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RECENT VIDEOS

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11.15.2012
26 mins
ILP Video

Connection Science: Reinventing Companies, Governments, and Society in the Wake of Big Data

Alex Pentland
Toshiba Professor of Media Arts and Sciences
Head, Human Dynamics Group
Director, Media Lab Entrepreneurship Program
MIT Media Laboratory
With Big Data we can now begin to actually look at the details of social interaction and how those play out, and are no longer limited to averages like market indices or election results. This is an astounding change. The ability to see the details of the market, of political revolutions, and to be able to predict and control them is definitely a case of Promethean fire?it could be used for good or for ill, and so Big data brings us to interesting times. We're going to end up reinventing what it means to be a company, a country, and even human society.
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11.15.2012
33 mins
ILP Video

Can Financial Engineering Cure Cancer, Solve the Energy Crisis, and Stop Global Warming?

Andrew W. Lo
Charles E and Susan T Harris Professor of Finance
Director, Laboratory for Financial Engineering
MIT Sloan School of Management
As disruptive as the financial crisis has been, the important lessons to be learned from the spectacular failure of financial technologies gone awry may actually pave the way for some of the most significant achievements of the 21st century. In this talk, Prof. Lo will provide a brief overview of the origins of the crisis and how a deeper understanding of human nature may allow financial engineers to focus the enormous power of global financial markets on some of societies most pressing challenges.
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11.15.2012
44 mins
ILP Video

Big Data @ CSAIL

Samuel R. Madden
Professor of Computer Science and Engineering
MIT Department of Electrical Engineering and Computer Science
In this talk, Prof. Madden will summarize recent work at MIT CSAIL in the big data area, including recent work on data management, cloud computing, algorithms, and interfaces and visualization.
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11.15.2012
1 hr 13 mins
ILP Video

Panel Discussion: Big Data

Moderator:
- Scott Kirsner
Panelists:
- Sam Madden
- Sandy Pentland
- Willard Simmons
- Rama Ramakrishnan
Our world is drowning in data! Recently, everyone working on information technology issues is talking about or expressing worry about the rather ill-defined topic of "Big Data."

Every company in every industry is collecting an increasing amount of data: data about its customers; data about its products, data about its processes. Every human with a job or a car a bank account has data about their particulars stored in multiple databases. Anyone with an electronic device (mobile phone, laptop, scanner, camera) is creating a trail of data about their activities. On any given day, an individual moving through the world and initiating any kind of transaction (e.g. purchasing groceries, driving on a toll road, withdrawing money from an ATM), is generating data that is being collected somewhere. Corporate participation and investments in social media (e.g. Facebook, Twitter, LnkedIn), has added to the data we're generating and collecting. Individual participation at these same web sites generates even more data. And finally, an increasing number of sensors in the world, all collecting and broadcasting data in real time, are responsible for even more data that can be used to help make decisions. There is so much data in the world, that we're now looking to services that can store is and back it up elsewhere -- somewhere in "the cloud." And now we need to worry about how secure this data is!

While many companies appear to be focused on collection, storage and the security of this data, some companies (led by online social media and search firms such as Google, Amazon and Facebook) are capitalizing on this data to develop extremely accurate profiles of their customers, in order to provide each individual with services that would be most useful to that person. These are the firms that currently have some of the leading researchers who can interpret, analyze and make decisions on this data. Today, as we continue to face a mountain of data, we are realizing that there is a great shortage of skilled mathematicians, computer scientists, programmers, business analysts and decision makers that know what's relevant to collect, how to interpret this data from multiple sources, and how to make sense of any of it.

This panel brings together researchers and start-up companies in Big Data to explore the following questions:

- What types of problems are most frequently being considered when the topic of "Big Data" comes up? In what industries?
- How does an organization pull together a team of statisticians, experts in machine learning, analysts and decision makers to determine what data is relevant to your organization?'
- Is it just the data? What about the connections?
- How does one know that correlations in data and the conclusions/interpretations that may result really make sense?
- What are examples of an organization's strategy around "Big Data" that the panelists could share?
- Do discussions about where to store and how to access this data (e.g. cloud vs. in-house) often cloud users' discussions about what to do with this data?
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11.15.2012
29 mins
ILP Video

Engineering Alloys, Ten Times Better: How Controlling the Grain Boundaries in Materials Can Improve Performance and Lower Cost (RD2012)

Christopher A. Schuh
Danae and Vasilis (1961) Salapatas Professor of Metallurgy
Department Head / Materials Science and Engineering
Margaret MacVicar Faculty Fellow
MIT Department of Materials Science and Engineering
Most engineering materials are polycrystalline—that is, they are made of many crystals. And because they comprise many crystals, they also contain an even greater number of internal interfaces between those crystals. These interfaces, or grain boundaries, have a remarkably wide range of structures and properties, often spanning orders of magnitude in properties of direct engineering relevance. Modern metallurgical science aims to better understand and control the population of grain boundaries in engineering materials, to bring out the best properties they have to offer, and to mitigate negative properties. This talk will highlight several case studies in grain boundary engineering, spanning from basic scientific studies at MIT to commercial implementation. These studies include examples of how we can control the crystallographic types, geometry, and density of grain boundaries, in materials ranging from commodity metals, to engineering coatings, and even “smart” materials. They are unified by a common value proposition: engineering alloys, ten times better.
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