WHISTLEBLOWER Reveals Complete AGI TIMELINE, 2024 - 2027 (Q*, QSTAR)

so there was a recent document that

actually apparently reveals open ai's

secret plan to create AGI by 2027 now

I'm going to go through this document

with you Page by Page I've read it over

twice and there are some key things that

actually did stand out to me so without

further Ado let's not waste any time and

of course just before we get into this

this is of course going to contain a lot

of speculation remember that this

document isn't completely 100% factual

so just take this video with a huge of

salt so you can see here the document

essentially says revealing open ai's

plan to create AGI by 2027 and that is a

rather important date which we will come

back to if we look at this first thing

you can see there's an introduction okay

and of course remember like I said there

is a lot of speculation in this document

there are a lot of different facts and

of course like I said anyone can write

any document and submit it to um you

know Twitter or Reddit or anything but I

think this document does contain a

little bit more than that so it starts

out by stating that in this document I

will be revealing information I have

gathered regarding opening eyes delayed

plans to create human level AGI by 2027

not all of it will be easily verifiable

but hopefully there's enough evidence to

convince you summary is basically that

openai has started training a 125

trillion parameter multimodal model in

August of 2022 and the first stage was a

rakus also called qstar and the model

finished training in December of 2023

but the launch was cancelled due to the

high inference cost

and before you guys think it's just

document with like just words I'm going

to show you guys later on like all of

the crazy kind of stuff that is kind of

verifiable that does actually um line up

with some of the stuff that I've seen as

someone that's been paying attention to

this stuff so this is literally just the

introduction um the juicier stuff does

come later but essentially they actually

talk about the and this is just like an

overview so you're going to want to

continue watching they essentially state

that you know this is the original GPT 5

which was planned for release in 2025

bobi GPT 4.5 has been renamed name to

gbt 5 because the original gbt 5 has

been cancelled now I got to be honest

this paragraph here is a little bit

confusing um but I do want to say that

the words arus and the words GOI are

definitely models that were referred to

by several articles that were referring

to leaks from open eye and I think they

were actually on the information so this

is some kind of stuff that I didn't

really hear that much about but the

stuff that I did hear was pretty crazy

so um this arus and this goby thing

although you might not have heard a lot

about it of course it is like like a

kind of like half and half leak but like

I was saying this stuff is kind of true

so you can see here open AI dropped work

on a new arus model in rare AI setback

and this one actually just talks about

um how by the middle of open AI you know

scrapping an araus launch after it

didn't run as efficiently so there's

actually some references to this but a

lot of the stuff is a little bit

confusing but we're going to get on to

the main part of this story now I just

wanted to include that just to show you

that you know these names aren't made up

because if I was watching this video for

the first time and I hadn't seen some of

the prior articles before I'd be

thinking what on Earth is a r what on

Earth is GOI I've only heard about qar

so essentially let's just take a look

and it says the next stage of qstar

originally GPT 6 but since renamed

gpt7 originally for release in 2026 has

been put on hold because of the recent

lawsuit by Elon Musk if you haven't been

paying attention to the space

essentially Elon Musk just f a lawsuit

released a video yesterday um stating

that open ey have strayed far too long

from their goals and if they are

creating some really advanced technology

the public do deserve to have it open

source because that was their goal um

and essentially you can see here it says

qar GBC planned to be released in 2027

achieving full AGI and one thing that I

do want to say about this because

essentially they're stating that you

know they're doing this up to gpt7 and

then after gpt7 they do get to AGI one

thing that I do think okay and I'm going

to come back to this as well is that the

dates kind of do line up and I say kind

of because not like 100% because we

don't know but presuming let's just

presume okay because GPT 4 was released

in 2023 right um let's just say you know

every year release a new model okay um

that would mean that you know in 2024 we

would get gbt 5 in 2025 we get GPT 6 in

2026 we would get GPT 7 and in 2027 we

would get GPT 8 which is of course AGI

now one thing I do think about this that

is kind of interesting and remember I'm

going to come back to this so pay

attention Okay what I'm basically saying

is that if openi are consistent with

their year releases so for example if

they are going to release a new model

every year and if we continue at the

same rate like a new GPC every single

