Google Duplex: An AI System for Accomplishing Real-World Tasks Over the Phone
“Today we announce Google Duplex, a new technology for conducting natural conversations to carry out “real world” tasks over the phone. The technology is directed towards completing specific tasks, such as scheduling certain types of appointments. For such tasks, the system makes the conversational experience as natural as possible, allowing people to speak normally, like they would to another person, without having to adapt to a machine.”
Duplex shows Google failing at ethical and creative AI design
It really makes you wonder whether, at some foundational level, Google lacks trust in both what AI technology can do and in its own creative abilities to breath new life into these emergent synthetic experiences.
THE TRUMP ADMINISTRATION PLAYS CATCH-UP ON ARTIFICIAL INTELLIGENCE
AMERICA IS GREAT at artificial intelligence — and it’s going to get even greater.
Artificial Neural Nets Grow Brainlike Navigation Cells
Faced with a navigational challenge, neural networks spontaneously evolved units resembling the grid cells that help living animals find their way.
Tearing Apart Google’s TPU 3.0 AI Coprocessor
Google did its best to impress this week at its annual IO conference. While Google rolled out a bunch of benchmarks that were run on its current Cloud TPU instances, based on TPUv2 chips, the company divulged a few skimpy details about its next generation TPU chip and its systems architecture. The company changed from version notation (TPUv2) to revision notation (TPU 3.0) with the update, but ironically the detail we have assembled shows that the step from TPUv2 to what we will call TPUv3 probably isn’t that big; it should probably be called TPU v2r5 or something like that.
MLPerf — Will New Machine Learning Benchmark Help Propel AI Forward?
Let the AI benchmarking wars begin. Today, a diverse group from academia and industry — Google, Baidu, Intel, AMD, Harvard, and Stanford among them — released MLPerf, a nascent benchmarking tool “for measuring the speed of machine learning software and hardware.”
Self-driving cars are here
Self-driving cars are no longer a futuristic AI technology. They’re here, and will soon make transportation cheaper and more convenient.
Cambridge Analytica: how did it turn clicks into votes?
Whistleblower Christopher Wylie explains the science behind Cambridge Analytica’s mission to transform surveys and Facebook data into a political messaging weapon
How Health Care Changes When Algorithms Start Making Diagnoses
Imagine that the next time you see your doctor, she says you have a life-threatening disease. The catch? A computer has performed your diagnosis, which is too complex for humans to understand entirely. What your doctor can explain, however, is that the computer is almost always right.
Alexa and Siri Can Hear This Hidden Command. You Can’t.
Researchers can now send secret audio instructions undetectable to the human ear to Apple’s Siri, Amazon’s Alexa and Google’s Assistant.
How Long Until a Robot Cries?
Identifying the mechanics of emotions.
Privacy and machine learning: two unexpected allies?
In many applications of machine learning, such as machine learning for medical diagnosis, we would like to have machine learning algorithms that do not memorize sensitive information about the training set, such as the specific medical histories of individual patients. Differential privacy is a framework for measuring the privacy guarantees provided by an algorithm. Through the lens of differential privacy, we can design machine learning algorithms that responsibly train models on private data.
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.
