't put anything on my body

that's not silky silky smooth and also

so nerdy it's going to make everyone

else in your life go oh

so if you want to drape some of this on

your body and stop thinking about neural

networks for just another about 10

seconds you know where to go shop

so we are finally ready to fully

describe what chat GPT actually does on

a fundamental technological level and

let me just I'm just going to put put my

hair up real quick okay so you give chat

GPT a prompt it then turns the last word

of that prompt into numbers it encodes

it and then multiplies this number

by everything that it learned about how

words are associated with each other in

the English language are embedding this

gives us a big 12 000 dimensional Matrix

we run this Matrix and all the numbers

associated with it

through those attention Transformers

that we talked about so that some words

in the prompt or the last word is paid

attention to more than other words in

the rest of the prompt to generate the

output we then

normalize this so that we get something

more akin to what we started with in

terms of matrices and then we feed this

forward to the next layer of attention

Transformers and do it all over again

how many times 95 times there are 96

total layers in chant GPT after that

after all this data Gauntlet we then

basically do the reverse of what we

started with we take the big Matrix that

went through the 96 layers and we

reverse the embedding

and so we use that 12 000 Dimensions

again oh

to turn

the words

from numbers

and you get a single word

after all that so you can imagine that

if you have to do this for every single

word

do a calculation that involves

175 billion separate operations it can

take a large language model

a long time to get back to you

that's why there's a delay

that was a lot of real running that I

did

and notice that even with all of this at

no point does chat GPT know what you are

asking it if you ask it for example what

is the sixth element on the periodic

table the neural network is not thinking

about the periodic table it doesn't even

know what atoms are it is just

determining given the statistical

distribution of words in its vast

training text what word is most likely

to follow the sequence what is the sixth

element on the periodic table and a

reasonable next word based on everything

ever written online is carbon

that's it

so that in a nutshell no I'm not paying

kurz gazoc every time I say in a

nutshell they're rich so that in a

nutshell is how chat GPT works what's

next well people are obviously very

excited about this technology it's the

best chat bot ever it can summarize

unreadable amounts of text and and other

books for you it can write poetry it can

generate code that you can actually use

that's all awesome yes but I still think

there is actually a large risk if we

don't mitigate it

to our information ecosystem here

remember we fundamentally do not know

how chat GPT came up with the embedding

that it did or how it chose all those

175 billion different weights of course

this isn't really surprising chat GPT is

a neural network and it operates like

your brain and you can't even tell me

the decisions you make why you make them

what they look like on the inside here

I'll show you just a sec I'm gonna I'm

gonna get a kitty I'm gonna show you

with a cat I'm gonna get a cat to show

you

look at this adorable little kitty why

is this a kitty you will quickly say

something like it's whiskers or it's

tiny little cute little peats but the

more you go down that line of

questioning you realize you don't have a

full description for what Katniss is you

can't describe how your brain arrives at

cattitude similarly for neural networks

like chat GPT we can't right now just

open them up and see exactly how they

work and what they are doing for it's

for example look at a neural network

that is looking at cats and trying to

recognize pictures of cats at the first

layer it looks like it's looking for

cat-like shapes but at the tenth layer

of its brain

what is that how does that describe

Katniss Everdeen we can't right now look

into something like chat GPT and figure

out exactly how it's working we don't

know that's important to understand lady

no plastic eating please no plastic

what is interesting though is what chat

GPT seems to understand about human

language producing human-like responses

that could pass the Turing test used to

be science fiction just a few months ago

it still seemed like the ultimate

problem and within just months multiple

AI have blown past it it appears that

human language is computationally easier

than anyone thought

maybe there are laws of language to

discover like there are laws of physics

and with chat gpt4 already being called

a step towards artificial general

intelligence who knows what these

systems will figure out but be unable to

tell humans how or why

she ate all the plastic in the world

maybe the Striking success of chat GPT

shouldn't have been surprising it has

about as many connections and weights

between those connections in its brain

as neurons in your brain maybe it just

so happens that a neural network a

sufficient size can handle a problem as

complex as human language I just hope

that after all of this a video of

sufficient size

can help you understand a problem as

complex as rapidly emerging AI

Technologies

until next time

she found more plastic and then she

threw up

now exiting the facility

how do they find more plastic thank you

so much to the very nerdy staff at the

facility for the direct and substantial

support in the creation of this here

video if you want to join the facility

if you want to drape on a silky white

lab coat and stop my cat lady from

eating all plastic on planet Earth you

can go to patreon.com Kyle Hill to join

the facility today if you're on mobile

go into the description of this video

click the link or if you're on desktop

just click join and you get private

members only live streams you get behind

the scenes photos and videos and bloopy

bloops you get to talk to me on our

private Discord and if you support us

just enough you get your name on Arya

here on each and every episode as you

can see there's hundreds and hundreds of

you I don't even I haven't even figured

out how if you pressed me the one thing

I am still worried about with uh well

I'm worried about a couple of things but

with large language models like this is

once they start outputting more text

than has ever been written by humans in

history will we get to a point where we

don't actually know if anything we read

see or hear is real and at that point

it's going to be a dis and

misinformation apocalypse where we have

to re down to some sort of established

way of determining the veracity of stuff

and most of the human population is just

going to tune out and then we're going

to have to rethink our media landscape

and it's gonna oh it could be bad

[Music]

thanks for watching a chatbot wrote that