AI is a Lie.

Summary

This 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

00:00

🤖 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.

05:00

🚗 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.

10:01

📈 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.

Keywords

💡AI (Artificial Intelligence)

AI refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. In the video, it's discussed that what is commonly referred to as AI in consumer products is actually a subset known as narrow AI, which is limited to specific tasks rather than possessing general intelligence. The script uses the term to highlight the difference between the public's perception of AI and the reality of current technology capabilities.

💡Narrow AI

Narrow AI, also mentioned as AI Ani in the script, is a type of AI designed to perform specific tasks by using specialized algorithms and data processing. It is contrasted with general AI, which would have a broader range of cognitive abilities. The video emphasizes that while narrow AI can be highly effective within its designed scope, it lacks the general reasoning and learning capabilities of human intelligence.

💡Machine Learning

Machine learning is a subset of AI that involves algorithms capable of learning from and making predictions or decisions based on data. The script explains that most AI applications today are actually machine learning models that identify patterns through statistical probability and can be further trained through reinforcement learning, which is a method of training where correct outputs are rewarded and incorrect ones are punished.

💡GPT (Generative Pre-trained Transformer)

GPT is a type of large language model that is trained to understand and generate natural language. The script uses GPT as an example of a narrow AI system, highlighting its ability to process information based on learned patterns but also pointing out its limitations, such as the inability to generate images, videos, or audio, and its dependence on training data.

💡Reinforcement Learning

Reinforcement learning is a method of training AI models where the model learns to make decisions by receiving rewards for correct outputs and penalties for incorrect ones. In the script, it is mentioned as a way to further train machine learning algorithms, allowing them to improve their performance over time by adjusting their behavior based on feedback.

💡AGI (Artificial General Intelligence)

AGI, or artificial general intelligence, is the hypothetical ability of an AI to understand, learn, and apply knowledge across a wide range of tasks at a level equal to or beyond that of a human. The video discusses AGI as a goal for AI development, contrasting it with the current state of narrow AI, and notes that even with sophisticated software, we are far from being able to achieve AGI.

💡Hype Machine

The term 'hype machine' in the script refers to the tech industry's eagerness to label products with the AI label, regardless of whether they truly embody the capabilities of AI. It is used to critique the misleading marketing practices that overpromise on the capabilities of AI, creating unrealistic expectations among consumers.

💡Edge Case

An edge case in the context of AI refers to a situation or scenario that is atypical and falls outside the range of normal conditions that the AI has been trained on. The script uses the term to illustrate the limitations of narrow AI, which may not be able to handle such cases effectively, unlike a human driver who can adapt to unexpected situations.

💡Marketing Buzzword

The script discusses AI as a 'marketing buzzword,' indicating how the term has been overused and diluted to the point of losing its original meaning. This overuse can lead to confusion and misrepresentation of a product's capabilities, as seen in the example of Tesla's self-driving software.

💡Generative Models

Generative models, such as those mentioned in the script for image, video, or audio generation, are AI systems that can create new content based on learned patterns. The video points out that these models are limited by their training data and can sometimes produce unrealistic or 'hallucinated' outputs when faced with unfamiliar concepts.

💡Tokens

In the context of AI language models like GPT, tokens refer to the basic units of text, such as words or phrases, that the model uses to process and generate language. The script notes that AI models have a finite number of tokens they can remember, which limits their ability to maintain a coherent train of thought over long sequences of text.

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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