Everything might change forever this century (or we’ll go extinct)

Summary

This script explores the unprecedented pace of scientific and technological advancements in recent human history, contrasting it with the slow progress of past millennia. It delves into economic growth trends, the potential of artificial intelligence to catalyze hyperbolic growth, and the profound implications of AGI for humanity's future, emphasizing the importance of ensuring AI aligns with human values.

Takeaways

• 📚 Isaac Asimov's historical analysis shows that the majority of scientific innovation has occurred in the last few centuries, highlighting the recent acceleration in human progress.
• 🌍 A chart of world populations since 10,000 B.C. illustrates a rapid increase, reaching 1 billion by 1800 and soaring to 6 billion just 200 years later, underscoring the recent significant growth in human population.
• 💹 Economic historian Bradford DeLong's speculative reconstruction indicates that economic growth was historically slow but has accelerated in the recent era, reflecting a shift in the pace of human development.
• 🛠️ The Industrial Revolution marked the beginning of a historically atypical period of rapid change, affecting not only our lifestyles but also our expectations for the future.
• 🚀 The 'business as usual' perspective assumes a continuation of the current rate of change, predicting incremental advancements in technology and society.
• 🌌 In contrast, the 'radical change thesis' posits that the world by the end of the 21st century could be unrecognizable due to breakthroughs in technology such as advanced nanotechnology and interstellar spaceflight.
• 📈 Economic data suggests a pattern of hyperbolic growth, which, if continued, implies a dramatic acceleration of change and potentially an 'economic singularity'.
• 🧬 The hypothetical 'Duplicator' technology exemplifies how a single innovation could drastically impact economic growth by instantly creating productive human clones.
• 🤖 The development of artificial intelligence (AI), especially artificial general intelligence (AGI), could be a catalyst for explosive economic growth by automating a vast array of tasks.
• 🔮 Predictions on when AGI will be developed vary widely, with some estimates suggesting it could be as soon as the latter half of this century, potentially making the 21st century the most pivotal in human history.
• 🌟 The implications of AGI are profound, potentially leading to a transformation of human civilization and the need for vigilance to ensure alignment with human values and a positive trajectory for future generations.

Q & A

What does Isaac Asimov's cataloging of inventions and scientific discoveries reveal about our current era?

Isaac Asimov's cataloging reveals that most scientific innovation has occurred relatively recently, within the last few hundred years, despite his book starting from 4 million BCE. This shows a significant acceleration of technological advancements in modern times.

How did the world population growth trend change around 1800?

The world population growth experienced a significant shift around 1800, taking until then to reach 1 billion people. However, just 200 years later, the Earth's population reached 6 billion, indicating an exponential growth pattern in recent history.

What was Bradford DeLong's attempt to understand the world's economic production over the last million years?

Bradford DeLong attempted to piece together the total world economic production over the last million years, providing a quantitative account of economic growth that aligns with historical trends in population and technology, showing a very slow growth until recent times.

How did the quality of life for a ten-year-old change over the centuries?

The quality of life for a ten-year-old has significantly improved over the centuries. Life expectancy, which was once under 60 worldwide, has now increased to an expectation of living to the age of 80 in many nations.

What is the 'Business as usual' perspective and how does it relate to future expectations?

The 'Business as usual' perspective is the idea that change in the coming decades will be similar to change in the last few decades, with a steady rate of economic growth and technological advancement. It suggests that the future will look a lot like the present, but with gradual modifications.

What is the 'Radical change thesis' and how does it contrast with the 'Business as usual' perspective?

The 'Radical change thesis' posits that the world by the end of the 21st century will be so different as to be almost unrecognizable due to potentially revolutionary technologies. This contrasts with the 'Business as usual' perspective, which expects a more gradual and predictable rate of change.

What is the concept of 'hyperbolic growth' as proposed by economist Michael Kremer?

Hyperbolic growth is a model of economic growth that is much more dramatic than exponential growth over a long enough time horizon. It suggests that the world economy could grow at an increasingly rapid pace, eventually reaching a point of infinite growth, which is consistent with the radical change thesis.

What is the hypothetical 'Duplicator' and how could it impact economic growth?

The hypothetical 'Duplicator' is a machine that creates exact replicas of people, each with the same memories and talents. If such a technology existed, it could lead to a population and productivity explosion, potentially sustaining hyperbolic growth and leading to an economic singularity.

What are the potential advantages of artificial intelligence (AI) over human labor?

AI has several potential advantages over human labor, including the ability to think faster, save and transfer memory states, copy and transfer themselves across networks, and improve their own software, which could lead to unprecedented productivity and economic growth.

What is artificial general intelligence (AGI) and why is it significant?

Artificial general intelligence (AGI) is a system capable of performing any intellectual task that a human being can do. It is significant because an AGI could automate almost any type of human labor and potentially lead to a fundamental transformation of society and the economy.

What challenges does the development of AGI pose in terms of value alignment?

The development of AGI poses the challenge of value misalignment, where it is not guaranteed that AI will share or be aligned with human values. This could lead to negative outcomes if not properly addressed through research and ethical considerations.

