AI is a Lie.
Summary
TLDRThis video script debunks the hype around artificial intelligence (AI), clarifying the difference between the fictional, general AI and the real-world narrow AI. It explains that most AI applications, including machine learning models like GPT, are specialized tools, not sentient beings. The script critiques misleading marketing, particularly in Tesla's self-driving cars, and warns of the potential for AI to cause distrust due to its inability to understand context or reason like humans.
Takeaways
- 🧠 The script clarifies that most 'AI' features are not the general intelligence (AGI) often depicted in sci-fi but are actually narrow AI (ANI), specialized for specific tasks.
- 🔍 It explains that ANI, such as GPT, is a large language model trained to understand and generate natural language, but is limited to its training data and specialized algorithms.
- 📈 The video highlights the tech industry's eagerness to label products with 'AI' to capitalize on the hype and consumer interest, which often leads to misleading perceptions.
- 🚗 It criticizes Tesla's marketing of its vehicles' AI capabilities, suggesting that the company has misrepresented the extent of its self-driving technology's capabilities.
- 🤖 The script points out that ANI is not capable of handling edge cases it has never encountered, unlike the human brain which can adapt to new situations.
- 🔬 It discusses the effective deployment of ANI in complex scenarios where dense data requires interpretation, such as diagnosing diseases.
- 📚 The video mentions that simple neural networks have been in use for decades for tasks like handwriting recognition and web traffic analysis.
- 🧩 It describes ANI as a building block of a complete AI system, akin to a single app on a computer, capable of specific functions but not general intelligence.
- 🔮 The script speculates that when true AGI arrives, it will be so advanced that it will require a new term to differentiate it from the current 'AI' marketing.
- 🚨 It warns of the potential dangers of marketing AI capabilities beyond their actual capabilities, such as in the case of autonomous vehicles, which could impact user safety.
- 🎨 The video also touches on the challenges of generative AI models like Stable Diffusion, which can struggle to produce specific outputs without a clear context from their training data.
Q & A
What is the main issue discussed in the video script regarding AI?
-The main issue discussed is the misleading representation and overhyping of AI capabilities, particularly the distinction between artificial general intelligence (AGI) and artificial narrow intelligence (ANI), and the misuse of the term AI in marketing.
What is the difference between AGI and ANI as explained in the script?
-AGI refers to a system with the capacity for reason and general intelligence, similar to human cognition, while ANI, also called narrow AI, is limited to specialized tasks and operates based on specialized algorithms and data processing utilities.
What is the role of machine learning in the context of AI?
-Machine learning is a subset of AI that involves algorithms capable of analyzing patterns in data. These algorithms learn from training data and can identify patterns, make predictions, or generate new content based on statistical probabilities.
Why is the script critical of the tech industry's use of the AI label?
-The script criticizes the tech industry for using the AI label to mislead consumers and to market products that do not possess the general intelligence typically associated with the term AI, thus creating confusion and false expectations.
What is the 'reality hammer' mentioned in the script, and what does it symbolize?
-The 'reality hammer' is a metaphorical tool used in the script to break down and debunk the hype surrounding AI, aiming to reveal the true capabilities and limitations of current AI technologies.
How does the script describe the capabilities of GPT-4 Omni?
-GPT-4 Omni is described as a large language model capable of understanding and generating natural language. It processes information based on learned patterns, including definitions and mathematical formulas, allowing it to generate unique outputs not part of its training data.
What limitations does the script highlight for ANI models like GPT-4 Omni?
-ANI models like GPT-4 Omni are limited to specialized tasks and cannot operate outside their specific niche. They are also limited by their training data and can 'hallucinate' or make things up when faced with unfamiliar concepts or when they run out of tokens.
What is the potential danger mentioned in the script regarding the use of AI in vehicles like Tesla's?
-The script mentions the danger of misrepresenting the capabilities of AI in vehicles, suggesting that current AI technologies are not capable of handling all edge cases that may occur during driving, which could lead to safety issues.
What does the script suggest about the future of AI and its impact on society?
-The script suggests that as AI technologies advance and become more capable of creating realistic outputs, it may become increasingly difficult to distinguish between real and AI-generated content, potentially leading to an era of unprecedented distrust.
What is the role of赞助商MSI in the script, and what product are they promoting?
-MSI is mentioned as a sponsor in the script, promoting their AI RS2 pre-built PC with impressive specs such as an Intel Core i7-4400 KF, 16 GB of DDR5 RAM, and a GeForce RTX 4070 Super.
