The founder of China’s most valuable artificial intelligence startup was only half-joking when he asked an MIT audience last year what company they instantly thought of when he said “AI.”
“Yes, it’s SenseTime,” quipped Tang Xiao’ou, even though the obvious answer was Google. Someday soon, it might not seem like a joke at all.
“Tang told me his dream was to make SenseTime as great as Google,” Jeff Shi, president of the company’s Asia-Pacific business, told the Nikkei Asian Review.
To achieve that, the startup known for its surveillance technology is expanding beyond China and into burgeoning industries such as autonomous driving. Read More
Monthly Archives: August 2019
Biological learning curves outperform existing ones in artificial intelligence algorithms
Recently, deep learning algorithms have outperformed human experts in various tasks across several domains; however, their characteristics are distant from current knowledge of neuroscience. The simulation results of biological learning algorithms presented herein outperform state-of-the-art optimal learning curves in supervised learning of feedforward networks. The biological learning algorithms comprise asynchronous input signals with decaying input summation, weights adaptation, and multiple outputs for an input signal. In particular, the generalization error for such biological perceptrons decreases rapidly with increasing number of examples, and it is independent of the size of the input. This is achieved using either synaptic learning, or solely through dendritic adaptation with a mechanism of swinging between refecting boundaries, without learning steps. The proposed biological learning algorithms outperform the optimal scaling of the learning curve in a traditional perceptron. It also results in a considerable robustness to disparity between weights of two networks with very similar outputs in biological supervised learning scenarios. The simulation results indicate the potency of neurobiological mechanisms and open opportunities for developing a superior class of deep learning algorithms. Read More
Turn digital transformation from a project into a capability
Technology changes quickly, but organizations change much more slowly,” says MIT Sloan Professor George Westerman, whose recent research focuses on the leadership challenge posed by digital transformation.
“The relentless march of technology is very good for companies that sell technology, and for the analysts, journalists, and consultants who sell technology advice to managers,” writes Westerman in a recent article for MIT Sloan Management Review. “But it’s not always so good for the managers themselves.”
He claims that while computing power grows exponentially (Moore’s Law), organizations do not, making digital transformation more of a leadership challenge than a technical one. Read More
Inside DeepMind's epic mission to solve science's trickiest problem
DeepMind is best known for its breakthroughs in machine learning and deep learning that have resulted in highly publicised events in which neural networks combined with algorithms have mastered computer games, beaten chess grandmasters and caused Lee Sedol, the world champion of Go – widely agreed to be the most complex game man has created – to declare: “From the beginning of the game, there was not a moment in time when I thought that I was winning.”
For Demis Hassabis,Shane Legg, and Mustafa Suleyman, the proof points offered by gameplay will define the next ten years: namely, to use data and machine learning to solve some of the hardest problems in science. Read More
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Israel’s Carmel program: Envisioning armored vehicles of the future
Israel is developing combat vehicles of the future that will use the latest in artificial intelligence technology to reduce collateral damage and enable formations of manned and unmanned vehicles working in concert.
The director of defense research and development at Israel’s Ministry of Defense revealed the Carmel program concept behind the multi-year initiative, showcasing new technologies at a base in northern Israel Aug. 4.
Carmel is a “revolutionary program,” said head of R&D, Brig. Gen. Yaniv Rotem, in an interview during a visit by Defense News. The goal is to reduce the number of soldiers inside armored vehicles from the usual four to two while enhancing mission performance, all in a closed cockpit or turret. The system uses the latest optics outside and screens inside to allow maneuverability and a 360-degree view. Read More
With a Second Repeating Radio Burst, Astronomers Close In on an Explanation of Fast Radio Bursts (FRBs)
Between this past Christmas and New Year’s Day, Brian Metzger realized he had his home to himself — no emails coming in, no classes to teach — and maybe, just maybe, the glimmer of an answer to one of astronomy’s most stubborn mysteries.
He chased hard after the lead, worried a little error could unravel everything or that someone else would put together the same pieces first. “You’re racing a little bit against the clock, because other people probably see this as well,” said Metzger, an astrophysicist at Columbia University. “It can kind of become all-consuming.”
Along with scores of other researchers around the world, Metzger has spent the last few years brainstorming ways to understand fast radio bursts (FRBs). Read More
What is “The Art of Thinking Like a Data Scientist” Workbook and Why It Matters
To survive in today’s digital economy, it’s imperative for organizations to convert their key business stakeholders into “Citizens of Data Science.” Meaning, they should not only understand where and how to apply data science to power the business, but champion a data-first approach toward decision-making across the entire organization.
That’s the subject my new workbook, “The Art of Thinking Like A Data Scientist”, seeks to accomplish. It’s designed to be a pragmatic tool that can help your organization leverage data and analytics to power its business and operational models. The content is jammed with templates, worksheets, examples, and hands-on exercises — all composed to help reinforce and deploy the fundamental concepts of “Thinking Like A Data Scientist.” Read More
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