Tag Archives: Artificial intelligence

#441130 The Brief History of Artificial ...

To see what the future might look like it is often helpful to study our history. This is what I will do in this article. I retrace the brief history of computers and artificial intelligence to see what we can expect for the future.

How Did We Get Here?
How rapidly the world has changed becomes clear by how even quite recent computer technology feels ancient to us today. Mobile phones in the ‘90s were big bricks with tiny green displays. Two decades before that the main storage for computers was punch cards.

In a short period computers evolved so quickly and became such an integral part of our daily lives that it is easy to forget how recent this technology is. The first digital computers were only invented about eight decades ago, as the timeline shows.

Since the early days of this history, some computer scientists have strived to make machines as intelligent as humans. The next timeline shows some of the notable artificial intelligence systems and describes what they were capable of.

The first system I mention is the Theseus. It was built by Claude Shannon in 1950 and was a remote-controlled mouse that was able to find its way out of a labyrinth and could remember its course.1 In seven decades the abilities of artificial intelligence have come a long way.

Language and Image Recognition Capabilities of AI Systems Are Now Comparable to Those of Humans
The language and image recognition capabilities of AI systems have developed very rapidly.

The chart shows how we got here by zooming into the last two decades of AI development. The plotted data stems from a number of tests in which human and AI performance were evaluated in five different domains, from handwriting recognition to language understanding.

Within each of the five domains the initial performance of the AI system is set to -100, and human performance in these tests is used as a baseline that is set to zero. This means that when the model’s performance crosses the zero line is when the AI system scored more points in the relevant test than the humans who did in the same test.2

Just 10 years ago, no machine could reliably provide language or image recognition at a human level. But, as the chart shows, AI systems have become steadily more capable and are now beating humans in tests in all these domains.

Outside of these standardized tests the performance of these AIs is mixed. In some real-world cases these systems are still performing much worse than humans. On the other hand, some implementations of such AI systems are already so cheap that they are available on the phone in your pocket: image recognition categorizes your photos and speech recognition transcribes what you dictate.

From Image Recognition to Image Generation
The previous chart showed the rapid advances in the perceptive abilities of artificial intelligence. AI systems have also become much more capable of generating images.

This series of nine images shows the development over the last nine years. None of the people in these images exist; all of them were generated by an AI system.

The series begins with an image from 2014 in the top left, a primitive image of a pixelated face in black and white. As the first image in the second row shows, just three years later AI systems were already able to generate images that were hard to differentiate from a photograph.

In recent years, the capability of AI systems has become much more impressive still. While the early systems focused on generating images of faces, these newer models broadened their capabilities to text-to-image generation based on almost any prompt. The image in the bottom right shows that even the most challenging prompts—such as “A Pomeranian is sitting on the King’s throne wearing a crown. Two tiger soldiers are standing next to the throne”—are turned into photorealistic images within seconds.4

Language Recognition and Production Is Developing Fast
Just as striking as the advances of image-generating AIs is the rapid development of systems that parse and respond to human language.

Shown in the image are examples from an AI system developed by Google called PaLM. In these six examples, the system was asked to explain six different jokes. I find the explanation in the bottom right particularly remarkable: the AI explains an anti-joke that is specifically meant to confuse the listener.

AIs that produce language have entered our world in many ways over the last few years. Emails get auto-completed, massive amounts of online texts get translated, videos get automatically transcribed, school children use language models to do their homework, reports get auto-generated, and media outlets publish AI-generated journalism.

AI systems are not yet able to produce long, coherent texts. In the future, we will see whether the recent developments will slow down—or even end—or whether we will one day read a bestselling novel written by an AI.

Where We Are Now: AI Is Here
These rapid advances in AI capabilities have made it possible to use machines in a wide range of new domains:

When you book a flight, it is often an artificial intelligence, and no longer a human, that decides what you pay. When you get to the airport, it is an AI system that monitors what you do at the airport. And once you are on the plane, an AI system assists the pilot in flying you to your destination.

AI systems also increasingly determine whether you get a loan, are eligible for welfare, or get hired for a particular job. Increasingly they help determine who gets released from jail.

Several governments are purchasing autonomous weapons systems for warfare, and some are using AI systems for surveillance and oppression.

AI systems help to program the software you use and translate the texts you read. Virtual assistants, operated by speech recognition, have entered many households over the last decade. Now self-driving cars are becoming a reality.

In the last few years, AI systems helped to make progress on some of the hardest problems in science.