year which is possible him stating that

gp7 being the last release before GPT 8

which is Agi does actually kind of make

sense because once again and I know you

guys are going to hate this but if we

look at the trademarks okay remember

that they trademarked this around the

same time okay around that 2023 time

when all of this crazy stuff was going

on and I think it's important to note as

well is that like there's no gp8 you

might might argue that if they're going

to use all the GPT names why wouldn't

they just trademark GPT a and I think

maybe because like the document States

the model after gpt7 could be AGI and

I'm going to give you guys another

reason on top of that um another reason

is and I'm going to show you guys that

later on in the video but essentially um

open ey's timeline on super alignment

actually does coincide with this Theory

which is a little bit Co coincidental of

course like I said pure speculation

could be completely false open ey like I

said before can go ahead and completely

change their entire plans you know they

can go ahead and drop two models in one

year the point I'm trying to make is

that um certain timelines do align but

just remember this because I'm going to

come back to this because of some

documents stuff that you're going to see

in this document at the end of the video

so anyways um you know it says Elon Musk

caused a delay because of his lawsuit

this why I'm revealing the information

now because no further harm can be done

so I guess Elon musk's lawsuit has kind

of um you know if you wanted bought you

some time so he says I've seen many

definitions of AGI artificial general

intelligence but I will Define AGI

simply as an artificial general

intelligence that can do any

intellectual task a smart human can this

is how most people Define the term now

2020 was the first time that I was

shocked by an AI system so this is just

some um of course you know talk about

his experience with you know AI systems

I'm guessing the person who wrote this

but you know AGI if you don't know AGI

is like an AI system that can do any

task human can but one thing that is

important to discern is that you know

AGI there was a recent paper that

actually talks about the levels of AGI

and I think it's important to remember

that AGI isn't just you know one AI that

can do absolutely everything there are

going to be levels to this AGI system

that we've seen so far and in this paper

levels of AGI they actually talk about

how you know we're already at emerging

AGI which is you know emerging which is

equal or somewhat better than an

unskilled human so we are at level one

AGI and then of course we've got um you

know competent AGI which is going to be

at least 50% of the 50th percentile of

skilled adults and that's competent AGI

that's not yet achieved and then of

course we've got expert AGI which is

90th percentile of skilled adults which

is not yet achieved then we've got

virtuoso AGI which is not yet achieved

which is 99th percentile of all skilled

adults and then we've got artificial

super intelligence which is just 100% so

I think it's important to understand

that there are these levels to AGI

because once someone says AGI I mean is

it 90 9 can it do like half you know

it's like it's just it's just pretty

confusing but I think this is uh a

really good framework for actually

looking at the definition because trust

me it's an industry standard but it is

very very confusing so here's where he

basically says that you know um you know

GPT 3.5 which powered the famous chat

GPT and of course gpt3 which was the not

the successor but the predecessor of 3.5

it says you know these were a massive

step forward towards AGI but the note is

you know gbt2 and all chatbot since

Eliza had no real ability to respond

coherently so while such gpt3 a massive

leap and of course this is where we talk

about parameter count and of course he

says deep learning is a concept that

essentially goes back to the beginning

of AI research in the 1950s first new

network was created in the 50s y y y so

basically this is where he's giving the

description of a parameter and he says

you may already know but to give a brief

digestible summary it's a nalist to a

synapse in a biological brain which is a

connection between neurons and each

neuron in a biological brain has roughly

a thousand connections to other neurons

obviously digital networks OB vular to

biological brains basically saying that

you know of course we're comparing them

but different but um how many synapses

or parameters are in a human brain the

most commonly cited figure for synapse

count in the brain is roughly 100

trillion which would mean each neuron is

100 billion in the human brain has

roughly 1,000 connection and remember um

this number 100 trillion because it's

going to actually be a very very big

number that uh you do need to remember

so of course you can see here the human

brain consists of 100 billion urans and

over 100 trillion synaptic connections

okay and essentially this is trying to

you know um just pair the similarities

between parameters and synapses so

entially stating here that you know if

each neuron in a brain and trust me guys

this is just all going to come into