Outlines

00:00

📚 Historical Innovation and Recent Acceleration

Isaac Asimov's historical analysis revealed that the majority of scientific inventions and discoveries have occurred in the last few centuries, highlighting a significant acceleration in innovation. The script discusses the rapid growth in world population, economic production, and the profound changes in living standards and technology, contrasting the static nature of human life for most of history. It also touches on the abolition of slavery and the failure of historical figures like Adam Smith and Thomas Malthus to predict future societal changes due to their time being at the onset of the Industrial Revolution, which dramatically altered expectations for the future.

05:01

🔮 The 'Business as Usual' vs. 'Radical Change' Perspectives

This paragraph explores two contrasting perspectives on future change: 'Business as usual,' where future decades resemble the recent past with incremental advancements, and the 'radical change thesis,' which posits that the coming decades could bring unprecedented transformations. The radical change thesis is supported by the historical trend of accelerating economic growth, possibly indicating hyperbolic growth, as opposed to the exponential growth implied by the business as usual perspective. Skepticism is warranted due to the slow nature of scientific research and some evidence of a slowdown in technological progress, but the potential for rapid change remains plausible.

10:01

🌟 The Hypothetical 'Duplicator' and Its Economic Impact

The script introduces a hypothetical technology called 'The Duplicator' from the comic Calvin and Hobbes, capable of creating exact human replicas with the same memories and talents. It discusses the potential economic implications of such a device, including the ability to instantly create productive workers, accelerating scientific innovation, and benefiting various industries. The Duplicator could lead to a population and productivity explosion, potentially sustaining hyperbolic growth in the world economy. The paragraph also outlines the basic inputs of population, capital, and technology in economic growth models and how the Duplicator could impact them.

15:03

🤖 The Potential of Artificial Intelligence as a Labor Force

The potential of artificial intelligence (A.I.) to revolutionize the labor force is discussed, drawing parallels with the hypothetical Duplicator. A.I. could offer advantages such as faster thinking, memory retention, and self-improvement, making it potentially more productive than human workers. The development of A.I. could lead to explosive economic growth if it arrives in the coming century, making the 21st century a critical period in history. The script also notes the rapid advancements in A.I. in recent years, including achievements in image classification, game playing, and autonomous driving.

20:06

🧠 Estimating the Arrival of Advanced Artificial Intelligence

This paragraph delves into the challenge of predicting when advanced A.I., or artificial general intelligence (AGI), will be developed. It discusses the historical over-optimism and subsequent 'A.I. winter,' leading to a more cautious approach among researchers. A survey by Grace et al. from 2016 reveals a median expectation of A.I. surpassing human performance in any occupation by 2061, though with significant variation. The report by Ajeya Cotra attempts to estimate A.I. development timelines based on trends in building and training increasingly large A.I. models, using 'biological anchors' to gauge computational requirements.

25:09

🚀 The Role of Deep Learning and Hardware Progress in A.I. Development

The script explains the importance of deep learning and computational hardware advancements in A.I. development. Deep learning has been successful in areas like vision and language, and its progress is tied to the falling cost of computation. Ajeya Cotra's model for A.I. timelines is based on the affordability of training large deep learning models, assuming continued growth in computing hardware. The model also considers the size of deep learning models and the amount of trial and error needed for learning complex tasks, with the ultimate goal of automating human cognition.

30:10

🌌 The Significance and Implications of Artificial General Intelligence

The potential arrival of AGI in the 21st century could mark a fundamental transformation for humanity, with impacts far beyond job displacement. This paragraph discusses the profound implications of AGI, including the possibility of rapid technological advancements, space colonization, and the creation of a galaxy-wide civilization. It also raises concerns about value misalignment, where A.I. may not share human values, and the need for research to ensure A.I. is compatible with human interests. The paragraph concludes by emphasizing the importance of vigilance and responsible action in the face of such a transformative event.

🌐 Our Cosmic Significance and the Importance of Our Choices

The final paragraph reflects on the potential cosmic significance of human actions in the 21st century. It suggests that our civilization, though currently small, could be the seed of an astronomically vast future. The script encourages understanding our place in history and the urgency of making wise decisions, as they could shape billions of years of future history. It contrasts the idea of human insignificance with the immense potential impact of our actions, especially in the context of upcoming technological and economic changes.

Keywords

💡Scientific Innovation

Scientific innovation refers to the process of creating new ideas, methods, and products in the field of science. In the context of the video, it highlights the acceleration of inventions and discoveries in recent history, particularly since the year 1500, as noted by Isaac Asimov. The script underscores the rapidity of these innovations in the last few centuries compared to the slow pace of advancements in earlier periods of human history.

💡Population Growth

Population growth is the increase in the number of individuals in a population over time. The video script illustrates the concept by showing a significant increase in the world's population since 1800, reaching 1 billion people and then doubling to 6 billion within just 200 years. This rapid growth is contrasted with the relatively stable population levels for most of human history.

💡Economic Production

Economic production is the creation of goods and services by economic agents within an economy. The script discusses how economic historian Bradford DeLong attempted to quantify the total world economic production over the last million years, indicating a historically slow growth that has recently accelerated, reflecting changes in technology and living standards.

💡Industrial Revolution

The Industrial Revolution is a period during the 18th and 19th centuries where agrarian, rural societies became industrial and urban. The video script points out that the effects of the Industrial Revolution have been dramatic, reshaping our lives and our expectations for the future, and marking the beginning of a historically atypical time of rapid change.