Large AIs called recommender systems determine what you see on social media, which products are shown to you in online shops, and what gets recommended to you on YouTube. Increasingly they are not just recommending the media we consume, but based on their capacity to generate images and texts, they are also creating the media we consume.

Artificial intelligence is no longer a technology of the future; AI is here, and much of what is reality now would have looked like sci-fi just recently. It is a technology that already impacts all of us, and the list above includes just a few of its many applications.

The wide range of listed applications makes clear that this is a very general technology that can be used by people for some extremely good goals—and some extraordinarily bad ones, too. For such ‘dual use technologies’, it is important that all of us develop an understanding of what is happening and how we want the technology to be used.

Just two decades ago the world was very different. What might AI technology be capable of in the future?

What Is Next?
The AI systems that we just considered are the result of decades of steady advances in AI technology.

The big chart below brings this history over the last eight decades into perspective. It is based on the dataset produced by Jaime Sevilla and colleagues.7

Each small circle in this chart represents one AI system. The circle’s position on the horizontal axis indicates when the AI system was built, and its position on the vertical axis shows the amount of computation that was used to train the particular AI system.

Training computation is measured in floating point operations, or FLOP for short. One FLOP is equivalent to one addition, subtraction, multiplication, or division of two decimal numbers.

All AI systems that rely on machine learning need to be trained, and in these systems training computation is one of the three fundamental factors that are driving the capabilities of the system. The other two factors are the algorithms and the input data used for the training. The visualization shows that as training computation has increased, AI systems have become more and more powerful.

The timeline goes back to the 1940s, the very beginning of electronic computers. The first shown AI system is ‘Theseus’, Claude Shannon’s robotic mouse from 1950 that I mentioned at the beginning. Towards the other end of the timeline you find AI systems like DALL-E and PaLM, whose abilities to produce photorealistic images and interpret and generate language we have just seen. They are among the AI systems that used the largest amount of training computation to date.

The training computation is plotted on a logarithmic scale, so that from each grid-line to the next it shows a 100-fold increase. This long-run perspective shows a continuous increase. For the first six decades, training computation increased in line with Moore’s Law, doubling roughly every 20 months. Since about 2010 this exponential growth has sped up further, to a doubling time of just about 6 months. That is an astonishingly fast rate of growth.8

The fast doubling times have accrued to large increases. PaLM’s training computation was 2.5 billion petaFLOP, more than 5 million times larger than that of AlexNet, the AI with the largest training computation just 10 years earlier.9

Scale-up was already exponential and has sped up substantially over the past decade. What can we learn from this historical development for the future of AI?

Studying the Long-Run Trends to Predict the Future of AI
AI researchers study these long-term trends to see what is possible in the future.11

Perhaps the most widely discussed study of this kind was published by AI researcher Ajeya Cotra. She studied the increase in training computation to ask at what point in time the computation to train an AI system could match that of the human brain. The idea is that at this point the AI system would match the capabilities of a human brain. In her latest update, Cotra estimated a 50% probability that such “transformative AI” will be developed by the year 2040, less than two decades from now.12

In a related article, I discuss what transformative AI would mean for the world. In short, the idea is that such an AI system would be powerful enough to bring the world into a ‘qualitatively different future’. It could lead to a change at the scale of the two earlier major transformations in human history, the agricultural and industrial revolutions. It would certainly represent the most important global change in our lifetimes.

Cotra’s work is particularly relevant in this context as she based her forecast on the kind of historical long-run trend of training computation that we just studied. But it is worth noting that other forecasters who rely on different considerations arrive at broadly similar conclusions. As I show in my article on AI timelines, many AI experts believe that there is a real chance that human-level artificial intelligence will be developed within the next decades, and some believe that it will exist much sooner.

Building a Public Resource to Enable the Necessary Public Conversation
Computers and artificial intelligence have changed our world immensely, but we are still at the early stages of this history. Because this technology feels so familiar, it is easy to forget that all of these technologies that we interact with are very recent innovations, and that most profound changes are yet to come.

Artificial intelligence has already changed what we see, what we know, and what we do. And this is despite the fact that this technology has had only a brief history.

There are no signs that these trends are hitting any limits anytime soon. To the contrary, particularly over the course of the last decade, the fundamental trends have accelerated: investments in AI technology have rapidly increased, and the doubling time of training computation has shortened to just six months.

All major technological innovations lead to a range of positive and negative consequences. This is already true of artificial intelligence. As this technology becomes more and more powerful, we should expect its impact to become greater still.

