THIS BILLION SECONDS 

AI @ 70 -- EPISODE ONE: AI TAKES THE PENSION 

On the 17th August 1956, artificial intelligence was born. The founders of the field reckoned they'd have thinking machines equivalent to humans in five year, perhaps ten at the most. What looked easy at the outset turned into a seven-decade struggle - across two "AI winters", when the discipline nearly faded away - before we got to ChatGPT.

 

TRANSCRIPT: 

MARK PESCE, HOST: On the 17th August 1956, artificial intelligence was born.

The final day of a six week workshop that ran through a hot lazy summer at Dartmouth University. 

A workshop on “artificial intelligence”. 

Words that had never been put together before - and would never be separate again. 

G'day, I'm Mark Pesce and this billion seconds are already unfolding as the most significant of this century.

In this miniseries, celebrating the 70th anniversary of artificial intelligence, we're looking at where we’ve come from, how we got here - and where we seem to be going.

Because we’re travelling at the speed of thought.

The future looked bright at the beginning of artificial intelligence.

This episode looks at how what seemed like a very straightforward goal - making machines that could think - turned into a 70 year struggle. 

That’s on this episode of THIS billion seconds.

Claude Shannon.

Marvin Minsky.

John Conway.

These aren’t household names, but in the halls of computer science and in the history of artificial intelligence they are renowned. 

Three of the founders of the field.

They were among around 60 different individuals who passed through Dartmouth in the summer of 1956.

The workshop was purposely constructed very casually. 

You could come in and stay for the whole thing - or just drop by check it out and move on.

People were encouraged to give talks. 

They were encouraged to share their ideas. 

They were encouraged to brainstorm.

On the very last day, the 17th of August, they had a set of presentations covering some of the things they’d learned from one another over those six weeks.

This is the first moment when the shape of artificial intelligence - as we think of it today - becomes visible.

The workshop participants returned to their respective institutions filled good ideas, high hopes - and completely unrealistic expectations.

They reckoned they’d have machines that could think in five years. 

10 years, tops.

It didn’t work out that way.

The most obvious obstacle was that no one had a workable definition of intelligence. 

Not human intelligence, and certainly not artificial intelligence.

If you don’t know how to describe intelligence how can you build that into a machine?

So the search for artificial intelligence became a quest to understand intelligence more broadly.

That’s a hard problem. 

70 years later it’s not clear that we have a much better idea of what intelligence is. 

But that didn’t stop these first pioneers.

Instead, they borrowed from what they all agreed was a marker of high intelligence, deciding that if the computer could reproduce that capability, the computer must be intelligent.

It's all so logical. And all so very wrong.

Almost all of them were excellent chess players. 

All of them agreed that chess was an excellent example of intelligence. 

So why not teach the computer to play chess?

It’s an interesting idea. But it was quickly disproven.  Yes, you could teach a computer to play chess. Turns out, that wasn't even terrifically hard.

A computer can think so much faster than a person it can simply work its way through every chess move available to it, selecting the best one.

It has the advantage of speed. But not intelligence.

By 1977 you could buy a little computer that could play chess - and beat the pants off of almost anyone except a master player. 

Those computers weren’t intelligent.

Another approach taken early on worked from the idea that it might be possible to teach a computer enough facts about the world that the computer would be able to make intelligent decisions about how to operate in real world.

This is called the 'top-down' approach to artificial intelligence. 

On the surface it sounds like a good idea. In practice it’s basically unworkable. 

Because there is so much knowledge that is embedded in this world, it is almost impossible to teach it to a machine. 

There have been multiple attempts. 

The last of them only drew to a close in 2017.

None of them worked.

Artificial intelligence was an incredibly active field of research in the 1960s and the 1970s as that first generation of pioneers worked its way through the hard problems.

Only to learn that the hard problems didn’t get any easier.

The people paying for all of this work got disappointed in a lack of progress, withdrew their funding, and forward progress in AI ground to a halt.

This is known as the first “AI Winter“.

Then came along the second generation of researchers, led by a brilliant Australian by the name of Rodney Brooks.

Brooks took a look at the world. 

Creatures as simple as insects could make very good decisions about the world. 

With very little intelligence. 

How did they do that?

Brooks decided to design computers and robots that took their cues from those insects. 

Suddenly robots could climb staircases. 

They could walk upright. 

All sorts of things that have been very hard to do before with the top-down approach - and which we don’t think of as intelligence but actually require a great deal of smarts - they all worked worked with Brooks' 'bottom-up' approach to artificial ingtelligence.

This caused an enormous burst of enthusiasm. 

If it was possible to use simple techniques to solve complex problems, maybe, just maybe, we could build artificial intelligence from the bottom up rather than the top down.

They gave it a red-hot go. 

And it's not that the approach failed.

