Risks and Impact of AI

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Updated November 2, 2022

You’re reading an excerpt of Making Things Think: How AI and Deep Learning Power the Products We Use, by Giuliano Giacaglia. Purchase the book to support the author and the ad-free Holloway reading experience. You get instant digital access, plus future updates.

Surveillance

If you want to keep a secret, you must also hide it from yourself.George Orwell, 1984*

On a Saturday evening, Ehmet woke up as on any other day and decided to go to the grocery store near his home. But on the way to the store, he was stopped by a police patrol. Through an app that uses face recognition, the police force identified him as one of the few thousand Uyghur that lived in the region. Ehmet was sent to one of the “re-education camps” with more than a million other Uyghur Muslims.*

Even though this seems like a dystopian future, where people are identified by an all-present surveillant state, it is already happening under the Chinese Communist Party. George Orwell’s novel 1984 couldn’t be closer to reality. This scenario is unlikely to happen in other countries, but in this chapter, I go over some companies that are using the power of AI to surveil citizens elsewhere.

One of these companies turning the dystopian version of the future into reality is Clearview AI. Police departments across the United States have been using Clearview AI’s facial recognition tool to identify citizens. In fact, the main immigration enforcement agency in the US, the Department of Justice, and retailers including Best Buy and Macy’s are among the thousands of government entities and companies around the world that have used Clearview AI’s database of billions of photos to identify citizens.*

The company has users at the FBI, Customs and Border Protection (CBP), Interpol, and the New York Police Department.

Clearview’s system works by crawling through the open web for photos of people, creating a database based on those images and combining different photos based on people’s facial features.* It searches for pictures on websites like Facebook, Twitter, LinkedIn, MySpace, and even Tumblr. And it creates an offline database that is updated frequently, joining together all the photos pertaining to a single person.*

Someone at a police department who wants to search for a specific person can use the Clearview AI iPhone app to upload their picture, and the app can return the person’s full name as well as other pictures associated with them.

This tool is not only being used by governmental agencies to identify citizens; it has also been used by private companies to surveil people. Buzzfeed has uncovered through Clearview’s logs that about 2,900 institutions have used the company’s service to search for citizens around the world.*

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In the US and other countries, some law enforcement agencies are even unaware that their officers and employees are using Clearview’s services. It is worrisome that this tool is being used without any oversight.

Audio

ShotSpotter is another tool using machine learning to aid police departments around the world. It has networks of microphones deployed in 110 different communities in the US, including New York City.*

The microphones record and share live audio to a central server. An algorithm then analyzes sounds to identify possible shootings in nearby areas. Police departments using this tool are then notified of the possible shooting with the location of the incident. They can either verify that the sound is actually from a shooting or relabel the sound as something else.*

The technology is not only being used by police departments to figure out if there is a possible shooting, but also by prosecutors as evidence of crimes, even though ShotSpotter hasn’t been fully tested for accuracy.

That is really troublesome as the tool has not been proven to be fully accurate and could falsely label other sounds as shots. The Associated Press has found that ShotSpotter evidence has been used in 200 court cases nationwide. Could this potentially lead to innocent people ending up in jail?

In one such case, court records show that ShotSpotter initially labeled a sound as fireworks. It was then relabeled by a human as a gunshot and used as evidence in a case. Either the human or the machine were wrong. Either case is not great.

Recommendation Algorithms

We’ve all been there. You start watching a video on YouTube. Before you realize it, it’s 1 a.m., and you are watching videos about Greek philosophers and their influence in the modern world.

This is known as the “YouTube rabbit hole”—the process of watching YouTube videos nonstop. Most of these videos are presented by YouTube’s recommendation algorithm, which determines what to suggest you watch based on your and other users’ watch histories.

TikTok, Netflix, Twitter, Facebook, Instagram, Snapchat, and all services that present content have an underlying algorithm that distributes and determines the material presented to users. This is what drives YouTube’s rabbit hole.

