Ayanna Howard
New Year

Ayanna Howard: Training in Ethical AI

As AI engineers, we have tools to design and build any technology-based solution we can dream of. But many AI developers don’t consider it their responsibility to address potential negative consequences as a part of this work.
Dawn Song
New Year

Dawn Song: Taking Responsibility for Data

Datasets are critical to AI and machine learning, and they are becoming a key driver of the economy. Collection of sensitive data is increasing rapidly, covering almost every aspect of people’s lives.
Richard Socher
New Year

Richard Socher: Boiling the Information Ocean

Ignorance is a choice in the Internet age. Virtually all of human knowledge is available for the cost of typing a few words into a search box.
David Patterson
New Year

David Patterson: Faster Training and Inference

Billions of dollars invested to create novel AI hardware will bear their early fruit in 2020. Google unleashed a financial avalanche with its tensor processing unit in 2017.
Kai-Fu Lee
New Year

Kai-Fu Lee: AI Everywhere

Artificial intelligence has moved from the age of discovery to the age of implementation. Among our invested portfolios, primarily in China, we see flourishing applications using AI and automation in banking, finance, transportation, logistics, supermarkets, restaurants, warehouses, factories...
Yann LeCun
New Year

Yann LeCun: Learning From Observation

How is it that many people learn to drive a car fairly safely in 20 hours of practice, while current imitation learning algorithms take hundreds of thousands of hours, and reinforcement learning algorithms take millions of hours? Clearly we’re missing something big.
Chelsea Finn
New Year

Chelsea Finn: Robots That Generalize

Many people in the AI community focus on achieving flashy results, like building an agent that can win at Go or Jeopardy. This kind of work is impressive in terms of complexity.
Oren Etzioni
New Year

Oren Etzioni: Tools For Equality

In 2020, I hope the AI community will grapple with issues of fairness in ways that tangibly and directly benefit disadvantaged populations.
Anima Anandkumar
New Year

Anima Anandkumar: The Power of Simulation

We’ve had great success with supervised deep learning on labeled data. Now it’s time to explore other ways to learn: training on unlabeled data, lifelong learning, and especially letting models explore a simulated environment before transferring what they learn to the real world.
Illustration of a reindeer with security cameras pointing at it
New Year

Face Recognition Meets Resistance

An international wave of anti-surveillance sentiment pushed back against the proliferation of face recognition systems.
Illustration of a crystal snowball
New Year

Simulation Substitutes for Data

The future of machine learning may depend less on amassing ground-truth data than simulating the environment in which a model will operate. Deep learning works like magic with enough high-quality data. When examples are scarce, though, researchers are using simulation to fill the gap.

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