Videos uploaded by user “Association for Computing Machinery (ACM)”
CACM Jun 2019 - 2018 ACM Turing Award
Once treated by the field with skepticism (if not outright derision), the artificial neural networks that 2018 ACM A.M. Turing Award recipients Geoffrey Hinton, Yann LeCun, and Yoshua Bengio spent their careers developing are today an integral component of everything from search to content filtering. So what of the now-red-hot field of deep learning and artificial intelligence (AI)? Here, the three researchers share what they find exciting, and which challenges remain. In this video, Yoshua Bengio, Geoffrey Hinton, and Yann LeCun discuss their breakthrough work and the path that led the three of them to receiving the 2018 ACM A.M. Turing Award. This interview is also printed in “Reaching New Heights with Artificial Neural Networks,” in the June 2019 issue of Communications of the ACM.
CACM Mar. 2018 - A Programmable Programming Language
In the ideal world, software developers would analyze each problem in the language of its domain and then articulate solutions in matching terms. In the real world, however, programmers use a mainstream programming language someone else picked for them. The Racket project seeks to address this problem by utilizing the emerging idea of language-oriented programming. In this video, Matthias Felleisen discusses "A Programmable Programming Language" (cacm.acm.org/magazines/2018/3/225475), a Contributed Article in the March 2018 issue of Communications of the ACM. (Racket is available at http://racket-lang.org/).
CACM Mar. 2019 - The Seven Tools of Causal Inference
The dramatic success in machine learning has led to an explosion of artificial intelligence (AI) applications and increasing expectations for autonomous systems that exhibit human-level intelligence. These expectations have, however, met with fundamental obstacles that cut across many application areas. One such obstacle is adaptability, or robustness. Machine learning researchers have noted current systems lack the ability to recognize or react to new circumstances they have not been specifically programmed or trained for. Intensive theoretical and experimental efforts toward "transfer learning," "domain adaptation," and "lifelong learning"4 are reflective of this obstacle. In this video, Judea Pearl discusses "The Seven Tools of Causal Inference with Reflections on Machine Learning," a Contributed Article in the March 2019 Communications of the ACM. Read the full article here: https://cacm.acm.org/magazines/2019/3/234929-the-seven-tools-of-causal-inference-with-reflections-on-machine-learning/fulltext
CACM Oct. 2018 - Human-Level Intelligence or Animal-Like Abilities?
The recent successes of neural networks in applications like speech recognition, vision, and autonomous navigation has led to great excitement by members of the artificial intelligence (AI) community, as well as by the general public. Over a relatively short time, by the science clock, we managed to automate some tasks that have defied us for decades, using one of the more classical techniques due to AI research. The triumph of these achievements has led some to describe the automation of these tasks as having reached human-level intelligence. This perception, originally hinted at in academic circles, has gained momentum more broadly and is leading to some implications. In this video, Adnan Darwiche discusses "Human-Level Intelligence or Animal-Like Abilities?" (https://cacm.acm.org/magazines/2018/10/231373), a Contributed Article in the October 2018 Communications of the ACM.
Why I Belong to ACM
Bryan Cantrill, Vice President of Engineering at Joyent, Ben Fried, Chief Information Officer at Google, and Theo Schlossnagle, Chief Executive Officer at OmniTI, discuss motivations and benefits of joining the Association for Computing Machinery (ACM). To join ACM: http://www.acm.org/join/professional/PWEBVID More information about ACM: http://www.acm.org
ACM A.M. Turing Award - Whitfield Diffie and Martin E. Hellman
Whitfield Diffe and Martin Hellman received the 2015 ACM A.M. Turing Award for critical contributions to modern cryptography. The ability for two parties to use encryption to communicate privately over an otherwise insecure channel is fundamental for billions of people around the world. On a daily basis, individuals establish secure online connections with banks, e-commerce sites, email servers and the cloud. Diffie and Hellman's groundbreaking 1976 paper, "New Directions in Cryptography," introduced the ideas of public-key cryptography and digital signatures, which are the foundation for most regularly-used security protocols on the Internet today. The Diffie-Hellman Protocol protects daily Internet communications and trillions of dollars in financial transactions.
