EE380 Computer Systems Colloquium

Leela is a semantic artificially intelligent agent modeled on the theories of Jean Piaget. She builds increasingly abstract semantic models of the world from her experiences of exploration, play, and experimentation. As an agent she is able to formulate, execute, and explain her own plans.

This talk will provide an introduction to Leela's background and design and will show her in action.

How do we choose and remember our secure access codes? So far biometrics, password managers, and systems like Facebook connect have not been able to guarantee the security we need. Remembering dozens of different passwords becomes a usability nightmare. 25+ years into online experience, each of us have many hard-to-remember or easy-to-guess passwords, with all the risks and frustration they imply.

We describe experiments showing how to make easy to remember codes and passwords and the system to make them, called Cue-Pin-Select. It can generate (and regenerate) passwords on the go using only the user's brain for computation. It has the advantage of creating memorable passwords, not requiring any external storage or computing device, and can be executed in less than a minute to create a new password.

This talk will summarize recent usable security work done with Ted Selker. It will start with the Cue-Pin-Select algorithm, cover an improvement we found that applies to all passphrase-based security systems, and explain some of the work currently underway to have better tools to study password schemes and human computation.

New social movements, technologies, and public-health initiatives often struggle to take off, yet many diseases disperse rapidly without issue. Can the lessons learned from the viral diffusion of diseases be used to improve the spread of beneficial behaviors and innovations? In this talk, I discuss several new breakthroughs in the science of network diffusion, and how these advances have improved our understanding of how changes in societal behavior--in voting, health, technology, and finance--occur, and the ways social networks can be used to influence how they propagate. The findings show that the same conditions accelerating the viral expansion of an epidemic unexpectedly inhibit the spread of behaviors. I show how many of the most well-known, intuitive ideas about how social networks function have in fact been responsible for causing past diffusion efforts to fail. I present new findings and new network methods that have been used to enable social change efforts to succeed much more effectively.

For futher reading please consult:

• Research Group Site: https://ndg.asc.upenn.edu/
• Book Site: https://www.amazon.com/How-Behavior-Spreads-Contagions-Analytical/dp/0691175314

***The talk for October 31, 2018 is drawn from our back list of videos and will not be a live presentation. This talk was originally given November 1, 2017. ***
We have been planning to have a speaker for this slot to address the issues of elections in a technological state. Despite many discussions and invitations, we have been unable to find anyone willing to take on speak about the current juncture of politics, technology, and economics.

The U.S. Supreme Court is considering a case this term, Gill v Whitford, that might lead to the first constitutional constraints on partisanship in redistricting. Eric McGhee is the inventor of the efficiency gap, a measure of gerrymandering that the court is considering in the case. He will describe the case's legal background, discuss some of the metrics that have been proposed for measuring gerrymandering, and reflect on the role of social science in the litigation.

Related NPR Science Friday Talk (Nov 3):

Does Math Have A Place In The Courtroom. Audio is 17 minutes.
So is it possible that these Ivy League-educated Supreme Court justices really don't understand the math of this case? Oliver Roeder, senior writer for FiveThirtyEight joins Ira to discuss whether the Supreme Court is allergic to math, and what that means for future cases. And Moon Duchin, associate professor of mathematics at Tufts University, returns to discuss the best math to use for rooting out gerrymandering.

TBA

A focus on energy efficiency in the late CMOS design era, requires extra careful attention to system reliability and resilience to hardware-sourced errors. At the same time, the emergence of AI (cognitive) applications as a key growth segment is quite obvious. This talk will attempt to address the special challenges that next generation AI (or cognitive) systems pose, with a particular focus on next generation cognitive IoT architectures. We will discuss this primarily from the point of view of providing energy-efficient resilience in environments that are likely to have built-in vulnerability to errors. Such uncertainty stems not just from potentially error-prone (late CMOS) hardware designed for extreme efficiency, but also from algorithmic brittleness of the most prevalent forms of machine learning/deep learning (ML/DL) solution strategies today. In that context, we will briefly examine the promise of the Adaptive Swarm Intelligence (ASI) architectural paradigm that we have recently started investigating at IBM Research. This is a form of distributed or decentralized computing applied to the world of mobile cognitive IoT, backed by resilient support from back-end cloud (server) systems. In addition to examining the promises of inherent system architectural scalability and in-field, continuous learning that ASI offers, we will argue (albeit philosophically!) about why this could open the door to new models of self-aware systems that mimic cooperative and conscious problem solving in a human setting.