But there are limits. 

Insect intelligence is incredibly useful.

It's given us all sorts of crazy robots that can balance themselves and perform dexterous tasks.

But those body smarts aren't the smarts we think of as smarts - even though they are legitimately smart.

The kinds of things researchers wanted to do with artificial intelligence... 

[ John Sculley on Agents.wav  ] 

That's John Sculley, the man who famously fired Steve Jobs when running Apple Computer. 

Sculley wanted to leave his own mark on computing, so he pointed to an amazing future of autonomous agents.

But there was one problem.

Autonomous agents need artificial intelligence.

And in 1987 - when Sculley introduced agents to the world - there wasn't any artificial intelligence they could use.

Insects can't do research.

And that was the great disappointment of the 2nd age of AI.

Not that it didn't work.It did.

But it didn't do hold the answer to the great promise of artificial intelligence: machines that think like people do.

And so - despite the great robots and the robot vacuum cleaners that popped up in living rooms - interest in AI waned again.

The second AI winter.

That winter lasted for more than a decade. From the late 1990s through around 2010.

And we come to what we think of as the 'modern' age of AI.

Two great minds played complementary roles.

Fei-Fei Li and Ilya Sutskever.

Fei-Fei Li realised that you were going to need a lot of data to train artificial intelligence systems.

So she put together the IMAGENET project.

14 million images.

Each of them carefully labeled.

A massive project that took years.

When that was done, then Ilya Sustskever built software known as AlexNet on top of it.

IMAGENET provided raw information, AlexNet provided the intelligence that allowed a computer program to recognise the difference bertween a dog, a boat and a cloud.

To do that took huge amounts of information - and massive, massive amounts of computer power.

But it worked. And because it worked, it kicked off the modern age of AI.

Five years after AlexNet, eight researchers at Google invented the 'Transformer'.

The foundation for all modern language models like ChatGPT, Claude - oh, yes, named after Claude Shannon, one of the founders of the field - and Google's Gemini.

But it took another five years for for the Transformer to prove its worth.

Because, like IMAGENET before it, it had to be trained on billions and billions and billions of words.

And who was doing that?  Ilya Sutskever - who had moved on from AlexNet to work at a not-for-profit research company.

OpenAI.

Everything he'd learned from AlexNet went into the tool OpenAI was building on top of the Transformer.

On the 30th of November, 2022 - more than 66 years after the Dartmouth workshop on artificial intelligence, OpenAI launched their tool.

ChatGPT.

And - finally - AI had arrived.

Sort of.

In our next episode we'll take a look at the last three years of horrible AI, bad AI, mid AI, and finally, 'good enough' AI.

And how that changed everything.

That's on the next episode of THIS BILLION SECONDS.

THIS BILLION SECONDS was written and recorded by Mark Pesce.

Produced with assistance from Ampel Myrtle and Pine.

If you like this show, please share it with a friend.

And make sure to follow or subscribe to get all of the episodes in this series.

This is Mark Pesce, thanking you for listening.