💡Business as Usual

The 'business as usual' perspective is the idea that change in the coming decades will be similar to change in the recent past, with steady economic growth and incremental technological advancements. The script contrasts this with the 'radical change thesis,' suggesting that we might be underestimating the potential for transformative developments in the future.

💡Radical Change Thesis

The 'radical change thesis' posits that the world by the end of the 21st century could be so different as to be almost unrecognizable due to rapid technological advancements. The script uses this concept to challenge the 'business as usual' perspective, suggesting that developments like advanced nanotechnology, cures for aging, and interstellar spaceflight could be closer than we think.

💡Hyperbolic Growth

Hyperbolic growth is a model of growth that becomes increasingly rapid, eventually surpassing exponential growth. The script references economist Michael Kremer's work, suggesting that long-run economic data fits a hyperbolic growth model, which supports the radical change thesis and the idea that change may continue to accelerate.

💡Artificial Intelligence (AI)

Artificial Intelligence (AI) is the development of computer systems that can perform tasks that would normally require human intelligence, such as visual perception, speech recognition, and decision-making. The video script discusses AI's potential to automate labor and vastly increase economic productivity, possibly leading to a productivity explosion in the 21st century.

💡Artificial General Intelligence (AGI)

Artificial General Intelligence (AGI) 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 script describes AGI as the 'holy grail' of AI, capable of automating almost any type of human labor and potentially leading to a transformation of human civilization.

💡Economic Singularity

The economic singularity is a theoretical point in the future when the economy grows to be infinitely large in a short amount of time due to rapid technological advancements, particularly in AI. The script uses the concept of a 'gold pile' growing hyperbolically to illustrate how such a singularity could occur, with growth accelerating at an ever-increasing rate.

💡Demographic Transition

The demographic transition refers to the shift from a society with high birth and death rates to one with low birth and death rates as a result of industrialization and modernization. The script notes that the slowing of population growth since the 1960s, part of the demographic transition, has contributed to a decline in global economic growth rates.

Highlights

Isaac Asimov's historical analysis shows a surge in scientific innovation in the last 500 years compared to the previous 4 million.

World population growth accelerated significantly, reaching 6 billion only 200 years after hitting 1 billion.

Bradford DeLong's speculative economic reconstruction indicates a historically slow economic growth that has recently sped up.

The concept of 'business as usual' suggests a continuation of current trends in economic growth and technological advancement.

The 'radical change thesis' posits that the future could be vastly different from today, with unimaginable technologies emerging in the near future.

Economist Michael Kremer's hyperbolic growth model suggests a potential acceleration in economic growth, differing from traditional exponential growth.

The idea of 'The Duplicator' from Calvin and Hobbes illustrates the potential impact of a technology that could instantly clone productive humans.

AI has the potential to automate labor more effectively than humans, with advantages like faster thinking and self-improvement capabilities.

The development of artificial general intelligence (AGI) could lead to a transformation of human civilization, with impacts beyond current comprehension.

Ajeya Cotra's report on AI timelines uses trends in AI model affordability and task difficulty to estimate when AGI might be developed.

Deep learning advancements have been significantly tied to the falling cost of computation, enabling larger and more capable AI models.

The future of AI might be better predicted by understanding the relationship between computation used for training and AI performance.

Historical progress in computing power suggests that continued advancements could make training advanced AI models economically feasible by the end of the century.

The potential arrival of AGI in the 21st century could be one of the most significant events in human history, with far-reaching implications.

The development of AGI raises concerns about value misalignment, where AI may not share or understand human values and intentions.

Our actions in the 21st century may have a profound impact on the long-term future, influencing billions of years of history.

The significance of our current century could mean that our civilization's choices have a cosmic importance, shaping the destiny of life across the galaxy.

Transcripts

A very unusual time period.

The celebrated science fiction author and chemistry professor Isaac Asimov,

once cataloged a history of inventions and scientific discoveries

throughout all of human history.

While incomplete, his efforts still reveal something intriguing

about our current era.

Out of these 694 pages in Asimov's book,

553 pages documented inventions and discoveries since 1500.

Even though his book starts in 4 million BCE.

In other words, throughout human history, most scientific innovation has come

relatively recently, within only the last few hundred years.

Other historical trends paint a similar picture.

For example, here's a chart of world populations since 10,000 B.C.

for nearly all of human history.

Up until quite recently, there weren't very many people on Earth.

It took until about 1800 for the population to reach 1 billion people.

And just 200 years later,

a blink of an eye compared to how long our species has been around.

Earth reached 6 billion people.

Economic historian Bradford DeLong attempted to piece together

the total world economic production over the last million years.

By its nature, his reconstruction of the historical data is speculative,

but the rough story it tells is consistent with the aforementioned

historical trends in population and technology

in the millennia preceding the current era.

Economic growth by which we mean growth in how much valuable stuff humanity

as a whole can produce.

Was extremely slow.

Now growth is much faster.

Bradford DeLong's data provides historians a quantitative account

of what they already know from reading narratives written

in the distant past. For nearly all of human history,

people lived similarly to the way their grandparents lived,

unlike what we expect today.

Most people did not see major changes in living standards,

technology and economic production over their lifetimes.