everything like I know you guys might be

thinking what is the point of talking

about this I just want to hear about qar

but just trust me all of this stuff it

does actually make sense like I've read

this a lot of times so I'm going to skip

some pages but the pages I'm talking

about now just trust me guys you're

going to want to read them it basically

says here if each neuron in a brain has

a th000 connections this means a cat has

roughly 250 billion synapses and a dog

has roughly 530 billion synapses synapse

count generally seems to predict to

intelligence with a few exceptions for

instance elephants techn have a higher

signups count than humans but yet

display lower intelligence of course

basically here's where he's actually

talking about how you know the simplest

explanation for larger signups accounts

with lower intelligence is a smaller

amount of quality data and from an

evolutionary perspective brains are

quote unquote trained on billions of

years of epigenetic data and human

brains evolve from higher quality

socialization communication data than

elephants leading to our Superior

ability to reason but the point he's

trying to make here is that you know

while there are nuances that you know

don't make sense synapse count is

definitely important and I think we've

definitely seen that um with the

similarities in the parameter size with

the explosion of llms and what we've

seen in these multimodal models and

their capabilities and it says again the

explosion in a capabilties since the

early 20110 has been a result of far

more computing power and far more data

gbt2 had 1.5 billion connections Which

is less than a mouse's brain and DBT 3

had 175 billion connections which is get

somewhat closer to a cat's brain and

obviously it's intuitively obvious that

an AI system the size of a cat's brain

would be superior to a system than the

size of a mouse's brain so so here's

where things start to get interesting so

he says in 2020 after the release of the

175 billion parameter gbt 3 many

speculated about the potential

performance of a Model 600 times larger

at 100 trillion parameters just remember

this number because this number is about

to just keep you know repeating in your

head and of course he says the big

question is is it possible to predict AI

performance by parameter count and as it

turns out the answer is yes as you'll

see on the next page and this is where

he actually references this article

which is called extrapolating GPT and

performance by lrien and it was not

score written in 2022 and basically it

talks about how as you scale up in

parameter count you approach Optimal

Performance so essentially this graph

seems to be illustrating the

relationship between neuron networks

measured by the number of parameters

which can be thought of as the strength

of connections between neurons and their

performance on various tasks and these

tasks included language related

challenges like translation read and

comprehension and question and answering

among others and the performance on

these task is measured in the vertical

axis higher values indicating better

performance and the graph shows that as

the number of parameters in increases

the performance on these tasks also

tends to but of course it does have

diminishing returns As you move right

because the curves actually do tend to

Plateau as they reach the higher

parameter counts of course the various

colors on this chart just essentially

represent different tasks and each dot

on those lines represents a neural

network model of a certain size and

certain parameter count being tested on

that you can see right down here this is

where the trained G you can see the gbt

performance so it says flop us TR at gp3

and then of course you can see right

here this is apparently the number of

synap in the brain and just remember the

number 100 trillion or 200 trillion

because it's going to be really

important so s it then says as Lan

Illustrated extrapolations show that air

performance inexplicably seems to reach

human level at the same time as a human

level brain size is matched with the

parameter count his count for the

synapse the brain is roughly 200

trillion parameters as opposed to the

commonly cited 100 trillion figure but

the point still stands 100 trillion

parameters is remarkably close to

Optimal by the way an important thing to

not is that although 100 trillion is

slightly suboptimal in performance there

is an engineering technique that openi

is using to bridge this cap and I'll

explain this towards the very end of

this document because it is crucial to

open ey is building and Lan's post is

one of many similar posts online it's an

extrapolation of Performance Based on

the jump between previous models and

open ey certain has much more detailed

metrics and they've come to the same

conclusion as lanon as I'll show later

in this document so if AI performance is

predictable based on parameter count and

100 trillion parameters is enough for

human level performance when will 100

trillion parameter AI model be released

in the future so here's where we go okay

it says that gbt 5 achieved Proto AGI in

late 2023 with an IQ of 48 now that is a

statement that um you know with the IQ