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how-artificial-intelligence-will-boost-these-8-stocks

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A dozen or so companies are well-positioned to reap big profits from the burgeoning market for artificial intelligence (AI), Barron's reports. Among these companies are: semiconductor manufacturers Micron Technology Inc. (MU) and Nvidia Corp. (NVDA); Google parent Alphabet Inc. (GOOGL); database management software developer Oracle Corp. (ORCL); online merchant and cloud-computing leader Amazon.com In 1997, IBM scored a major milestone in AI history when its Deep Blue program beat reigning world chess champion Gary Kasparov, still considered by many experts to be the best player of all time. IBM's Watson question answering system passed a high-profile test in 2011, beating two top former champs on Jeopardy!, the long-running quiz show on TV. Since then, