Gadi Singer discusses the major questions organizations confront as they integrate deep learning.
Continue reading Fast, flexible, and functional: 4 real-world AI deployments at enterprise scale.
Showing posts with label All - O'Reilly Media. Show all posts
Showing posts with label All - O'Reilly Media. Show all posts
Saturday, 20 April 2019
Data fueling AI of the future
Thomas Henson considers how AI will shape the experiences of future generations.
Continue reading Data fueling AI of the future.
Is AI human-ready?
Aleksander Madry discusses roadblocks preventing AI from having a broad impact and approaches for addressing these issues.
Continue reading Is AI human-ready?.
Highlights from the O'Reilly Artificial Intelligence Conference in New York 2019
Watch highlights from expert talks covering AI, machine learning, deep learning, ethics, and more.
People from across the AI world came together in New York for the O'Reilly Artificial Intelligence Conference. Below you'll find links to highlights from the event.
AI and the robotics revolution
Martial Hebert offers an overview of challenges in AI for robotics and a glimpse at the exciting developments emerging from current research.
- Watch "AI and the robotics revolution."
Applied machine learning at Facebook
Kim Hazelwood discusses the hardware and software Facebook has designed to meet its scale needs.
- Watch "Applied machine learning at Facebook."
Decoding the human genome with deep learning
How can machine learning decode the mysteries of life? Olga Troyanskaya explores this and other big questions through the prism of deep learning.
Computational propaganda
Sean Gourley considers the repercussions of AI-generated content that blurs the line between what's real and what's fake.
- Watch "Computational propaganda."
Software 2.0 and Snorkel
Christopher Ré discusses Snorkel, a system for fast training data creation.
- Watch "Software 2.0 and Snorkel."
Is AI human-ready?
Aleksander Madry discusses roadblocks preventing AI from having a broad impact and approaches for addressing these issues.
- Watch "Is AI human-ready?"
Machine learning for personalization
Tony Jebara explains how Netflix is personalizing and optimizing the images shown to subscribers.
- Watch "Machine learning for personalization."
Checking in on AI tools
Ben Lorica and Roger Chen assess the state of AI technologies and adoption in 2019.
- Watch "Checking in on AI tools."
Fast, flexible, and functional: 4 real-world AI deployments at enterprise scale
Gadi Singer discusses the major questions organizations confront as they integrate deep learning.
Making real-world distributed deep learning easy with Nauta
Carlos Humberto Morales offers an overview of Nauta, an open source multiuser platform that lets data scientists run complex deep learning models on shared hardware.
Automated ML: A journey from CRISPR.ML to Azure ML
Danielle Dean explains how cloud, data, and AI came together to help build Automated ML.
Toward ethical AI: Inclusivity as a messy, difficult, but promising answer
Kurt Muehmel explores AI within a broader discussion of the ethics of technology, arguing that inclusivity and collaboration are necessary.
How AI adaptive technology can upgrade education
Joleen Liang explains how AI and precise knowledge points can help students learn.
Artificial intelligence: The “refinery” for data
Nick Curcuru explains how Mastercard is using AI to improve security without sacrificing the customer experience.
Automation of AI: Accelerating the AI revolution
Ruchir Puri discusses the next revolution in automating AI, which strives to deploy AI to automate the task of building, deploying, and managing AI tasks.
Simple, scalable, and sustainable: A methodical approach to AI adoption
Rajendra Prasad explains how leaders in large enterprises can make AI adoption successful.
Data fueling AI of the future
Thomas Henson considers how AI will shape the experiences of future generations.
- Watch "Data fueling AI of the future."
Continue reading Highlights from the O'Reilly Artificial Intelligence Conference in New York 2019 .
Four short links: 17 April 2019
Infocom Source, Twitter Design, New Ways of Seeing, and Software Blowouts
- Infocom Source Code Uploaded -- with some version control (retroactively manufactured from different versions of the source code). Uploaded from a hard drive of Infocom material copied at the time of the acquisition. Jason Scott described the contents. See also DECWAR source.
- I Kind of Hate Twitter (Jason Lefkowitz) -- a very good product analysis of why Twitter drives unproductive behaviour. Example: Push delivery makes it hard to ignore what people are saying about you. If someone’s talking about you on the web, you have to go into Google and search to find that out. If someone’s talking about you on Twitter, though, it’s very likely right in your face. This can be flattering if people are saying nice things, but if they’re not, it can feel embarrassing and/or painful; and people who are embarrassed or wounded tend to do stupid things, like lash back at the person who did the wounding, that they regret later when the pain has worn off.
- New Ways of Seeing -- new BBC show from James Bridle which looks to be great. (via The Guardian)
- Why Software Projects Take Longer Than You Think—a Statistical Model -- A reasonable model for the “blowup factor” would be something like a log-normal distribution. If the estimate is one week, then let’s model the real outcome as a random variable distributed according to the log-normal distribution around one week. This has the property that the median of the distribution is exactly one week, but the mean is much larger [...]
Continue reading Four short links: 17 April 2019.
