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
Monthly Archives: June 2019
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
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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
Beyond the AI Arms Race
The idea of an artificial intelligence (AI) arms race between China and the United States is ubiquitous. Before 2016, there were fewer than 300 Google results for “AI arms race” and only a handful of articles that mentioned the phrase. Today, an article on the subject gets added to LexisNexis virtually every week, and Googling the term yields more than 50,000 hits. Some even warn of an AI Cold War.
One question that looms large in these discussions is if China has, or will soon have, an edge over the United States in AI technology. Dean Garfield, the president of a U.S. trade group called the Information Technology Industry Council, recently told Politico that such fears are “grounded in hysteria.” But many prominent figures disagree. Former Alphabet CEO Eric Schmidt, for instance, warned in 2017 that “By 2020, [the Chinese] will have caught up [to the United States]. By 2025, they will be better than us. And by 2030, they will dominate the industries of AI.” And former Deputy Defense Secretary Bob Work, among others, has argued that China’s advances in AI should spark a “Sputnik moment” for the United States, inspiring a national effort comparable to the one that followed the Soviet Union’s early victories in the space race. Read More
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What is Quantum Computing and How is it Useful for Artificial Intelligence?
After decades of a heavy slog with no promise of success, quantum computing is suddenly buzzing! Nearly two years ago, IBM made a quantum computer available to the world. The 5-quantum-bit (qubit) resource they now call the IBM Q experience. It was more like a toy for researchers than a way of getting any serious number crunching done. But 70,000 users worldwide have registered for it, and the qubit count in this resource has now quadrupled. With so many promises by quantum computing and data science being at the helm currently, are there any offerings by quantum computing for the AI? Let us explore that in this blog! Read More
Analyst 2.0 – a “How To” Guide to Embracing AI in the Intel Community
In my previous post of the Government Technology Insider, I shared the findings of a recently-released Thought Piece by the global government consultancy, Booz Allen Hamilton, which delved into the role that Artificial Intelligence (AI) and Machine Learning (ML) can play in the analysis of intelligence data.
Ultimately, the Thought Piece concluded that AI and ML could help to alleviate some of the more redundant and tedious tasks that normally fall on the plate of the analyst community – tasks such as reviewing countless hours of ISR video, watching for the slightest of changes and discrepancies that could be of interest to the mission and national security. This could shift the role of the analyst from searching for red flags, to analyzing the red flags for pertinence to the mission.
However, the Thought Piece also laid out a problem with the adoption of AI and ML in the intelligence community and military. That problem was effectively fear – fear that the machines couldn’t do an extremely important job as effectively as humans, and fear that jobs could be eliminated if the machines did the task too well. Read More
Top GAN Research Papers Every Machine Learning Enthusiast Must Peruse
In the early 1960s, AI pioneer Herbert Simon observed that in a span of two decades, machines will match the cognitive abilities of humankind. Predictions like these motivated theorists, sceptics and thinkers from a cross-section of domains to find ways to use computers to perform routine tasks. From Heron’s Automatons in the first century to Google’s Deep Mind in the 21st century, mankind has yearned to make machines more ‘human’.
The latest developments in AI, especially in the applications of Generative Adversarial Networks (GANs), can help researchers tackle the final frontier for replicating human intelligence. With a new paper being released every week, GANs are proving to be a front-runner for achieving the ultimate — AGI.
Here are a few papers that verify the growing popularity of GANs: Read More
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