The AI Hype is OVER! Have LLMs Peaked?
Summary
TLDRThe video script discusses the current perception of AI, particularly generative AI and large language models (LLMs), and counters the notion that AI hype is fading. It references the Gartner Hype Cycle to illustrate the stages of technological maturity and adoption, arguing that while some believe we are at a plateau, significant advancements are still to come. The speaker highlights key developments such as OpenAI's Sora, advancements in voice recognition, and the potential for AI to exceed human performance in complex tasks. They also emphasize the importance of energy and compute capacity as potential bottlenecks for future AI growth. The script suggests that internal developments at companies like OpenAI are far from slowing down, with暗示 (hints) at groundbreaking models like GPT 5 that could revolutionize the field. The summary concludes by emphasizing that despite external appearances, the AI industry is on the cusp of significant breakthroughs that will propel it into a new era of innovation and capability.
Takeaways
- 🚀 **AI Hype and Progression**: Despite perceptions that AI hype is waning, significant advancements are still being made in the field of generative AI, with large language models (LLMs) continuing to evolve.
- 📈 **Gartner Hype Cycle**: The Gartner Hype Cycle is a useful tool for understanding the trajectory of technological development, but some argue that AI, particularly LLMs, is not following the traditional pattern of the cycle.
- 💡 **Innovation in AI**: Companies like OpenAI are making consistent breakthroughs in AI, some of which are not publicly disclosed, indicating that internal progress is much faster than what is visible externally.
- 🔥 **Energy and Compute Bottlenecks**: Energy consumption and compute capacity are significant bottlenecks for the future of AI, with the need for vast amounts of power and advanced infrastructure to support increasingly complex models.
- 💼 **Business Dynamics**: The shift from open research to closed, proprietary development means that companies are less likely to share their innovations, leading to a perception that progress is slower than it actually is.
- 🌐 **Global AI Race**: There is a global race in AI development, with companies investing heavily to stay competitive, which is driving rapid advancements and the potential for new breakthroughs.
- 🔬 **Advanced Reasoning Engines**: The incorporation of advanced reasoning engines on top of existing models like GPT-4 is pushing the boundaries of what AI can achieve, particularly in complex problem-solving tasks.
- 📊 **Benchmarking and Competition**: Companies are incentivized to surpass the current benchmarks like GPT-4, leading to a focus on marginal improvements rather than acknowledging the potential for more significant leaps in capability.
- 🔍 **Internal Developments at OpenAI**: There is an expectation that OpenAI is significantly ahead of its current public releases, suggesting that future releases could bring substantial advancements in AI capabilities.
- 🔥 **Sora and Other Innovations**: OpenAI's release of Sora, a video generation model, and other undisclosed projects indicate that the company is exploring various AI applications beyond just text-based models.
- ⏳ **Future Predictions**: Industry leaders like Sam Altman suggest that the current state-of-the-art models will be considered primitive in the near future, hinting at the potential for massive jumps in AI performance.
Q & A
What is the current debate about AI on social media platforms like Twitter?
-The current debate is centered around whether the hype surrounding AI, particularly generative AI, is wearing off and if we have reached an exhaustion point in terms of its capabilities and future developments.
What is Sam Altman's stance on the investment in building AGI, despite the high costs?
-Sam Altman is quoted as saying that he doesn't care if they burn $50 billion a year because they are building AGI, and he believes it will be worth it.
What is the Gartner Hype Cycle and how is it relevant to AI technologies?
-The Gartner Hype Cycle is a graphical representation used to illustrate the maturity, adoption, and social application of specific technologies. It provides a conceptual framework to understand how technologies evolve from introduction to mainstream adoption, which is particularly useful for evaluating emerging technologies like generative AI and large language models.
What are some of the current criticisms of large language models (LLMs)?
-Criticisms include biases in the models, the need for vast data training, high operational and inference costs, environmental impacts, and issues like hallucination where the model generates incorrect or misleading information.
What is the 'slope of Enlightenment' in the Gartner Hype Cycle?
-The 'slope of Enlightenment' is a phase where previous issues with a technology are ironed out. For LLMs, this would involve improvements in model efficiency, reduced biases, and increased reliability.