Four short links: 16 April 2019
Data Brokers, AI Research Ethics, Overclaimed Science, and Hardware for ML
- Facebook Transparency Tool (Buzzfeed) -- A transparency tool on Facebook inadvertently provides a window into the confusing maze of companies you’ve never heard of who appear to have your data.
- Microsoft’s AI Research with Chinese Military University Fuels Concerns (SCMP) -- “The new methods and technologies described in their joint papers could very well be contributing to China’s crackdown on minorities in Xinjiang, for which they are using facial recognition technology,” said Helena Legarda, a research associate at the Mercator Institute for China Studies, who focuses on China’s foreign and security policies.
- @justsaysinmice -- points out bogus science claims by adding "in mice" where appropriate. Genius.
- What Machine Learning Needs from Hardware (Pete Warden) -- More arithmetic; Inference; Low Precision; Compatibility; Codesign.
Continue reading Four short links: 16 April 2019.
Four short links: 15 April 2019
Making a Group, Robot Arms, Human Contact, and a Personal Archive
- You Should Organize a Study Group/Book Club/Online Group/Event! Tips on How to Do It (Stephanie Hurlburt) -- good advice on how to get people together.
- Berkeley Open Arms -- Berkeley Open Arms manufactures the BLUE robot arm that was developed at UC Berkeley's Robot Learning Lab. Paper (arXiv link).
- Human Contact is a Luxury Good (NYT) -- Life for anyone but the very rich—the physical experience of learning, living, and dying—is increasingly mediated by screens. Not only are screens themselves cheap to make, but they also make things cheaper. [...] The rich do not live like this. The rich have grown afraid of screens. They want their children to play with blocks, and tech-free private schools are booming. Humans are more expensive, and rich people are willing and able to pay for them. Conspicuous human interaction—living without a phone for a day, quitting social networks and not answering email—has become a status symbol.
- ArchiveBox -- The open source self-hosted web archive. Takes browser history/bookmarks/Pocket/Pinboard/etc., saves HTML, JS, PDFs, media, and more.
Continue reading Four short links: 15 April 2019.
Tuesday, 2 April 2019
Four short links: 1 April 2019
Communist RuneScape, API Versioning, Computer Graphics, User Stories
- The Communist Revolution inside RuneScape (Emilie Rākete) -- In 2007, a communist RuneScape clan was formed to bring proletarian rule to Server 32 of the world of Gielinor. In a context of scattered clan infighting, the RuneScape communist party was a rampantly victorious social force. Under the wise leadership of SireZaros, the communists waged a revolutionary struggle against reactionary and bourgeois clans that saw more than 5,000 player characters killed in the fighting.
- Back-end/Front-end Versioning (Christian Findlay) -- A submission can be rejected [from Google/Apple App Store] for any number of reasons, and it can take up to several days for any one submission to reach the store. On top of this, any user can choose to delay an upgrade, and many users will be on older phones that are not compatible with your current front-end API version. This leaves leaves a situation where front-end versions may be out of sync with each other, or out of sync with the latest back-end version. Here is a quick look at two patterns that might emerge as a strategy to solve the problem.
- Introduction to Computer Graphics -- a free, online textbook covering the fundamentals of computer graphics and computer graphics programming.
- Engineering Guide to Writing User Stories -- the headings are: Using consistent language; Users do not want your stuff; Removing technical details; Clarifying roles; Making user stories verifiable; Spotting the incompleteness; Ranking user stories.
Continue reading Four short links: 1 April 2019.
Four short links: 29 March 2019
Programming Languages, Asset Graphing, Statistical Tests, and Embeddable WebAssembly
- Programmer Migration Patterns -- I made a little flow chart of mainstream programming languages and how programmers seem to move from one to another.
- cartography -- a Python tool that consolidates infrastructure assets and the relationships between them in an intuitive graph view powered by a Neo4j database. Video.
- Common statistical tests are linear models (or: how to teach stats) -- the linear models underlying common parametric and non-parametric tests. Formulating all the tests in the same language highlights the many similarities between them.
- lucet -- a native WebAssembly compiler and runtime. It is designed to safely execute untrusted WebAssembly programs inside your application.. Open source, from Fastly. Announcement.
Continue reading Four short links: 29 March 2019.
Highlights from the Strata Data Conference in San Francisco 2019
Watch highlights from expert talks covering AI, machine learning, data analytics, and more.
People from across the data world came together in San Francisco for the Strata Data Conference. Below you'll find links to highlights from the event.
Hacking the vote: The neuropolitical universe
Elizabeth Svoboda explains how biosensors and predictive analytics are being applied by political campaigns and what they mean for the future of free and fair elections.
Likewar: How social media is changing the world and how the world is changing social media
Peter Singer explores the new rules of power in the age of social media and how we can navigate a world increasingly shaped by "likes" and lies.
Forecasting uncertainty at Airbnb
Theresa Johnson outlines the AI powering Airbnb’s metrics forecasting platform.
- Watch "Forecasting uncertainty at Airbnb."
Chatting with machines: Strange things 60 billion bot logs say about human nature
Lauren Kunze discusses lessons learned from an analysis of interactions between humans and chatbots.