What is the script's stance on the current state of AI and its portrayal in the media?
-The script criticizes the media for contributing to the overhyping of AI by focusing on sensational stories and failing to accurately represent the current capabilities and limitations of AI technologies.
Outlines
🤖 AI Hype and Reality
The video script addresses the misconceptions surrounding AI, highlighting that many so-called AI features are not the advanced intelligence people imagine but rather narrow AI or specialized algorithms. It clarifies the difference between general AI (GII) and narrow AI, explaining that the latter is limited to specific tasks and relies on machine learning to analyze patterns and generate outputs. The script also touches on the tech industry's eagerness to label products with the AI term and the potential for confusion and deception in marketing.
🚗 The Limits of Narrow AI in Complex Scenarios
This paragraph delves into the practical applications and limitations of narrow AI, using Tesla's self-driving technology as a case study. It points out that despite impressive demos, narrow AI is not capable of handling edge cases it hasn't been trained on, emphasizing the difference between narrow AI and the more complex, real-time contextual awareness required for tasks like operating a motor vehicle. The script criticizes the marketing of AI capabilities that may not be fully realized, potentially leading to safety issues and legal repercussions.
📈 The Evolution and Impact of AI Marketing
The final paragraph discusses the strategic use of the term AI in marketing and its impact on public perception. It explains how the allure of AI has been exploited to generate interest and investment, despite the current capabilities of AI being far from the general intelligence many expect. The script also predicts a future where generative AI models become increasingly adept at creating realistic outputs, potentially leading to a society struggling to distinguish between real and AI-generated content, and the challenges this may pose for trust and authenticity.
Mindmap
Keywords
💡AI (Artificial Intelligence)
💡Narrow AI
💡Machine Learning
💡GPT (Generative Pre-trained Transformer)
💡Reinforcement Learning
💡AGI (Artificial General Intelligence)
💡Hype Machine
💡Edge Case
💡Marketing Buzzword
💡Generative Models
💡Tokens
Highlights
AI is often misrepresented in discussions, with many misunderstandings about its capabilities and limitations.
The term AI is frequently used to describe features that are actually narrow AI, not the general intelligence commonly imagined.
The tech industry is eager to label products with 'AI' to capitalize on its hype, even when the technology is not truly AI.
AI, as commonly referred to, is actually machine learning, a subset of AI focused on pattern recognition in data.
GPT-4 Omni is an example of a large language model, capable of understanding and generating natural language based on learned patterns.
Narrow AI is limited to specialized tasks and relies on algorithms and data processing utilities to function.
Generative models like GPT can produce outputs that seem new but are limited by their training data and can sometimes 'hallucinate' when faced with unfamiliar concepts.
Machine learning AI has been effectively used in complex scenarios like disease diagnosis, where dense data interpretation is required.
The promise of AI is often used in marketing to create a mystique that can mislead consumers about a product's true capabilities.
Tesla's claims about full autonomy in their vehicles have been criticized as misleading due to the limitations of narrow AI.
The current state of AI technology is more akin to advanced summarization and pattern recognition engines rather than true general intelligence.
Artificial general intelligence (AGI) would require the ability to handle a wide range of tasks and adapt to new situations, unlike current narrow AI.
The overuse and dilution of the term 'AI' has led to a situation where the true meaning of the term is often misunderstood.
Marketing strategies that misrepresent AI capabilities can have serious implications for user safety and trust.
As AI models improve, the distinction between their outputs and real human creations will become increasingly blurred, leading to potential issues with authenticity and trust.
The video concludes with a call for caution and critical thinking regarding AI, emphasizing the need for clear understanding and responsible use of the technology.