What are some of the advancements in AI that suggest it is not stagnating?
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What does Sam Altman imply about the future of AI models beyond GPT-4?
-Sam Altman implies that future models, such as GPT-5, will represent a significant leap from the current state-of-the-art, suggesting that GPT-4 will be considered 'dumb' in comparison to what is to come.
What are the potential bottlenecks that could slow down the progress of generative AI?
-The potential bottlenecks include energy constraints, due to the high power requirements for training and running large models, and compute capacity, as the demand for processing power may exceed current supply.
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Why might companies be hesitant to publicly release all their AI research and advancements?
-Companies like OpenAI may keep their research and advancements secret to maintain a competitive edge. Revealing too much could allow competitors to replicate their successes and potentially surpass them.
How does the release of GPT-4 as a benchmark affect the AI industry?
-The release of GPT-4 as a benchmark creates an incentive for other companies to train models that just surpass GPT-4, rather than aiming for a significant leap in capabilities. This can create an illusion of a plateau in AI progress.
What does the future hold for AI capabilities according to the advancements and statements made by industry leaders?
-The future of AI is expected to be transformative, with significant advancements in reasoning capabilities, efficiency, and the potential for AI agents to perform complex tasks at a level surpassing current human capabilities.
Outlines
🤖 AI Hype and the Gartner Hype Cycle
The first paragraph discusses the perception that the hype around AI might be fading, particularly in the context of generative AI. It references a clip from Sam Altman, emphasizing the commitment to developing AGI despite the costs. The paragraph also introduces the Gartner Hype Cycle as a tool for understanding the trajectory of technological development, from introduction to mainstream adoption. It outlines the cycle's stages, including the technology trigger, peak of inflated expectations, and the trough of disillusionment, which is where many believe generative AI currently stands due to issues like biases, high costs, and environmental impact.
🚀 The Upcoming Surge in AI Advancements
This paragraph challenges the notion that AI is stagnating and argues that we are on the cusp of significant breakthroughs. It points out that while some benchmarks show AI nearing human performance, there are still complex tasks where AI lags, suggesting room for growth. The speaker disagrees with the idea that we have reached the limits of generative AI and alludes to ongoing advancements in various AI domains, such as voice recognition and video generation, indicating that the field is far from plateauing.
📉 The Downturn in AI Hype and Future Predictions
The third paragraph focuses on the idea that despite the perceived downturn in AI hype, industry leaders hint at upcoming revolutionary AI models. Sam Altman's statements suggest that current state-of-the-art models like GPT-4 are considered 'dumb' compared to what's coming next, indicating a significant leap in AI capabilities. The paragraph also discusses the potential for energy consumption and GPU production to become bottlenecks in AI development, highlighting the infrastructure challenges that could slow progress.
💡 The Impact of Energy and Compute Constraints on AI
This paragraph delves into the challenges of energy consumption and the supply constraints of GPUs, which are critical for training large language models. It discusses the potential for energy to be a limiting factor in AI's growth, given the extensive power requirements for training clusters. It also touches on the regulatory hurdles and long lead times associated with building new power plants and infrastructure, suggesting that these factors could slow the pace of AI advancement.
🌐 The Unseen Progress in AI and the Shift to Closed Research
The fifth paragraph emphasizes that while external appearances may suggest a slowdown in AI progress, internal developments at companies like OpenAI are likely far more advanced. It highlights the shift from open research to closed, proprietary research as companies protect their innovations. The paragraph also mentions the competitive drive among companies to surpass GPT-4, suggesting that the current benchmark does not indicate a plateau but rather a strategic pause before the next significant release.
🔮 Future AI Capabilities and the Race for Advancements
The final paragraph speculates on the potential for AI to surpass current benchmarks significantly, leading to transformative changes across industries. It discusses the potential integration of advanced reasoning engines with current models like GPT-4 and the impact of iterative agent workflows. The speaker expresses optimism about the unseen advancements within companies like OpenAI and predicts that the next year will bring groundbreaking developments in AI.
