You can learn more about the Cloud Vision API, which is now in GA ("General Availability") at https://cloud.google.com/vision/
I'm very excited that we can finally discuss this in public. Today at Google I/O revealed the TPU (Tensor Processing Unit), a custom ASIC that Google has designed and built specifically for machine learning applications. We've had TPUs deployed in Google datacenters for more than a year, and they are an order of magnitude faster and more power efficient per operation than other computational solutions for the kinds of models we are deploying to improve our products. This computational speed allows us to use larger, more powerful machine learned models, expressed and seemlessly deployed using TensorFlow (tensorflow.org) into our products, and to deliver the excellent results from those models in less time.
TPUs are used on every Google Search to power RankBrain (https://en.wikipedia.org/wiki/RankBrain), they were a key secret ingredient in the recent AlphaGo match against Lee Sedol, they are used for speech and image recognition, and they are powering a growing list of other smart products and features.
and the rest of the team that developed this ASIC did a fabulous job, and it's great to see it discussed in public!
Link to the part of the keynote where Sundar discusses TPUs:
Edit: Added a link and some text.
This is absurd.
I'm also excited to see this topic being addressed openly, in a collaboration across many different institutions.
Actual paper: https://arxiv.org/abs/1606.06565
Google Research blog post: https://research.googleblog.com/2016/06/bringing-precision-to-ai-safety.html
Open AI blog post:
I signed it, and added the following comments:
I am a computer scientist. I believe strongly in the ability of computing to change the world, and also that every person, regardless of their school, socioeconomic background, or other factors should have the opportunity to be exposed to computer science and computational thinking. Our field needs a diversity of opinions and backgrounds, and our world will be a better place when more people understand the power and capabilities of computing.
Exposing all students to computer science at an early enough age will go a long way to ensuring that the field of computer science reflects the diversity of the world's people.
- Google Senior Fellow, present
Prior to joining Google, I was at DEC/Compaq's Western Research Laboratory, where I worked on profiling tools, microprocessor architecture, and information retrieval. Prior to graduate school, I worked at the World Health Organization's Global Programme on AIDS, developing software for statistical modeling and forecasting of the HIV/AIDS pandemic.
I earned a B.S. in computer science and economics (summa cum laude) from the University of Minnesota and received a Ph.D. and a M.S. in computer science from the University of Washington. I was elected to the National Academy of Engineering in 2009, which recognized my work on "the science and engineering of large-scale distributed computer systems."
- University of WashingtonComputer Science
- University of MinnesotaComputer Science and Economics
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