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T 2/28 Latest Innovations in Machine Learning 6 pm to 9 pm Shearman & Sterling Menlo Park

From: Rob
Sent on: Monday, February 27, 2017 1:19 PM

The deadline to register and pay online is:

2 pm  T 2/28

 

There is a cover charge for this event.

A "YES"  RSVP on our meetup site does not confirm your reservation.

Space is limited.  Please only RSVP "Yes" if you will be attending.

Take advantage of online registration pricing!

Slots are limited.  First come, first served!

In order to attend, you must register and pay here:

https://www.eventbrite.com/e/latest-innovations-in-machine-learning-tickets-31693001621

Please bring your printed ticket to the event.

Or, pay $20 (cash) at the door.

 

"Machine learning is the subfield of computer science that "gives computers the ability to learn without being explicitly programmed" (Arthur Samuel, 1959).[1] Evolved from the study of pattern recognition and computational learning theory in artificial intelligence,[2]machine learning explores the study and construction of algorithms that can learn from and make predictions on data[3] – such algorithms overcome following strictly static program instructions by making data driven predictions or decisions,[4]:2 through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms is infeasible; example applications include spam filtering, detection of network intruders or malicious insiders working towards a data breach,[5] optical character recognition (OCR),[6] search enginesand computer vision.

Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses in prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining,[7] where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning.[4]:vii[8] Machine learning can also be unsupervised[9] and be used to learn and establish baseline behavioral profiles for various entities[10] and then used to find meaningful anomalies.

Within the field of data analytics, machine learning is a method used to devise complex models and algorithms that lend themselves to prediction; in commercial use, this is known as predictive analytics. These analytical models allow researchers, data scientists, engineers, and analysts to "produce reliable, repeatable decisions and results" and uncover "hidden insights" through learning from historical relationships and trends in the data.[11]"

-  Wikipedia

 

Our distinguished panel will discuss the latest developments in machine learning.

Please come with questions and comments.

If you are a machine learning startup and would like space on a demo table for the networking portion of the event, please send an email message to:

[address removed]

 

Put this in the subject header:

Machine Learning Startup Demo Table

 

Agenda

6:00 pm to 7:00 pm   Check In, Food, Networking Reception

7:00 pm to 8:30 pm    Panel Discussion,  Q & A

8:30 pm to 9:00 pm    More Networking

 

About the Moderator:

 

Matt Berkowitz is a partner in Shearman & Sterling’s Litigation Group.  His practice focuses on patent litigation in federal district courts and the International Trade Commission (ITC), as well as post-grant proceedings in the US Patent and Trademark Office.  Mr. Berkowitz has experience across a broad spectrum of technologies, including consumer electronics, memory systems, hybrid vehicles, pre-collision vehicle systems, aircraft components, and pharmaceuticals.

Mr. Berkowitz has been named one of the “Top 40 under 40 Intellectual Property Lawyers” by the American Society of Legal Advocates as well as a 2015 New York Metro Rising Star by Super Lawyers.  In 2016, Benchmark Litigation recognized Mr. Berkowitz in its inaugural “Under 40 Hot List.”

 

About the Panelists:

(More panelists TBA)

Ed Fernandez  is an early stage start up venture capitalist,  investor, advisor  and entrepreneur. Ed is a member of the Young Presidents' Organization, a worldwide non-profit association of leaders and entrepreneurs under 45.

He is a partner and co-founder of Naiss.io, a venture capital and advisory boutique in Palo Alto focusing on technology based start ups (machine learning in particular) and early stage companies, preparing them for M&A's and IPO's in alternative markets.  Ed  began investing in startups in 2012 as an investor and venture partner in a $10MM start up venture fund with 12 companies in their portfolio. He is currently a board observer and an advisor to some of them.

Ed's first computer was a Commodore 64 back in the 1980s, a 64Kb RAM machine running on a 1 MHz CPU.  An avid assembler programmer at the time, he became an amateur hacker.

Ed lives in Palo Alto with his family. He is mission-driven, and a parallel multi-tasker with nerd traits: curious, ENTP, self-made, resilient, highly functional and a finisher of Madrid's marathon (all adding up to a euphemism for modern ADHD).

Twitter: @efernandez

 

Patrick Ehlen is Chief Scientist at Loop AI Labs. He earned a PhD in Cognitive Psychology from the New School for Social Research, did a post-doc at Stanford University in the Computational Semantics Laboratory where he worked on the $200M DARPA CALO AI project, and has helped to develop semantic technologies at AT&T Labs-Research, Dragon Systems, and AT&T Interactive before joining Loop AI.

Patrick has pursued artificial intelligence since 1977, when he read Arthur C. Clarke’s 2001: A Space Odyssey shortly before his econometrician father bought a DEC PDP-11 to keep in their home. In 1990, inspired by the recently-published Parallel Distributed Processingvolumes, he programmed his first neural network.

Patrick has produced 47 research publications and has been awarded four U.S. patents in the areas of computational semantics, cognitive linguistics, psycholinguistics, word sense disambiguation, human concept learning, and artificial intelligence. His work is cited in over 350 papers.

He lives in San Francisco.

 

David Gerster is Vice President of Data Science at BigML, an organization founded in 2010 "with the mission of making machine learning easy and beautiful for everyone".  David's role is to promote the idea that data science is easy by speaking at conferences and teaching workshops. Since joining BigML in July 2013, he has spoken at CERN, Big Data Spain, Papis.io, DataLead (UC Berkeley), DataBeat (VentureBeat), and more than a dozen other events.

Prior to BigML, David held postions at Groupon and Yahoo.  At Groupon, he built an elite data science team that trained the first machine-learned models for mobile deal relevance. At Yahoo, he led the project to collect billions of URL clickstreams in Hadoop and use them to improve Yahoo’s main web search algorithm.

David holds an MBA from the University of California at Berkeley and a bachelor’s degree from Harvard University.

linkedin.com/in/gerster.

 

Jose Luis Agúndez leads the Big Data Silicon Valley technology innovation team at AT&T, exploring innovative concepts with data analytics, trusted computing and machine intelligence. Prior to moving to the San Francisco Bay Area, he led the Big Data Innovation program for data monetization at Telefonica's digital services in Europe.

Jose Luis is a computer scientist who went from C developer at startups to senior architect in complex real-time products, building a solid technology background. For the last 14 years, he has focused on discovering differential technology and ways to take it to market, including product and business development, partnerships and Venture Capital. He has five granted patents, and has contributed to Standardization (3G) and Licensing (4G patent pool).

Jose Luis is a venture capital investor, speaker at public events, panelist and blogger, with a passion for personal data privacy and the applications of Big Data to society and people. He co-founded the consortium DataTransparencyLab.org and authored a series of articles on the "Data-sharing economy" (http://en.blogthinkbig.com/?s=ciberjos)



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