Mindmap
Keywords
💡AI Hype
💡Generative AI
💡Gartner Hype Cycle
💡Large Language Models (LLMs)
💡Sam Altman
💡AI Efficiency and Inference Costs
💡Bias in AI
💡Energy Constraints
💡Compute Capacity
💡Closed Research Environment
💡Multimodal AI Agents
Highlights
AI hype is not wearing off; generative AI's capabilities are still growing.
Sam Altman's statement about investing heavily in AGI development signifies ongoing commitment to AI advancement.
Generative AI is not slowing down; it's evolving with new developments in voice recognition and video generation.
Gartner hype cycle is used to illustrate the maturity, adoption, and application of technologies like generative AI.
The peak of inflated expectations for LLMs was reached with GPT 3.5 and GPT 4, indicating significant media attention and public interest.
The trial of disillusionment phase is characterized by the recognition of issues like biases, high operational costs, and environmental impacts of large language models.
The slope of Enlightenment suggests that LLMs are improving in efficiency, reducing biases, and increasing reliability.
AI performance on benchmarks is approaching human levels in areas like image classification and natural language inference, but complex tasks remain a challenge.
Sam Altman hints at significant advancements beyond GPT 4, suggesting that future models will be dramatically more capable.
Energy consumption and GPU supply constraints are potential bottlenecks for the growth of AI systems.
NVIDIA's Blackwell GPU architecture promises significant performance improvements for training large language models.
OpenAI's shift from open research to closed research environment means they are making consistent breakthroughs without immediate public disclosure.
GPT 4 as a benchmark creates an illusion of a plateau, as companies aim to just surpass it before releasing their models.
The incorporation of advanced reasoning engines on top of existing models like GPT 4 is pushing the boundaries of AI capabilities.
Andrew NG's research on iterative agent workflows shows significant improvements in AI performance, indicating ongoing progress in the field.
Internal advancements at companies like OpenAI suggest that there will be shocking releases in the future as they advance beyond current benchmarks.
The next 365 days are predicted to bring about significant changes and advancements in AI, particularly in multimodal agents and open-ended tasks.
Transcripts
so one of the current questions that
I've been seeing floating around on
Twitter and on social media is that AI
hype might be wearing off and when I'm
talking about AI hype this is referring
to the trend that generative AI has I
guess you could say reached an
exhaustion point now I disagree and I'm
going to tell you guys why but I'm going
to go over some of the key examples and
what people are stating so essentially
there was a clip here and a lot of
people were stating that this clip from
Sam Altman people were stating that the
hype is wearing off The Vibes are
shifting you can feel it and basically
in this clip clip here Sam Alman
literally is he's only really stating
that you know I don't care if we burn
$50 billion a year we're building AGI
and it's going to be worth it so it's
not a crazy crazy statement I think why
people are stating that the hype is
wearing off and that Vibes are wearing
off and this video is going to be and
essentially what this is referring to
here is the fact that you know I guess a
lot of people are thinking that you know
I guess you could say the generative AI
is currently slowing down in terms of
the capabilities and what we're likely
to see in the future now like I said
before it's just mainly due to this clip
and a bunch of other different factors
but let's get into how Cycles work in
terms of technology and a graph that a
lot of people have been referencing when
they talk about llms plateauing so
something that I also saw quite a bit
being you know passed around as like
this infographic is of course the
Gartner hype cycle so essentially it's
just a graphical rep representation used
to illustrate maturity adoption and
social application of specific
Technologies it basically just provides
a conceptual framework that helps
stakeholders and individuals understand
how Technologies evolve from the time
they introduced until they reach
mainstream adoption and the hype cycle
can be particularly useful for
evaluating emerging Technologies like
generative Ai and of course large
language models so of course we
essentially have the technology trigger
this phase initially occurs when a new
technology is conceptualized or when a
significant development makes it
publicly known creating interest and
significant media attention for example
this is when GPT 3.5 chat GPT was
released and it demonstrated the ability
to create text in the long
format and then of course that's when we
got to GPT 4 which is stage two so this
is where there is the peak of inflated
expectations now I've got to be honest
it's not just llms that were going crazy
at this point there was also 11 labs and
other image generation services like mid
journey and of course other services
like stable diffusion so I would argue
that the problem with this is that with
generative AI experiencing some kind of
increase in the actual media coverage I
would say that this is something that is
cumulatively increasing in terms of the
