Interviews & Essays

Without question, 2023 has been the most exciting and interesting year in technology that I’ve seen over a fairly long career. It bears mention that I’m pretty sure I said more or less the same thing at the close of 2022...

One of my favourite flavours of conversation is listening to reinforcement learning experts talk about their children as reinforcement learning agents. These conversations highlight just how comically far behind humans our machine learning models are.

Before this past year, when I told people I worked in AI, more often than not I was met with a blank stare and sometimes a question along the lines of: “You mean like robots?”

Only a year ago, ChatGPT woke the world up to the power of foundation models. But this power is not about shiny, jaw-dropping demos.

The past year has seen incredible innovation in AI, and I expect as much or more in 2024. The coming year undoubtedly will be a year of rapid progress in models – multimodal, multilingual, and (hopefully) smaller and faster.

The year 2023 was an inflection point in the development of broadly useful AI systems across text, image, video, audio, and other modalities. At Runway alone, we saw the release of video-generation models such as Gen-1 and Gen-2...

The important question of how companies and organizations use our data has received a lot of attention in the technology and policy communities. An equally important question that deserves more focus in 2023 is how...

As we enter the new year, there is a growing hope that the recent explosion of generative AI will bring significant progress in active learning. This technique, which enables machine learning systems to generate their own training examples and request them to be labeled...

It’s an exciting time for AI, with fascinating advances in generated media and many other applications, some even in science and medicine. Some folks may dream about what more AI can create and how much bigger models we may engineer.

This year we really started to see the mainstreaming of AI. Systems like Stable Diffusion and ChatGPT captured the public imagination to an extent we haven’t seen before in our field.

Recent advances in deep learning largely have come by brute force: taking the latest architectures and scaling up compute power, data, and engineering. Do we have the architectures we need, and all that remains is to develop better hardware and datasets so we can keep...

There’s a reason why artificial intelligence is sometimes referred to as “software 2.0”: It represents the most significant technological advance in decades. Like any groundbreaking invention, it raises concerns about the future, and much of the media focus is on the threats it brings.

Large models pretrained on immense quantities of text have been proven to provide strong foundations for solving specialized language tasks. My biggest hope for AI in 2022 is...

I believe that natural language processing in 2022 will re-embrace symbolic reasoning, harmonizing it with the statistical operation of modern neural networks. Let me explain what I mean by this.

Until recently, big data processing has been dominated by batch systems like MapReduce and Spark, which allow us to periodically process a large amount of data very efficiently.