For TikTok, an investigation done by the Wall Street Journal found that the app only needs one important piece of information to figure out what a user wants: the total amount of time a user lingers on a piece of content.* Through that powerful signal, TikTok can learn people’s interests and drive users into rabbit holes of content. YouTube’s and TikTok’s algorithms are all engagement-based, but according to Guillaume Chaslot, TikTok’s algorithms learn much faster.*

These services drive engagement by recommending content that users are likely to watch, but Netflix went a step further and personalizes thumbnail images of its shows to increase the click-through rate and total watch time. Netflix figured out that the thumbnail image that attracts a user to click depends on the type of movies that person likes to see. For example, if a user watches a lot of romance movies, the thumbnail should show an image of a romantic scene.

Let’s dive into one of these recommendation systems. We’ll look at YouTube’s system as that has been discussed publicly. Others’ systems work similarly.

System Overview

The YouTube recommendation system works in two different stages. The first is for candidate generation, which selects videos that are possible options to be presented to users. The second stage is for ranking, which determines which videos are at the top and which are at the bottom of users’ feeds.*

Candidate generation takes users’ YouTube history as input. The ranking network operates a little differently. It assigns a score to each video using a rich set of features describing the video and the user. Let’s go over both stages.

Stage 1: Candidate Generation

The first stage’s model is inspired by the architecture of a continuous bag of words language model.* The continuous bag of words is a way of representing sentences as data points. It tries to predict the current target word (the center word) based on the source context words (surrounding words). That means that it just uses a small context around the target word to represent it.

The model will generate a representation of the video called an embedding. Then, the neural network is fed embeddings which have been learned from each video and are organized in a fixed vocabulary.

Data about each user’s viewing history is transformed into varying arrays of video IDs and mapped into a dense vector representation. With that, YouTube’s algorithm uses training data of past videos and their watch time to train their neural network to figure out the expected viewing time for other videos.

Models are typically biased from making predictions based on past data. But recent relevant content is vital to YouTube as a platform, as it helps keep users engaged and up to date. To correct for this, YouTube sets the age of the training data as a feature and optimizes it so that more recent videos are more likely to show up as candidates and at the top of the list.

Stage 2: Ranking

The second part of the recommendation system involves ranking videos. In order to recommend quality content, YouTube needs a way to determine which content users are watching and enjoying.

The authors observed that previous interactions with a particular video or ones similar to it were very important when predicting recommendations. This is intuitive because if a viewer enjoys particular types of content, they are likely to view many videos in that niche. They also noticed that videos coming from particular channels were also very important in deciding what to recommend next. So they used these features and others for the neural network to predict the ranking of a video.

Videos that retain the viewer’s attention are usually regarded as higher quality. In order to recommend quality videos, the model is trained so that it can predict how long a viewer will watch a video. This aspect also plays into how the algorithm ranks the videos.

With all of that, the team trained a neural network that takes inputs like the video ID, the watched video IDs, the video language, user language, time since last watch, number of previous impressions, and other features to predict the expected watch time. The click-through rate and the total amount spent per user increased based on YouTube’s recommendations.

Summary

YouTube’s algorithm is based on neural networks that aim to maximize engagement. That might be a good proxy for whether the user is enjoying watching those videos as the user is spending more time watching them. But there is not as much understanding of exactly what these neural networks are optimizing.

There is a risk that because these algorithms serve such a large percentage of the views, they can be controlled by a small group of people. For example, most of the social media platforms in China do not allow Chinese citizens to post images of Winnie the Pooh because it looks like the Chinese dictator, Xi Jinping.*

In the next section, I go over how researchers are trying to understand what these neural networks are doing under the hood.

Interpretability of Neural Networks

”By the help of microscopes, there is nothing so small, as to escape our inquiry; hence there is a new visible world discovered to the understanding.”Robert Hooke*

Mary spent the whole morning on her TikTok getting videos about how lamps work. Her TikTok feed is mostly that and cute videos of dogs. As with many who have interacted with TikTok or other social media apps, she never noticed that most of her social media feed is determined mostly by algorithms that tell her what to watch next.

