How AIs, like ChatGPT, Learn

Summary

This video script delves into the pervasive yet mysterious world of algorithms that govern our digital experiences. It explains how these complex bots, initially built through a process of trial and error by simpler bots, evolve to perform tasks beyond human comprehension. Despite their creators' inability to fully understand their inner workings, these algorithms shape our online interactions, from video recommendations to financial transactions, often driven by the relentless pursuit of data to improve their accuracy. The script concludes with a humorous nod to the irony of being governed by bots while also asking viewers to engage with the content to influence algorithmic decisions.

Takeaways

• 🌐 Algorithms are pervasive on the internet, influencing what we see, buy, and how we interact with various platforms.
• 👀 Companies are reticent to disclose how their algorithms work due to the competitive advantage and trade secret nature of their technology.
• 🤖 Modern algorithms are often too complex for humans to fully understand, even the developers who created them.
• 🧠 The process of creating algorithms involves 'builder bots' that construct other bots and 'teacher bots' that test them, using a trial-and-error methodology.
• 🔄 The iterative process of testing, building, and refining bots leads to increasingly effective algorithms over time.
• 📈 The effectiveness of algorithms is measured by how well they perform in specific tasks, such as recognizing images or recommending content.
• 📊 More data leads to longer and more comprehensive tests, which in turn results in the development of better algorithms.
• 🔍 Algorithms are not just tools; they are also used to test and manipulate human behavior, as seen in the way content is recommended to keep users engaged.
• 🔑 The success of an algorithm is often determined by its performance on tests designed by humans, highlighting the ongoing interplay between human intention and machine learning.
• 🌟 Despite the complexity and sometimes mysterious inner workings of algorithms, they are an integral part of our digital lives and are here to stay.

Q & A

How do algorithms influence our online experiences?

Algorithms shape our online experiences by determining what content we see, set prices, monitor transactions for fraud, and even decide which videos to recommend on platforms like NetMeTube.

Why is it difficult for humans to provide explicit instructions for complex algorithmic tasks?

Many problems are too large and complex for humans to write simple instructions for. For instance, identifying fraudulent transactions among billions of financial transactions per second or selecting videos from an almost infinite library for recommendation.

How do companies protect the secrets of their algorithmic bots?

Companies treat their algorithmic bots as valuable employees and guard the details of their functioning as fiercely protected trade secrets.

What is the process of creating an algorithmic bot that can recognize images?

A builder bot creates student bots with random connections, which are then tested by a teacher bot using a set of labeled images. The best-performing bots are selected, replicated with variations, and the process repeats until a bot can recognize images effectively.

Why do algorithms sometimes fail to understand complex or nuanced tasks?

Algorithmic bots are often good at specific tasks they have been trained for but may fail at others, such as understanding videos, recognizing upside-down images, or distinguishing between unrelated objects.

How does the process of building and testing algorithmic bots relate to the concept of evolution?

The process resembles evolution, where the builder bot selects the 'fittest' bots (those that perform best in tests), replicates them with variations, and discards the rest, leading to gradual improvement over many iterations.

What role does data play in improving the performance of algorithmic bots?

Data is crucial for creating longer and more comprehensive tests, which in turn lead to better-performing bots. More data allows for more extensive testing and fine-tuning of the bots' abilities.

How do algorithmic bots determine what content to show users on platforms like NetMeTube?

Bots are tested on their ability to keep users engaged on the platform. They analyze user behavior and select content that maximizes the time users spend on the site.

Why do companies collect vast amounts of user data?

Companies collect data to create more extensive tests for their algorithmic bots, which helps improve the bots' performance and accuracy in tasks such as content recommendation or user engagement.

What is the significance of the 'Are you human?' tests on websites?

These tests serve a dual purpose: they verify that the respondent is a human, and they also contribute to the training data used to improve bots' ability to recognize and respond to human-like inputs.

How do algorithmic bots impact the way content is selected and presented on social media platforms?

Bots are used to analyze user preferences and interactions to select and prioritize content that is most likely to engage users, such as posts, articles, or videos, based on their past behavior and preferences.

