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.

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.

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.

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.

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...

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.

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.

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

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.

An international wave of anti-surveillance sentiment pushed back against the proliferation of face recognition systems.

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.