Program your next server in Go
Author: Sameer Ajmani Abstract: Go is a new general-purpose programming language for professionals who build and maintain production systems. Hundreds of companies and thousands of open-source projects are using Go, including Google, DropBox, Docker, Apcera, and SoundCloud. This talk will present Go to the experienced service developer and show how its radically simple approach to software construction can make teams more productive. ACM DL: http://dl.acm.org/citation.cfm?id=2960078 DOI: http://dx.doi.org/10.1145/2959689.2960078
UIST 2015 Technical Program Preview
A glimpse at the exciting technical program coming up at UIST 2015 in Charlotte, 8-11 November 2015. www.uist.org ----------------------------- Music is Big Car Theft by Jason Shaw http://freemusicarchive.org/music/Jason_Shaw/Audionautix_Tech_Urban_Dance/TU-BigCarTheft
CACM Mar. 2016 - Lessons Learned from 30 Years of MINX
Andrew S. Tanenbaum, the author of the MINX operating system, discusses "Lessons Learned from 30 Years of MINIX" (cacm.acm.org/magazines/2016/3/198874), his Contributed Article in the March 2016 CACM.
Bryan Cantrill on why he belongs to ACM
Bryan Cantrill, Vice President of Engineering at Joyent, on ability of the Association for Computing Machinery (ACM) to inspire professional excellence, broaden personal horizons, and bridge the academic/practitioner divide. To join ACM: http://www.acm.org/join/professional/PWEBVID More information about ACM: http://www.acm.org
CACM June 2014 - Leslie Lamport, recipient of the 2013 ACM A.M. Turing Award
ACM's 2013 A.M. Turing Award recipient Leslie Lamport was cited for discovering the field of distributed computing systems that work as intended, making it possible for computers to cooperate, avoid error, and reach consensus. The June 2014 issue of Communications of the ACM details Lamport's innovative advances in an article (cacm.acm.org/news/175166), a Q&A, and an original video highlighting some of his renowned colleagues. In his own voice, he asserts that the best logic for stating things clearly is mathematics, a concept, he notes, that some find controversial. Assessing his body of work, he concludes that he created a path that others have followed to places well beyond his imagination. cacm.acm.org
ACM Queue Inspirations with Jim Waldo HD
Jim Waldo, Chief Technology Officer at Harvard University, discusses his work on data de-identification, and the question of how to protect user privacy while aggregating accurate models. Download the Queue app here: https://queue.acm.org/app/landing.cfm
From L3 to seL4 what have we learnt in 20 years of L4 microkernels?
The L4 microkernel has undergone 20 years of use and evolution. It has an active user and developer community, and there are commercial versions which are deployed on a large scale and in safety-critical systems. In this paper we examine the lessons learnt in those 20 years about microkernel design and implementation. We revisit the L4 design papers, and examine the evolution of design and implementation from the original L4 to the latest generation of L4 kernels, especially seL4, which has pushed the L4 model furthest and was the first OS kernel to undergo a complete formal verification of its implementation as well as a sound analysis of worst-case execution times. We demonstrate that while much has changed, the fundamental principles of minimality and high IPC performance remain the main drivers of design and implementation decisions. In the ACM Digital Library: http://dl.acm.org/citation.cfm?id=2522720
CACM Mar 2015 - Local Laplacian Filters  Edge aware Image Processing with a Laplacian Pyramid HD
Co-author Sylvain Paris discusses "Local Laplacian Filters: Edge-aware Image Processing with a Laplacian Pyramid," the Research Highlights article published in the March 2015 Communications of the ACM (cacm.acm.org/magazines/2015/3/183587).
