Dec 29, 2021

8 Posts

Andrew Ng writing his learning goals for 2022
Dec 29, 2021

The Batch: Hopes for 2022 from Alexei Efros, Abeba Birhane, Yoav Shoham, Chip Huyen, Matt Zeiler, Wolfram Burgard, Yale Song

As we approach the end of the year, many of us consider setting goals for next year. I wrote about setting learning goals in a previous letter. In this one, I’d like to share a framework that I’ve found useful: process goals versus outcome goals.
Matt Zeiler
Dec 29, 2021

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.
Photograph of Yale Song
Dec 29, 2021

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...
Yoav Shoham
Dec 29, 2021

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.
Chip Huyen
Dec 29, 2021

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.
Alexei Efros
Dec 29, 2021

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.
Wolfram Burgard
Dec 29, 2021

Wolfram Burgard: Train robots in the real world.

Robots are tremendously useful machines, and I would like to see them applied to every task where they can do some good. Yet we don’t have enough programmers for all this hardware and all these tasks.
Abeba Birhane
Dec 29, 2021

Abeba Birhane: Clean up web datasets.

From language to vision models, deep neural networks are marked by improved performance, higher efficiency, and better generalizations. Yet, these systems are also marked by perpetuation of bias and injustice.

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