Outlines

00:00

🤖 The Ubiquity and Complexity of Algorithms

This paragraph delves into the omnipresence of algorithms in our digital lives, highlighting how they influence our online experiences. Algorithms are responsible for showing us videos, curating content, setting prices, and even monitoring financial transactions for fraud. The paragraph discusses the evolution of these bots from simple 'If this, then that' instructions to complex systems that can handle vast amounts of data beyond human comprehension. It introduces the concept of machine learning, where bots are not programmed with explicit instructions but are instead trained through a process of trial and error, using large datasets to improve their performance. The limitations of understanding how these advanced algorithms work are also touched upon, emphasizing the black-box nature of modern AI systems.

05:01

📊 The Evolution of Learning Algorithms

The second paragraph explores the process of training algorithms through a cycle of building, testing, and refining. It describes a system where a 'builder bot' creates student bots with random connections, which are then tested by a 'teacher bot' that can only evaluate their performance. The best-performing bots are selected, modified, and the process repeats, gradually improving the bots' abilities. This iterative process is likened to natural selection, where only the most effective bots survive. The paragraph also touches on the limitations of these bots, which are often highly specialized and may not perform well outside their trained domain. It concludes with a commentary on the role of data in training these bots and the implications of using algorithms that are not fully understood, even by their creators.

Keywords

💡Algorithm

An algorithm is a set of rules or steps used to solve a problem or perform a computation. In the context of the video, algorithms are pervasive in the digital world, influencing what content users see, how data is processed, and even setting prices. The video discusses how algorithms are becoming increasingly complex and opaque, to the point where even their creators may not fully understand their inner workings, as exemplified by the statement, 'But how these bots work exactly, more and more, no one knows.'

💡Algorithmic bots

Algorithmic bots refer to automated software agents that perform tasks using algorithms. The video uses the term to describe the systems that curate content, detect fraud, and make decisions at scale. These bots are portrayed as both incredibly powerful and somewhat mysterious, as they are built and evolve through a process that even their creators may not fully comprehend, as indicated by the narrative about building bots that build bots.

💡Linear algebra

Linear algebra is a branch of mathematics that deals with linear equations, linear transformations, and vector spaces. The video humorously suggests that the cutting edge of algorithm development might require a liking for linear algebra, implying that advanced mathematical concepts are foundational to the complex algorithms that drive modern technology.

💡Machine learning

Machine learning is a subset of artificial intelligence that allows computers to learn from data and improve from experience without being explicitly programmed. The video touches on machine learning through the process of 'building a bot that builds bots,' which is a metaphor for how machine learning models are trained and refined through iterative processes to improve their performance.

💡Neural networks

Neural networks are a set of algorithms modeled loosely after the human brain that are designed to recognize patterns. They are a key component in machine learning and are mentioned in the video as a complex system that is difficult to fully understand, even by the humans who design them, which relates to the broader theme of the video about the enigmatic nature of advanced algorithms.

💡Data

Data refers to the raw facts and statistics collected through observation and used for reference or analysis. The video emphasizes the importance of data in training algorithms, suggesting that 'more data equals longer tests equals better bots.' It highlights how companies are obsessed with collecting data to improve their algorithms' performance and decision-making capabilities.

💡Fraud detection

Fraud detection involves identifying potentially deceitful transactions or activities. In the video, it is mentioned as one of the tasks that algorithms perform, watching transactions for fraud at banks. This illustrates the practical application of algorithms in safeguarding financial systems.

💡Recommendation systems

Recommendation systems are algorithms that suggest items or content to users based on their preferences. The video discusses how these systems decide what content users see on platforms like NetMeTube, highlighting the influence of algorithms on user experience and content consumption.

💡Trade secret

A trade secret refers to confidential business information that gives an enterprise a competitive edge. The video mentions that companies guard the inner workings of their algorithms as trade secrets, emphasizing the commercial value and proprietary nature of these complex systems.

💡User interaction

User interaction refers to the ways in which users engage with a system or service. The video discusses how algorithms are designed to increase user interaction, such as keeping users watching videos on platforms, which underscores the role of algorithms in shaping user behavior and experience.

💡Watch time

Watch time is a metric used to measure the total amount of time users spend watching content. In the video, it is used as an example of how algorithms are trained to maximize user engagement, indicating the importance of this metric in the performance evaluation of algorithmic bots.

Highlights

Algorithms are pervasive on the internet, shaping user experiences.

User actions on platforms are influenced and monitored by algorithms.

Algorithms decide content visibility on social media platforms like TweetBook.

Algorithms assist in photo organization and creation of automated content.

Financial transactions are influenced by algorithms that detect fraudulent activity.

The stock market heavily relies on algorithmic trading systems.