Celebrating 50 Years of the ACM A.M. Turing Award and Computing's Greatest Achievements
Since its inauguration in 1966, the ACM A. M. Turing Award has recognized major contributions of lasting importance in computing. Through the years, it has become the most prestigious technical award in the field, often referred to as the “Nobel Prize of computing.” During the next several months, ACM will celebrate 50 years of the Turing Award and the visionaries who have received it. Our aim is to highlight the significant impact of the contributions of the Turing Laureates on computing and society, to look ahead to the future of technology and innovation, and to help inspire the next generation of computer scientists to invent and dream. Our celebration will culminate with a conference on June 23 - 24, 2017 at the Westin St. Francis in San Francisco with lively moderated discussions exploring how computing has evolved and where the field is headed. We hope you can join us there, or via the web—we will be streaming the sessions in real time. For more information please visit, http://www.acm.org/turing-award-50
ACM Turing Award 2012
Shafi Goldwasser, Silvio Micali Receive 2012 ACM Turing Award For Advances In Cryptography Shafi Goldwasser and Silvio Micali laid the foundations of modern theoretical cryptography, taking it from a field of heuristics and hopes to a mathematical science with careful definitions and security models, precise specifications of adversarial capabilities, and rigorous reductions from formally defined computational problems. Their results, jointly and with others, established the now-standard definitions of security for the fundamental primitives of encryption and digital signatures, and provided exemplary implementations meeting the stated security objectives. Even more importantly, their work helped to establish the tone and character of modern cryptographic research. Jointly and in collaboration with others, they provided stunning innovations in the form of random functions, interactive proofs, and zero-knowledge protocols, with implications beyond cryptography to theoretical computer science in general. http://amturing.acm.org
CACM July 2016 - The Rise of Social Bots
A social bot is a computer algorithm that automatically produces content and interacts with humans on social media, trying to emulate and possibly alter their behavior. These bots have become more prevalent on social networking sites in the past few years. In this video, Emilio Ferrara discusses "The Rise of Social Bots" (cacm.acm.org/magazines/2016/7/204021), a Review Article in the July 2016 Communications of the ACM.
Inside Websockets
Author: Leah Hanson Abstract: This talk will focus on how WebSockets work -- the details of the protocol and why they are the way they are. Protocol design is about tradeoffs, and if you pick the wrong tradeoff, you may regret it for a very long time. Were going to take a look at the tradeoffs that the WebSockets protocol made and talk about how you can apply the same principles to your own protocols. ACM DL: http://dl.acm.org/citation.cfm?id=2960084 DOI: http://dx.doi.org/10.1145/2959689.2960084
Celebrating 50 Years of Computing's Greatest Achievements
Since its inauguration in 1966, the ACM A. M. Turing Award has recognized major contributions of lasting importance in computing. Through the years, it has become the most prestigious technical award in the field, often referred to as the “Nobel Prize of computing.” During the next several months, ACM will celebrate 50 years of the Turing Award and the visionaries who have received it. Our aim is to highlight the significant impact of the contributions of the Turing Laureates on computing and society, to look ahead to the future of technology and innovation, and to help inspire the next generation of computer scientists to invent and dream. Our celebration will culminate with a conference on June 23 - 24, 2017 at the Westin St. Francis in San Francisco with lively moderated discussions exploring how computing has evolved and where the field is headed. We hope you can join us there, or via the web—we will be streaming the sessions in real time. For more information please visit, http://www.acm.org/turing-award-50
Discovery of Meaningful Rules in Time Series
Authors: Mohammad Shokoohi-Yekta, Yanping Chen, Bilson Campana, Bing Hu, Jesin Zakaria, Eamonn Keogh Abstract: The ability to make predictions about future events is at the heart of much of science; so, it is not surprising that prediction has been a topic of great interest in the data mining community for the last decade. Most of the previous work has attempted to predict the future based on the current value of a stream. However, for many problems the actual values are irrelevant, whereas the shape of the current time series pattern may foretell the future. The handful of research efforts that consider this variant of the problem have met with limited success. In particular, it is now understood that most of these efforts allow the discovery of spurious rules. We believe the reason why rule discovery in real-valued time series has failed thus far is because most efforts have more or less indiscriminately applied the ideas of symbolic stream rule discovery to real-valued rule discovery. In this work, we show why these ideas are not directly suitable for rule discovery in time series. Beyond our novel definitions/representations, which allow for meaningful and extendable specifications of rules, we further show novel algorithms that allow us to quickly discover high quality rules in very large datasets that accurately predict the occurrence of future events. ACM DL: http://dl.acm.org/citation.cfm?id=2783306 DOI: http://dx.doi.org/10.1145/2783258.2783306
CACM May 2018 - Speech Emotion Recognition
In the 22 years since what is arguably first research paper on the topic of Speech Emotion Recognition was published, the field has come a long way -- SRE technologies by the names of Alexa, Cortana, Siri, and many others are now on the consumer market on a broader basis than ever. But do any of them truly notice our emotions and react to them like a human conversational partner would? In this video, Björn Schuller discusses "Speech Emotion Recognition: Two Decades in a Nutshell, Benchmarks, and Ongoing Trends," (cacm.acm.org/magazines/2018/5/227191), a Review Article in the May 2018 Communications of the ACM.
