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China Set to Approve Qualcomm-NXP Deal, a Sign of Easing Trade Tensions
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Chinese Smartphone Maker Xiaomi Stakes Its Future on Europe
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India’s Biggest Competitors to Walmart and Amazon? Mom and Pop
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Corona Introduces First Bottle Redesign for Summer 2018
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The Fable of the Dragon-Tyrant – Prof. Nick Bostrom
Nick Bostrom is a Swedish philosopher at the University of Oxford known for his work on existential risk, the anthropic principle, human enhancement ethics, superintelligence risks, and the reversal test.
https://nickbostrom.com/fable/dragon.html
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Take a Step Backwards to Take a Step Forward for the Rest of Your Life | DailyVee 455
For OTT Giants Netflix, Amazon, Hulu, Roku And Apple, It’s A Jump Ball For Brand Budgets
“On TV And Video” is a column exploring opportunities and challenges in advanced TV and video. Today’s column is written by Lance Neuhauser, CEO at 4C Insights. The new reality of consumer channel choice has thrown a wrench into the decades-old media model that funnels millions of brand advertising dollars to linear television. Competition for these… Continue reading »
The post For OTT Giants Netflix, Amazon, Hulu, Roku And Apple, It’s A Jump Ball For Brand Budgets appeared first on AdExchanger.
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How Machine Intelligence Can Improve Health Care – Prof. Suchi Saria
Recorded May 1st, 2018 ICLR2018
Augmenting Clinical Intelligence with Machine Intelligence
“Healthcare is rapidly becoming a data-intensive discipline, driven by increasing digitization of health data, novel measurement technologies, and new policy-based incentives. Critical decisions about whom and h ow to treat can be made more precisely by layering an individual’s data over that from a population. In this talk, I will begin by introducing the types of health data currently being collected and the challenges associated with learning models from these data. Next, I will describe new techniques that leverage probabilistic methods and counterfactual reasoning for tackling the aforementioned challenges. Finally, I will introduce areas where statistical machine-learning techniques are leading to new classes of computational diagnostic and treatment planning tools—tools that tease out subtle information from “messy” observational datasets, and provide reliable inferences given detailed context about the individual patient.” – Suchi Saria
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