Artificial intelligence is moving faster than almost anyone predicted, and the conversation around it swings wildly between utopia and doom. Will it cure diseases and lift billions out of drudgery — or take our jobs, flood the world with misinformation, and slip beyond our control? This course steps back from the hype in both directions to look honestly at what's actually at stake. (It complements our courses on how AI and computers work, focusing here on consequences rather than mechanics.)You'll start with a balanced map of how AI could reshape work, science and daily life, then sit with the high-stakes view that this could be one of humanity's most transformative — and risky — inventions. You'll hear a deliberately contrarian take arguing that the real dangers are nearer and more mundane than killer robots — bias, surveillance and concentrated power — before finishing with the genuine technical puzzle of keeping a system smarter than us aligned with what we actually want. Honest note: experts sincerely disagree about how big these risks are and how soon they arrive; this course presents competing views rather than telling you which to believe.
Humans rule Earth without competition. But we are about to create something that may change that: our last invention, the most powerful tool, weapon, or maybe even entity: Artificial Super intelligence. This sounds like science fiction, so let’s start at the beginning. Intelligence is the ability to learn, reason, acquire knowledge and skills and use them to solve problems. Intelligence is power, and we’re the species that exploited it the most. So much so that humanity broke the game of nature and took control. But the journey there wasn’t straightforward. For most animals intelligence costs too much energy to be worth it. Still, if we track intelligence in the tree of species over time, we can see lots of diverse forms of intelligence emerge. The earliest brains were in flatworms 500 million years ago. Just a tiny cluster of neurons to handle basic body functions. It took hundreds of millions of years for species to diversify and become more complex. Life conquered new environments, gained new senses and had to contend with fierce competition over resources. But in nature all that matters is survival and brains are expensive, so for almost all animals a narrow intelligence, fit for a narrow range of tasks was enough. In some environments, animals like birds, octopuses and mammals evolved more complex neural structures. For them it paid off to have more energy-consuming skills like advanced navigation and communication. Until seven million years ago, the Hominins emerged. We don’t know why, but their brains grew faster than their relatives’. Something was different about their intelligence – very slowly, it turned from narrow, to general. From a screwdriver to a multi tool. Able to think about diverse problems. Two million years ago, Homo Erectus saw the world differently from anyone before – as something to be understood and transformed. They controlled fire, invented tools and created the first culture. We probably emerged from them around 250,000 years ago with an even larger and more complex brain. It enabled us to work together in large groups and to communicate complex thoughts. We used our intelligence to improve our lives, to ask how things worked and why things are the way they are. With each discovery, we asked more questions and pushed forward, preserving what we learned, outpacing what evolution could do with genes. Knowledge builds on knowledge. Progress was slow at first and then sped up exponentially. Agriculture, writing, medicine, astronomy or philosophy exploded into the world. 200 years ago science took off and made us even better at learning about the world and speeding up progress. 35 years ago the internet age began. Today we live in a world made to suit our needs, created by us, for us. This is incredibly new. We forget how hard it was to get here. How enormous the steps on the intelligence ladder were and how long it took to climb them. But once we did, we became the most powerful animal in the world in a heartbeat. But we may be in the process of changing this. We are building machines that could be better at the very thing that gave us the power to conquer the planet. Humanity's final invention. Artificial Intelligence Artificial intelligence, or AI, is software that performs mental tasks with a computer. Code that uses silicon, instead of neurons, to solve problems. In the beginning, AI was very simple. Lines of code on paper, mere proofs of concept to demonstrate how machines could perform mental tasks. Only in the 1960s did we start seeing the first examples of what we would recognize as AI. A chatbot in 1964, a program to sort through molecules in 1965. Slow, specialised systems requiring experts to use them. Their intelligence was extremely narrow, built for a single task inside a controlled environment. The equivalent of flatworms a billion years ago, doing the minimum amount of mental work. Progress in AI research paused several times when researchers lost hope in the technology. But just like changing environments create new niches for