THIS BILLION SECONDS 
AI @ 70 -- EPISODE ONE: AI TAKES THE PENSION&nbsp;
On the 17th August 1956, artificial intelligence was born. The founders of the field reckoned they'd have thinking machines equivalent to humans in five year, perhaps ten at the most. What looked easy at the outset turned into a seven-decade struggle - across two "AI winters", when the discipline nearly faded away - before we got to ChatGPT.
&nbsp;
TRANSCRIPT:&nbsp;
MARK PESCE, HOST: On the 17th August 1956, artificial intelligence was born.
The final day of a six week workshop that ran through a hot lazy summer at Dartmouth University.&nbsp;
A workshop on &ldquo;artificial intelligence&rdquo;.&nbsp;
Words that had never been put together before - and would never be separate again.&nbsp;
G'day, I'm Mark Pesce and&nbsp;this billion seconds are already unfolding as the most significant of this century.
In this miniseries, celebrating the 70th anniversary of artificial intelligence, we're looking at where we&rsquo;ve come from, how we got here - and where we seem to be going.
Because we&rsquo;re travelling at the speed of thought.
The future looked bright at the beginning of artificial intelligence.
This episode looks at how what&nbsp;seemed like a very straightforward goal - making machines that could think - turned into a 70 year struggle.&nbsp;
That&rsquo;s on this episode of&nbsp;THIS billion seconds.
Claude Shannon.
Marvin Minsky.
John Conway.
These aren&rsquo;t household names, but in the halls of computer science and in the history of artificial intelligence they are renowned.&nbsp;
Three of the founders of the field.
They were among around 60 different individuals who passed through Dartmouth in the summer of 1956.
The workshop was purposely constructed very casually.&nbsp;
You could come in and stay for the whole thing - or just drop by check it out and move on.
People were encouraged to give talks.&nbsp;
They were encouraged to share their ideas.&nbsp;
They were encouraged to brainstorm.
On the very last day, the 17th of August, they had a set of presentations covering some of the things they&rsquo;d learned from one another over those six weeks.
This is the first moment when the shape of artificial intelligence - as we think of it today - becomes visible.
The workshop participants returned to their respective institutions filled good ideas, high hopes - and completely unrealistic expectations.
They reckoned they&rsquo;d have machines that could think in five years.&nbsp;
10 years, tops.
It didn&rsquo;t work out that way.
The most obvious obstacle was that no one had a workable definition of intelligence.&nbsp;
Not human intelligence, and certainly not artificial intelligence.
If you don&rsquo;t know how to describe intelligence how can you build that into a machine?
So the search for artificial intelligence became a quest to understand intelligence more broadly.
That&rsquo;s a hard problem.&nbsp;
70 years later it&rsquo;s not clear that we have a much better idea of what intelligence is.&nbsp;
But that didn&rsquo;t stop these first pioneers.
Instead, they borrowed from what they all agreed was a marker of high intelligence, deciding that if the computer could reproduce that capability, the computer&nbsp;must be intelligent.
It's all so logical. And all so very wrong.
Almost all of them were excellent chess players.&nbsp;
All of them agreed that chess was an excellent example of intelligence.&nbsp;
So why not teach the computer to play chess?
It&rsquo;s an interesting idea. But it was quickly disproven.&nbsp; Yes, you could teach a computer to play chess. Turns out, that wasn't even terrifically hard.
A computer can think so much faster than a person it can simply work its way through every chess move available to it, selecting the best one.
It has the advantage of speed. But not intelligence.
By 1977 you could buy a little computer that could play chess - and beat the pants off of almost anyone except a master player.&nbsp;
Those computers weren&rsquo;t intelligent.
Another approach taken early on worked from the idea that it might be possible to teach a computer enough facts about the world that the computer would be able to make intelligent decisions about how to operate in real world.
This is called the 'top-down' approach to artificial intelligence.&nbsp;
On the surface it sounds like a good idea. In practice it&rsquo;s basically unworkable.&nbsp;
Because there is so much knowledge that is embedded in this world, it is almost impossible to teach it to a machine.&nbsp;
There have been multiple attempts.&nbsp;
The last of them only drew to a close in 2017.
None of them worked.
Artificial intelligence was an incredibly active field of research in the 1960s and the 1970s as that first generation of pioneers worked its way through the hard problems.
Only to learn that the hard problems didn&rsquo;t get any easier.
The people paying for all of this work got disappointed in a lack of progress, withdrew their funding, and forward progress in AI ground to a halt.
This is known as the first &ldquo;AI Winter&ldquo;.
Then came along the second generation of researchers, led by a brilliant Australian by the name of Rodney Brooks.
Brooks took a look at the world.&nbsp;
Creatures as simple as insects could make very good decisions about the world.&nbsp;
With very little intelligence.&nbsp;
How did they do that?
Brooks decided to design computers and robots that took their cues from those insects.&nbsp;
Suddenly robots could climb staircases.&nbsp;
They could walk upright.&nbsp;
All sorts of things that have been very hard to do before with the top-down approach - and which we don&rsquo;t think of as intelligence but actually require a great deal of smarts - they all worked worked with Brooks' 'bottom-up' approach to artificial ingtelligence.
This caused an enormous burst of enthusiasm.&nbsp;
If it was possible to use simple techniques to solve complex problems, maybe, just maybe, we could build artificial intelligence from the bottom up rather than the top down.
They gave it a red-hot go.&nbsp;
And it's not that the approach failed.
But there are limits.&nbsp;
Insect intelligence is incredibly useful.
It's given us all sorts of crazy robots that can balance themselves and perform dexterous tasks.
But those body smarts aren't the smarts we think of as smarts - even though they are legitimately smart.
The kinds of things researchers wanted to do with artificial intelligence...&nbsp;
[ John Sculley on Agents.wav&nbsp; ]&nbsp;
That's John Sculley, the man who famously fired Steve Jobs when running Apple Computer.&nbsp;
Sculley wanted to leave his own mark on computing, so he pointed to an amazing future of autonomous agents.
But there was one problem.
Autonomous agents need artificial intelligence.
And in 1987 - when Sculley introduced agents to the world - there wasn't any artificial intelligence they could use.
Insects can't do research.
And that was the great disappointment of the 2nd age of AI.
Not that it didn't work.It did.
But it didn't do hold the answer to the great promise of artificial intelligence: machines that think like people do.
And so - despite the great robots and the robot vacuum cleaners that popped up in living rooms - interest in AI waned again.
The second AI winter.
That winter lasted for more than a decade. From the late 1990s through around 2010.
And we come to what we think of as the 'modern' age of AI.
Two great minds played complementary roles.
Fei-Fei Li and Ilya Sutskever.
Fei-Fei Li realised that you were going to need a lot of data to train artificial intelligence systems.
So she put together the IMAGENET project.
14 million images.
Each of them carefully labeled.
A massive project that took years.
When that was done, then Ilya Sustskever built software known as AlexNet on top of it.
IMAGENET provided raw information, AlexNet provided the intelligence that allowed a computer program to recognise the difference bertween a dog, a boat and a cloud.
To do that took huge amounts of information - and massive, massive amounts of computer power.
But it worked. And because it worked, it kicked off the modern age of AI.
Five years after AlexNet, eight researchers at Google invented the 'Transformer'.
The foundation for all modern language models like ChatGPT, Claude - oh, yes, named after Claude Shannon, one of the founders of the field - and Google's Gemini.
But it took another five years for for the Transformer to prove its worth.
Because, like IMAGENET before it, it had to be trained on billions and billions and billions of words.
And who was doing that? &nbsp;Ilya Sutskever - who had moved on from AlexNet to work at a not-for-profit research company.
OpenAI.
Everything he'd learned from AlexNet went into the tool OpenAI was building on top of the Transformer.
On the 30th of November, 2022 - more than 66 years after the Dartmouth workshop on artificial intelligence, OpenAI launched their tool.
ChatGPT.
And - finally - AI had arrived.
Sort of.
In our next episode we'll take a look at the last three years of horrible AI, bad AI, mid AI, and finally, 'good enough' AI.
And how that changed everything.
That's on the next episode of THIS BILLION SECONDS.
THIS BILLION SECONDS was written and recorded by Mark Pesce.
Produced with assistance from Ampel Myrtle and Pine.
If you like this show, please share it with a friend.
And make sure to follow or subscribe to get all of the episodes in this series.
This is Mark Pesce, thanking you for listening.