This isn’t a problem when she is watching videos of dogs, but one day she was browsing around and started watching depressing videos, and the algorithm just reinforced that.

A neural network is behind the videos that she watches, recommending 70% of them.* And the algorithm is mostly a black box. That is, the humans that wrote the neural network don’t know its exact inner workings. Most of what they know is that using these algorithms increases engagement. But is that enough?

If a lot of our lives is determined by what neural networks decide, from housing prices to driving our cars, it might be worth understanding how and why these neural networks are making their decisions.

That’s where interpretability of neural networks comes in. Understanding how these “black boxes” work might be important for understanding why different decisions are made and whether they are correct.

Neural Network Microscope

Many scientific discoveries have been made when scientists were able to “zoom in.” For example, microscopes let scientists see cells, and X-ray crystallography lets them see DNA. In the same way, AI scientists led by a young researcher, Chris Olah, have been studying and “zooming in” on neural networks that are used for image classification.*

In order to study those neural networks, the team at OpenAI analyzed each neuron on different neural networks and their features, as well as the connections between different neurons. To observe what different neurons represent in each neural network, the team analyzed how the neurons fire and activate when different images are run through the neural network. What they found was really interesting.*

The team created the equivalent of a microscope but for “visual” neural networks—neural networks that are used to detect objects in images. With Microscope, researchers can systematically visualize every neuron in common neural networks including InceptionV1. In contrast to the typical picture of neural networks as a black box, the researchers were surprised by how approachable the network is on this scale.

The neurons became understandable. Some represent abstract concepts like edges or curves, and others, features like dog eyes or snouts. The team also was able to explain the connections between each neuron. The connections represent meaningful algorithms. For example, a connection may correspond to joining two different layers together, one representing dogs in one orientation and the other representing dogs in another orientation. These connections, or “circuits,” can even represent simple logic, such as AND, OR, or XOR, over high-level visual features.

The researchers at OpenAI laid out a foundation to show that these neurons are probably mapping to these features. They didn’t prove that it was the case, but by testing the activation of such neurons with many different examples, they showed a causal link between the firing of these neurons and the images that they are purportedly representing. They’ve also shown that the neurons do not fire with images that are close to but not the same as those that these neurons are identifying.

Figure: InceptionV1 neural network representations and the union of the bottom two neural networks.

The OpenAI team showed that neurons can be understood and are representing real features.

That was not the only surprise found by these researchers. They also have found that the same features were detected across different neural networks. For example, curve detectors were found in the following neural networks: AlexNet, InceptionV1, VGG19, and ResnetV2-50.

The scientists detected that when training the same dataset with different neural networks, the same neurons were present in those networks. With that, they came up with a hypothesis that there is a universality of features in different networks. That is, if there are different architectures of neural networks trained in the same dataset, there are neurons that are likely to be present in all the different architectures.

Not only that, but they found complex Gabor detectors, which are usually found in biological neurons. They are similar to some classic “complex cells” of neuroscience. Could it be that our brain also has the same neurons present in artificial neural networks?

Language Interpretability Tool

For now the Microscope has only been used to analyze neural networks that classify images, but it can be imagined that the same technique could be applied to other areas, including natural language processing.

Other tools have been developed for neural networks used in natural language processing. One recently developed by a group at Google is called the Language Interpretability Tool* and is used to understand NLP tasks. The open-source tool allows for rich visualizations of model predictions and includes aggregate analysis of metrics and slicing of the dataset.*

The tool uses a technique called UMAP (Uniform Manifold Approximation and Projection for Dimension Reduction). With UMAP, you can visualize the classification of certain datasets on a projection of the dataset into a smaller plane. In that way, you can identify unexpected results from data. That means that if a dataset contains many features or can be represented in a multi-dimensional space, UMAP will transform the data points and the dataset into a representation in a lower dimension. For example, you can reduce the dimension of the data points so that you can see the points in a 3D graph. It includes several other capabilities, but is not as developed as OpenAI Microscope.*

All these tools to understand and interpret neural networks are in their infancy. Microscope and the Language Interpretability Tool are just two examples of tools that are starting to be developed to understand the internals of neural networks.