CACM July 2018 - Digital Nudging: Guiding Online User Choices through Interface Design
Life is full of choices, often in digital environments. People interact with e-government applications; trade financial products online; buy products in Web shops; book hotel rooms on mobile booking apps; and make decisions based on content presented in organizational information systems. All such choices are influenced by the environments in which they take place, and designers of these environments can subtly guide users' behavior by gently "nudging" them toward certain choices. “Digital Nudging: Guiding Online User Choices through Interface Design,” a contributed article in the July issue of Communications of the ACM, shows how designers can consider the effects of nudges when designing digital choice environments.
Andrew Ng on Building a Career in Machine Learning
Title: Break Into AI: A Q&A with Andrew Ng on Building a Career in Machine Learning Speaker: Andrew Ng Date: 12/4/2018 Abstract Andrew Ng will share tips and tricks on how to break into AI. He will discuss some of the most valuable skills for today's machine learning engineers, how to gain the experience to successfully switch careers, and how to build a habit of lifelong learning. He will also take questions from aspiring engineers and business professionals who want to work on AI-powered products. SPEAKER Andrew Ng, General Partner, AI Fund; CEO, Landing AI; Adjunct Professor, Stanford University Dr. Andrew Ng, a globally recognized leader in AI, is a General Partner at AI Fund and CEO of Landing AI. As the former Chief Scientist at Baidu and the founding lead of Google Brain, he led the AI transformation of two of the world’s leading technology companies. A longtime advocate of accessible education, Dr. Ng is the Co-founder of Coursera, an online learning platform, and founder of deeplearning.ai, an AI education platform. Dr. Ng is also an Adjunct Professor at Stanford University’s Computer Science Department. MODERATOR Juan Miguel de Joya, UN ITU; ACM Practitioners Board Juan Miguel de Joya is the in-house consultant for Artificial Intelligence and Emerging Technologies at the United Nations International Telecommunications Union. Prior to this role, he served as a contractor at Facebook/Oculus and Google, worked at Pixar Animation Studios and Walt Disney Animation Studios, and was an undergraduate researcher in graphics at the Visual Computing Lab at the University of California, Berkeley. In his spare time, he is part of the ACM Practitioners Board, the ACM Professional Development Committee, and the ACM SIGGRAPH Strategy Group. His current interests include artificial intelligence, computer vision, mixed reality, computational physics, the web, and the human impact of computing in society at large.
Naiad: a timely dataflow system
Naiad is a distributed system for executing data parallel, cyclic dataflow programs. It offers the high throughput of batch processors, the low latency of stream processors, and the ability to perform iterative and incremental computations. Although existing systems offer some of these features, applications that require all three have relied on multiple platforms, at the expense of efficiency, maintainability, and simplicity. Naiad resolves the complexities of combining these features in one framework. A new computational model, timely dataflow, underlies Naiad and captures opportunities for parallelism across a wide class of algorithms. This model enriches dataflow computation with timestamps that represent logical points in the computation and provide the basis for an efficient, lightweight coordination mechanism. We show that many powerful high-level programming models can be built on Naiad's low-level primitives, enabling such diverse tasks as streaming data analysis, iterative machine learning, and interactive graph mining. Naiad outperforms specialized systems in their target application domains, and its unique features enable the development of new high-performance applications. In the ACM Digital Library: http://dl.acm.org/citation.cfm?id=2522738
John Hennessy and David Patterson 2017 ACM A.M. Turing Award Lecture
2017 ACM A.M. Turing Award recipients John Hennessy and David Patterson delivered their Turing Lecture on June 4 at ISCA 2018 in Los Angeles. The lecture took place from 5 - 6 p.m. PDT and was open to the public. Titled “A New Golden Age for Computer Architecture: Domain-Specific Hardware/Software Co-Design, Enhanced Security, Open Instruction Sets, and Agile Chip Development,” the talk will cover recent developments and future directions in computer architecture. Hennessy and Patterson were recognized with the Turing Award for “pioneering a systematic, quantitative approach to the design and evaluation of computer architectures with enduring impact on the microprocessor industry.”