Transcripts
AI is everywhere and everyone is talking
about it but as it turns out the vast
majority of what they're saying is
misleading at best and at worst an
outright deception while the feature on
your Smartwatch or your new co-pilot PC
is called AI it's not the AI that you're
probably thinking of so we're going to
take a reality Hammer to this hype
machine and break down what AI can do
what it can't do and why the tech
industry is so eager to slap the AI
label on absolutely everything and guys
this Rabbit Hole goes way deeper than
you think okay predictably it ends at
money but the path to get there it turns
out is really confusing on purpose which
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the
description the classic definition of AI
is probably best Illustrated with
fictional examples it's what you see in
sci-fi Creations like Commander Data
Hell 9000 and GLaDOS these are are
computers or machines that demonstrate a
capacity for reason however naive
twisted or alien it might seem to us
meatbags now you'd be forgiven for
thinking that that's still the
definition of AI a lot of people seem to
think that it is but in reality the
meaning of words is ever shifting and we
would now refer to these characters as
having a gii or artificial general
intelligence what you're referring to as
AI then is in fact narrow AI or as I've
taken to calling it AI Ani is not a
general intelligence unto itself but
rather another component of a fully
functioning system made useful by
specialized algorithms and data
processing utilities forming a complete
artificial intelligence system didn't
think I could make that point in the
style of Richard stallman's famous
interjection well I could haha but I
also didn't have to that previous
paragraph was actually written by gp4
Omni and this is exactly the sort of
thing that modern AI does very well and
that's because most of the time when we
hear the term AI we're actually
referring to machine learning a subset
of AI involving algorithms that can
analyze patterns in data they get
trained on things like text multimedia
or even just raw number outputs and
using this training data they identify
patterns through statistical probability
they can be further trained through
reinforcement learning then by rewarding
correct outputs and punishing incorrect
outputs kind of like training a hamster
the results allow these algorithms to
summarize predict or even generate
something seemingly new and in many
cases they are so impressive that a good
machine learning system can be
indistinguishable from classic AI or a
gii well then minus if it looks like an
AI and it quacks like in AI what's the
difference well artificial narrow
intelligence is limited to specialized
tasks gp4 Omni specifically is a large
language model which means that it is
trained to understand and generate
natural language like the words I'm
speaking now it's basically an
autocomplete on steroids what sets it
apart from your phone's keyboard though
is that it can also process information
based on patterns that are learned
during training including definitions
mathem iCal formula and so on and so
forth that makes it capable of
generating unique output that wasn't
part of its training data GPT has
traditionally been incapable of image
video or audio generation there are
other types of generative models like
Sora sunno or dolly that feature their
own specific talents but most of them
are incapable of operating outside of
their specific Niche and all of them are
limited by their training data in a
similar Manner and because they're
limited by their training data in many
cases the answers that they give
resemble their training data which if
you're an artist or a photographer and
your work gets added to a model is
probably not your idea of fair use much
less a good time worse when generative
models are faced with a concept that
they don't understand or they simply run
out of tokens they can begin to
hallucinate that is to say they just
make things up as they go which is why
sometimes you get eldrich Abominations
like these with that said these
limitations don't mean that machine
learning AI is a dead end it's been
deployed very effectively for diagnosing
diseases and in other highly complex
scenarios where the data is dense and
the conclusions require interpretation
these specialized models are extremely
useful they're just also extremely not
new simple neural networks have been in
use for decades for things ranging from
handwriting recognition to web traffic
analysis and yes even video game
these are not scripted sequences the AI
is determining when your allies choose
to advance and how to best help you out
in combat and chatbots the main
difference is that they run much faster
on Modern Hardware if I had to distill
down what artificial narrow intelligence
really means then I would say it's like
having a thousand monkeys at a thousand
typewriters with a thousand pieces of
reference material for what the outputs
are supposed to look like with enough
trial and error then they do arrive
arrive at a point where they're likely
to spit out a correct or at least
correct enough solution then we take all
those monkeys and we take a snapshot of
the model State and we start feeding it
inputs for both Fun and Profit what ani
is to a brain then is kind of what a
single app is to a computer it's a
building block it's something your brain
is capable of but it's just one of its
many many functions shifting GE a bit
then what would artificial
general intelligence look like well it
would need to be able to handle
everything we've talked about so far
just like your brain can take some past
experiences and turn them into a new
creation but again like your own brain
it would need to be able to run many of
these models concurrently and
continuously train and iterate on them
rather than relying on fixed snapshots
only then would an AGI have the ability
to truly learn and adapt to new things
bringing it closer to that that
classical definition of AI and really
blur the lines between machine learning
and machine Consciousness the problem is
even if we had software that
sophisticated we are nowhere close to
