Imagine for a minute that you’re running a profitable business, and that part of your sales strategy is to send the occasional mass email to people who’ve signed up to be on your mailing list. For a while, this approach leads to a reliable flow of new sales, but then one day, that abruptly stops. What happened? You pour over logs, looking for an explanation, but it turns out that the problem wasn’t with your software; it was with your data. Maybe the new intern accidentally added a character...

Historically, AI systems have been slow learners. For example, a computer vision model often needs to see tens of thousands of hand-written digits before it can tell a 1 apart from a 3. Even game-playing AIs like DeepMind’s AlphaGo, or its more recent descendant MuZero, need far more experience than humans do to master a given game. So when someone develops an algorithm that can reach human-level performance at anything as fast as a human can, it’s a big deal. And that’s exactly why I asked...

There once was a time when AI researchers could expect to read every new paper published in the field on the arXiv, but today, that’s no longer the case. The recent explosion of research activity in AI has turned keeping up to date with new developments into a full-time job. Fortunately, people like YouTuber, ML PhD and sunglasses enthusiast Yannic Kilcher make it their business to distill ML news and papers into a digestible form for mortals like you and me to consume. I highly recommend...

Today, most machine learning algorithms use the same paradigm: set an objective, and train an agent, a neural net, or a classical model to perform well against that objective. That approach has given good results: these types of AI can hear, speak, write, read, draw, drive and more. But they’re also inherently limited: because they optimize for objectives that seem interesting to humans, they often avoid regions of parameter space that are valuable, but that don’t immediately seem...

It’s no secret that governments around the world are struggling to come up with effective policies to address the risks and opportunities that AI presents. And there are many reasons why that’s happening: many people — including technical people — think they understand what frontier AI looks like, but very few actually do, and even fewer are interested in applying their understanding in a government context, where salaries are low and stock compensation doesn’t even exist. So there’s a...

AI ethics is often treated as a dry, abstract academic subject. It doesn’t have the kinds of consistent, unifying principles that you might expect from a quantitative discipline like computer science or physics. But somehow, the ethics rubber has to meet the AI road, and where that happens — where real developers have to deal with real users and apply concrete ethical principles — is where you find some of the most interesting, practical thinking on the topic. That’s why I wanted to speak...

Over the last two years, the capabilities of AI systems have exploded. AlphaFold2, MuZero, CLIP, DALLE, GPT-3 and many other models have extended the reach of AI to new problem classes. There’s a lot to be excited about. But as we’ve seen in other episodes of the podcast, there’s a lot more to getting value from an AI system than jacking up its capabilities. And increasingly, one of these additional missing factors is becoming trust. You can make all the powerful AIs you want, but if no one...

On the face of it, there’s no obvious limit to the reinforcement learning paradigm: you put an agent in an environment and reward it for taking good actions until it masters a task. And by last year, RL had achieved some amazing things, including mastering Go, various Atari games, Starcraft II and so on. But the holy grail of AI isn’t to master specific games, but rather to generalize — to make agents that can perform well on new games that they haven’t been trained on before. Fast forward...

Bias gets a bad rap in machine learning. And yet, the whole point of a machine learning model is that it biases certain inputs to certain outputs — a picture of a cat to a label that says “cat”, for example. Machine learning is bias-generation. So removing bias from AI isn’t an option. Rather, we need to think about which biases are acceptable to us, and how extreme they can be. These are questions that call for a mix of technical and philosophical insight that’s hard to find. Luckily, I’ve...

As impressive as they are, language models like GPT-3 and BERT all have the same problem: they’re trained on reams of internet data to imitate human writing. And human writing is often wrong, biased, or both, which means language models are trying to emulate an imperfect target. Language models often babble, or make up answers to questions they don’t understand. And it can make them unreliable sources of truth. Which is why there’s been increased interest in alternative ways to retrieve...

Corporate governance of AI doesn’t sound like a sexy topic, but it’s rapidly becoming one of the most important challenges for big companies that rely on machine learning models to deliver value for their customers. More and more, they’re expected to develop and implement governance strategies to reduce the incidence of bias, and increase the transparency of their AI systems and development processes. Those expectations have historically come from consumers, but governments are starting...

The more powerful our AIs become, the more we’ll have to ensure that they’re doing exactly what we want. If we don’t, we risk building AIs that use dangerously creative solutions that have side-effects that could be undesirable, or downright dangerous. Even a slight misalignment between the motives of a sufficiently advanced AI and human values could be hazardous. That’s why leading AI labs like OpenAI are already investing significant resources into AI alignment research. Understanding that...