expectations and that's because like I
said already there were many several
different categories that came together
at the same time and of course this
could include llms being at the peak of
inflated expectations now I want to say
that I do disagree with this hype cycle
for AI I do think that this is nowhere
near it its peak where it should be but
there of course is inflated expectations
when a new technology comes to fruition
a lot of people may exaggerate what the
technology can really do for example
some people say it's going to replate
replace entire careers or entire tasks
and revolutionize entire Industries now
whilst yes that might happen in the
future I don't think that that is
completely happening with GPT 4 and gbt
3.5 so some are arguing that this is
where we are at currently okay and then
of course the trial of disillusionment
and this is where Technologies enter
this phase when they fail to meet the
inflated expectations and stakeholders
become increasingly delusion so issues
related to the technology start to arise
such as the biases and large language
models need for vast data training the
high operational costs the high
inference costs the environmental
impacts they become more apparent and
then they become criticized and one of
the main things that many people are
talking about with llms is of course
things like the hallucination and the
high inference costs and of course the
training cost because these models are
certainly not cheap and it seems like
there's only a few companies that can
run and train these large models now of
course there we have the slope of
Enlightenment and this is essentially
where with l M all of these previous
issues get ironed out so things like the
issues of hallucination and the biases
they get ironed out right here and this
is where as more experimentations as
more implementations occur the market
matures and second third generation
products appear and with llms this would
involve developments that address the
earlier criticisms such as improving the
model efficiency the inference costs
reducing the biases and of course the
reliability of the model and this is
arguably where people think AI is going
to go technology becomes stable and
accepted and this means widespread
adoption however I think the graph isn't
going to look anything like that I think
it's probably going to look something
like this where we go up we dip a little
bit and then we continue a trajectory
upwards because like I said before
whilst yes that it does seem to many
different pieces of Statistics that it
looks like we are slowing down in terms
of AI versus human performance and even
on this Stanford AI index we can see
that AI has surpassed human performance
on several benchmarks including someon
image classification visual reasoning
and English understanding yet it Trails
behind on more complex tasks like
competition level mathematics visual
Common Sense reasoning and planning and
you can see here that if we take a look
at actually how the AI is moving we can
see that in image classification visual
Common Sense reasoning natural language
inference all of these seem to be coming
towards the human level Baseline but
they don't seem to be going upwards you
know like in a crazy level on on the
graph and some people would argue that
this is because AI generative AI large
language models whatever you want to
call it if you just want to group
everything together that we have reached
our limit in terms of where we are and
new architectures are going to be needed
now I would firmly disagree with this
for several reasons that I'm about to
explain and I'm going to show you guys
some really key evidence on why things
are about to actually get very very
crazy in the world of AI and why we're
about to go into a very very abundant
era due to AI so one of the things that
many people are not actually taking into
account is the fact that currently there
are many different things going on in
the world of AI that people aren't
paying attention to some people are just
paying attention to large language
models and this doesn't make sense
because there are vast and many
different categories in which AI is
currently exceeding for example in voice
recognition and in voice generation
opening ey has recently developed their
voice engine which was actually
something that they developed in around
2022 and it was basically a
state-of-the-art system that could
recreate anyone's voice in addition to
that if you do remember open ey also did
talk about and quote unquote release
their sore up model to Showcase us how
far they've come in creating a text
video model now I think you have to
understand how crazy this is because for
people to say that generative AI is
stagnating is a crazy statement when
literally this year we literally got
Sora which blew everyone's Minds in
terms of the capabilities so with Sora I
still find it absolutely incredible that
people can say AI is stagnating because
with Sora as you've all seen this was
something that was truly just
mind-blowing this piece of technology
showed us how crazy it is when you get a
group of AI researchers dedicated to
doing something and I'm going to give
you guys a quick memory joke remember
that opening ey isn't a company that's
Focus fed on video generation this is
just a subsection of their company so
the video generation aspect was