Conclusion

It is clear that we are still in the early days of creating tools for interpreting and understanding neural networks in different applications. Neural networks might still be complex to understand, but there are ways to investigate what each neuron in a network might be doing independently.

As we take for granted the microscope as an important scientific instrument, the creation of a neural network microscope might be an important step to understand them and may even help fix possible bugs that neural networks create.

Economic Impact of AI

We wanted flying cars, instead we got 140 characters.Peter Thiel*

Jennifer woke up early on Monday morning. Before going to work, she received a personalized message distilling all information that she needed to know for the day. She walked out of her house and hailed an autonomous car that was waiting for her. As her car rode from her home to her office, Jennifer’s AI assistant briefed her about her day and helped her make some decisions. She arrived at her office in just under ten minutes, going through an underground tunnel.

That’s a future that seems far off, but it might be closer than we think. Deep learning might make most of these predictions reality. It is starting to change the economy and might have a significant economic impact. ARK Invest, an investment firm based in New York, predicts that in 20 years, deep learning will create a $17 trillion market opportunity.* That is bigger than the economic impact that the internet had.

Even though these predictions are far off, deep learning is already having an impact on the world. It is already revolutionizing some fields in artificial intelligence. In the past seven years, machine learning models for vision and language have been completely overtaken by deep learning models. These new models outperform any other “old” artificial intelligence techniques. And every few months, a bigger and newer model outperforms state-of-the-art results.*

In recent years, due to the rapid progress in natural language processing and understanding, the AI community has had to develop new and harder tests for AI capabilities. Models are getting better so fast that researchers have to come up with new benchmarks almost every year.*

We are starting to see deep learning slowly affect our lives. The technology is being added to most major software packages to help people be more productive. Gmail’s Smart Complete is one of them. It helps people write emails faster by auto-completing sentences. Google is adding similar features to other products. With Android 10, Smart Reply was embedded into the operating system.

Other companies are also looking to improve their software with deep learning. Recently, Microsoft featured the work that OpenAI is doing with its language models. It demonstrated that it could automate* some of the work that software engineers do.

These features seem to have a small impact right now, but their effect on our lives will accelerate, and they will have a bigger impact than most predict.

From self-driving car systems to music recommendation engines, traditional software is slowly being replaced by trained neural networks. That, in turn, increases productivity for software engineers.

Deep learning is not only increasing productivity for software engineers and white-collar workers; other markets are also being disrupted. Transportation will see an increasing influence of artificial intelligence. Currently, there are around 3.5M truckers working in the United States.* With self-driving cars and trucks, most of these jobs will be replaced by computers.

Jobs being replaced doesn’t mean that the economy will implode. With automation, productivity in some areas increases, which frees capital to other areas of the economy. Other sectors of the economy have been growing steadily. For example, consumer spending as a percentage of GDP in food services and recreation services has been growing since the 60s.*

Figure: Spending on leisure and hospitality as percentage of the total economy.*

In a 2017 interview, Marc Andreessen, a famous investor in Silicon Valley, explained that there are two kinds of sectors of the economy: the fast-change sectors and the slow-change sectors.

The fast-change sectors include retail, transportation, and media. They are sectors in which technology has had an enormous impact. There is a massive change in those sectors, and there are massive productivity improvements, which cause gigantic churn in jobs. And at the same time, prices have fallen rapidly.

The other sectors, the slow-change sectors, include healthcare, education, construction, elder care, childcare, and government. In those sectors, the opposite is happening: there is a price crisis. The prices for products and services in these areas are rising fast. The Financial Times* showed that 88% of all the price inflation since 1990 is attributed to healthcare, construction, and education.