Because of the importance of AI, we should all be able to form an opinion on where this technology is heading and to understand how this development is changing our world. For this purpose, we are building a repository of AI-related metrics, which you can find on OurWorldinData.org/artificial-intelligence.

We are still in the early stages of this history and much of what will become possible is yet to come. A technological development as powerful as this should be at the center of our attention. Little might be as important for how the future of our world—and the future of our lives—will play out.

Acknowledgements: I would like to thank my colleagues Natasha Ahuja, Daniel Bachler, Julia Broden, Charlie Giattino, Bastian Herre, Edouard Mathieu, and Ike Saunders for their helpful comments to drafts of this essay and their contributions in preparing the visualizations.

This article was originally published on Our World in Data and has been republished here under a Creative Commons license. Read the original article.

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#441118 AI Timelines: What Do Experts in ...

Artificial intelligence that surpasses our own intelligence sounds like the stuff from science fiction books or films. What do experts in the field of AI research think about such scenarios? Do they dismiss these ideas as fantasy, or are they taking such prospects seriously?

A human-level AI would be a machine, or a network of machines, capable of carrying out the same range of tasks that we humans are capable of. It would be a machine that is “able to learn to do anything that a human can do,” as Norvig and Russell put it in their textbook on AI.1

It would be able to choose actions that allow the machine to achieve its goals and then carry out those actions. It would be able to do the work of a translator, a doctor, an illustrator, a teacher, a therapist, a driver, or the work of an investor.

In recent years, several research teams contacted AI experts and asked them about their expectations for the future of machine intelligence. Such expert surveys are one of the pieces of information that we can rely on to form an idea of what the future of AI might look like.

The chart shows the answers of 352 experts. This is from the most recent study by Katja Grace and her colleagues, conducted in the summer of 2022.2

Experts were asked when they believe there is a 50% chance that human-level AI exists.3 Human-level AI was defined as unaided machines being able to accomplish every task better and more cheaply than human workers. More information about the study can be found in the fold-out box at the end of the text on this page.4

Each vertical line in this chart represents the answer of one expert. The fact that there are such large differences in answers makes it clear that experts do not agree on how long it will take until such a system might be developed. A few believe that this level of technology will never be developed. Some think that it’s possible, but it will take a long time. And many believe that it will be developed within the next few decades.

As highlighted in the annotations, half of the experts gave a date before 2061, and 90% gave a date within the next 100 years.

Other surveys of AI experts come to similar conclusions. In the following visualization, I have added the timelines from two earlier surveys conducted in 2018 and 2019. It is helpful to look at different surveys, as they differ in how they asked the question and how they defined human-level AI. You can find more details about these studies at the end of this text.

In all three surveys, we see a large disagreement between experts and they also express large uncertainties about their own individual forecasts.5

What Should We Make of the Timelines of AI Experts?
Expert surveys are one piece of information to consider when we think about the future of AI, but we should not overstate the results of these surveys. Experts in a particular technology are not necessarily experts in making predictions about the future of that technology.

Experts in many fields do not have a good track record in making forecasts about their own field, as researchers including Barbara Mellers, Phil Tetlock, and others have shown.6 The history of flight includes a striking example of such failure. Wilbur Wright is quoted as saying, “I confess that in 1901, I said to my brother Orville that man would not fly for 50 years.” Two years later, ‘man’ was not only flying, but it was these very men who achieved the feat.7

Additionally these studies often find large ‘framing effects’, two logically identical questions get answered in very different ways depending on how exactly the questions are worded.8

What I do take away from these surveys however, is that the majority of AI experts take the prospect of very powerful AI technology seriously. It is not the case that AI researchers dismiss extremely powerful AI as mere fantasy.

The huge majority thinks that in the coming decades there is an even chance that we will see AI technology which will have a transformative impact on our world. While some have long timelines, many think it is possible that we have very little time before these technologies arrive. Across the three surveys more than half think that there is a 50% chance that a human-level AI would be developed before some point in the 2060s, a time well within the lifetime of today’s young people.

The Forecast of the Metaculus Community
In the big visualization on AI timelines below, I have included the forecast by the Metaculus forecaster community.

The forecasters on the online platform Metaculus.com are not experts in AI but people who dedicate their energy to making good forecasts. Research on forecasting has documented that groups of people can assign surprisingly accurate probabilities to future events when given the right incentives and good feedback.9 To receive this feedback, the online community at Metaculus tracks how well they perform in their forecasts.

What does this group of forecasters expect for the future of AI?

