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Google launches artificial intelligence research lab in Bengaluru - Times of India

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NEW DELHI: Google has launched an artificial intelligence (AI) research lab in Bengaluru to "tackle big problems", the technology giant announced on Thursday during its fifth edition of Google for India event. Other key announcements at the flagship event included its partnership with BSNL to bring "fast, reliable and secure public WiFi to villages in Gujarat, Maharashtra and Bihar" and with National Skills Development Corporation (NSDC) for their Skill India programme to make entry-level jobs easily discoverable online. According to Google, the lab will be led by Manish Gupta, a SEM (Society for Experimental Mechanics) fellow. Director of Harvard Centre for Computation & Society, professor Milind Tambe, will serve as director of AI for social good. "Professor Tambe will build a research programme around applying AI to tackle big problems in areas like healthcare, agriculture, or education," the company said.


Generate More Training Data When You Don't Have Enough

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Computers outperform humans in image and object recognition. Big corporations like Google and Microsoft have beat the human benchmark on image recognition [1, 2]. On average, human makes an error on image recognition tasks about 5% of the time. As of 2015, Microsoft's image recognition software reached an error rate of 4.94%, and at around the same time, Google announced that its software achieved a reduced error rate of 4.8% [3]. This was possible by training deep convolutional neural networks on millions of training examples from ImageNet dataset which contains hundreds of object categories [1].


Everyday Examples of Artificial Intelligence and Machine Learning Emerj

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With all the excitement and hype about AI that's "just around the corner"--self-driving cars, instant machine translation, etc.--it can be difficult to see how AI is affecting the lives of regular people from moment to moment. What are examples of artificial intelligence that you're already using--right now? In the process of navigating to these words on your screen, you almost certainly used AI. You've also likely used AI on your way to work, communicating online with friends, searching on the web, and making online purchases. We distinguish between AI and machine learning (ML) throughout this article when appropriate. At Emerj, we've developed concrete definitions of both artificial intelligence and machine learning based on a panel of expert feedback. To simplify the discussion, think of AI as the broader goal of autonomous machine intelligence, and machine learning as the specific scientific methods currently in vogue for building AI.


iPhones today, Alexa Wednesday and more to come this holiday

USATODAY - Tech Top Stories

The new iPhones are in stores now. With their release, the 2019 tech buying season has officially begun. Facebook released its fall hardware lineup on Wednesday, and Roku updated its streaming player offeringsThursday. Meanwhile, Amazon is set for this upcoming Wednesday, and Google and Microsoft have October events lined up. The e-tailer will host press next week at its Seattle headquarters, where the company is expected to introduce several new Amazon Echo speakers and other products.


Machine Learning and Artificial Intelligence in Healthcare Market Projected to Witness Vigorous Expansion by 2019-2027 Intel, IBM, Nvidia, Microsoft, Alphabet (Google), General Electric, Enlitic, Verint Systems, General Vision, Welltok, iCarbonX – Market Expert24

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Artificial Intelligence (AI), machine learning, and deep learning are taking the healthcare industry by storm. They are not pie in the sky technologies any longer; they are practical tools that can help companies optimize their service provision, improve the standard of care, generate more revenue, and decrease risk. Nearly all major companies in the healthcare space have already begun to use the technology in practice; here I present some of the important highlights of the implementation, and what they mean for other companies in healthcare. AI, machine learning, and deep learning are already increasing profits in the healthcare industry. For example, according to research firm Frost & Sullivan by 2021, AI systems will generate $6.7 billion in global healthcare industry revenue.


Android of the Auto Industry? How Baidu May Race Ahead Of Google, Tesla, And Others In Autonomous Vehicles

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As Baidu accelerates its capabilities in self-driving vehicle technology, we dive into the Chinese tech giant's uniquely collaborative approach. Baidu has become the "dark horse" in the autonomous vehicle arms race. In an effort to play catch up to frontrunners in the US and gain an edge on emerging players in China, Baidu has taken a novel approach to developing self-driving software. From autonomy to telematics to ride sharing, the auto industry has never been at more risk. Get the free 67-page report PDF. The company's Apollo project, which it launched in April 2017, is an open source software platform that's designed to encourage collaboration across the auto industry to accelerate the development of self-driving cars.


New Era of Machine Learning in Medicine Market is growing in Huge Demand in 2019 Google, Bio Beats, Jvion, Lumiata, DreaMed, Healint, Arterys, Atomwise, Health Fidelity, Ginger – The Industry UpTo Date

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Machine Learning (ML) provides methods, techniques, and tools that can help solving diagnostic and prognostic problems in a variety of medical domains. The rising technology in Machine Learning in Medicine market is also depicted in this research report. Factors that are boosting the growth of the market, and giving a positive push to thrive in the global market is explained in detail. The report delivers a comprehensive overview of the crucial elements of the market and elements such as drivers, restraints, current trends of the past and present times, supervisory scenario, and technological growth. A thorough analysis of these elements has been accepted for defining the future growth prospects of the global Machine Learning in Medicine market.


Simplifying Google AI's Best Paper from ICML 2019

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There are only a handful of machine learning conferences in the world that attract the top brains in this field. One such conference, which I am an avid follower of, is the International Conference on Machine Learning (ICML). Folks from top machine learning research companies, like Google AI, Facebook, Uber, etc. come together and present their latest research. It's a conference any data scientist would not want to miss. ICML 2019, held last week in Southern California, USA, saw records tumble in astounding fashion.


Want to Reduce CPA? Try Machine Learning–Driven Retargeting.

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The way we do retargeting is restrictive. Creating a tailored, personalized campaign is often done with micro-triggers: Did the user spend more than X minutes on the site? Did they view more than Y pages? Did they add to cart? Are they visiting on mobile?


Google: We weren't being sexist giving assistant a female voice

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In May, the United Nations released a troubling report, arguing that female-sounding voices for AI assistants such as Apple's Siri and Amazon's Alexa perpetuate gender biases and encourage users to be sexist. Now, Google has come out to explain why it chose to give its Assistant a female-sounding voice -- and the search giant says it has nothing to do with gender biases and everything to do with the available technology. According to Google Assistant product manager Brant Ward, Google initially planned to launch Assistant with a male voice. The problem, he told Business Insider, was that the audio produced by text-to-speech systems was easier to understand if delivered in a higher-pitched, female-sounding voice. "At the time, the guidance was the technology performed better for a female voice," Ward said.