CACM Jan 2015 - Distributed Information Processing in Biological and Computational Systems HD
Co-author Saket Navlakha discusses "Distributed Information Processing in Biological and Computational Systems," his Review Article in the January 2015 Communications of the ACM.
Large-scale cluster management at Google with Borg
Authors: Abhishek Verma, Luis Pedrosa, Madhukar Korupolu, David Oppenheimer, Eric Tune, John Wilkes Abstract: Google's Borg system is a cluster manager that runs hundreds of thousands of jobs, from many thousands of different applications, across a number of clusters each with up to tens of thousands of machines. It achieves high utilization by combining admission control, efficient task-packing, over-commitment, and machine sharing with process-level performance isolation. It supports high-availability applications with runtime features that minimize fault-recovery time, and scheduling policies that reduce the probability of correlated failures. Borg simplifies life for its users by offering a declarative job specification language, name service integration, real-time job monitoring, and tools to analyze and simulate system behavior. We present a summary of the Borg system architecture and features, important design decisions, a quantitative analysis of some of its policy decisions, and a qualitative examination of lessons learned from a decade of operational experience with it. ACM DL: http://dl.acm.org/citation.cfm?id=2741964 DOI: http://dx.doi.org/10.1145/2741948.2741964
CACM Feb. 2017 - Model Learning
A combination of automated queries can give computers the ability to solve puzzles and accomplish unfamiliar tasks in the same way human beings can. Frits Vaandrager, a professor of software science at Radboud University in the Netherlands, discusses this in “Model Learning," a Review Article in the February 2017 issue of CACM: (cacm.acm.org/magazines/2017/2/212445).
IronFleet: proving practical distributed systems correct
Authors: Chris Hawblitzel, Jon Howell, Manos Kapritsos, Jacob R. Lorch, Bryan Parno, Michael L. Roberts, Srinath Setty, Brian Zill Abstract: Distributed systems are notorious for harboring subtle bugs. Verification can, in principle, eliminate these bugs a priori, but verification has historically been difficult to apply at full-program scale, much less distributed-system scale. We describe a methodology for building practical and provably correct distributed systems based on a unique blend of TLA-style state-machine refinement and Hoare-logic verification. We demonstrate the methodology on a complex implementation of a Paxos-based replicated state machine library and a lease-based sharded key-value store. We prove that each obeys a concise safety specification, as well as desirable liveness requirements. Each implementation achieves performance competitive with a reference system. With our methodology and lessons learned, we aim to raise the standard for distributed systems from "tested" to "correct." ACM DL: http://dl.acm.org/citation.cfm?id=2815400.2815428 DOI: http://dx.doi.org/10.1145/2815400.2815428
CACM Apr. 2017 - Attack of the Killer Microseconds
Luis Barroso discusses "Attack of the Killer Microseconds" (cacm.acm.org/magazines/2017/4/215032), a Contributed Article in the April 2017 CACM.
CACM Mar. 2016 - An Interview with Stanford University President John Hennessy
Stanford University President John Hennessy discusses the future of business, technology, and Silicon Valley with UC Berkeley Computer Science Professor David Patterson (cacm.acm.org/magazines/2016/3/198871), featured in the March 2015 Communications of the ACM.
CACM Jan. 2019 - Face2Face  Real Time Face Capture and Reenactment of RGB Videos
Face2Face is an approach for real-time facial reenactment of a monocular target video sequence (e.g., Youtube video). The source sequence is also a monocular video stream, captured live with a commodity webcam. This video and accompanying article present research with the goal of developing methods for animating facial expressions of the target video by a source actor and re-render the manipulated output video in a photo-realistic fashion. The result is a convincing re-render okf the synthesized target face on top of the corresponding video stream that seamlessly blends with the real-world illumination. In this video, Justus Thies discusses "Face2Face: Real-Time Face Capture and Reenactment of RGB Videos," a Research Highlights article in the January 2019 Communications of the ACM. Read the full article here: https://cacm.acm.org/magazines/2019/1/233531-face2face/fulltext
The History of Rust
Author: Steve Klabnik Abstract: The Rust programming language recently celebrated its one year anniversary since 1.0. While that's not a long time, there were eight years of development before that, which saw radical changes in the language. In this talk, Steve will show off some of Rust's history, with all of the decisions and changes that were made along the way. ACM DL: http://dl.acm.org/citation.cfm?id=2960081 DOI: http://dx.doi.org/10.1145/2959689.2960081
On Methodology: Turing Laureates Discuss their Approach to Work
In this video from ACM's Celebration of 50 Years of the A.M. Turing Award, Turing Laureates Andrew Yao, Marvin Minsky, Herbert Simon, Shafi Goldwasser, James Gray, Edmund Clarke and Richard Karp discuss their approach to work and share advice for those who aspire to follow in their footsteps.
