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Data Big and Small
February 16–17, 2017
San Francisco

FIELD TRIP
SRI International
February 15, 2017
9:15AM–4:00PM

Library Selection
Data for the People: How to Make Our Post-Privacy Economy Work for You (Basic Books, Jan. 2017)
By Andreas Weigend


 




agenda


WEDNESDAY, FEBRUARY 15
 

Field Trip to SRI International, Menlo Park
SRI International (formerly Stanford Research Institute) is a non-profit research institute that for 70 years has been at the forefront of many of Silicon Valley’s most remarkable innovations, including ground-breaking advances in artificial intelligence, network communications, security, robotics, health, and STEM education. Today, researchers across SRI collaborate with clients and partners to develop solutions to pressing problems, ranging from cybersecurity to meeting the challenges of an aging population.

9:15 AM

Buses depart from hotel
(independent drivers should use parking lot on Laurel St at Mielke Dr)

10:30 AM

Tour begins
Lab visits will span a range of disciplines: Robotics (MOTOBOT Yamaha-motorcycle-riding humanoid, PROXI walking humanoid, and robotic surgery); a soft-exoskeleton wearable for aging populations; advanced space radar applications; IoT security solutions; and rapid triage of radiation exposure.

2:30 PM

Buses return to hotel (approximate arrival, 4:00 PM)

6:00 PM
First-Timers Reception—Terrace Room, 2nd Level
6:30 PM
Welcome Reception—Terrace Room, 2nd Level
7:00 PM
Welcome Dinner (Buffet)—Terrace Courtyard, 2nd Level

THURSDAY, FEBRUARY 16
7:30 AM

Breakfast—Gallery 1 (Ballroom Foyer, First Level)

8:30 AM
Len Kleinrock, TTI/Vanguard Advisory Board
Conference Welcome
8:50 AM

Elan Kriegel (@blue_labs), BlueLabs
From Big Data to the Individual
Predictive modeling is a way to maximize return on investment when dealing with large sets of individuals whose behavior is hard to predict and where the costs of reaching them can be high—whether the set is likely voters in an election or likely purchasers in a supermarket. Based on the model, specialized controlled 2 experiments can be conducted that pinpoint individuals who are most likely to respond to a message through a certain medium or at a certain time. Other targets that fit that same model can then be identified. Other algorithms can then identify the television programs and websites that provide the best bang for a client’s buck—that is, identifying who is most likely to change their behaviors and attitudes as a result of outreach.

9:35 AM

Ben Horowitz (@bhorowitz), Co-Founder and Partner, Andreessen Horowitz, and Len Kleinrock, TTI/Vanguard Advisory Board
Fireside Chat: The Hard Thing About Hard Data
Management and self-help books never talk about what’s really hard—which is managing situations for which there are no recipes. There’s no recipe for leading a group of people out of trouble; there’s no recipe for making a series of hit songs or playing NFL quarterback; there’s no recipe for building a high-tech company. Here are some other questions for which there are no recipe answers: Is machine learning an important area for investors? Will machine learning and other developments dramatically reduce the number of jobs that are done by humans? Overall, will that be good for society? How best should this generation’s college graduates prepare for the next 50 years?

10:25 AM
Coffee Break—Ballroom Foyer
10:55 AM

Emil Eifrem (@emileifrem), Chief Executive Officer, Neo Technology
How Graph Technology Cracked the Panama Papers
2016 was quite the year for graph databases. Many of the world’s most influential organizations, from Walmart to eBay, doubled down on their use of graph technology, recognizing the value of the relationships between their data and leveraging that to change the way they do business. Graph technology also famously played an integral role in helping the International Consortium of Investigative Journalists crack the Panama Papers. While 2016 showed the huge value of graph technology for specific use cases, in the near future graphs will become a standard part of every major enterprise’s infrastructure. This presentation will look at how the explosion of graph technology will transform the way we store, analyze and leverage data, from Walmart’s real-time recommendation engine to finding offshore tax structures buried within the 11.5 million documents of the Panama Papers.

11:40 AM

Lindsey Dillon (@lindseyld), Assistant Professor, Department of Sociology, UC Santa Cruz, and Steering Committee Chair, Environmental Data and Governance Initiative and Matt Price, Lecturer, Faculty of Information and Department of History, University of Toronto
Keeping Environmental Data Public
The Environmental Data and Governance Initiative is building online tools, events, and research networks to proactively archive public environmental data and ensure its continued publicly availability. It is also monitoring changes to federal regulation, enforcement, research, funding, websites, and general management at agencies including EPA, DoE, NASA, NOAA, and OSHA. It aims to create an open, collaborative network of individuals, non-profits, universities, and companies who believe that science and data are vital for environmental governance.

12:10 PM
Members’ Working Lunch—Terrace Courtyard, 2nd Level
1:25 PM
Claudia Perlich (@claudia_perlich), Chief Scientist, Dstillery
Data-Driven Marketing
Digital advertising is one of the largest and most open playgrounds for machine learning, data mining, and related analytic approaches. This development is fueled by unparalleled access to consumer activity across all digital devices and the rise of programmatic advertising, where about 100 Billion advertising opportunities are sold daily in real time auctions. Predictive marketing is one of the most effective advanced automated machine learning applications, and its future could very well make advertising truly meaningful for both the consumer and the brand. But the very power of this technology can also lead to rather unintended outcomes and is in fact susceptible to manipulation by malicious third parties.
2:10 PM
Gourab De, DataRobot
Automated Machine Learning
Machine learning and data science are transforming businesses, but, ironically, data science has become a business that needs to be transformed. The right machine learning platform can help data scientists build and deploy accurate predictive models more rapidly, in a wide variety of fields. One of the biggest hurdles is not technical—getting regulators to accept models not created by humans.
2:50 PM
Coffee Break—Ballroom Foyer
3:20 PM