something that I guess they just wanted
to see if they could do well at and they
literally surpassed state-of-the-art
models Google Runway pabs they
completely surpassed them and this is
absolutely crazy I mean the demo
literally absolutely shocked everyone I
was truly truly speechless when this
technology was here and I'm someone that
pays attention to all of the AI news and
of course we do have Devon cognition
Labs released Devon their first AI
software engineer and this was a AI
rapper around GPT 4 but it did a few
things in a unique way where it was able
to surpass GPT 4 on certain software
engineering benchmarks so one of the
first things the point here I'm making
is that you might think that llms are
stagnating but that is not the truth at
all we're going to get into llms later
but overall voice engine Sora and Devon
show us that generative AI is really
really not going to be stagnating
anytime soon but if that didn't convince
you let me show you guys some of the
recent statements that show you that
we're absolutely in for a crazy ride so
Sam Alman recently said in an interview
at Stanford's entrepreneurship talk he
spoke about gp4 now remember GPT 4
currently is a state-of-the-art system
meaning that it is the best of the best
that we can currently get our hands on
for public use which means that Sam
Alman currently probably has access to
Frontier that are being developed by
open Ai and remember the big Labs like
anthropic and Google are currently
behind them in terms of what they're
creating so take a listen to this
statement open ey is phenomenal chat gbt
is phenomenal um everything else all the
other models are phenomenal it burned
you've burned $520 million of cash last
year that doesn't concern you in terms
of thinking about the economic model of
how do you actually where's going to be
the monetization source well first of
all that's nice of you to say but Chachi
BT is not phenomenal like Chachi BT is
mildly embarrassing in a best um gp4 is
the dumbest model any of you will ever
ever have to use again by a lot um but
you know it's like important to ship
early and often so if you weren't paying
attention there Sam mman literally just
said that this is going to be the
dumbest model that we will have to use
or that we will have had to use by far
so he didn't just state that this was
going to be an incremental increase he
clearly stated that gp4 was dumb he
stated this model was not you know great
he stated that this model was not that
good he clearly stated that GPT 4 a
current state-ofthe-art system that
people were literally able to get you
know increasingly capabilities just by
wrapping the system and being able to do
software engineering tasks and a lot of
people even using GPT 4 to be able to
train robotic systems like recently we
saw in a research paper and this was
literally where we had language model
guided Sim to real transfer so we
basically had large language models were
basically writing the reward functions
for an AI system and it was able to do
it very effectively and it literally
just went from simulation to real life
which means that this is going to
immediately speed up how quickly we're
able to train robots and have them you
know doing well in novel environments
it's pretty pretty insane so it's an llm
guided Sim toal approach so that is
pretty crazy and remember I'm guessing
that they were using GPT 4 that's
actually a study that I haven't covered
just yet but the point is is that samman
states that the current state-of-the-art
model the current model that other
industry labs are trying to be millions
and millions of dollars he states that
that model is bad and it's dumb he
didn't just say it was kind of smart he
said that it was dumb which leads me to
believe that we are truly truly not even
scratching the surface for what AI
systems are he could have just said
future models will be interesting he
could have just said they will be you
know kind of good but he literally said
okay I'm going to play it one more time
first of all that's nice of you to say
but Chachi BT is not phenomenal like
Chachi PT is mildly embarrassing at best
um GPT 4 is the dumbest model any of you
will ever ever have to use again Again
by a lot um but you know it's like
important to ship early and often so
that just goes to show how clear there's
going to be a distinction in the future
now he also stated that there will be a
massive jump from DPT 3.5 to 4 and there
will be a similar jump from GPT 4 to GPT
5 so it's important to know the jump
from GPT 3.5 to GPT 4 was incredible
because of GPT 3.5 limitations it meant
that it couldn't be extended to certain
tasks but if we have that same jump from
GPT 4 to GPT 5 then things are truly
about to change there's so many
questions uh first of all also amazing
it's looking back it'll probably be this
kind of historic pivotal moment with 35
and four which had your BT maybe five
will be the pivotal moment I don't know
hard to say that looking forwards we
never know that's the annoying thing
about the future it's hard to predict
but for me looking back GPT 4 Chad GPT
is pretty damn impressive like
historically impressive so allow me uh
to ask what's been the most impressive
capabilities of GPT 4 to you and gp4
turbo I think it kind of sucks H typical
human also gotten used to an awesome
thing no I think it is an amazing thing
um but relative to where we need to get