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Hosted by award-winning podcast creator, journalist & futurist Mark Pesce, The Next Billion Seconds is everything you need to know about the future — so you can make the best decisions today. The rate of change we are experiencing is the fastest humanity has ever seen. Stay informed as Mark simplifies the complex technological and societal changes we face. How we will work, connect, use money, drive, eat is all changing at a rapid pace. For more information on The Next Billion seconds with Mark Pesce, please check out https://nextbillionseconds.com 

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Hosted by award-winning podcast creator, journalist &amp; futurist Mark Pesce, The Next Billion Seconds is everything you need to know about the future — so you can make the best decisions today. The rate of change we are experiencing is the fastest humanity has ever seen. Stay informed as Mark simplifies the complex technological and societal changes we face. How we will work, connect, use money, drive, eat is all changing at a rapid pace. For more information on The Next Billion seconds with Mark Pesce, please check out <a href="https://nextbillionseconds.com">https://nextbillionseconds.com </a><a href="https://ampel.com.au"></a>
For more information about this podcast and The Next Billion Seconds, please visit<a href="https://nextbillionseconds.com"> https://nextbillionseconds.com.</a> The Next Billion Seconds with Mark Pesce is produced by Ampel -<a href="https://ampel.com.au"> https://ampel.com.au</a>.

The future of tech:  the next billion seconds are the most important in human history as technology transforms the way we live and work.  The rate of change we will experience will be the fastest humanity has ever seen....

The Next Billion Seconds with Mark Pesce

Hosted by award winning podcast creator, journalist & futurist Mark Pesce, The Next Billion Seconds is everything you need to know about the future — so you can make the best decisions today. The rate of change we are experiencing is the fastest humanity has ever seen. Stay informed as Mark simplifies the complex technological and societal changes we face. How we will work, connect, use money, drive, eat is all changing at a rapid pace. For more information on The Next Billion seconds with Mark Pesce, visit https://nextbillionseconds.com

The Next Billion Seconds with Mark Pesce is produced by Ampel -https://ampel.com.au. Chief Audio Officer - Josh Butt

Hosted by award winning podcast creator, journalist &amp; futurist Mark Pesce, The Next Billion Seconds is everything you need to know about the future — so you can make the best decisions today. The rate of change we are experiencing is the fastest humanity has ever seen. Stay informed as Mark simplifies the complex technological and societal changes we face. How we will work, connect, use money, drive, eat is all changing at a rapid pace. For more information on The Next Billion seconds with Mark Pesce, visit <a href="https://nextbillionseconds.com">https://nextbillionseconds.com</a><a href="https://ampel.com.au"></a>
The Next Billion Seconds with Mark Pesce is produced by Ampel -<a href="https://ampel.com.au"> </a><a href="https://ampel.com.au">https://ampel.com.au</a>. Chief Audio Officer - Josh Butt