Today, the world of AI ethics is a harmonious ecosystem of organizations with uncontroversial and reasonable, respectable aims.
They share conferences, include each other in thought leadership, and develop white papers together – in order to discover frameworks for governing AI and handling issues around privacy, security, bias, and individual rights.
This makes sense, for now – because AI ethics is a means to great power. Read More
Daily Archives: June 17, 2019
Software Engineering for Machine Learning: A Case Study
Recent advances in machine learning have stimulated widespread interest within the Information Technology sector on integrating AI capabilities into software and services.This goal has forced organizations to evolve their development processes. We report on a study that we conducted on observing software teams at Microsoft as they develop AI-based applications. We consider a nine-stage workflow process informed by prior experiences developing AI applications (e.g., search and NLP) and data science tools (e.g. application diagnostics and bug reporting). We found that various Microsoft teams have united this workflow into preexisting, well-evolved, Agile-like software engineering processes, providing insights about several essential engineering challenges that organizations may face in creating large-scale AI solutions for the marketplace. We collected some best practices from Microsoft teams to address these challenges.In addition, we have identified three aspects of the AI domain that make it fundamentally different from prior software application domains: 1) discovering, managing, and versioning the data needed for machine learning applications is much more complex and difficult than other types of software engineering, 2) model customization and model reuse require very different skills than are typically found in software teams, and 3) AI components are more difficult to handle as distinct modules than traditional software components — models may be “entangled” in complex ways and experience non-monotonic error behavior. We believe that the lessons learned by Microsoft teams will be valuable too their organizations. Read More
AI adoption is being fueled by an improved tool ecosystem
In this post, I share slides and notes from a keynote that Roger Chen and I gave at the 2019 Artificial Intelligence conference in New York City. In this short summary, I highlight results from a — survey (AI Adoption in the Enterprise) and describe recent trends in AI. Over the past decade, AI and machine learning (ML) have become extremely active research areas: the web site arxiv.org had an average daily upload of around 100 machine learning papers in 2018. With all the research that has been conducted over the past few years, it’s fair to say that we now have entered the implementation phase for many AI technologies. Companies are beginning to translate research results and developments into products and services. Read More
Federated Learning — Google
Practical Secure Aggregation for Privacy-Preserving Machine Learning
We design a novel, communication-efficient, failure-robust protocol for secure aggregation of high-dimensional data. Our protocol allows a server to compute the sum of large, user-held data vectors from mobile devices in a secure manner (i.e. without learning each user’s individual contribution), and can be used, for example,in a federated learning setting, to aggregate user-provided model updates for a deep neural network. We prove the security of our protocol in the honest-but-curious and active adversary settings,and show that security is maintained even if an arbitrarily chosen subset of users drop out at any time. We evaluate the efficiency of our protocol and show, by complexity analysis and a concrete implementation, that its runtime and communication overhead remain low even on large data sets and client pools. For 16-bit input values, our protocol offers 1.73×communication expansion for210users and220-dimensional vectors, and 1.98×expansion for214users and224-dimensional vectors over sending data in the clear. Read More
MIT CSAIL’s AI can visualize objects using touch
Robots that can learn to see by touch are within reach, claim researchers at MIT’s Computer Science and Artificial Intelligence Laboratory. Really. In a newly published paper that’ll be presented next week at the Conference on Computer Vision and Pattern Recognition in Long Beach, California, they describe an AI system capable of generating visual representations of objects from tactile signals, and of predicting tactility from snippets of visual data.
“By looking at the scene, our model can imagine the feeling of touching a flat surface or a sharp edge,” said CSAIL PhD student and lead author on the research Yunzhu Li, who wrote the paper alongside MIT professors Russ Tedrake and Antonio Torralba and MIT postdoc Jun-Yan Zhu. “By blindly touching around, our [AI] model can predict the interaction with the environment purely from tactile feelings. Bringing these two senses together could empower the robot and reduce the data we might need for tasks involving manipulating and grasping objects.” Read More
How the Artificial-Intelligence Program AlphaZero Mastered Its Games
A few weeks ago, a group of researchers from Google’s artificial-intelligence subsidiary, DeepMind, published a paper in the journal Science that described an A.I. for playing games. While their system is general-purpose enough to work for many two-person games, the researchers had adapted it specifically for Go, chess, and shogi (“Japanese chess”); it was given no knowledge beyond the rules of each game. At first it made random moves. Then it started learning through self-play. Over the course of nine hours, the chess version of the program played forty-four million games against itself on a massive cluster of specialized Google hardware. After two hours, it began performing better than human players; after four, it was beating the best chess engine in the world.The best of The New Yorker, in your in-boxReporting, commentary, culture, and humor. Sign up for our newsletters now.
The program, called AlphaZero, descends from AlphaGo, an A.I. that became known for defeating Lee Sedol, the world’s best Go player, in March of 2016. Sedol’s defeat was a stunning upset. In “AlphaGo,” a documentary released earlier this year on Netflix, the filmmakers follow both the team that developed the A.I. and its human opponents, who have devoted their lives to the game. We watch as these humans experience the stages of a new kind of grief. At first, they don’t see how they can lose to a machine: “I believe that human intuition is still too advanced for A.I. to have caught up,” Sedol says, the day before his five-game match with AlphaGo. Then, when the machine starts winning, a kind of panic sets in. In one particularly poignant moment, Sedol, under pressure after having lost his first game, gets up from the table and, leaving his clock running, walks outside for a cigarette. He looks out over the rooftops of Seoul. (On the Internet, more than fifty million people were watching the match.) Meanwhile, the A.I., unaware that its opponent has gone anywhere, plays a move that commentators called creative, surprising, and beautiful. In the end, Sedol lost, 1-4. Before there could be acceptance, there was depression. “I want to apologize for being so powerless,” he said in a press conference. Eventually, Sedol, along with the rest of the Go community, came to appreciate the machine. “I think this will bring a new paradigm to Go,” he said. Fan Hui, the European champion, agreed. “Maybe it can show humans something we’ve never discovered. Maybe it’s beautiful.” Read More
AI TED Talks
3100+ talks to stir your curiosity Read More
Data, Surveillance, and the AI Arms Race
According to foreign policy experts and the defense establishment, the United States is caught in an artificial intelligence arms race with China — one with serious implications for national security. The conventional version of this story suggests that the United States is at a disadvantage because of self-imposed restraints on the collection of data and the privacy of its citizens, while China, an unrestrained surveillance state, is at an advantage. In this vision, the data that China collects will be fed into its systems, leading to more powerful AI with capabilities we can only imagine today. Since Western countries can’t or won’t reap such a comprehensive harvest of data from their citizens, China will win the AI arms race and dominate the next century. Read More
The AI Cold War That Threatens Us All
In the spring of 2016, an artificial intelligence system called AlphaGo defeated a world champion Go player in a match at the Four Seasons hotel in Seoul. In the US, this momentous news required some unpacking. Most Americans were unfamiliar with Go, an ancient Asian game that involves placing black and white stones on a wooden board. And the technology that had emerged victorious was even more foreign: a form of AI called machine learning, which uses large data sets to train a computer to recognize patterns and make its own strategic choices.
Still, the gist of the story was familiar enough. Computers had already mastered checkers and chess; now they had learned to dominate a still more complex game. Geeks cared, but most people didn’t. In the White House, Terah Lyons, one of Barack Obama’s science and technology policy advisers, remembers her team cheering on the fourth floor of the Eisenhower Executive Building. “We saw it as a win for technology,” she says. “The next day the rest of the White House forgot about it.” Read More
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