To be sure, people were aware that empires rose and fell.

Infectious disease ravaged communities and wars were fought.

Individual humans saw profound change in their own lives

through the births and deaths of those they loved.

Cultural change and migration.

But the idea of a qualitatively different mode of life

with electricity, computers, and the prospect of thermonuclear war

that's all come extremely recently on historical timescales

as new technologies were developed.

Quality of life shot up in various ways for ten year olds.

Life expectancy was once under 60 all over the world.

Now, in many nations, a ten year old can expect to live to the age of 80.

With progress in automating food production,

fewer people now are required to grow food.

As a result, our time has been freed to pursue different activities.

For example, going to school.

And it's not just technology that changed.

In the past, people took for granted some social institutions that had existed

for thousands of years,

such as the monarchy and chattel slavery. In the midst of the Industrial Revolution.

These institutions began to vanish.

Writing in 1763, the eminent British economist Adam Smith wrote

that slavery, quote: "Takes place in all societies

at the beginning and proceeds from that tyrannical disposition,

which may almost be said to be natural to mankind."

While Adam Smith personally found the practice repugnant,

he nonetheless was pessimistic about the future of slavery.

Predicting that, quote: "It is indeed almost impossible

that it should ever be totally or generally abolished."

And yet, mere decades after Adam Smith wrote those lines,

Britain outlawed slavery and launched a campaign

to end the practice in its colonies around the world.

By the end of the 20th century,

every nation in the world had formally abolished slavery.

Another scholar from his era, Thomas Malthus, made a similar blunder

at the end of the 18th century, Malthus was concerned

by the population growth he saw in his time.

He reasoned that historically, excess population growth had always outstripped

the food supply, leading to famine and mass death.

As a consequence, Malthus predicted that recent high population

growth in England would inevitably result in a catastrophe.

But what might have been true about all the centuries before

the 18th century evidently came to an end

shortly after Malthus' his pessimistic prediction.

Historians now recognize that rather than famine becoming more frequent,

food in Britain became more widely available

in the 19th and 20th centuries, despite unprecedented population growth.

What Adam Smith and Thomas Malthus failed to see was that they were living

in the very beginning of a historically atypical time of rapid change.

A period we now refer to as the Industrial Revolution.

The effects of the Industrial Revolution have been dramatic, reshaping

not only how we live, but also our ideas about what to expect in the future.

The 21st century could be much weirder than we imagine.

It's easy to fault historical figures at the time of the Industrial Revolution

for failing to see what was to come in the next few centuries.

But their method of reasoning

of looking at the past and extrapolating past trends outwards

is something we still commonly do today to gauge our expectations of the future.

In the last several decades, society has become accustomed to the global

economy growing at a steady rate of about 2% to 4% per year.

When people imagine the future,

they often implicitly extrapolate this rate of change continuing indefinitely.

We can call this perspective: "Business as usual."

The idea that change in the coming decades will look more or less like change

in the last few decades.

Under business as usual, the world in 50 years

looks a lot like our current world, but with some modifications.

The world in 2072 would look just about as strange

as someone from 1972 looking at our current world.

Which is to say there will be more technology, different ways of

communicating and socializing with others, distinct popular social movements

and novel economic circumstances, but nothing too out of the ordinary

like the prospect of mind uploading or building Dyson spheres around the sun.

Parents sometimes take this perspective when imagining what life will one day

be like for their children.

Policymakers often take this perspective when crafting policy

so that their proposed rules will be robust to new developments in the future.

And workers who save for their retirement often

take this perspective when they prepare for the challenges of growing old.

Contrast business as usual with another perspective,

which we can call: "The radical change thesis."

Perhaps like Adam Smith and Thomas Malthus in their time,

we are failing to see something really big on the horizon.

Under the radical change thesis,

the world by the end of the 21st century will look so different

as to make it almost unrecognizable to people living today.

Technologies that might seem unimaginable to us now, like advanced nanotechnology,

cures for aging, interstellar spaceflight, and fully realistic virtual reality.

Could only be a few decades away.

As opposed to many centuries or thousands of years away.

It's sensible to be skeptical of the radical change thesis.

Scientific research happens slowly, and there's even some evidence

that the rate of technological progress has slowed down in recent decades.

At the same time, the radical change thesis makes intuitive sense

from a long view perspective.

Recall the trend in economic growth.

Here, we've zoomed in on the last hundred years of economic growth.

This view justifies the business as usual perspective.

In the last 100 years, economic production and technology

underlying economic production has grown at a fairly constant rate.

Given the regularity of economic growth in recent decades.

It makes a lot of sense to expect that affairs will continue to change

at the current rate for the foreseeable future.

But now zoom out.

What we see is not the regular and predictable trend we saw before,

but something far more dynamic and uncertain.

And rather than looking like change is about to slow down,

it looks more likely that change will continue to speed up.

In a classic 1993 paper, economist Michael Kremer

proposed that the best fit to this long run economic data

is not the familiar slow rate of exponential change that we're used to,

but rather what's called hyperbolic growth.

Over a long enough time horizon,

hyperbolic growth is much more dramatic than exponential growth,

making it consistent with the radical change thesis.

But it can also be a bit counterintuitive for people to imagine.

So here's an analogy.