The recent success of large transformer models in AI raises new questions about the limits of current strategies: can we expect deep learning, reinforcement learning and other prosaic AI techniques to get us all the way to humanlike systems with general reasoning abilities? Some think so, and others disagree. One dissenting voice belongs to Francesca Rossi, a former professor of computer science, and now AI Ethics Global Leader at IBM. Much of Francesca’s research is focused on deriving...

AI research is often framed as a kind of human-versus-machine rivalry that will inevitably lead to the defeat — and even wholesale replacement of — human beings by artificial superintelligences that have their own sense of agency, and their own goals. Divya Siddarth disagrees with this framing. Instead, she argues, this perspective leads us to focus on applications of AI that are neither as profitable as they could be, nor safe enough to prevent us from potentially catastrophic consequences...

2020 was an incredible year for AI. We saw powerful hints of the potential of large language models for the first time thanks to OpenAI’s GPT-3, DeepMind used AI to solve one of the greatest open problems in molecular biology, and Boston Dynamics demonstrated their ability to blend AI and robotics in dramatic fashion. Progress in AI is accelerating exponentially, and though we’re just over halfway through 2021, this year is already turning into another one for the books. So we decided to...

Many AI researchers think it’s going to be hard to design AI systems that continue to remain safe as AI capabilities increase. We’ve seen already on the podcast that the field of AI alignment has emerged to tackle this problem, but a related effort is also being directed at a separate dimension of the safety problem: AI interpretability. Our ability to interpret how AI systems process information and make decisions will likely become an important factor in assuring the reliability of AIs in...

Cruise is a self-driving car startup founded in 2013 — at a time when most people thought of self-driving cars as the stuff of science fiction. And yet, just three years later, the company was acquired by GM for over a billion dollars, having shown itself to be a genuine player in the race to make autonomous driving a reality. Along the way, the company has had to navigate and adapt to a rapidly changing technological landscape, mixing and matching old ideas from robotics and software...

There are a lot of reasons to pay attention to China’s AI initiatives. Some are purely technological: Chinese companies are producing increasingly high-quality AI research, and they’re poised to become even more important players in AI over the next few years. For example, Huawei recently put together their own version of OpenAI’s massive GPT-3 language model — a feat that leveraged massive scale compute that pushed the limits of current systems, calling for deep engineering and technical...

This special episode of the Towards Data Science podcast is a cross-over with our friends over at the Banana Data podcast. We’ll be zooming out and talking about some of the most important current challenges AI creates for humanity, and some of the likely future directions the technology might take.

Few would disagree that AI is set to become one of the most important economic and social forces in human history. But along with its transformative potential has come concern about a strange new risk that AI might pose to human beings. As AI systems become exponentially more capable of achieving their goals, some worry that even a slight misalignment between those goals and our own could be disastrous. These concerns are shared by many of the most knowledgeable and experienced AI...

How can you know that a super-intelligent AI is trying to do what you asked it to do? The answer, it turns out, is: not easily. And unfortunately, an increasing number of AI safety researchers are warning that this is a problem we’re going to have to solve sooner rather than later, if we want to avoid bad outcomes — which may include a species-level catastrophe. The type of failure mode whereby AIs optimize for things other than those we ask them to is known as an inner alignment failure in...

When OpenAI announced the release of their  GPT-3 API last year, the tech world was shocked. Here was a language model, trained only to perform a simple autocomplete task, which turned out to be capable of language translation, coding, essay writing, question answering and many other tasks that previously would each have required purpose-built systems. What accounted for GPT-3’s ability to solve these problems? How did it beat state-of-the-art AIs that were purpose-built to solve tasks it...

In 2016, OpenAI published a blog describing the results of one of their AI safety experiments. In it, they describe how an AI that was trained to maximize its score in a boat racing game ended up discovering a strange hack: rather than completing the race circuit as fast as it could, the AI learned that it could rack up an essentially unlimited number of bonus points by looping around a series of targets, in a process that required it to ram into obstacles, and even travel in the wrong...

We all value privacy, but most of us would struggle to define it. And there’s a good reason for that: the way we think about privacy is shaped by the technology we use. As new technologies emerge, which allow us to trade data for services, or pay for privacy in different forms, our expectations shift and privacy standards evolve. That shifting landscape makes privacy a moving target. The challenge of understanding and enforcing privacy standards isn’t novel, but it’s taken on a new...

When OpenAI developed its GPT-2 language model in early 2019, they initially chose not to publish the algorithm, owing to concerns over its potential for malicious use, as well as the need for the AI industry to experiment with new, more responsible publication practices that reflect the increasing power of modern AI systems. This decision was controversial, and remains that way to some extent even today: AI researchers have historically enjoyed a culture of open publication and have...