to and where I believe we will get to uh
you know at the time of like gpt3 people
were like oh this is amazing this is
this like Marvel of technology and it is
it was uh you know now we have gp4 and
look at GB3 and you're like that's
unimaginably horrible um I expect that
the Delta between 5 and four will be the
same as between four and three and I
think it is our job to live a few years
in the future and remember that the
tools we have now are going to kind of
suck looking backwards at them so you
can clearly see here that he's basically
stating that a year from now when we
have gbt 5 or the next level of Frontier
Model is released we're going to look
back at GPT 4 and think it's pretty
pretty bad so as much as people are
stating that AI hype is wearing off Sam
alman's feeling it there's this Gartner
hype cycle it's important to remember
the subtle cues the subtle tells the
subtle statements that we've clearly
seen from industry leaders about these
future future models that only they
currently have access to now if
generative AI was actually slowing down
it would be due to a bottleneck and
there are only a couple bottlenecks that
are really the issues okay one of the
issues is of course energy and this one
isn't really talked about enough because
energy isn't something that people think
of but trust me when I say these
inference costs are really really
expensive and a lot of people you know
including Mark Zuckerberg are basically
stating that one of the major
limitations for future AI systems and
where things might actually start to
slow down is due to the energy cost the
rampant energy cost for these AI systems
and just how much electricity they
consume and there were even a few rumors
talking about a GPT 6 training cluster
project that would arguably shut down
the power grid something along those
lines I know it does sound crazy and I
know rumors are rumors but energy is
expensive and a lot of it is required to
run inference on these large language
models which is why they often restrict
us I think there was this issue of um
GPU production yeah right so even
companies that had the money to pay for
the gpus um couldn't necessarily get as
many as they wanted because there was
there were all these Supply constraints
now I think that's sort of getting less
so now I think you're seeing a bunch of
companies
think about wow we should just like
really invest a lot of money in building
out these things and I think that will
go for um for some period of time there
is a capital question of like okay at
what point does it stop being worth it
to put the capital in but I actually
think before we hit that you're going to
run into energy constraints right
because I just I mean I don't think
anyone's built a gigawatt single
training cluster yet I me just to I
guess put this in perspective I think a
gigawatt it's like around the size of
like a meaningful nuclear power plant
only going towards training a model and
then you run into these things that just
end up being slower in the world like
getting energy permitted is like a very
heavily regulated government function
and if you're talking about building
large new power plants or large build
outs and then building transmission
lines that cross other private or public
land that is just a heavily regulated
thing so you're talking about many years
of lead time so if we wanted to stand up
just some like massive facility um to
power that I I think that that is that's
that's a very long-term project so
basically what Mark Zuckerberg is
stating here is that energy is going to
be a huge bottleneck because unlike
software where you can make it more
efficient and you can do things quickly
or you can get a GPU produced quickly
trying to build a nuclear power plant it
takes time trying to build this these
kind of infrastructure it's not
something you could just do in a day and
it's something that's quite important
when it comes to ative AI because as the
race continues they're going to be
spending a lot more money they're going
to be spending a lot more in terms of
how much they're spending on cooling
because these gpus heat up quite a bit
so I think this is something that you
know this is probably where the
constraints actually will come from in
the future and this is where things
might actually slow down a little bit
because there are going to be a few
things that make this truly hard to
continue with I think we would probably
build out bigger clusters than we
currently can if we could get the energy
to do it so I think that that's um
that's fundamentally money bottl in the
limit like if you had a trillion dollar
I think it's time right um but it
depends on how far the the exponential
curves go right like I think a number of
companies are working on you know right
now I think you know like a lot of data
centers are on the order of 50 megawatts
or 100 megawatts or like a big one might
be50 megawatts okay so you take a whole
Data Center and you fill it up with just
all the stuff that you need to do for
training and you build the biggest
cluster you can I think you're that's
kind of I think a bunch of companies are
running at stuff like that so it will be
interesting to see where gen of AI does
fall down but like I've said it's not
slowing down anytime soon and if you
remember open a and Microsoft are
building a100 billion Stargate AI
supercomputer to power the AGI or ASI