Modern algorithmic bots are built to solve problems too complex for human-crafted instructions.

Algorithmic solutions to large-scale problems like fraud detection are not fully understood, even by their creators.

Companies consider the inner workings of their algorithms as highly valuable trade secrets.

The current state of algorithm development is driven by linear algebra and other complex mathematical concepts.

A method for building algorithms involves creating 'builder bots' that construct other bots.

Teacher bots test the effectiveness of student bots without truly understanding the tasks themselves.

Through a process of trial and error, algorithms evolve to perform tasks with increasing accuracy.

The complexity of modern algorithms makes their decision-making processes incomprehensible.

Algorithms are trained on vast amounts of data to improve their performance.

User interactions, such as completing CAPTCHA tests, contribute to the training of algorithms.

Algorithms can be designed to maximize user engagement on platforms like NetMeTube.

The process of algorithm development involves iterative testing and refinement.

Algorithms are selected based on their performance in tests designed by humans.

The understanding of how algorithms operate is limited, even by their creators.

The reliance on algorithms is increasing, and we must learn to guide them through the tests we design.

The video humorously suggests that viewer engagement through likes and subscriptions influences algorithmic visibility.

Transcripts

On the internet, the algorithms are all around you.

You are watching this video because an algorithm brought it to you (among others) to click,

which you did, and the algorithm took note.

When you open the TweetBook, A the algorithm decides what you see.

When you search through your photos, A the algorithm does the finding.

Maybe even makes a little movie for you.

When you buy something, A the algorithm sets the price

and A the algorithm is at your bank watching transactions for fraud.

The stock market is full of algorithms

trading with algorithms.

Given this, you might want to know how these little algorithmic bots shaping your world work,

especially when they don't.

In Ye Olden Days,

humans built algorithmic bots by giving them instructions the humans could explain.

"If this, then that."

But many problems are just too big and hard for a human to write simple instructions for.

There's a gazillion financial transactions a second, which ones are fraudulent?

There's octillion videos on NetMeTube.

Which eight should the user see as recommendations? Which shouldn't be allowed on the site at all?

For this airline seat, what is the maximum price this user will pay right now?

Algorithmic bots give answers to these questions.

Not perfect answers, but much better than a human could do.

But how these bots work exactly, more and more, no one knows.

Not even the humans who built them,

or "built them",

as we will see...

Now companies that use these bots don't want to talk about how they work

because the bots are valuable employees.

Very, VERY valuable.

And how their brains are built is a fiercely guarded trade secret.

Right now the cutting edge is most likely very

'I hope you like linear algebra',

but what the current hotness is on any particular site

and how the bots work, is a bit "I dunno", and always will be.

So let's talk about one of the more quaint but understandable ways bots CAN be "built"

without understanding how their brains work.

Say you want a bot that can recognize what is in a picture.

Is it a bee, or is it a three?

It's easy for humans (even little humans),

but it's impossible to just tell a bot in bot language how to do it,

because really we just know that's a bee and that's a three.

We can say in words what makes them different, but bots don't understand words.

And it's the wiring in our brains that makes it happen anyway.

While an individual neuron may be understood, and clusters of neurons' general purpose vaguely grasped,

the whole is beyond.

Nonetheless, it works.

So to get a bot that can do this sorting,

you don't build it yourself.

You build a bot that builds bots, and a bot that teaches bots.

These bots' brains are simpler, something a smart human programmer can make.

The builder bot builds bots, though it's not very good at it.

At first it connects the wires and modules in the bot brains almost at random.

This leads to some very...

"special" student bots sent to teacher bot to teach.

Of course, teacher bot can't tell a bee from a three either;

if the human could build teacher bot to do that, well, then, problem solved.

Instead the human gives teacher bot a bunch of "bee" photos, and "three" photos,

and an answer key to which is what.

Teacher bot can't teach,

but teacher bot can TEST.

The adorkable student bots stick out their tongues, try very hard,

but they are bad at what they do.

Very, VERY, bad.

And it's not their fault, really, they were built that way.

Grades in hand, the student bots take a march of shame back to builder bot.

those that did best are put to one side,

the others recycled.

Builder bot still isn't good at building bots,

but now it takes those left and makes copies with changes in new combinations.

Back to school they go.

Teacher bot teaches - er, tests again, and builder bot builds again.

And again, and again.