Keynote - JSON Graph: Reactive REST at Netflix
Every user of a web application wants to believe that all of the data in the cloud is sitting right on their device. Netflix's data platform "JSON Graph" creates this illusion for the web developer. One Model, Available Everywhere. Using an innovative combination of reactive programming techniques and RESTful principles, JSON Graph allows web developers to create a virtual server JSON model for their web application and transparently access it from any cloud-connected device. The Data is the API. Using JSON Graph, Netflix developers retrieve data from the virtual server model the same way they would from an in-memory JSON object. Efficient client/server interactions are ensured by batching concurrent idempotent requests, transparently optimizing requests into point queries, and caching recently-used data. Come learn about the innovative data platform the powers the Netflix UIs, and the new design patterns it enables. Jafar Husain http://dx.doi.org/10.1145/2742580.2742640
ACM Prize in Computing 2017: Dina Katabi, Massachusetts Institute of Technology
The ACM Prize in Computing is presented to Dina Katabi for creative contributions to wireless networking. Recognized as one of the most innovative researchers in the field of networking, Katabi applies methods from communication theory, signal processing and machine learning to solve problems in wireless networking. Among her contributions, she is cited for co-authoring several highly influential papers on overcoming interference in wireless networks to improve the flow of data traffic. And in inventing a device that seems to be lifted out of the pages of science fiction, she and her team pioneered the use of the wireless signals in the environment to sense humans behind walls, determine their movements and even surmise their emotional states. These trailblazing human-sensing technologies hold out promise for use in several applications of daily life including helping the house-bound elderly, and-perhaps determining survivors within buildings during search and rescue operations. Katabi, along with MIT colleague Piotr Indyk and students, developed a new algorithm, the Sparse Fast Fourier Transform (SFFT) that processes data 10 to 100 times faster than previous methods. The ACM Prize in Computing recognizes an early to mid-career fundamental innovative contribution in computing that, through its depth, impact and broad implications, exemplifies the greatest achievements in the discipline. The award carries a prize of $250,000. Financial support is provided by Infosys. The ACM Prize in Computing was previously known as the ACM-Infosys Foundation Award in the Computing Sciences from 2007 through 2015.
Predicting Ambulance Demand: a Spatio-Temporal Kernel Approach
Authors: Zhengyi Zhou, David S. Matteson Abstract: Predicting ambulance demand accurately at fine time and location scales is critical for ambulance fleet management and dynamic deployment. Large-scale datasets in this setting typically exhibit complex spatio-temporal dynamics and sparsity at high resolutions. We propose a predictive method using spatio-temporal kernel density estimation (stKDE) to address these challenges, and provide spatial density predictions for ambulance demand in Toronto, Canada as it varies over hourly intervals. Specifically, we weight the spatial kernel of each historical observation by its informativeness to the current predictive task. We construct spatio-temporal weight functions to incorporate various temporal and spatial patterns in ambulance demand, including location-specific seasonalities and short-term serial dependence. This allows us to draw out the most helpful historical data, and exploit spatio-temporal patterns in the data for accurate and fast predictions. We further provide efficient estimation and customizable prediction procedures. stKDE is easy to use and interpret by non-specialized personnel from the emergency medical service industry. It also has significantly higher statistical accuracy than the current industry practice, with a comparable amount of computational expense. ACM DL: http://dl.acm.org/citation.cfm?id=2788570 DOI: http://dx.doi.org/10.1145/2783258.2788570
Michael Stonebraker 2014 ACM A.M. Turing Lecture, June 13 2015