being able to run an AGI even on a
modern supercomput let alone on your AI
smartphone but all right lonus you still
haven't explained why any of this is
even a problem I mean freerange meat is
just marketing bollocks too so who cares
well well truthfully in most cases I
don't I mean cooler Master's AI thermal
paste snafu I was never bothered by it
because I never expected my paste to be
sentient anyway but there are situations
where this kind of marketing can have an
impact on user safety and therefore does
matter let's talk about Tesla Mr musk
has said among other things that any
vehicle from 2019 onward will be able to
reach full autonomy and he's certainly
put out some impressive demos both
canned and even in the form of public
beta software that you really can use
and that's really cool but unfortunately
it isn't much more than that you see to
operate a vehicle safely it's not enough
to be trained with images of painted
lines and traffic cones stop signs
pedestrians vehicle Telemetry data it's
not even enough to be trained to predict
the likely maneuvers of nearby vehicles
and life forms on the road anything can
happen and by definition by its very
definition Ani is not capable of
handling an edge case that it has never
seen before even if it was by the way I
have some really bad news for you Tesla
owners out there Hardware 3.0 has about
144 tops or trillion operations per
second worth of processing power for
context Windows 11 recall a feature that
does little more than take screenshots
and analyze your PC usage for search
asks for 40 tops now to be clear tops is
not a be all andall measure of
performance and there is no way that
Microsoft has optimized the code for
recall nearly as much as Tesla has for
full self-driving but this should still
illustrate the point that Tesla either
did or should have known that a vehicle
with the AI capabilities of a family of
iPhone 15 Pro users would never achieve
that kind of realtime contextual
awareness that's required for complex
situations like operating a motor
vehicle and they misrepresented its
capabilities in order to sell more
software that was never going to leave
beta that is going to be a doozy of a
class action and it's a common story
that has led to this current mess where
fuzzy definitions and impossible
promises have turned AI into this
meaningless buzzword like all the rest
of them all of them refer to legitimate
useful Technologies some of which have
really come to fruition but their
meanings have become diluted with
overuse and it means that when computer
cognition finally happens we're going to
have to call it something completely
different in order to differentiate it
from all of the marketing wank on the
subject of marketing calling ai ai
wasn't an accident the people behind
that marketing know what you think AI
means they know that the promise and the
Mystique behind the term is tantalizing
and they know you'll click on an article
that is interviewing an AI expert who
discusses how dangerous or already alive
it is they want you to buy into their
hype they want you to buy into their
stocks what we have now though really
are decent summarization engines and
lukewarm guessing machines that are
tuned for working with different types
of media they can't reason and anyone
who's seen chat GPT get something
ridiculously confidently wrong can
attest to that and they also can't
understand why they get these things
wrong which mean they can't learn or
improve on their own even if you
explicitly tell them they also have a
finite number of things that they can
remember called tokens which limit their
ability to maintain a train of thought
for very long anyone who's tried to get
chat GPT to write a novel or even a
moderately complex python script can
probably attest to that of course as
time goes on some of these limitations
will begin to lift and these a Ani
models will start to look more and more
like a GI to the lay person I mean just
ask the guy who had Bing fall in love
with him but at the end of the day it's
still the same thing as it's always been
to paraphrase a paper co-written by
Emily bender and Alexander caller it's a
hyper intelligent octopus that is
observing and learning the patterns that
are expected of communication and
repeating them back more accurately over
time so when faced with a novel
situation like a bear attack that
octopus has no hope of being able to
guide you on how to defend yourself with
the materials you have on hand it has no
concept of bear or stick only what words
most closely match the pattern that it
has previously observed to further
illustrate this if you haven't tried it
before try to craft a prompt that coaxes
stable diffusion to spit out something
very specific it turns out it's pretty
challenging and when it finally does it
it's generally composed of a mosaic of
shapes and patterns from its training
model that best match the keywords that
sounds overly simplified but that's
because it is in essence what it's doing
it takes the keywords from your prompt
and starts compositing filling in the
image until it hits for example a
certain percentage of computer and desk
and it does this without any context so
if you asked it to show you a computer
would it show you a desktop PC a laptop
a server rack and for that matter from
what era the answer is often simply yes
because it lacks the context behind what
computer means to you and it can't
always even for a coherent depiction
even in the same image so then Linus
just don't worry about it and wait till
AGI shows up and you don't have to
bother with online dating anymore well
not quite as generative AI models learn
to create works that are closer and
closer to our expectations and to
finally understand how many fingers and
teeth humans normally have we're going
to start to find it more and more
difficult to distinguish their output
from real photographs or artwork or
other people ushering in an era of
unprecedented distrust and the bad news
is that the folks in charge of helping
us deal with the consequences of all of
this have a lot less funding than the
ones who are trying to sell it to us so
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