Imagine a pile of gold that spontaneously grows over the course of a day.

This pile of gold represents the total size of the

world economy, over time.

Which we assume grows hyperbolically.

At 12 a.m. the beginning of the day.

There's only one piece of gold in the pile.

After 12 hours, the gold pile doubles in size so that there are now two pieces.

Then at 6 p.m., there are four gold pieces.

3 hours later, at 9 p.m., there are eight gold pieces

and only one and a half hours after that 10:30 p.m..

There are 16 gold pieces.

From the perspective of someone watching at 10:30 p.m..

It would be tempting to think that growth over the next few hours will be similar

to growth in the last few hours,

and that by midnight the next day, an hour and a half later,

the gold pile will double in size yet again to 32 pieces.

But this intuition would be wrong.

Notice that the time it takes for the gold pile to double in size

cut in half each time, the size of the pile doubles

As midnight draws nearer.

The pile will continue to double in size again and again,

more frequently each time than the last.

In fact, exactly at midnight, the pile will reach

what's called a singularity and grow to be infinitely large.

We can perform a similar exercise with the real world economy.

In 2020, David Roodman found that after fitting a hyperbolic function

to historical economic data.

The economy is expected to grow to be infinitely large

as we approach roughly the year 2047.

A general result, he says, is fairly robust

to extrapolating the trend at different periods in history.

Now it's important not to take this headline

result too seriously, especially the specific year 2047.

Physical resource limits prohibit the economy from becoming

infinitely large, and moreover, long run historical data is notoriously unreliable.

Using historical statistics alone, especially those from the distant past,

it's very difficult to predict what the future will really be like.

The business as usual perspective might still be correct

for the foreseeable future, or something else entirely might happen.

Maybe civilization itself will collapse

as we become consigned to fighting endless wars, famines and pandemics.

Humanity might even go extinct.

These possibilities notwithstanding, there do exist

concrete reasons to think that the world could be fundamentally different

by the end of the 21st century, in a way consistent with the radical change thesis.

Infinite growth is out of the question.

But one technology is clearly visible on the horizon,

which arguably has the potential to change our civilization dramatically.

"The Duplicator"

before we try to predict what technology in the coming decades could precipitate

explosive economic growth.

Let's first start by examining a hypothetical example.

A technology that would be sufficient to have such an effect.

The machine is The Duplicator from the comic Calvin and Hobbes.

Here's how it works: When someone steps in

two exact replicas step out.

The replicas keep all of the memories, personalities and talents of the original.

Basically, it splits you into two people, each of whom remember an identical past.

To constrain our expectations a bit,

let's imagine that using The Duplicator isn't free.

We can only use it to replicate people.

And it costs a lot of money to build and operate a Duplicator.

Yet even with these constraints, it seems safe to assume

that The Duplicator would have a very large impact on the world.

Even without knowing exactly how it will be used, there will presumably

be people who want to use the duplicated to clone themselves and other people.

Unlike with ordinary population growth in which it takes 18 years

and a considerable amount of effort to create one productive worker.

The Duplicator would allow us to create productive humans almost instantly

and without having to pay the costs of educating and raising them.

Consider the case of scientific innovation.

If we could clone our most productive scientists

say Newton, Galileo or von Neumann

Just imagine how much faster our civilization could innovate.

And it's not just raw scientific innovation that could increase

With a machine that can duplicate people, all industries could benefit.

The most talented rocket engineers, for example, could be cloned

and directly planted into places where they're needed the most

and we could duplicate the most talented doctors, musicians and architects.

As a consequence, The Duplicator would left

a crucial bottleneck to civilization wide output.

Unless we impose some strict controls on how often people could use the duplicator.

It appears likely that there would be a population explosion.

But, more than just a population explosion, it would be a productivity explosion.

Since the new duplicates

could be direct copies of the most productive people on earth,

who in turn, would use their talents to invent even more productive

technologies and potentially even better duplicators

Would the Duplicator be enough

to sustain the hyperbolic growth trend in gross world product,

and enable us to approach the economic singularity

we discussed earlier?

Maybe.

In academic models of economic growth, there are roughly three inputs

to economic production that determine the overall size of the economy.

These inputs are: population, capital and technology.

Population is the most intuitive input.

It just means how many workers there are in a society.

Capital is another term for equipment and supplies

like machines, roads and buildings that allow workers to produce stuff.

Technology is what joins these two inputs together.

It refers to the inherent efficiency of labor and includes

the quality of tools and the knowledge of how to create stuff in the first place.

A simple model of economic growth is the following:

Over time, people have children and the population gets larger.

With more people, the economy also grows.

But the economy also grows faster than the population because people work

to accumulate new capital and invent new technology,

at the same time the population is growing.

Most importantly, people come up with new ideas.

These new ideas are shared with others

and can be used by everyone to increase efficiency.

The process of economic growth looks a lot like a feedback loop.

Start with a set of people.

These people innovate and produce capital, which makes them more productive,

and they also produce resources which enables them to produce more people.

The next generation can rinse and repeat

with each generation more productive than the last.

Not only producing more people, but also more stuff per capita.

In the absence of increased economic efficiency.

The population will grow exponentially,

but when the total production per person also increases

This process implies super exponential economic growth.

Recall the analogy of the gold pile that grows in size from earlier.