life, the world around AI changed. Between 1950 and 2000 computers got a billion times faster, while programming became easier and widespread. In 1972, AI could navigate a room. In 1989, it could read handwritten numbers. But it remained a fancy tool, no match for humans! Until in 1997 an AI shocked the world by beating the world champion in Chess. Proving that we could build machines that could surpass us – but we calmed ourselves because a chess bot is quite stupid. Not a flatworm, but maybe a bee, only able to perform a specialised, narrow task. But in this narrow task it is so good that no human will ever again beat AI at chess. As computers continued to improve, AI became a powerful tool for more and more task: in 2004 it drove a robot on Mars, in 2011 it began recommending Youtube videos to you. But this was only possible because humans broke down problems into easy-to-digest chunks that computers could solve quickly. Until we taught AIs to teach themselves. Rise of the Self-Learning Machines This is not a technical video, so we are massively oversimplifying here. In a nutshell, the sheer power of supercomputers was combined with the almost endless data collected in the information age to make a new generation of AI. AI experts began drastically improving forms of AI software called neural networks, enormously huge networks of artificial neurons that start out being bad at their tasks. They then used machine learning, which is an umbrella term for many different training techniques and environments, that allows algorithms to write their own code and improve themselves. The scary thing is that we don’t exactly know how they do it and what happens inside them. Just that it works and that what comes out the other end is a new type of AI. A capable black box of code. These new AIs could master complex skills extremely quickly, with much less human help. They were still narrow intelligences, but a huge step up. In 2014, Facebook AI could identify faces with 97% accuracy. In 2016 an AI beat the best humans in the incredibly complex game of Go. In 2018, a self-learning AI learned chess in four hours just by playing against itself – and then defeated the best specialised chess bot. Since then machine learning has been applied to reading, image processing, solving tests and much more. Many of these AIs are already better than humans for whatever narrow task they were trained, but they still remained a simple tool. AI still didn’t seem that big of a deal for most people. And then came the chatbot ChatGPT. The work that went into it is massive. It trained on nearly everything written on the Internet to learn how to handle language, which it now does better than most people. It can summarise, translate and help with some maths problems. It is incredibly more broad than any other system just a few years ago, not crushing any single benchmark but all of them at once. Many large tech companies are spending billions to build powerful competitors. AI is already transforming customer service, banking, healthcare, marketing, copywriting, creative spaces and more. AI-generated content has already taken hold of social media, youtube and news websites. Elections are expected to be inundated by propaganda and misinformation. No-one is sure how much good or harm can come from adopting AI everywhere. Change is scary. There will be winners and losers. One of the biggest questions governments and corporations have now is how to manage the transition to an AI-boosted economy. All these potential gains or risks are just the result of today’s AI. ChatGPT’s intelligence is a major step up, but it remains narrow. While it can write a great essay in seconds, it doesn’t understand what it is writing. But what if the AIs stopped being narrow? General AI What makes humans different from current AI is our general intelligence. Humans can technically absorb any piece of knowledge and start working on any problem. We are great at many very different skills and tasks, from playing chess to writing or solving science puzzles – not equally of course. Some of us are experts in some fields and beginners in others, but we can technically do all of them. In the past AI was narrow and able to become good at one skill but was rather bad in all the others. Simply by building faster computers and pouring more money into AI training will get us new, more powerful generations of AI. But what if the next step for AI is to become a general intelligence like us? An AGI? If the AI improvement process continues as it has been, it is not unlikely that AGI could be better in most or even all skills than humans can do. We don’t know how to build AGI, how it will work or what it will be able to do. Since narrow AIs today are capable of mastering one mental task quickly, AGI might be able to do the same with all mental tasks. So even if it starts out stupid, an AGI might be able to become as smart and capable as a human. While this sounds like science fiction, most AI researchers think this will happen some time this century, maybe already in a few years. Humanity is not ready for what will happen next. Not socially, not economically, not morally. Earlier we defined