now there's also another bottleneck
which is what I've titled here which is
the compute problem essentially this
just means that the compute capacity for
AI systems is far too great and it kind
of exceeds the demand that we require so
that's why they're building out this1
billion supercomputer to meet the
demands of future generative AI systems
or to essentially power the next
Industrial Revolution because my oh my
if an AGI or air size here it's going to
be used pretty much everywhere to power
the economy and you're going to need the
compute and the infrastructure to do
that and currently this is one of our
biggest things because openi and
Microsoft don't even have enough chips
and don't even have enough computes
currently to compete with the likes of
Google so what we have here is we have
updates to the chips and you can see
that nvidia's recent Blackwell is pretty
pretty incredible and this was one of
the most important developments for AI
because this accelerates the training of
large language models and generative AI
systems so the Blackwell GPU
architecture with its 208 billion
transistors and its enhanced Transformer
engine is designed to dramatically
increase increase the training of large
language models like GPT 4 according to
Nvidia Blackwell can provide up to 30
times higher performance for generative
AI inference compared to the previous
h100 gpus four times faster training
performance for large language models
and this essentially means large
language models like GPT 4 which took
around 90 days to train on 8, h100 gpus
consuming 15 megawatt of power could
potentially be just trained in just 30
days on two 2,000 black C gpus only
using 4 megawatt which is pretty pretty
incredible and that curve definitely
reminds me of some that we've seen
before where things are starting to
exponentially increase so the point
right here is that these are the actual
bottlenecks of generative AI because a
lot of people are thinking that you know
things are slowing down things are
getting worse and trust me guys if you
been paying attention things I would
argue are actually speeding up
internally and one of the things that I
didn't even include in this presentation
because I forgot about it is the fact
that right now it's like open ey lit a
match under every other company because
now other companies are realizing that
whoa there's a huge huge AI race going
on and if we partake we could definitely
be getting billions and billions of
dollars and that means that other
companies and other startups are all
rushing down the corridor to see if they
can get piece of the piie which means
that we're about to see a complete
Revolution and a complete new industry
in terms of all of these products and
services now one of the biggest things
that I think that most people need to
consider is that open AI are no longer
open okay and if there's one thing you
take away from this video please
understand this okay things might be
slowing down externally but things are
not slowing down internally and what I
mean by that statement is that currently
we're at a stage where things have moved
from an open research environment to a
closed research environment the reason
this has happened is because opening up
they're no longer essentially a company
that's just focused on Research they are
a business and businesses you know they
hide their secrets and they hide their
Innovations because they don't want
their competitors to have them if open I
shared all their secrets then other
companies could easily build gbt 4 with
remarkable accuracy and op essentially
has secret Source the thing is openai
also doesn't publicize their research
I'm sure breakthroughs are made every
single month okay and you have to think
about it if openai did their whatever
they did with gbt so long ago they must
have some secret kind of breakthrough
they must have some secret source and
they must have something that others
don't which essentially means that open
ey are making consistent breakthroughs
and remember Sora they had Sora we had
no news no indication that they were
even developing some video AI there was
literally no indication whatsoever there
was no interview from Sam Alman there
was literally nothing we could have
picked up on on the fact that they were
even training such a model and boom they
just you know put it out into the open
the point is is that we know we have no
idea on what's going on at you know
closed AI open AI whatever you want to
call it the point is is that internally
I can guarantee you guys they are like 2
to 3 years ahead from where they are and
the point there is that whilst you might
think ah they haven't released anything
in a while that doesn't mean things are
slowing down it just means that they're
thinking okay how can we not Shock the
public with this next release that we
know is literally going to take
everything remember other companies are
still playing catchup gbt 4 finished
training in August of 2022 which means
that we are very very lucky because
we're going to be in for a real surprise
when gbt 5 gets here now another thing
to note as well okay is that GPT 4 being
The Benchmark does not mean Plateau the
problem with this and like I said before
this is a business which mean things are
going to change GPT 4 is currently the
Benchmark which means that companies are
incentivized to train their models to
surpass GPT 4 and then release that