Michael Stonebraker has made fundamental contributions to database systems, which are one of the critical applications of computers today and contain much of the world's important data. He is the inventor of many concepts that were crucial to making databases a reality and that are used in almost all modern database systems. His work on Ingres introduced the notion of query modification, used for integrity constraints and views. His later work on Postgres introduced the object-relational model, effectively merging databases with abstract data types while keeping the database separate from the programming language. Stonebraker's implementations of Ingres and Postgres demonstrated how to engineer database systems that support these concepts; he released these systems as open software, which allowed their widespread adoption and their code bases have been incorporated into many modern database systems. Since the pathbreaking work on Ingres and Postgres, Stonebraker has continued to be a thought leader in the database community and has had a number of other influential ideas including implementation techniques for column stores and scientific databases and for supporting on-line transaction processing and stream processing. BACKGROUND Michael Stonebraker is adjunct professor at the Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory (MIT CSAIL) where he is also co-founder and co-director of the Intel Science and Technology Center for Big Data. Prior to MIT, Stonebraker was professor of computer science at the University of California at Berkeley for 29 years. A graduate of Princeton University, Stonebraker earned his master's degree and his Ph.D. from the University of Michigan. Stonebraker received the Software System award with Gerald Held and Eugene Wong for the development of Ingres (IBM’s System R was also recognized). He was the recipient of the inaugural SIGMOD Edgar F. Codd Innovations Award, and received the IEEE John von Neumann Medal. Stonebraker is an ACM Fellow and a member of the U.S. National Academy of Engineering. Read more at http://www.acm.org/turing-lecture-stonebraker .
The Challenges of Writing a Massive and Complex Go Application
Author: Ben Darnell Abstract: We opted for Go when building CockroachDB, a scale-out, relational database, because of its support for libraries, interfaces, and tooling. However, it has come with its own frustrations, often related to performance and synchronization. And as for Cgo, RocksDB, and other critical external libraries, we've had to hunt down or develop creative workarounds to ensure they work well the rest of the toolchain. In this talk, we'll share how we've optimized our memory usage to mitigate issues related to garbage collection and improved our use of channels to avoid deadlocks. We will also share creative techniques to integrate non-Go dependencies into the Go build process. ACM DL: http://dl.acm.org/citation.cfm?id=2960085 DOI: http://dx.doi.org/10.1145/2959689.2960085
CACM Apr. 2017 - Computational Thinking for Teacher Education
Co-author Aman Yadav discusses "Computational Thinking for Teacher Education" (cacm.acm.org/magazines/2017/4/215031), a Contributed Article in the April 2017 CACM.
Coz: finding code that counts with causal profiling
Authors: Charlie Curtsinger, Emery D. Berger Abstract: Improving performance is a central concern for software developers. To locate optimization opportunities, developers rely on software profilers. However, these profilers only report where programs spent their time: optimizing that code may have no impact on performance. Past profilers thus both waste developer time and make it difficult for them to uncover significant optimization opportunities. This paper introduces causal profiling. Unlike past profiling approaches, causal profiling indicates exactly where programmers should focus their optimization efforts, and quantifies their potential impact. Causal profiling works by running performance experiments during program execution. Each experiment calculates the impact of any potential optimization by virtually speeding up code: inserting pauses that slow down all other code running concurrently. The key insight is that this slowdown has the same relative effect as running that line faster, thus "virtually" speeding it up. We present Coz, a causal profiler, which we evaluate on a range of highly-tuned applications: Memcached, SQLite, and the PARSEC benchmark suite. Coz identifies previously unknown optimization opportunities that are both significant and targeted. Guided by Coz, we improve the performance of Memcached by 9%, SQLite by 25%, and accelerate six PARSEC applications by as much as 68%; in most cases, these optimizations involve modifying under 10 lines of code. ACM DL: http://dl.acm.org/citation.cfm?id=2815400.2815409 DOI: http://dx.doi.org/10.1145/2815400.2815409
Extracting Energy from the Turing Tarpit
Talk by ACM A.M. Turing Laureate Alan C. Kay during the ACM A.M. Turing Centenary Celebration, June, 2012. Abstract: Part of Turing's fame and inspiration came from showing how a simple computer can simulate every other computer, and so "anything is possible". The "Turing Tarpit" is getting caught by "anything is possible but nothing is easy". One way to get caught is to stay close to the underlying machine with our languages so that things seem comprehensible in the small but the code blows up into intractable millions of lines. What if we used "anything is possible" to make very different kinds of computers which require new learning but the code compactly fits the problem and stays small?
