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    meanwhile at Berkeley...
   
   
   https://rise.cs.berkeley.edu/
   
   
   RISELab Kicks Off
   
   Berkeley’s computer science division has an ongoing tradition of 5-year collaborative research labs. In the fall of 2016 we closed out the most recent of the series: the AMPLab. We think it was a pretty big deal, and many agreed.
   
   One great thing about Berkeley is the endless supply of energy and ideas that flows through the place — always bringing changes, building on what came before. In that spirit, we’re fired up to announce the Berkeley RISELab, where we will focus intensely for five years on systems that provide Real-time Intelligence with Secure Execution.
   
   Context
   RISELab represents the next chapter in the ongoing story of data-intensive systems at Berkeley; a proactive step to move beyond Big Data analytics into a more immersive world. The RISE agenda begins by recognizing that there are big changes afoot:
   
   

   1. Sensors are everywhere. We carry them in our pockets, we embed them in our homes, we pass them on the street. Our world will be quantified, in fine detail, in real time.
   2. AI is for real. Big data and cheap compute finally made some of the big ideas of AI a practical reality. There’s a ton more to be done, but learning and prediction are now practical tools in the computing toolbox.
   3. The world is programmable. Our vehicles, houses, workplaces and medical devices are increasingly networked and programmable. The effects of computation are extending to include our homes, cities, airspace, and bloodstreams.

   In short, the loop between data generation, computation, and actuation is closing. And this is no longer a niche scenario: it’s going to be a standard mode of technology going forward.
   
   Mission
   Our mission in the RISELab is to develop technologies that enable applications to interact intelligently and securely with their environment in real time.
   
   As in previous labs, we’re all in — working on everything from basic research to software development, all in the Berkeley tradition of open publication and open source software. We’ll use this space to lay out our ideas and progress as we go.
   
   Sponsors
   A final note: we’re extremely fortunate at Berkeley to be supported by — and working with — some of the world’s biggest and most innovative companies. The RISELab’s 11 founding sponsors are quite the crew: Amazon Web Services, Ant Financial, Capital One, Ericsson, GE Digital, Google, Huawei, IBM, Intel, Microsoft Research and VMware. Thanks to all.
   
   We RISE.
   
   — Ion, Joe, Joey, Raluca and team
   
   FEATURED PROJECT
   Clipper
   Machine learning is being deployed in a growing number of applications which demand real-time, accurate, and robust predictions under heavy query load. However, most machine learning frameworks and systems only address model training and not deployment.
   
   Clipper is a general-purpose low-latency prediction serving system. Interposed between end-user applications and a wide range of machine learning frameworks, Clipper introduces a modular architecture to simplify model deployment across frameworks. Furthermore, by introducing caching, batching, and adaptive model selection techniques, Clipper reduces prediction latency and improves prediction throughput, accuracy, and robustness without modifying the underlying machine learning frameworks.
   

   jfieb, 4 minutes agoEditDeleteReport

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   jfiebWell-Known Member

   New
   

   
   
   

   
   https://rise.cs.berkeley.edu/
   
   
   RISE Camp 2017
   
   
   November 2017
   
   Welcome to our Fall 2017 Newsletter! This is the place in which we keep you informed about our recent progress.
   
   We had a great RISECamp at the beginning of the semester! Here is the web page, and here are the videos that we feel show the enormous success of the camp. People seemed to have a great experience learning in real time about what we are doing. You may ask yourself: How does one actually put on such an event? Making this happen smoothly and seamlessly can be quite complex, so to that end, Jey Kottalam has put together this nice blog describing some of the technical issues that went on behind the scenes.
   
   Two other items are of particular interest: one is that the RISELab faculty have put together a vision paper, which should give you an idea of some of our thoughts going forward, and the other is that RISELab faculty Michael Mahoney and Michael Jordan, along with statistics professors Bin Yu and Fernando Perez and EECS professor Richard Karp were awarded an NSF grant to create a Foundations of Data Analysis (FODA) Institute here at UC Berkeley. Details can be found here. The FODA Institute will focus on theoretical foundations of data science at the intersection of computer science theory, statistics theory, and applied mathematics. Having a mix of foundational theory inspired by and leading to real applications and implementations is one of the great aspects of the RISELab!Thank you for your continued support.
   
   Professor Michael Mahoney
   RISELab Faculty
   
   
   Notice mathematics. AI is more math.