Intelligence as the ability to learn, reason, acquire knowledge and skills and use them to solve problems. All things humans excel at. An AGI as intelligent as even an average human would already disrupt modern civilization, because they are not bound by the same limitations as we are. Today’s AIs like ChatGPT already think and solve the tasks they were made for at least ten times faster than even very skilled humans. Maybe AGI will be slower but it may also be faster, maybe much faster. And since AGIs are software, you could copy them endlessly as long as you have enough storage and run them in parallel. There are 8 million scientists in the world – Now imagine an AGI, copied a million times and put to work. Imagine one million scientists working 24/7, thinking ten times faster than humans, without being distracted, only focused on the task they have been given. What if suddenly AGI could do all intelligence based jobs in the world, from interpreting law, to coding to creating animated youtube videos – better, faster and much cheaper than humans? Would whoever controls this AGI suddenly own the economy? And thinking bigger: Human progress is our intelligence applied to problems, so what could a million AGIs achieve? Solve fundamental questions of science, like dark energy? Invent new technology that gives us limitless energy, fix climate change, cure ageing and cancer? But then again, sadly humans apply their intelligence not just for the benefit of all. What if the AGIs are tasked to guide drones or pull the triggers in war? Or to engineer a virus that only kills people with green eyes? Or to create the most profitable social media, so addictive that people starve in front of their screens? The creation of AGI could reasonably be as big of an event as taming fire or electricity – and give whoever invents it equally as much power. But now let us go one step further. What if the potential of AGI does not stop here? Intelligence Explosion Intelligence and knowledge build and accelerate each other but humans are limited by biology and evolution. Once we evolved the right hardware, our software outpaced evolution by orders of magnitudes and within a heartbeat we ruled this planet. But our software basically hasn’t changed much since then, which is why we have obesity and destroy the climate for short term gains. Since AGI is software on a computer, once it is smart enough to do AI research, the rate of AI progress should speed up a lot. And that results in better AI that's better at AI research without much human involvement. It may even be possible that AI could learn how to directly improve itself, in which case some experts fear this feedback loop could be incredibly fast. Maybe just months or years after the first self improving AGI is switched on. Maybe it would actually take decades. We simply don’t know, this is all speculative. But such an intelligence explosion might lead to a true superintelligent entity. We don’t know what such a being would look like, what its motives or goals would be, what would go on in its inner world. We could be as laughably stupid to a superintelligence as squirrels are to us. Unable to even comprehend its way of thinking. This hypothetical scenario keeps many people up at night. Humanity is the only example we have of an animal becoming smarter than all others – and we have not been kind to what we perceive as less intelligent beings. AGI might be the last invention of humanity. It is possible that it could become the most intelligent, and therefore most powerful being on Earth. A ‘God in a Box’ that could exercise its power to bring unimaginable wealth and happiness to humans while securing our future. Or, it could subvert civilization and bring about our end, with humanity unable to come up with a way to stop it. We will look at some of these potential futures in more videos but let us wrap up now. The only thing we know for sure is that today, right now, many of the largest and richest companies in the world are racing to create ever more powerful AIs. Whatever our future is, we are running towards it. Who knows how long we have until we must confront our AI future. Luckily, you still have plenty of time to prepare for it—if you're learning on Brilliant.org, that is. Brilliant will make you a better thinker and problem solver in just minutes a day, with thousands of bite-sized, hands-on lessons on just about anything you may be curious about—including AI. Their latest course, “How LLMs Work” takes you under the hood of real language models. It demystifies technologies like ChatGPT with interactive lessons on everything from how models build vocabulary to how they choose their next word. You’ll learn how to tune LLMs to produce output with exactly your desired tonality, whether it’s poetry or a cover letter. And you’ll understand why training is really everything, by comparing models trained on Taylor Swift lyrics and the legal speech of Big Tech’s Terms and Conditions. It’s an immersive AI workshop, allowing you to experience and harness the mechanics of today’s most advanced tool. We’ve also partnered with Brilliant to create a series of lessons to take your scientific knowledge to the next level. These lessons let you further explore the topics in our most popular videos, from rabies and mammalian metabolism to climate science and supernovae. Each lesson on Brilliant is interactive, like a one-on-one version of a Kurzgesagt video. And you can get started whenever, wherever—right from whatever device you’d like. To get hands-on with Kurzgesagt lessons and explore everything Brilliant has to offer—from AI and programming to math, science, and beyond—start your free 30-day trial by signing up at Brilliant.org/nutshell/. There’s even an extra perk for Kurzgesagt viewers:
So I've been an AI researcher for over a decade. And a couple of months ago, I got the weirdest email of my career. A random stranger wrote to me saying that my work in AI is going to end humanity. Now I get it, AI, it's so hot right now. (Laughter) It's in the headlines pretty much every day, sometimes because of really cool things like discovering new molecules for medicine or that dope Pope in the white puffer coat. But other times the headlines have been really dark, like that chatbot telling that guy that he should divorce his wife or that AI meal planner app proposing a crowd pleasing recipe featuring chlorine gas. And in the background, we've heard a lot of talk about doomsday scenarios, existential risk and the singularity, with letters being written and events being organized to make sure that doesn't happen. Now I'm a researcher who studies AI's impacts on society, and I don't know what's going to happen in 10 or 20 years, and nobody really does. But what I do know is that there's some pretty nasty things going on right now, because AI doesn't exist in a vacuum. It is part of society, and it has impacts on people and the planet. AI models can contribute to climate change. Their training data uses art and books created by artists and authors without their consent. And its deployment can discriminate against entire communities. But we need to start tracking its impacts. We need to start being transparent and disclosing them and creating tools so that people understand AI better, so that hopefully future generations of AI models are going to be more trustworthy, sustainable, maybe less likely to kill us, if that's what you're into. But let's start with sustainability, because that cloud that AI models live on is actually made out of metal, plastic, and powered by vast amounts of energy. And each time you query an AI model, it comes with a cost to the planet. Last year, I was part of the BigScience initiative, which brought together a thousand researchers from all over the world to create Bloom, the first open large language model, like ChatGPT, but with an emphasis on ethics, transparency and consent. And the study I led that looked at Bloom's environmental impacts found that just training it used as much energy as 30 homes in a whole year and emitted 25 tons of carbon dioxide, which is like driving your car five times around the planet just so somebody can use this model to tell a knock-knock joke. And this might not seem like a lot, but other similar large language models, like GPT-3, emit 20 times more carbon. But the thing is, tech companies aren't measuring this stuff. They're not disclosing it. And so this is probably only the tip of the iceberg, even if it is a melting one. And in recent years we've seen AI models balloon in size because the current trend in AI is "bigger is better." But please don't get me started on why that's the case. In any case, we've seen large language models in particular grow 2,000 times in size over the last five years. And of course, their environmental costs are rising as well. The most recent work I led, found that switching out a smaller, more efficient model for a larger language model emits 14 times more carbon for the same task. Like telling that knock-knock joke. And as we're putting in these models into cell phones and search engines and smart fridges and speakers, the environmental costs are really piling up quickly. So instead of focusing on some future existential risks, let's talk about current tangible impacts and tools we can create to measure and mitigate these impacts. I helped create CodeCarbon, a tool that runs in parallel to AI training code that estimates the amount of energy it consumes and the amount of carbon it emits. And using a tool like this can help us make informed choices, like choosing one model over the other because it's more sustainable, or deploying AI models on renewable energy, which can drastically reduce their emissions. But let's talk about other things because there's other impacts of AI apart from sustainability. For example, it's been really hard for artists and authors to prove that their life's work has been used for training AI models without their consent. And if you want to sue someone, you tend to need proof, right? So Spawning.ai, an organization that was founded by artists, created this really cool tool called “Have I Been Trained?” And it lets you search these massive data sets to see what they have on you. Now, I admit it, I was curious. I searched LAION-5B, which is this huge data set of images and text, to see if any images of