Marc Andreessen also stated that the worries of unemployment and job displacement come from the lump of labor fallacy.*

The lump of labor fallacy is the recurrent panic that happens every twenty-five to fifty years over whether the job market pool is fixed, meaning that an influx of workers, such as younger people, immigrants, or machines, will take all the jobs, driving out other workers. This effect never actually happens.

A good example of this fallacy happened with cars. When the automobile went mainstream 100 years ago, the same panic happened that may occur in the future with self-driving cars. At the time, people worried that all jobs for people whose livelihood depended on taking care of horses—everybody running stables, all the blacksmiths—were going to disappear.

But in reality, more jobs were created with the creation of cars. Manufacturing jobs in auto plants became a large sector of the economy. Car companies became such a huge employer that the US government had to bail out these companies in 2008 to keep all their employees working.

Not only that, but there were jobs created to pave streets for cars. A lot of concepts were built from what the creation of cars allowed. The idea of restaurants, motels, hotels, conferences, movie theaters, apartment complexes, office complexes, and suburbs were all expanded after the creation of cars.

The number of jobs created by the second, third, and fourth order effects of the creation of cars was one hundred times the number that disappeared. Marc Andreessen argues that with the creation of new technology, the efficiency of that market goes up, liberating capital that can be invested in other areas.

Others that are more concerned about the lack of innovation than the economic effects of innovation. In a few presentations, Peter Thiel argued that he is far more worried about the lack of good technologies than the danger of evil in technology applications or their consequences.

Peter Thiel argues that there hasn’t been much innovation in past years. For example, he argues that the nuclear industry has been dead for decades, while other promises like cleantech just became toxic words for losing money badly.

If technology has had such an impact on society, then the price of goods would have gone down. But Peter argues that, for example, the price of commodities has not gone down as technology expanded.

In fact, there was a famous bet between two economists, Simon and Ehrlich,* in the 80s. Simon said that the price of commodities would go down in the next decade, while Ehrlich said that it would go up. Simon was right in the 80s, meaning that commodity prices went down in that decade.

But if you look at the next decades, from 1993 to 2003, and 2003 to 2013, commodity prices have gone up, which would show that technology has not had as significant an effect on the economy as some people have predicted.

Peter Thiel stated that most innovation has happened only in the world of bits, and not the world of atoms, and that computers alone can’t do everything. He argued that people are free to do things in the world of bits, and not free to do stuff in the world of things.

But we might start to see the effects in the world of atoms. Battery prices have been falling for years, following Wright’s Law.** Batteries cost around $1,000/kWh in 2010 and have since fallen to around $100/kWh. Solar panel prices have followed the same curve. The cost to decode the human genome has fallen faster than Moore’s Law.* The world of atoms might be at the tipping point of disruption.

Artificial General Intelligence

Detective Del Spooner: Human beings have dreams. Even dogs have dreams, but not you, you are just a machine. An imitation of life. Can a robot write a symphony? Can a robot turn a … canvas into a beautiful masterpiece?

Robot Sonny: Can you?

I, Robot (2004)

Using the past as an indicator of the future, this final chapter addresses how artificial intelligence systems might evolve into artificial general intelligence. It explains the difference between knowing that versus knowing how. And given that the brain is a good indicator of how AI systems evolve, we know that for the animal kingdom there is a high correlation of intelligence to the number of pallial and cortical neurons. The same has been true for deep learning. The higher the number of neurons, the more performant a multilayer neural network is. While artificial neural networks still have a few orders of magnitude less neurons than the human brain, we are marching toward that milestone. Finally, we’ll talk about the Singularity, a point where artificial intelligence might be hard to control.

The Past as an Indicator of the Future

Arthur C. Clarke has an interesting quote where he says, “Any advanced technology is indistinguishable from magic.”* If you were to go back to the 1800s, it would be unthinkable to imagine cars traveling at 100 mph on the highway or living with handheld devices for connecting with people on the other side of the planet.