model the reason this creates the
illusion that things are plateauing
around GPT 4 is because these companies
are no longer incentivized to go duly
pass GPT 4 they're only incentivized to
just beat it and that is because of
course with GPT 4 that is something that
people State oh it's the best system is
the best system so if another company
like gemini or anthropic or Google can
come out and say look our system
surpasses gbt 4 or benchmarks they're
going to immediately release that model
after it's fine- tuned or after after
it's whatever they've done with it and
then run with that so that they can
Market that and get the customer base
because they know that open AI are
waiting to release GPT 5 potentially
after the elections and that gives them
some time to reclaim the market share
understand that where GPT 4 is is just
an indication of where other models are
going to stop and if you think that that
is just a pure speculative argument look
at how close gp4 is to some of these
models you have to understand that if
they didn't beat gp4 they wouldn't be
releasing these models this is
86.4% they literally got it up to 86.8%
another one here 92% they got this up to
95% okay it's not like it's completely
surpassing them and I guess some people
like look it all you know slows down
around here no they just want it to be
as close as possible so that they can
get this out as quickly as possible
because they know by the time open hour
releases next again they're going to be
even behind and you can see here that
Gemini Ultra a lot of people were even
debating this because this was Chain of
Thought at 32 um and that what they did
in order to beat this because I'm
guessing that when they had finished
training the model and when they
finished fine-tuning it they had to you
know increasingly developed certain
methods just to get this metric right
here and that's why I state that gp4
being the Benchmark does not mean we're
currently at a plateau at all because
it's likely that these companies are
just benchmarking their models up to GPT
4 so that they can get them out now
here's why things are going to go even
crazy and remember I said this because
the next 365 days are going to be
absolutely insane agents are still early
okay and someone actually recently
created a benchmark where they're
talking about multimodal agents for
open-ended tasks in real computer
environments and essentially with this
you can see that humans can accomplish
over 72% of the task and the best
current AI agent can only do
12.24% what happens when AI agents can
get to above 80% that is truly going to
change everything and with the advanced
reasoning and with the advanced
capabilities of future models we going
to see a future that we've never seen
before now there was also something that
I covered in a previous video that I'm
guessing the majority of people are just
completely glossing over and I'm sure
it's because during the video I was kind
of sick because I had some kind of flu
whatever but I still made the video
anyways so essentially there was this
thing right here okay this is mesa's kpu
now this is a little bit speculative
because they haven't released too much
information but if you check the
benchmarks here you can see that this
surpasses claw 3 Opus Gemini Ultra and
Mr large at all benchmarks okay and this
is because they use a advanced reasoning
engine on top of gp4 Turbo now this is
pretty interesting because this shows us
that we are still very early on the
reasoning capabilities which is why I
argue that samman here says that gp4 is
dumb and why he also says here that it
was not a very good AI system so there
was one demo released by M's kpu in
which they showcased an AI system
actually doing reasoning with an
advanced task and recently on their
Twitter I'm not sure why it's not
getting any love or any actual you know
tweets about it they've shown that this
system is able to you know use some
reasoning steps and this is their system
they're messing around with it and
they're showing that it's able to
complete a lot of tasks really really
correctly so you have to remember that
internally things are going at light
speed things like qar things like other
companies now trying to get a piece of
the pie uh incorporating different
reasoning engines on top of gbt 4 are
going to push things further and
remember it was recently that Andrew NG
actually spoke about agentic workflows
and basically said that GPT 3.5 zero
shot was
48.3 five and of course it was Andrew NG
that did some research and found out
that GPT 3.5 zero shot was 48.1% correct
GPT 4 zero shot does better at 67% but
the Improvement was dwarfed by
incorporating an iterative agent
workflow and wrapped in an agent Loop
GPT 3.5 gets up to 95.1% so the point
here is that there's still a lot of
different architectures that we haven't
fully explored with some of the AI
systems that we do have which means that
we are far far far away from any sort of
plateau and things are going to keep
increasing number one we've got the gpus
increasing in terms of efficiency we've
got the data centers we've got all of
these things getting increasingly better
and of course we've got the fact that
internally open AI they are blisteringly
so far ahead that I'm guessing that
things are going to be shocking when
they are finally released
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