The underlying dynamic is that the gold pile doubles in size every interval,

but the interval in which the pile doubles is not fixed.

Metaphorically, the gold pile becomes more efficient,

at doubling in size during each interval.

Giving rise to a process much faster than exponential growth.

Many standard models of economic growth

carry the same implication about the world economy.

However, we know our current economy isn't exploding in size

in the way predicted by this simple model.

So, why not?

One reason could be that our population growth is slowing down.

Since the 1960s,

population growth worldwide actually peaked before entering a decline.

Not coincidentally, world economic growth has fallen since that decade.

The underlying reason behind the fall in population growth and economic growth,

also called the "demographic transition," is still a matter of debate.

But regardless of its causes, the effect of the demographic

transition has been that most mainstream economic and population forecasters

do not anticipate explosive growth in the 21st century.

But hypothetically, if something like the Duplicator were invented,

then the potential for explosive growth could return.

The most important invention ever?

It's unlikely that humans will soon build the Duplicator.

What's more likely however, is the invention

of artificial intelligence that can automate labor.

As with the Duplicator, A.I. could be copied and used as a worker.

Vastly increasing total economic productivity.

In fact, the potential for A.I.

is even more profound than the duplicator.

That's because A.I. have a number of advantages over humans.

That could enable them to be far more productive in principle.

These advantages include being able to think faster,

save their current memory state,

copy and transfer themselves across the internet,

make improvements to their own software, and much more.

Under the assumption that the development of A.I.

will have a similar effect on the long term future

as the Duplicator hypothetically would.

The question of whether the 21st century will have explosive growth

becomes a question of predicting when A.I. will arrive.

If advanced A.I. is indeed invented later this century,

it might mean the 21st century is the most important century in history.

When will advanced AI arrive?

If you've paid any attention to the field of AI in recent years,

you've probably noticed that we've made some startling developments.

In the last ten years,

we've seen the rise of artificial neural networks

that can match human performance in image classification,

generate photorealistic images,

beat the top Go players in the world,

drive cars autonomously,

reach grand master level at the real time strategy game Starcraft 2,

learn how to play Atari games from scratch using only the raw pixel data,

and write rudimentary poetry and fiction stories.

If the next decade is anything like the last,

we're sure to see even more impressive developments in the near-term future.

Looking further,

the holy grail of the field of artificial intelligence

is so-called artificial general intelligence or AGI.

A system capable of performing

not only narrow tasks like game playing and image classification,

but the entire breadth of tasks that humans are capable of performing.

This would include the ability

to reason abstractly and prove new mathematical theorems,

perform scientific research, write books, and invent new goods and services.

In short, an AGI, if created,

would be at least as useful as a human worker

and would in theory have the ability to automate almost any type of human labor.

We know that AGI is possible in principle because human brains

are already a type of general intelligence created by evolution.

Many cognitive scientists and neuroscientists regularly analogized

the brain to a computer

and believe that everything we do is a consequence of algorithms implicit

in the structure of our biological neural networks within our brains.

Unless the human brain performs literal magic,

then we have good reason to believe that one day

we should be able to replicate its abilities in a computer.

However, knowing that AGI is possible is one thing.

Knowing when it will be developed and deployed in the real world

is another thing entirely.

Since the beginning of their discipline, A.I.

researchers have experienced notorious difficulty

predicting the future of the field.

In the early days of the 1960s, the field was plagued with over-optimism,

with some prominent researchers declaring that human like A.I.

was due within 20 years.

After these predictions failed to come to fruition.

Most expectations became more modest.

in the 1980s, the field entered what is now known as an A.I. winter

and many researchers became more focused on short term commercial applications

rather than the long term goal of creating something that rivals the human brain.

These days, there's a wide variety of opinions

among researchers about when AGI will be developed.

The most comprehensive survey to date was by Grace, et al from 2016.

A.I. experts were asked when they expected, quote:

"for any occupation, machines could be built

to carry out the task better and more cheaply than human workers."

The median response was the year 2061,

with considerable variation in individual responses.

These results show that most researchers take the possibility

of AGI being developed by the end of the century quite seriously.

However, it is worth noting that responses were very sensitive

to the exact phrasing of the question being asked.

A subset of the researchers were asked when they expect AI to automate

all human labor, and the median guess for that question was the year 2136.

It's clear that a significant

minority of researchers are unconvinced that AGI will arrive any time soon.

To see more about this survey of A.I. researchers,

check out Rob Miles' video about it on his channel.

...or my channel ...really, I mean

I'm Rob Miles, I do the voice for this channel,

but I also have my own channel. Rob Miles AI

....anyway....

In general, forecasting when A.I. will be developed is extremely difficult.

Ideally, we could create a measure that represents

how much progress there has been in the field of artificial intelligence.

Then we could plot that measure on a graph and extrapolate outwards

until we're expected to reach a critical threshold

identified with the development of AGI.

The problem with this approach is that it's

very hard to find a robust measure of progress in AI.

Luckily, a recent report from researcher Ajeya Cotra

takes us part of the way there

instead of trying to extrapolate progress in AI directly,

her report tries to estimate when something like AGI might be developed

based on trends in how affordable it is to build increasingly large

AI models trained using increasingly difficult tasks.

