After studying four million game pitches, BU researcher suggests how to fix a broken baseball system.
This article is based on 11 seasons of Major League Baseball data, over four million pitches culled and analyzed over two months by Boston University Master Lecturer Mark T. Williams and a team of graduate students at the Questrom School of Business experienced in data mining, analytics, and statistics. Read More
Daily Archives: November 3, 2019
‘Tectonic shift’ of Space Command has intelligence community feeling aftershocks
Redefining space as a warfighting domain made waves throughout the defense community as they began thinking about defending assets in space. Maj. Gen. John Shaw, deputy commander for Air Force Space Command, called the creation of Space Command a “tectonic shift.” Now the aftershocks of that shift are being felt in the intelligence community as analysts have to reconsider space’s role in intelligence gathering.
“When you think of space and intelligence together, you might be like me: I spent my career thinking about intelligence collection in space coming down to the Earth, intelligence from space,” Shaw said on Agency in Focus: Intelligence Community. “We need to think really, really hard now about intelligence for space. Where is that intelligence expertise that processes the capabilities? We have to understand what’s actually happening in the space environment.” Read More
Deep Learning Explainability: Hints from Physics
Nowadays, artificial intelligence is present in almost every part of our lives. Smartphones, social media feeds, recommendation engines, online ad networks, and navigation tools are some examples of AI-based applications that already affect us every day. Deep learning in areas such as speech recognition, autonomous driving, machine translation, and visual object recognition has been systematically improving the state of the art for a while now.
However, the reasons that make deep neural networks (DNN) so powerful are only heuristically understood, i.e. we know only from experience that we can achieve excellent results by using large datasets and following specific training protocols. Recently, one possible explanation was proposed, based on a remarkable analogy between a physics-based conceptual framework called renormalization group (RG) and a type of neural network known as a restricted Boltzmann machine (RBM). Read More
Sidewalk Labs, Waterfront Toronto to proceed with Quayside project, but with significant changes
Sidewalk Labs’ controversial proposal to build a high-tech district on Toronto’s waterfront is moving forward but major changes will be incorporated by Waterfront Toronto as it moves to assert more control over the project.
The development has been criticized by Ontario’s premier, privacy advocates and those suspicious of Big Tech.
In a significant climb down, Google sister firm Sidewalk Labs has agreed to a “realignment” of its original master plan, one that had called for broad development in Toronto’s Port Lands area and a public commitment from Waterfront Toronto to secure funding and deliver the extension of Light Rail Transit on the eastern waterfront. Read More
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