Mike O'Neill, Chief Science Officer, TruTags
Tagging Medications for Uniqueness
Globalization, outsourcing, and e-retailing have significantly complicated the modern supply chain—and made it possible for illegitimate products to enter the supply chain and legitimate products to be diverted. Counterfeiting and diversion of medicine in particular poses significant threats to patient safety and has also resulted in substantial industry losses. Historically, product packaging has been the primary means for distinguishing authentic and suspect products. However, technological advances have made it increasingly easy for counterfeiters to replicate packaging and other security products such as holograms. Direct tagging of the product is a solution that can provide a secure and economic means of authenticating product; in the case of medicine, the product tag can take the form of a covert, safe and edible microtag. When a suspect event occurs in the supply chain, on-product authentication can yield instant and unequivocal product identification in seconds. This capability is critical in reducing supply chain disruptions such as product recalls and quarantines.

4:05 PM

Brian David Johnson (@BDJFuturist), Futurist, Frost and Sullivan
The Coming Age of Sentient Tools
Intelligent tools are aware, can make sense of their surroundings, and are socially cognizant of the people who are using them. Sentient tools are the next step in the development of computational systems, smart cities and environments, autonomous systems, artificial intelligence (AI), big data and data mining, and the Internet of Things (IoT). These tools are “what comes next” after five decades of advances in computational, sensing, and communications technologies. Sentient tools are “aware” in a sense comparable to a human level of consciousness. They are not meant to mimic, mirror, or replace human interaction. These tools are designed for specific physical and virtual tasks that could be vastly complex but are not meant to replace humans. Indeed, they are meant to work alongside the human labor force. The rise of sentient tools will have a significant impact on the global work force and education, leaving practically no industry unaffected.

4:50 PM

End of First Conference Day

5:45 PM
6:00 PM
6:30 PM
Buses to Dinner - (Ozumo Restaurant, 161 Steuart St, San Francisco) 
Reception
Dinner

FRIDAY, FEBRUARY 17
7:30 AM

Breakfast—Ballroom, Salon 3, 1st Level

8:30 AM

Jeff Jonas (@jeffjonas), Data Scientist
Context Computing
If machine learning is ever to move from hype to real-world value—especially in such realms as the processing of natural language text, classification and identity recognition from imagery, and detection of weak signals caused by bad, missing, or falsified data—we need tools that allow context to emerge and evolve, in real time. One tactic that is proving to be effective is the ability to incrementally integrate diverse observations into entity resolved graphs.

9:15 AM

Jans Aasman, (@jansaasman), Chief Executive Officer, Franz
Semantic Graph Databases and Analytics
Cognitive Probability Graphs are remarkable for their practicality as well as their potential. The knowledge gained—through the confluence of machine learning, semantics, visual querying, graph databases, and big data—not only displays links between objects, but also quantifies the probability of their occurrence. This approach will be transformative across numerous business verticals.

9:55 AM

Peter Brodsky, HyperScience
Several Counterintuitive Aspects of AI
Common wisdom fails us when it comes to AI. It dictates that experts do better than generalists in their area of expertise; that we can reason about what AI is good at; and that because AI continually improves, data will continue to be the great business moat that it has been for the past decade. In each case, the common wisdom is wrong.

10:40 AM
Coffee Break—Ballroom Foyer
11:10 AM

Andreas Weigend (@ourdata), Social Data Lab
Data for the People
Marshall McLuhan said, “As information itself becomes the largest business in the world, data banks know more about individual people than the people do themselves. The more the data banks record about each one of us, the less we exist.” As true as that was in 1970, it’s far truer almost 50 years later. What we need is a new deal whereby individuals, companies, and government can each tap into the value of this data, while retaining our individual privacy, convenience, identity, control, and, yes, existence.

12:00 PM

Andreas Weigend, Claudia Perlich, and Gam Dian
Workshop: Creating Possibilities with Data Big and Small
How do we, as organizations, provide value back to users?

12:40 PM
Members’ Working Lunch—Ballroom, Salon 3, 1st Level
1:55 PM

Jana Eggers (@jeggers), Chief Executive Officer, Nara Logics
Synaptic Intelligence for Better Decisions
For recommendation and matching systems to take a big leap forward, we need to explore a new path in artificial intelligence that moves beyond traditional neural networks. A focus on the mathematical logic of how brain circuits compute provides several benefits, notably that each and every recommendation has explicit connections in the network that aren’t “blown up” every time a network is trained—which is how even the most cutting-edge machine learning systems work today. The resulting synaptic intelligence algorithm is continually learning and getting smarter as new information is added.

2:35 PM

Eric Haseltine, TTI/Vanguard Advisory Board, & Chris Gilbert, M.D.
Democratizing Data Discovery
Data exploration used to be sole province of governments and large organizations. But a new realm of discovery has just emerged alongside the traditional ones. Massive open datasets and professional-level tools from Google and others give us the ability to see small but significant undiscovered phenomena. This gives anyone with a little imagination the ability to connect new dots in a wide variety of fields, from oncology to product marketing, not yielding scientific results so much as suggestive correlations worthy of study. In other words, discovery democracy isn’t about answers so much as finding the right questions. Which, as every researcher knows, is half the battle—one you can now consistently win right from your web browser.

3:15 PM

Bob Lucky, TTI/Vanguard Advisory Board
Conference Reflections

4:00 PM
Meeting Closes


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