Deep Learning Achievements Over the Past Year
Great developments in text, voice, and computer vision technologies
A non-comprehensive list of awesome things other people did in 2017
Editor’s note: For the last few years I have made a list of awesome things that other people did (2016,2015, 2014, 2013). Like in previous years I’m making a list, again right off the top of my head.
2017: The Year in Color
Colors matter. As events unfold around the world, journalists focus our attention on the people, places, and things by creating scenes, much as film directors do. It isn’t just a man waving to a crowd from an airplane. It is a man in a “dark blue suit”, a “crisp white shirt” and a “bold red tie”. It isn’t just a crowd of women marching in the street. It is a sea of “pointy-eared pink hats”. Looking back on 2017, what did our world look like in color? We analyzed over 30 million English-language news documents to find out.
Fair and Balanced? Thoughts on Bias in Probabilistic Modeling
In recent months and years, the Machine Learning community has conducting a notable amount of soul searching on the question of algorithmic bias: are our algorithms operating in ways that are fundamentally unfair towards specific groups within society?
Physical Adversarial Examples Against Deep Neural Networks
There have been several techniques proposed to generate adversarial examples and to defend against them. In this blog post we will briefly introduce state-of-the-art algorithms to generate digital adversarial examples, and discuss our algorithm to generate physical adversarial examples on real objects under varying environmental conditions. We will also provide an update on our efforts to generate physical adversarial examples for object detectors.
Google’s voice-generating AI is now indistinguishable from humans
A research paper published by Google this month — which has not been peer reviewed — details a text-to-speech system called Tacotron 2, which claims near-human accuracy at imitating audio of a person speaking from text.
The Pentagon’s New Artificial Intelligence Is Already Hunting Terrorists
After less than eight months of development, the algorithms are helping intel analysts exploit drone video over the battlefield.
The AI chip startup explosion is already here
This year, an array of startups that are all working on their own variations of hardware that will power future devices built on top of AI received enormous amounts of funding. Some of these startups have nowhere near a massive install base (or have yet to ship a product) but already appear to have no trouble raising financing.
Nobody’s Ready for the Killer Robot
A Q&A with General Robert Latiff on the ethics of warfare in the autonomous future.
A List of Chip/IP for Deep Learning (keep updating)
Machine Learning, especially Deep Learning technology is driving the evolution of artificial intelligence (AI). At the beginning, deep learning has primarily been a software play. Start from the year 2016, the need for more efficient hardware acceleration of AI/ML/DL was recognized in academia and industry. This year, we saw more and more players, including world’s top semiconductor companies as well as a number of startups, even tech giants Google, have jumped into the race. I believe that it could be very interesting to look at them together. So, I build this list of AI/ML/DL ICs and IPs on Github and keep updating. If you have any suggestion or new information, please let me know.