Since the Dartmouth Conference and the creation of the artificial intelligence field, great strides have been made. The original dream many had of computers, which was to perform any intellectual task better than humans, is much closer than before. Though, some argue that this may never happen or is still in the very distant future.

The past, however, may be a good indication of the future. Software is better than the best humans at playing checkers, chess, Jeopardy!, Atari, Go, and Dota 2. It already performs text translation for a few languages better than the average human. Today, these systems improve the lives of millions of people in areas like transportation, e-commerce, music, media, and many others. Adaptive systems help people drive on highways and streets, preventing accidents.

At first, it may be hard to imagine computer systems performing what once were cerebral tasks like designing and engineering systems or writing a legal brief. But at one time, it was also hard to imagine systems triumphing over the best humans at chess. People claim that robots do not have imagination or will never accomplish tasks that only humans can perform. Others say that computers cannot explain why something happens and will never be able to.

Knowing That versus Knowing How

The problem is that for many tasks humans cannot explain why or how something happens, even though they might know how to do it. A child knows that a bicycle has two wheels, its tires have air, and you ride it by pushing the pedals forward in circles. But this information is different than knowing how to ride a bicycle. The first kind of knowledge is usually called “knowing that,” while the skill of riding the bike is “knowing how.”

These two kinds of knowledge are independent of each other, but they might help each other. Knowing that you need to push the pedals forward can help a person ride a bike. But “knowing how” cannot be reduced to “knowing that.” Knowing how to ride a bike does not imply that you understand how it works. In the same way, computers and humans perform different tasks that require them to know how to do it but not “know that.” Many rules apply to the pronunciation of certain words in English. People know how to pronounce the words, but they cannot explain why. A person who has access to a Chinese dictionary may actually understand Chinese with the help of that resource. Computers, in the same way, perform tasks and may not be able to explain the details. Asking why computers do what they do might be the same as asking why someone swings a bat the way they do when playing baseball.

It is hard to predict how everything will play out in the future and what will come next. But looking at the advances of the different subfields of artificial intelligence and their performance over time may be the best predictor of what might be possible in the future. Given that, let’s look at the advances in the different fields of AI and how they stack up. From natural language processing and speech recognition to computer vision, systems are improving linearly, with no signs of stopping.

Figure: AI advances on different benchmarks over time.* First Image: Top-5 accuracy asks whether the correct label is in at least the classifier’s top five predictions. It shows that the error rate has improved from around 85% in 2013 to almost 99% in 2020. Second Image: CityScapes Challenge. Cityscapes is a large-scale dataset of diverse urban street scenes across 50 different cities recorded during the daytime. This task requires an algorithm to predict the per-pixel semantic labeling of the image. Third Image: SuperGLUE Benchmark. SuperGLUE is a single-metric benchmark that evaluates the performance of a model on a series of language understanding tasks on established datasets. Fourth Image: Visual Question Answering Challenge: Accuracy. The VQA challenge, introduced in 2015, requires machines to provide an accurate natural language answer, given an image and a natural language question about the image based on a public dataset.

Data Growth in AI

Algorithms can only solve problems like self-driving cars and winning Go games if they have the correct data. For these algorithms to exist, it is essential to have properly labeled data. In research circles, significant efforts are underway to reduce the size of the datasets needed to create the appropriate algorithms, but even with this work, there is still a need for large datasets.

Figure: Dataset size comparison with the number of seconds that a human lives from birth to college graduation.

Datasets are already comparable in size to what humans capture during their lifetime. The figure above compares the size of the datasets used to train computers to the number of seconds from birth to college graduation of a human on a logarithmic scale. One of the datasets in the figure is Fei-Fei Li’s ImageNet described earlier in this book. The last dataset in the picture is used by Google to create their model for understanding street numbers on the façades of houses and buildings.