The Stanford EE Computer Systems Colloquium (EE380) meets on Wednesdays 4:30-5:45 throughout the academic year. Talks are given before a live audience in Room B03 in the basement of the Gates Computer Science Building on the Stanford Campus. The live talks (and the videos hosted at Stanford and on YouTube) are open to the public.

Stanford students may enroll in EE380 to take the Colloquium as a one unit S/NC class. Enrolled students are required to keep and electronic notebook or journal and to write a short, pithy comment about each of the ten lectures and a short free form evaluation of the class in order to receive credit. Assignments are due at the end of the quarter, on the last day of examinations.

EE380 is a video class. Live attendance is encouraged but not required. We (the organizers) feel that watching the video is not a substitute for being present in the classroom. Questions are encouraged.

Many past EE380 talks are available on YouTube, see the EE380 Playlist.

Indexes are models: a B-Tree-Index can be seen as a model to map a key to the position of a record within a sorted array, a Hash-Index as a model to map a key to a position of a record within an unsorted array, and a BitMap-Index as a model to indicate if a data record exists or not. In this talk, we take this premise and explain how existing database index structures can be replaced with other types of models, which we term learned indexes. The key idea is that a model can learn the sort order or structure of indexed data and use this signal to effectively predict the position or existence of records. We offer theoretical analysis under which conditions learned indexes outperform traditional index structures and we will delve into the challenges in designing learned index structures. Through addressing these challenges, our initial results show that learned indexes are able to outperform cache-optimized B-Trees by up to 70% in speed while saving an order-of-magnitude in memory over several real-world data sets. Finally, we will discuss the broader implications of learned indexes on database design and future directions for the ML for Database Systems research.

TBA

The Stanford EE Computer Systems Colloquium (EE380) meets on Wednesdays 4:30-5:45 throughout the academic year. Talks are given before a live audience in Room B03 in the basement of the Gates Computer Science Building on the Stanford Campus. The live talks (and the videos hosted at Stanford and on YouTube) are open to the public.

Stanford students may enroll in EE380 to take the Colloquium as a one unit S/NC class. Enrolled students are required to keep and electronic notebook or journal and to write a short, pithy comment about each of the ten lectures and a short free form evaluation of the class in order to receive credit. Assignments are due at the end of the quarter, on the last day of examinations.

EE380 is a video class. Live attendance is encouraged but not required. We (the organizers) feel that watching the video is not a substitute for being present in the classroom. Questions are encouraged.

Many past EE380 talks are available on YouTube, see the EE380 Playlist.

Today's society is rapidly advancing towards robotics systems that interact and collaborate with humans, e.g., semi-autonomous vehicles interacting with drivers and pedestrians, medical robots used in collaboration with doctors, or service robots interacting with their users in smart homes. In this talk, I will first discuss interactive autonomy, where we develop algorithms for autonomous systems that influence humans, and further leverage these effects for better safety, efficiency, coordination, and estimation. I will then focus on our efficient active learning methods to build predictive models of humans's preferences by eliciting comparisons from a mixed set of humans, and further analyzing the generalizability and robustness of the learned human models for safe and seamless interaction with robots.

The Stanford EE Computer Systems Colloquium (EE380) meets on Wednesdays 4:30-5:45 throughout the academic year. Talks are given before a live audience in Room B03 in the basement of the Gates Computer Science Building on the Stanford Campus. The live talks (and the videos hosted at Stanford and on YouTube) are open to the public.

Stanford students may enroll in EE380 to take the Colloquium as a one unit S/NC class. Enrolled students are required to keep and electronic notebook or journal and to write a short, pithy comment about each of the ten lectures and a short free form evaluation of the class in order to receive credit. Assignments are due at the end of the quarter, on the last day of examinations.

EE380 is a video class. Live attendance is encouraged but not required. We (the organizers) feel that watching the video is not a substitute for being present in the classroom. Questions are encouraged.

Many past EE380 talks are available on YouTube, see the EE380 Playlist.

Python is a dynamically typed language, and some of its appeal derives from this. Nevertheless, especially for large code bases, it would be nice if a compiler could find type errors before the code is even run. Optional static type checking promises exactly this, and over the past four years we have successfully introduced this feature into Python 3. This talk introduces the type system we've adopted and the syntax used for type annotations, some tips on how to get started with a large existing code base, and our experience using the 'mypy' type checker at Dropbox. The entire system is open source, and has also been adopted by other companies such as Lyft, Quora and Facebook.