Microsoft


Understanding the Written Word Using Machine Learning and Natural Language Processing (NLP)

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Removing stop words ensures that NLP is only applied to words that add meaning to the sentence and thereby improves the speed at which NLP routines can be executed. Natural Language Processing: how can machines identify topics that people are talking about? The service has returned JSON, in the Key Phrases node, the key phrases "staff", "wonderful experience" and "rooms" have been identified. For example, in 2016, DeepMind, (Googles artificial intelligence company) formed a partnership with Moorfields Eye Hospital in London.


Artificial intelligence will create new kinds of work

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Digital workers classify e-mail queries from consumers, for instance, by content, sentiment and other criteria. But consumers and companies will also expect ever-smarter AI services: digital assistants such as Amazon's Alexa and Microsoft's Cortana will have to answer more complex questions. Accordingly, Ms Gray and Siddharth Suri, her collaborator at Microsoft Research, see services such as UpWork and Mechanical Turk as early signs of things to come. The printing press created new work for the wood engravers in Augsburg, but they quickly discovered that it had become much more repetitive.


Microsoft built a hardware platform for real-time AI

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It can handle Microsoft's own AI framework (Cognitive Toolkit), but it can also work with Google's TensorFlow and other systems. To no one's surprise, Microsoft plans to make Project Brainwave available through its own Azure cloud services (it's been big on advanced tech in Azure as of late) so that companies can make use of live AI. There's no guarantee it will receive wide adoption, but it's evident that Microsoft doesn't want to cede any ground to Google, Facebook and others that are making a big deal of internet-delivered AI. It's betting that companies will gladly flock to Azure if they know they have more control over how their AI runs.


Microsoft made its AI work on a $10 Raspberry Pi

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The idea came about from Microsoft Labs teams in Redmond and Bangalore, India. By doing that, they were able to make an image detection system run about 20 times faster on a Raspberry Pi 3 without any loss of accuracy. "There is just no way to take a deep neural network, have it stay as accurate as it is today, and consume 10,000 times less resources. To get some new ideas and help, they've made some of their early training tools and algorithms available to Raspberry Pi hobbyists and other researchers on Github.


Inside Microsoft's AI Comeback

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But while his peer scientists Yann LeCun and Geoffrey Hinton have signed on to Facebook and Google, respectively, Bengio, 53, has chosen to continue working from his small third-floor office on the hilltop campus of the University of Montreal. Shum, who is in charge of all of AI and research at Microsoft, has just finished a dress rehearsal for next week's Build developers conference, and he wants to show me demos. Shum has spent the past several years helping his boss, CEO Satya Nadella, make good on his promise to remake Microsoft around artificial intelligence. Bill Gates showed off a mapping technology in 1998, for example, but it never came to market; Google launched Maps in 2005.


AI startups are ready to take on Fortune 500

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In another example of disruption through AI, travel companies have begun using behavioral data and predictive analytics to customize brand experiences based on individuals' preferences and patterns. Automating IT functions alone reduces expenses by 14 to 28 percent, so companies that launch using automated services quickly establish a financial advantage over larger, legacy-burdened competitors. Some tech experts believe that the current generation of applied AI systems, such as predictive analytics, will give small businesses advantages through increased automation and efficiency. New BI platforms offer data visualization, customer relationship management programs, and other critical BI services.


5 Best Machine Learning APIs for Data Science

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There are many companies like Google, IBM, Amazon, and Microsoft helping businesses process big data by building Machine Learning APIs so that organizations can make the best use of the machine learning technology. Machine Learning is the big frontier in big data innovation but it is daunting for people who are not tech geeks or data science domain experts.Similar to how standard APIs help developers create applications, Machine Learning APIs make machine learning easy to use, for everyone. Machine Learning APIs provide businesses with the ability to bring together predictive analytics so that they can get to know their customers better, understand their requirements and deliver products or services based on the past data trends, thereby initiating the selling process.There is an increasing percentage of real time consumer interactions through Machine Learning APIs – making them an ideal option for exposing real time predictive analytics to app developers. Azure Machine Learning makes it easy for data scientists to use predictive models in IoT applications by providing APIs for fraud detection, text analytics, recommendation systems and several other business scenarios.


Apple Just Joined Tech's Great Race to Democratize AI

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Federighi announced new APIs that help coders building apps for Apple devices do things like recognize faces or animals in photos, or parse the meaning of text. The reasoning goes that if you can make your phones, operating system, or cloud the best place to build smart new software that leverages AI, more users and revenue will follow. For example, Federighi boasted that Apple's new tools help developers run machine learning on data without it having to leave a person's device, giving performance and privacy benefits. A company that needs to run image recognition inside apps on both Apple and Android devices might prefer to use Google's cloud machine learning APIs instead, for example.


Moore's Law may be out of steam, but the power of artificial intelligence is accelerating

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A paper from Google's researchers says they simultaneously used as many as 800 of the powerful and expensive graphics processors that have been crucial to the recent uptick in the power of machine learning (see "10 Breakthrough Technologies 2013: Deep Learning"). Feeding data into deep learning software to train it for a particular task is much more resource intensive than running the system afterwards, but that still takes significant oomph. Intel has slowed the pace at which it introduces generations of new chips with smaller, denser transistors (see "Moore's Law Is Dead. It also motivates the startups--and giants such as Google--creating new chips customized to power machine learning (see "Google Reveals a Powerful New AI Chip and Supercomputer").


5 Reasons You Should Own at Least 1 Artificial Intelligence Stock -- The Motley Fool

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AI helped triple NVIDIA's data center revenue in the most recent quarter, with the company's CFO, Colette Kress, saying: "AI has quickly emerged as the single most powerful force in technology. If you have an Amazon (NASDAQ:AMZN) Echo speaker in your home, have ever used Alphabet's (NASDAQ:GOOG) (NASDAQ:GOOGL) Google Assistant, or talked to Apple's Siri, then you've interacted with artificial intelligence on some level already. Amazon's most lucrative business, its Amazon Web Services (AWS), now offers machine-learning services (part of the broader AI market) to improve natural-language processing, image analysis, and speech generation across apps and services that use AWS. The Motley Fool owns shares of and recommends Alphabet (A shares), Alphabet (C shares), Amazon, Apple, Facebook, and Nvidia.