There is an entire field of research studies on how to combine machine learning models with how humans can fix and change labeled data. But it is clear that the amount of data that we can capture in our datasets is already equivalent to what humans do over their lifetime.

Computation Growth

But machine learning software does not depend solely on data. Another piece of the puzzle is computational power. One way of analyzing the computational power of neural networks deployed today versus what human brains use is to look at the size of the neural networks in these models. The figure below compares them on a logarithmic scale.

Figure: Comparison of the model size of neural networks and the number of neurons and connections of animals and humans.

Neural networks shown in this figure were used to detect and transcribe images for self-driving cars. The figure below compares the scale of both the number of neurons and the connections per neuron. Both are important factors for neural network performance. Artificial neural networks are still orders of magnitude away from the size of the human brain, but they are starting to become competitive to some mammals.*

Figure: 122 years of Moore’s Law: Calculations per second per constant dollar. This is an exponential/log scale, so a straight line is an exponential; each y-axis tick is 100x. This graph covers a 10,000,000,000,000,000,000x improvement in computation/$.

The price of computation has declined over time, and the incremental computation power available to society has increased. The amount of computing power one can get for every dollar spent has been increasing exponentially. In fact, in an earlier section, I showed that the amount of compute used in the largest AI training runs has been doubling every 3.5 months. Some argue that computing power cannot continue this trend due to physics constraints. Past trends, however, do not support this theory. Money and resources in the area have increased over time as well. More and more people work in the field, developing better algorithms and hardware. And, we know the power of the human brain has a limit that can be achieved because it satisfies the constraints of physics.

The Singularity

With more computing power and improved software, it may be that AI systems eventually surpass human intelligence. The point at which these systems become smarter and more capable than humans is called the Singularity. For every task, these systems will be better than humans. When computers outperform humans, some people argue that they can then continue to become better and better. In other words, if we make them as smart as us, there is no reason to believe that they cannot make themselves better, in a spiral of ever-improving machines, resulting in superintelligence.

Some predict that the Singularity will come as soon as 2045. Nick Bostrom and Vincent C. Müller conducted a survey of hundreds of AI experts at a series of conferences and asked by what year the Singularity (or human-level machine intelligence) will happen with a 10% chance, 50% chance, and 90% chance. The responses were the following:

  • Median optimistic year (10% likelihood): 2022

  • Median realistic year (50% likelihood): 2040

  • Median pessimistic year (90% likelihood): 2075*

So, that means that AI experts believe there is a good chance that machines will be as smart as humans in around 20 years.

This is a controversial topic, as there are experts, including John Carmack, who believe that we will start to have signs of AGI in a decade from now.* But others, such as Kevin Kelly, argue that believing that there will be an “Artificial General Intelligence” is a myth.* Either way, if the pessimistic timetable for achieving it is any indication, we will know by the end of the century whether it is starting to materialize.

The Singularity and Society?

If the Singularity is as near as many predict and it results in artificial general intelligence that surpasses human intelligence, the consequences are unthinkable to society as we now know it. Imagine that dogs created humans. Would dogs understand the result of creating such creatures in their lives? I doubt it. In the same way, humans are unlikely to understand this level of intelligence, even if we initially created it.

controversyOptimists argue that because of the surge of the Singularity, solutions to problems previously deemed impossible will soon be obvious, and this superintelligence will solve many societal problems, such as mortality. Pessimists, however, say that as soon as we achieve superintelligence, then human society as we know it will become extinct. There would be no reason for humans to exist. The truth is that it is hard to predict what will come after the creation of such technology, though many agree that it is near.

Emerging Developments in Artificial Intelligence16 minutes, 67 links

A Landscape of Top Artificial Intelligence Teams

This chapter reflects recent developments and was last updated in October of 2022.

This landscape of the top artificial intelligence teams aims to track the most prominent teams developing products and tools in each of several areas. Tracking these teams gives a good starting point of the activity of where future development will be.

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