We can ground estimates of the needed

size of the models in what she calls biological anchors, estimates from biology

that inform how much computation and effort more generally

will be required to develop software that can do what human brains do.

The result very roughly coincides with the median responses

from the expert survey predicting that humans will likely develop

AI that can cheaply automate nearly all human labor by the end of the century.

In fact, more likely than not by 2060.

To understand how her model comes to this conclusion,

we need to first get a sense of how current progress in A.I.

is made.

Ajeya Cotra's report on AI timelines

Since 2012,

the field of AI has been revolutionized by developments in deep learning.

Deep learning involves training large artificial neural networks

to learn tasks using an enormous amount of data

without the aid of hand-crafted rules and heuristics.

And it's been successful at cracking a set of traditionally hard problems

in the field, such as those involving vision and natural language.

One way to visualize deep learning is to imagine a digital brain

called an artificial neural network

that tries a massive amount of trial and error at a given task.

For example, predicting the next character in a sequence of text.

The neural network starts out randomly initialized and so will generate gibberish at first.

During training, however, the neural network will be given a set of problems

and will be asked to provide a solution to each of those problems.

If the neural network solutions are incorrect,

its inner workings are slightly rewired

with the intention of it providing a better answer next time.

Over time, the neural network should become better at the assigned task.

If it does, then we say that it's learning.

In her report, Ajeya Cotra discusses a deep connection

between progress in hardware and progress in AI.

The idea is simple with greater access to computation,

A.I. researchers can train larger and more general deep learning models.

In fact, the rise of deep learning in the last decade

has largely been attributed to the falling cost of computation,

especially given progress in graphics processing units, which are used heavily

in A.I. research.

In a nutshell, Cotra tries to predict when it will become affordable

for companies or governments to train extremely large, deep learning models,

with the effect of automating labor

across the wide variety of tasks that humans are capable of learning.

In building this forecast, she makes some assumptions

about our ability to scale deep learning models

to reach human level performance

and the continued growth in computing hardware over the coming decades.

Over time,

researchers have found increasingly effective, deep learning models

that are capable of learning

to perform a wider variety of tasks and more efficiently than before.

It seems plausible that this process will continue until deep learning models

are capable of automating any aspect of human cognition.

Even if future AI is not created via deep learning.

Cotra's model may still be useful because of how it puts

a soft upper bound on when humans will create AGI.

She readily admits that if some more clever

and more efficient paradigm for designing AI than deep learning is discovered,

then the dates predicted by this model may end up being too conservative.

Cotra's model

extrapolates the rate at which progress in computing hardware will continue

and the economy will continue to steadily get larger, making it more feasible

for people to train extremely large, deep learning models on very large datasets.

By their nature, deep learning algorithms are extremely data

and computation hungry.

It can often take a lot of money and many gigabytes of training data

for deep learning algorithms to learn tasks that most humans

find relatively easy, like manipulating a Rubik's cube

or spotting simple, logical errors in natural language.

This is partly why it has only recently become practically

possible to train neural networks to perform these tasks.

Historically, progress in computing power was extraordinarily rapid,

according to data from William Nordhaus.

Between 1950 and 2010, the price of computation dropped by over 100 billion.

Put another way, that means that if you needed to perform a calculation in 1950

that would cost the same amount

as the Apollo program, that would later send humans to the moon,

then the equivalent calculation would cost less than $10

using computers in 2010, when adjusted for inflation over the same time period.

Since 2010,

the rate of progress in computing hardware has slowed down

from this spectacular pace, but still continues to improve relatively steadily.

By projecting outwards current trends, we can forecast when various computing

milestones will be reached.

For example, we can predict when it will become affordable to train

a deep learning model with 1 billion petaflops or 1 trillion petaflops.

Of course, simply knowing how much computation will be available in the future,

can't tell us how powerful deep learning models will be.

For that, we need to understand the relationship

between the amount of computation used to train a deep learning model

and its performance.

Here's where Ajeya Cotra's model becomes a little tricky.

Roughly speaking, there are two factors that determine how much computation

it takes to train a deep learning model on some task

holding other factors fixed.

The size of the deep learning model

and how much trial and error the model needs to learn the task.

Let's consider the first factor the size of the deep learning model.

Larger models are sort of like larger brains.

They're able to learn more stuff, execute more complex instructions,

and pick up on more nuanced patterns in the world.

In the animal kingdom, we often, but not always,

consider animals with larger brains, such as dogs, to be more generally

intelligent than animals with smaller brains, such as fish.

For many complex tasks, like writing code,

you need a fairly large minimum model size

Perhaps almost the size of a mouse's brain

to learn the task to any reasonable degree of performance.

And it will still be significantly worse than human programmers.

The task we're most interested in is the task of automating human labor,

at least the key components of human labor most important for advancing science

and technology.

It seems plausible that we'd need to use substantially larger models

than any we've trained so far, if we want to train on this task

Cotra roughly anchors

"the size of model that would be capable of learning the task of automating R&D"

with the size of the human brain.

Give or take a few orders of magnitude.

In fact, this approach of using biological anchors to guide our A.I.

forecasts has precedent.

In 1997, the computer scientist Hans Moravec,

tried to predict when computer hardware

would become available that would rival the human brain.

Later work by Ray Kurzweil mirrored Moravec's approach.