At the time of writing, in November 2022, the forecasters believe that there is a 50/50-chance for an ‘Artificial General Intelligence’ to be ‘devised, tested, and publicly announced’ by the year 2040, less than 20 years from now.

On their page about this specific question, you can find the precise definition of the AI system in question, how the timeline of their forecasts has changed, and the arguments of individual forecasters for how they arrived at their predictions.10

The timelines of the Metaculus community have become much shorter recently. The expected timelines have shortened by about a decade in the spring of 2022, when several impressive AI breakthroughs happened faster than many had anticipated.11

The Forecast by Ajeya Cotra
The last shown forecast stems from the research by Ajeya Cotra, who works for the nonprofit Open Philanthropy.12 In 2020 she published a detailed and influential study asking when the world will see transformative AI. Her timeline is not based on surveys, but on the study of long-term trends in the computation used to train AI systems. I present and discuss the long-run trends in training computation in this companion article.

Cotra estimated that there is a 50% chance that a transformative AI system will become possible and affordable by the year 2050. This is her central estimate in her “median scenario.” Cotra emphasizes that there are substantial uncertainties around this median scenario, and also explored two other, more extreme, scenarios. The timelines for these two scenarios—her “most aggressive plausible” scenario and her “most conservative plausible” scenario—are also shown in the visualization. The span from 2040 to 2090 in Cotra’s “plausible” forecasts highlights that she believes that the uncertainty is large.

The visualization also shows that Cotra updated her forecast two years after its initial publication. In 2022 Cotra published an update in which she shortened her median timeline by a full ten years.13

It is important to note that the definitions of the AI systems in question differ very much across these various studies. For example, the system that Cotra speaks about would have a much more transformative impact on the world than the system that the Metaculus forecasters focus on. More details can be found in the appendix and within the respective studies.

What Can We Learn From the Forecasts?
The visualization shows the forecasts of 1128 people—812 individual AI experts, the aggregated estimates of 315 forecasters from the Metaculus platform, and the findings of the detailed study by Ajeya Cotra.

There are two big takeaways from these forecasts on AI timelines:

There is no consensus, and the uncertainty is high. There is huge disagreement between experts about when human-level AI will be developed. Some believe that it is decades away, while others think it is probable that such systems will be developed within the next few years or months. There is not just disagreement between experts; individual experts also emphasize the large uncertainty around their own individual estimate. As always when the uncertainty is high, it is important to stress that it cuts both ways. It might be very long until we see human-level AI, but it also means that we might have little time to prepare.
At the same time, there is large agreement in the overall picture. The timelines of many experts are shorter than a century, and many have timelines that are substantially shorter than that. The majority of those who study this question believe that there is a 50% chance that transformative AI systems will be developed within the next 50 years. In this case it would plausibly be the biggest transformation in the lifetime of our children, or even in our own lifetime.

The public discourse and the decision-making at major institutions have not caught up with these prospects. In discussions on the future of our world—from the future of our climate, to the future of our economies, to the future of our political institutions—the prospect of transformative AI is rarely central to the conversation. Often it is not mentioned at all, not even in a footnote.

We seem to be in a situation where most people hardly think about the future of artificial intelligence, while the few who dedicate their attention to it find it plausible that one of the biggest transformations in humanity’s history is likely to happen within our lifetimes.

Acknowledgements: I would like to thank my colleagues Natasha Ahuja, Daniel Bachler, Bastian Herre, Edouard Mathieu, Esteban Ortiz-Ospina and Hannah Ritchie for their helpful comments to drafts of this essay.

And I would like to thank my colleague Charlie Giattino who calculated the timelines for individual experts based on the data from the three survey studies and supported the work on this essay. Charlie is also one of the authors of the cited study by Zhang et al. on timelines of AI experts.

This article was originally published on Our World in Data and has been republished here under a Creative Commons license. Read the original article.

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#441102 Could artificial intelligence help us ...

A robot may not injure a human being or, through inaction, allow a human being to come to harm. A robot must obey the orders given it by human beings except where such orders would conflict with the First Law. A robot must protect its own existence as long as such protection does not conflict with the First or Second Law. Continue reading

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#441091 The smallest robotic arm you can imagine ...

Researchers used deep reinforcement learning to steer atoms into a lattice shape, with a view to building new materials or nanodevices. Continue reading

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#441014 Shoring up drones with artificial ...

Australian surf lifesavers are increasingly using drones to spot sharks at the beach before they get too close to swimmers. But just how reliable are they? Continue reading

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