The AI ethics and safety communities are at a crossroads that will determine whether they work together in the production of safer intelligent systems, or continue down separate conflicted paths.

I opportunistically recorded a video of a delivery robot that almost immediately got into trouble. What follows is an introduction to the design elements of delivery robots, followed by how these elements produced and recovered from a series of unfortunate events.

As someone that turned down an offer to work on cashierless checkout four years ago, I was keen to learn how far the technology has come. What I found was a fascinating case study in designing the human environment for computer vision systems, and I also went home with several items that I didn't pay for...

In "2001: a Space Odyssey," the astronaut falls into an monolith floating in space. Just before the radio cuts transmission to earth, the astronaut gasps, "my god, it's full of stars." This sense of wonder and impending revelation is the view I brought to my PhD studies in artifical intelligence. Surely, I thought, we were working together to unwind our own little piece of the great mysteries of the universe. I was quickly disabused of my higher notions. In falling into the mysteries of intelligence, I came to understand that the intelligence we were collectively working to build resembeled the mysterious power of the Space Odyssey monolith in more than one way, only as I fell into my research I muttered, "my god, it's full of bugs."

(Update: my Syntiant Core 2 presentation bested Samsung and Qualcomm to win Best Product at TinyML)

One of my great professional privileges when founding Syntiant's machine learning stack has been collaborating with a team of electrical engineers in the design, development, verification, and shipping of an ultra-power efficient tensor computation core for Syntiant products. Although I resisted interviewing with Syntiant because I am not an electrical engineer, the hardware collaboration process we have developed through now two "Syntiant Cores" has borne fruit after three years of trading between machine learning requirements and silicon capabilities.

Video has long been a trusted source of ground truth for world events. People know images can be manipulated, but video manipulation is rare. This has changed with the advent of manipulating videos with neural networks. Automatic video manipulation tools, like those that swapped Nicolas Cage's face onto Amy Adams, are now fast, realistic, and cheap.
Defending the public record requires methods for detecting these fakes. But is the deepfake detection game winnable?

"AI winters" follow when the hype backing artificial intelligence yields to the disappointment. Typically the purveyors of AI technologies are then banished until the next spectacular advance in computing capacity. This is not a story about life during an AI winter. It is a story of my life in 2017, that could only be described as an AI Summer. But it didn't start out that way.

What follows is a partial retelling of my personal and professional pathway through the year.

I recently test-drove a Tesla Model X, which was the first time I had ridden in an all-electric vehicle since my neighbor drove me around in his pre-production EV1 in the 90s. The Tesla experience cemented my view that the hype around electric vehicles is well-founded. However, a point of …

In the run up to the AI for Good Global Summit, the XPRIZE Foundation asked for my thoughts on how artificial intelligence can address 3 Sustainable Development Goals (SDGs).

You can read the complete piece at the XPRIZE website.

Jesse Hostetler, Dan Garmat, and I recently hacked on DeepMind's implementation of a neural network for the Arcade Learning Environment. The above video shows the agent learning to play a game called Breakout, in which the objective is to break bricks at the top of the screen using a ball …