Tech & Society
Percy Liang: Transparency for Foundation Models
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.
New Year
Tech & Society
Sara Hooker: Prioritize Inclusion
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.
Tech & Society
Anastasis Germanidis: New Tools to Tell New Stories
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...
Interviews & Essays
Reza Zadeh: Generative AI can bring a breakthrough in active learning, says Matroid founder Reza Zadeh
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...
Interviews & Essays
Been Kim: Google Brain researcher Been Kim envisions a scientific approach to interpretability
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.
Interviews & Essays
Douwe Kiela: Natural language processing researcher Douwe Kiela calls for less hype, more caution.
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.
Interviews & Essays
Yoshua Bengio: Deep learning pioneer Yoshua Bengio looks forward to neural nets that can reason.
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...
Interviews & Essays
Matt Zeiler: Advance AI for good
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.
Interviews & Essays
Yale Song: Foundation models for vision
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...
Interviews & Essays
Yoav Shoham: Language models that reason
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.
Interviews & Essays
Chip Huyen: AI that adapts to changing conditions
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.
Interviews & Essays
Alexei Efros: Learning from the ground up
Things are really starting to get going in the field of AI. After many years (decades?!) of focusing on algorithms, the AI community is finally ready to accept the central role of data and the high-capacity models that are capable of taking advantage of this data.