The journey to the data-driven enterprise from the edge to AI
Amy O'Connor explains how Cloudera applies an "edge to AI" approach to collect, process, and analyze data.
Streamlining your data assets: A strategy for the journey to AI
Dinesh Nirmal shares a data asset framework that incorporates current business structures and the elements you need for an AI-fluent data platform.
Scoring your business in the AI matrix
Jed Dougherty plots AI examples on a matrix to clarify the various interpretations of AI.
Data warehousing is not a use case
Google BigQuery co-creator Jordan Tigani shares his vision for where cloud-scale data analytics is heading.
- Watch "Data warehousing is not a use case."
The enterprise data cloud
Mike Olson describes the key capabilities an enterprise data cloud system requires, and why hybrid and multi-cloud is the future.
- Watch "The enterprise data cloud."
Winners of the Strata Data Awards 2019
The Strata Data Award is given to the most disruptive startup, the most innovative industry technology, the most impactful data science project, and the most notable open source contribution.
Continue reading Highlights from the Strata Data Conference in San Francisco 2019.
Chatting with machines: Strange things 60 billion bot logs say about human nature
Lauren Kunze discusses lessons learned from an analysis of interactions between humans and chatbots.
Continue reading Chatting with machines: Strange things 60 billion bot logs say about human nature.
Data warehousing is not a use case
Google BigQuery co-creator Jordan Tigani shares his vision for where cloud-scale data analytics is heading.
Continue reading Data warehousing is not a use case.
Winners of the Strata Data Awards 2019
The Strata Data Award is given to the most disruptive startup, the most innovative industry technology, the most impactful data science project, and the most notable open source contribution.
Continue reading Winners of the Strata Data Awards 2019.
Forecasting uncertainty at Airbnb
Theresa Johnson outlines the AI powering Airbnb’s metrics forecasting platform.
Continue reading Forecasting uncertainty at Airbnb.
Likewar: How social media is changing the world and how the world is changing social media.
Peter Singer explores the new rules of power in the age of social media and how we can navigate a world increasingly shaped by "likes" and lies.
Continue reading Likewar: How social media is changing the world and how the world is changing social media..
Hacking the vote: The neuropolitical universe
Elizabeth Svoboda explains how biosensors and predictive analytics are being applied by political campaigns and what they mean for the future of free and fair elections.
Continue reading Hacking the vote: The neuropolitical universe.
The enterprise data cloud
Mike Olson describes the key capabilities an enterprise data cloud system requires, and why hybrid and multi-cloud is the future.
Continue reading The enterprise data cloud.
It’s time for data scientists to collaborate with researchers in other disciplines
The O’Reilly Data Show Podcast: Forough Poursabzi Sangdeh on the interdisciplinary nature of interpretable and interactive machine learning.
In this episode of the Data Show, I spoke with Forough Poursabzi-Sangdeh, a postdoctoral researcher at Microsoft Research New York City. Poursabzi works in the interdisciplinary area of interpretable and interactive machine learning. As models and algorithms become more widespread, many important considerations are becoming active research areas: fairness and bias, safety and reliability, security and privacy, and Poursabzi’s area of focus—explainability and interpretability.
Continue reading It’s time for data scientists to collaborate with researchers in other disciplines.
Four short links: 28 March 2019
Data-Oriented Design, Time Zone Hell, Music Algorithms, and Fairness in ML
- Data Oriented Design -- A curated list of data-oriented design resources.
- Storing UTC is Not a Silver Bullet -- time zones will drive you to drink.
- Warner Music Signed an Algorithm to a Record Deal (Verge) -- Although Endel signed a deal with Warner, the deal is crucially not for “an algorithm,” and Warner is not in control of Endel’s product. The label approached Endel with a distribution deal and Endel used its algorithm to create 600 short tracks on 20 albums that were then put on streaming services, returning a 50/50 royalty split to Endel. Unlike a typical major label record deal, Endel didn’t get any advance money paid upfront, and it retained ownership of the master recordings.
- 50 Years of Unfairness: Lessons for Machine Learning -- We trace how the notion of fairness has been defined within the testing communities of education and hiring over the past half century, exploring the cultural and social context in which different fairness definitions have emerged. In some cases, earlier definitions of fairness are similar or identical to definitions of fairness in current machine learning research, and foreshadow current formal work. In other cases, insights into what fairness means and how to measure it have largely gone overlooked. We compare past and current notions of fairness along several dimensions, including the fairness criteria, the focus of the criteria (e.g., a test, a model, or its use), the relationship of fairness to individuals, groups, and subgroups, and the mathematical method for measuring fairness (e.g., classification, regression). This work points the way toward future research and measurement of (un)fairness that builds from our modern understanding of fairness while incorporating insights from the past.
Continue reading Four short links: 28 March 2019.
Streamlining your data assets: A strategy for the journey to AI
Dinesh Nirmal shares a data asset framework that incorporates current business structures and the elements you need for an AI-fluent data platform.
Continue reading Streamlining your data assets: A strategy for the journey to AI.
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