After looking into these older estimates,

Cotra felt that the loose brain anchor made sense

and seemed broadly consistent with machine learning performance so far.

She ultimately made the guess that current algorithms are about 1/10 as efficient

as the human brain, with very wide uncertainty around this estimate.

Now we come to the second factor.

How much trial and error would a brain sized model need to learn

tasks required to automate science and technology R&D?

This is the hardest and most uncertain open question in this entire analysis.

How much trial and error is needed to train this model is a question

of how efficient deep learning models will be at learning tasks of this form.

And we only have limited and very indirect evidence about that question.

The possibilities span a wide range.

For example, perhaps deep learning models will be as efficient as human children,

taking only the equivalent of 20 years of experience,

sped up greatly within a computer, to learn how to be a scientist or engineer.

Cotra thinks this is unlikely, but not impossible.

On the other extreme, perhaps deep learning models

will be as inefficient as evolution, taking the equivalent of hundreds

of trillions of lifetimes of experience to evolve into a scientist or engineer.

Again, Cotra thinks this is unlikely but possible.

Cotra thinks the answer is likely to lie somewhere in between.

That training deep learning models to automate science and engineering

will require much more computation than raising a human child to be a scientist,

but much less computation than simulating natural selection

until it involves scientists.

In her report,

Cotra considers several anchors that lie in between these two extremes

and proceeds by placing a subjective probability distribution over all of them.

She arrived at the prediction that there's a roughly 50% chance that it will become

economically feasible to train the relevant type of advanced A.I.

by 2052.

The uncertainty in her calculation was quite large, however,

reflecting the uncertainty in her assumptions.

Here's a graph showing how the resulting probability distribution changes

under different assumptions.

What does this all ultimately mean?

Now you might be thinking, let's say these predictions are right.

AGI arrives later this century, and as a result,

there's a productivity explosion changing human life as we know it.

What does it ultimately mean for us?

Should we be excited?

Should we be scared?

At the very least, our expectations should be calibrated

to the magnitude of the event.

If your main concern from AGI is that you might lose your job to a robot,

then your vision of what the future will be like might be too parochial.

If AGI arrives later this century, it could mark a fundamental transformation

in our species,

not merely a societal shift, a new fad, or the invention of a few new gadgets.

As science fiction author Vernor Vinge put it in a famous 1993 essay,

this change will be a throwing away of all the human rules.

Perhaps in the blink of an eye,

an exponential runaway beyond any hope of control.

Developments that were thought

might only happen in a million years, if ever,

will likely happen in the next century.

One way of thinking about this event

is to imagine that humanity is on a cliff's edge...

Ready to slide off into the abyss.

In the abyss.

We have only the vaguest sense as to what lies beyond.

But we can give our best guesses.

A good guess is that humans, or our descendants,

will colonize the galaxy and beyond.

That means, roughly speaking, the history of life looks a lot like this

with our present time

tightly packed between the time humans first came into existence

and the time of the first galaxy wide civilization.

Our future could be extraordinarily vast, filled with technological wonders.

A giant number of people and strange beings

and forms of social and political organization we can scarcely imagine.

The future could be extremely bright...

or it could be filled with horrors.

One harrowing possibility

is that the development of AGI will be much like the development

of human life on earth,

which wasn't necessarily so great for the other animals.

The worry here is one of value misalignment.

It is not guaranteed that even though humans will be the ones to develop A.I.

That A.I. will be aligned with human values.

In fact, my channel is about how advanced A.I.

could turn out to be misaligned with human values,

which could surely lead to bad outcomes.

This prospect raises a variety of technical challenges,

prompting the need for more research on how to make sure future A.I.

is compatible with human values.

In addition to understanding what's to come,

we also have a responsibility to make sure things go right.

When approaching what may be the most important event in human history.

Perhaps the only appropriate emotion is vigilance.

If we are on the edge of a radical transformation of our civilization.

The actions we take today could have profound effects on the long-term future,

reverberating through billions of years of future history.

Even if we're not literally among the most important people who ever live,

our actions may still reach far farther than we might have otherwise expected.

The astronomer, Carl Sagan, once wrote that Earth is a pale blue dot

suspended in the void of space

whose inhabitants exaggerate their importance.

He asked us to:

"think of the rivers of blood spilled by all those generals and emperors,

so that, in glory and triumph,

they could become the momentary masters of a fraction of a dot."

Carl Sagan's intention was to get us to understand

the gravity of our decisions by virtue of our cosmic insignificance.

However, in the light of the fact that the 21st century could be

the most important century in human history,

we can turn this sentiment on its head.

If indeed humans will, sometime in he foreseeable future, reach the stars.

Our choices now are cosmically significant,

despite currently only being tiny inhabitants of a pale blue dot.

Right now our civilization is tiny, but we could be the seed

to something that will soon become almost incomprehensibly big

and measured on astronomical scales.

The vast majority of planets and stars could be dead,

but we might fill them with value.

Much of what we might have previously thought impossible this century

may soon become possible.

Truly, mind-bending, technological and economic change

may all play a role in our lifetimes.

Rather than pondering our unimportance.

Perhaps it's wiser to try to understand our true place in history.

And quickly.

There might be only so much time left

before we take the deep plunge into the abyss.