Today’s episode is somewhat special, because we’re going to be talking about what might be the first solid quantitative study of the power-seeking tendencies that we can expect advanced AI systems to have in the future.
For a long time, there’s
Until recently, AI systems have been narrow — they’ve only been able to perform the specific tasks that they were explicitly trained for. And while narrow systems are clearly useful, the holy grain of AI is to build more flexible, general syste
2021 has been a wild ride in many ways, but its wildest features might actually be AI-related. We’ve seen major advances in everything from language modeling to multi-modal learning, open-ended learning and even AI alignment.
So, we thought, wh
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
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
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 dev
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, sp
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 peopl
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 h
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 ot
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, includi
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.
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 try
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 expec
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 da
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 wit
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
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 Dynami
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 ef
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 s
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
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
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 s
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 h
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 wr
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