1. Scaling Streams with Google
We’re excited to announce that the team behind Streams—our mobile app that supports doctors and nurses to deliver faster, better care to patients—will be joining Google. One of the reasons for joining forces with Google in 2014 was the opportunity to use Google’s scale and experience in building billion-user products to bring our breakthroughs more rapidly to the wider world.
Over the past three years we’ve built a team of experts in what it takes to deploy clinical tools in practice – engineers, clinicians, translational researchers and more. Read More
2. Pitt Researcher Uses Video Games to Unlock New Levels of AI
Pitt Researcher Uses Video Games to Unlock New Levels of AI
The University of Pittsburgh’s Daniel Jiang has developed algorithms that learn decision strategies in complex and uncertain environments, and tests them on a genre of video games called Multiplayer Online Battle Arena (MOBA).
MOBAs involve players controlling one of several “hero” characters in order to destroy opponents’ bases while protecting their own.
Jiang’s team designed the algorithm to evaluate 41 pieces of information and output one of 22 different actions; the most successful player used the Monte Carlo tree search method to generate data, which was fed into a neural network. Read More
3. [1811.04983] Unseen Word Representation by Aligning Heterogeneous Lexical Semantic Spaces
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4. [1811.05245] Interpretable Credit Application Predictions With Counterfactual Explanations
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5. [1811.05291] Intelligent Drone Swarm for Search and Rescue Operations at Sea
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6. Three Keys to Choosing a Data Center in the Age of AI
In this special guest feature, Steve Conner, Vice President of Solutions Engineering at Vantage Data Centers, discusses how AI is currently rewriting the rules of future computing. He specializes in solution design and engineering for data center applications. Today, companies that are engaged in AI require much more than a loose configuration of computers running applications. Read More
7. AI for Exploration & Production (Upstream) in the Oil and Gas Industry – Current Applications
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What you’ll find here at TechEmergence is the objective, jargon-free market research and competitive insight that you need to make strategic decisions about the uses of AI in your business and industry.
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8. TWiML Presents: AI Platforms 2018
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November 5, 2018November 12, 2018
As many of you know, part of my work involves understanding the way large companies are adopting machine learning, deep learning and AI. Read More
9. Announcing the 2019 Uber AI Residency
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10. Five projects that are harnessing big data for good
Data science has boomed over the past decade, following advances in mathematics, computing capability, and data storage. When used ethically, big data can help solve some of society’s most difficult social and environmental problems. 0 should be underwritten by values that ensure these technologies are trained towards the social good (known as Society 4. Read More
11. Self-driving cars expand the fight over airwaves
Hungry for more Wi-Fi capacity, the telecom industry is looking to snatch control of underutilized airwaves reserved for the auto industry. But this is coming just as carmakers begin to make progress on developing and adopting technologies for connected and autonomous cars that currently rely on that spectrum.
The big picture: Tech and telecom companies have been fighting for years over spectrum to support exploding demand for mobile services and smartphones. Read More
12. BBC newsreader ‘speaks’ languages he can’t
BBC newsreader Matthew Amroliwala only speaks English, but by using artificial intelligence software he suddenly appears to be speaking Spanish, Mandarin and Hindi.
Amroliwala was asked to read a script in BBC Click’s film studio and the same phrases were also read by speakers of other languages.
BBC Click’s Lara Lewington finds out more. Read More
13. Robots Getting a Grip on General Manipulation
While robots have prepared entire breakfasts since 1961, general manipulation in the real world is arguably an even more complex problem than autonomous driving. Indeed, you can build a machine for anything, from preparing fantastic espresso, doing your dishes, harvesting a field of wheat, or mass-producing a sneaker. While these competitions push for general solutions, it is still hard to define tasks such that they could not be better solved by a specialized solution. Read More
14. These awful AI song lyrics show us how hard language is for machines
Among creative applications for algorithms, writing lyrics and poetry has proved particularly challenging. The art of producing lyrics with a machine also differs greatly from other tasks in natural-language processing. They trained the other on words from 20 popular books, to build up a corpus of diverse vocabulary. Read More