CACM August 2016 - Ur/Web: A Simple Model for Programming the Web
Ur/Web, is a domain-specific, statically typed functional programming language with a much simpler model for programming modern Web applications. In this video, Adam Chlipala discusses "Ur/Web: A Simple Model for Programming the Web" (cacm.acm.org/magazines/2016/8/205041), his Research Highlights article in the August 2016 Communications of the ACM.
CACM Nov. 2016 - "Sex as an Algorithm"
When it comes to unlocking the secrets of evolution, much can be gained from exploring these stories from a computer science perspective. In this CACM video, Adi Livnat discusses "Sex as an Algorithm: The Theory of Evolution Under the Lens of Computation" (cacm.acm.org/magazines/2016/11/209128), a Review Article in the November 2016 Communications of the ACM.
Video Synopsis of the 2016 Heidelberg Laureate Forum
ACM has been an active part of the annual Heidelberg Laureate Forum (HLF) since its first gathering in 2013. HLF is a world-renowned networking event for mathematicians and computer scientists based on the Lindau Nobel Laureate Meetings. Each September, HLF brings the laureates of the ACM Turing Award, The Abel Prize, the Fields Medal, and the Nevanlinna Prize together with brilliant young researchers from around the world to Heidelberg for a week of intensive exchange.
Murray Gell Mann - The quality of information
ACM 97 Speaker: Murray Gell-Mann Position: Professor and Co-chair of the Science Board of the Santa Fe Institute, and Robert Andrews Millikan Professor Emeritus at Caltech Talk: The quality of information Running time: 38 minutes
CACM August 2016 - Computational Biology in the 21st Century
In the past two decades, biological data sets have become so massive that it has become difficult to analyze them to discover patterns that illuminate underlying biological processes. In this video, Bonnie Berger discusses "Computational Biology in the 21st Century,” a Review Article in the August 2016 Communications of the ACM.(http://cacm.acm.org/magazines/2016/8/205052-computational-biology-in-the-21st-century/fulltext)
CACM Feb. 2018 - The Next Phase in the Digital Revolution
Digital Platforms in the computing "cloud" are fundamental features of the digital revolution, entangled with what we term "intelligent tools." An abundance of computing power enabling generation and analysis of data on a scale never before imagined permits the reorganization/transformation of services and manufacturing. How will the increased movement of work to digital platforms provide real and rising incomes with reasonable levels of equality? In this video, John Zysman and Martin Kenney discuss "The Next Phase in the Digital Revolution: Intelligent Tools, Platforms, Growth, Employment," a Contributed Article in the February 2018 issue of Communications of the ACM. Read the full article here: https://cacm.acm.org/magazines/2018/2/224635-the-next-phase-in-the-digital-revolution
CACM Sept. 2015 - Commonsense Reasoning and Commonsense Knowledge in Artificial Intelligence
Ernest Davis and Gary Marcus discuss the shortcomings of AI systems and "Commonsense Reasoning and Commonsense Knowledge in Artificial Intelligence," their Review Article in the September 2015 Communications of the ACM. http://cacm.acm.org/magazines/2015/9/191169
"Advances in Deep Neural Networks," at ACM Turing 50 Celebration
Deep neural networks can be trained with relatively modest amounts of information and then successfully be applied to large quantities of unstructured data. Deep learning techniques have been applied with great success to areas such as speech recognition, image recognition, natural language processing, drug discovery and toxicology, customer relationship management, recommendation systems, and biomedical informatics. The capabilities of deep neural networks, in some domains, have proven to rival those of human beings. Panelists will explore how deep neural networks are changing our world and our jobs. They will also discuss how things may further change going forward. Moderator: Judea Pearl (2011 Turing Laureate), University of California, Los Angeles Panelists: Michael I. Jordan, University of California, Berkeley Fei-Fei Li, Stanford University Stuart Russell, University of California, Berkeley Ilya Sutskever, OpenAI Raquel Urtasun, University of Toronto