me were in there. Now those two first images, that's me from events I've spoken at. But the rest of the images, none of those are me. They're probably of other women named Sasha who put photographs of themselves up on the internet. And this can probably explain why, when I query an image generation model to generate a photograph of a woman named Sasha, more often than not I get images of bikini models. Sometimes they have two arms, sometimes they have three arms, but they rarely have any clothes on. And while it can be interesting for people like you and me to search these data sets, for artists like Karla Ortiz, this provides crucial evidence that her life's work, her artwork, was used for training AI models without her consent, and she and two artists used this as evidence to file a class action lawsuit against AI companies for copyright infringement. And most recently -- (Applause) And most recently Spawning.ai partnered up with Hugging Face, the company where I work at, to create opt-in and opt-out mechanisms for creating these data sets. Because artwork created by humans shouldn’t be an all-you-can-eat buffet for training AI language models. (Applause) The very last thing I want to talk about is bias. You probably hear about this a lot. Formally speaking, it's when AI models encode patterns and beliefs that can represent stereotypes or racism and sexism. One of my heroes, Dr. Joy Buolamwini, experienced this firsthand when she realized that AI systems wouldn't even detect her face unless she was wearing a white-colored mask. Digging deeper, she found that common facial recognition systems were vastly worse for women of color compared to white men. And when biased models like this are deployed in law enforcement settings, this can result in false accusations, even wrongful imprisonment, which we've seen happen to multiple people in recent months. For example, Porcha Woodruff was wrongfully accused of carjacking at eight months pregnant because an AI system wrongfully identified her. But sadly, these systems are black boxes, and even their creators can't say exactly why they work the way they do. And for example, for image generation systems, if they're used in contexts like generating a forensic sketch based on a description of a perpetrator, they take all those biases and they spit them back out for terms like dangerous criminal, terrorists or gang member, which of course is super dangerous when these tools are deployed in society. And so in order to understand these tools better, I created this tool called the Stable Bias Explorer, which lets you explore the bias of image generation models through the lens of professions. So try to picture a scientist in your mind. Don't look at me. What do you see? A lot of the same thing, right? Men in glasses and lab coats. And none of them look like me. And the thing is, is that we looked at all these different image generation models and found a lot of the same thing: significant representation of whiteness and masculinity across all 150 professions that we looked at, even if compared to the real world, the US Labor Bureau of Statistics. These models show lawyers as men, and CEOs as men, almost 100 percent of the time, even though we all know not all of them are white and male. And sadly, my tool hasn't been used to write legislation yet. But I recently presented it at a UN event about gender bias as an example of how we can make tools for people from all walks of life, even those who don't know how to code, to engage with and better understand AI because we use professions, but you can use any terms that are of interest to you. And as these models are being deployed, are being woven into the very fabric of our societies, our cell phones, our social media feeds, even our justice systems and our economies have AI in them. And it's really important that AI stays accessible so that we know both how it works and when it doesn't work. And there's no single solution for really complex things like bias or copyright or climate change. But by creating tools to measure AI's impact, we can start getting an idea of how bad they are and start addressing them as we go. Start creating guardrails to protect society and the planet. And once we have this information, companies can use it in order to say, OK, we're going to choose this model because it's more sustainable, this model because it respects copyright. Legislators who really need information to write laws, can use these tools to develop new regulation mechanisms or governance for AI as it gets deployed into society. And users like you and me can use this information to choose AI models that we can trust, not to misrepresent us and not to misuse our data. But what did I reply to that email that said that my work is going to destroy humanity? I said that focusing on AI's future existential risks is a distraction from its current, very tangible impacts and the work we should be doing right now, or even yesterday, for reducing these impacts. Because yes, AI is moving quickly, but it's not a done deal. We're building the road as we walk it, and we can collectively decide what direction we want to go in together. Thank you. (Applause)