The next big breakthroughs in technology are already running in labs, but how do you take them out of research centers and academic institutions and introduce them in to the world?
Join a select group of researchers, academics and investors at the third annual Andreessen Horowitz Academic Roundtable on September 21-23 to go deep on the future of technology.
This invitation-only, all-inclusive event will present cutting edge research in Data Science, Artificial Intelligence, Crypto-Currency, Systems Problems and more. The gathering will also explore how the future gets commercialized. How academics and researchers can leverage their own entrepreneurial strengths, and find ways to better collaborate with the commercial tech industry.
Andreessen Horowitz backs bold entrepreneurs who move fast, think big and are committed to building the next major franchises in technology. Founded by Marc Andreessen and Ben Horowitz, we provide entrepreneurs with access to our deep expertise and insights in innovation, business development, market intelligence, executive and technical talent, and marketing and brand building. Find us in Menlo Park, Calif., and at a16z.com.
Session 1: Big Data
Big data, small data; structured data, unstructured data; in memory, on SSDs, on disk—the world wants to generate, process, and generate insight from data in all forms and on all media. Come share ideas on how we’ll be doing this in 5 to 10 years as we move from data warehouses to Hadoop clusters to Spark processing to the Next Great Big Data Frameworks.
Chandra Krintz, UC Santa Barbara
Chris Re, Stanford University
Kathryn McKinley, Microsoft/University of Texas
Michael Jordan, UC Berkeley
Session 2: VR/AR
We all saw C3PO play space chess (dejarik for the fanboys) with Chewie on board the Millenium Falcon. Ever since, we’ve been waiting for immersive, interactive 3D experiences. What will go mass market first? Immersive VR-scapes inside the Oculus Rift and phones mounted inside Google Cardboard? Or with AR glasses sporting lightweight projectors that expand on the scenes in front of us? As latency decreases, image recognition improves, and smartphone wars are producing components cheaply enough, what fundamental computer science enablers do we need?
Derek Belch, STRIVR
Steve Seitz, University of Washington
Matthew Turk, UC Santa Barbara
Marc Andreessen & Chris Dixon, Andreessen Horowitz
Session 3: Machine Learning
Surprisingly smart and influential people are predicting an imminent Skynet: when AI algorithms achieve sentience and decide that slow, idiosyncratic human intelligence isn’t worth preserving. What’s happening at the frontiers of AI and ML research? Is this a redux of the early late 1980s, early 1990s enthusiasm with early neural networks and expert systems? Is the current scale of AI/ML data & computation leading us to genuine breakthroughs on machine intelligence? Where do we go after deep learning? Do we need data structures and algorithms to more faithfully mimic the brain?
Jason Mars, University of Michigan
Yubin Park, University of Texas
Mark Ring, University of Texas
Session 4: Biotech
Wet labs and software are getting more intertwined than ever before. In some areas, we’re seeing Moore’s Law-like price drops and increases in capability (as with genome sequencing, which looks like it’s headed to free just like Google Photo storage). And despite all the progress on the technology, healthcare expenses as a percentage of GDP remains stubbornly high. What academic breakthroughs at the intersection of biology and computation will help get us better health outcomes at lower prices?
DJ Kleinbaum, Emerald Therapeutics
Session 5: Security
The lesson of high profile hacks in the last 10 years seems to be this: if the hackers (who have morphed from bored teenagers to uniformed cyber-soliders drawing paychecks-plus-benefits benefits from nation states) want your data, they will get your data. Starting with custom designed phishing attacks, they seem to be able to inject custom malware and APTs into any network. The good news is it’s a great time to be a security startup as organizations rain money on every possible defense. The bad news is that no one’s data is safe. Is there a breakthrough on the horizon that can help make computing trustworthy? Or is the next decade another team-on-team struggle between the good guys and the bad guys?
Raluca Ada Popa, UC Berkeley
Giovanni Vigna, UC Santa Barbara
Kevin Fu, University of Michigan
Dan Boneh, Stanford University