Now a builder that builds at random, and a teacher that doesn't teach, just tests,

and students who can't learn, they just are what they are, in theory shouldn't work,

but in practice, it does.

Partly because in every iteration, builder bot's slaughterhouse keeps the best and discards the rest,

and partly because teacher bot isn't overseeing an old-timey, one-room schoolhouse with a dozen students,

but an infinite warehouse with thousands of students.

The test isn't ten questions, but a million questions.

And how many times does the test, build, test loop repeat?

As many as necessary.

At first students that survive are just lucky,

but by combining enough lucky bots, and keeping only what works,

and randomly messing around with new copies of that

eventually a student bot emerges that isn't lucky,

that can perhaps barely tell bees from threes.

As this bot is copied and changed, slowly the average test score rises,

and thus the grade needed to survive the next round gets higher and higher.

Keep this up and eventually from the infinite warehouse

(slaughterhouse)

a student bot will emerge, who can tell a bee from a three in a photo it's never seen before pretty well.

But how the student bot does this, neither the teacher bot nor the builder bot,

nor the human overseer, can understand.

Nor the student bot itself.

After keeping so many useful random changes, the wiring in its head is incredibly complicated,

and while an individual line of code may be understood, and clusters of code's general purpose vaguely grasped,

the whole is beyond,

nonetheless, it works.

But this is frustrating, especially as the student bot is very good at exactly

only the kinds of questions it's been taught to.

It's great with photos, but useless with videos or baffled if the photos are upside down,

or things that are obviously not bees, it's confident are.

Since teacher bot can't teach,

all the human overseer can do is give it more questions, to make the test even longer,

to include the kinds of questions the best bots get wrong.

This is important to understand.

It's a reason why companies are obsessed with collecting data.

More data equals longer tests equals better bots.

So when you get the "Are you human?" test on a website,

you are not only proving that you are human, (hopefully),

but you are also helping to build the test to make bots that can read, or count,

or tell lakes from mountains, or horses from humans.

Seeing lots of questions about driving lately?

Hmm...! What could that be building a test for?

Now figuring out what's in a photo, or on a sign, or filtering videos,

requires humans to make correct enough tests.

But there is another kind of test that makes itself.

Tests ON the humans.

For example, say entirely hypothetical NetMeTube wanted users to keep watching as long as possible?

Well, how long a user stays on the site is easy to measure.

So, teacher bot gives each student bot a bunch of NetMeTube users to oversee,

the student bots watch what their user watches, looks at their files,

and do their best to pick the videos that keep the user on the site.

The longer the average, the higher their test score.

Build, test, repeat.

A million cycles later, there's a student bot who's pretty good at keeping the users watching,

at least compared to what a human could build.

But when people ask: "How does the NetMeTube algorithm select videos?"

Once again, there isn't a great answer other than pointing to the bot,

and the user data it had access to,

and most vitally, how the human overseers direct teacher bot to score the test.

That's what the bot is trying to be good at to survive.

But what the bot is thinking, or how it thinks it, is not really knowable.

All that's knowable is this student bot gets to be the algorithm,

because it's point one percent better than the previous bot at the test the humans designed.

So everywhere on the internet, behind the scenes, there are tests to increase user interaction,

or set prices just right to maximize revenue,

or pick the posts from all your friends you'll like the most, or articles people will share the most, or whatever.

If it's testable, it's teachable. Well, "teachable",

and a student bot will graduate from the warehouse to be the algorithm of its domain.

At least, for a little while.

We're used to the idea that the tools we use, even if we don't understand them, someone does,

but with our machines that learn we are increasingly in a position where we use tools,

or are used by tools,

that no one, not even their creators, understand.

We can only hope to guide them with the tests we make,

and we need to get comfortable with that,

as our algorithmic bot buddies are all around, and not going anywhere.

OK. The bots are watching.

You know what's coming.

This is where I need to ask you...

To like...

comment...

...and subscribe.

And bell me.

And share on the TweetBook.

The algorithm is watching.

It won't show people the video...

unless you do this.

Look what you've reduced me to, bots.

What do you want? Do you want watch time?

Is that what you want?

Fine.

(sigh...) Hey guys, did you know I also have podcasts you can listen to?

Maybe even just in the background while you're tidying up your all room for hours? Or whatever?

There's hours of audio entertainment for you, and watch time for the bots overseeing your actions.

Go ahead and - and take a click. Entertain yourself.

Help me.

Help the bots.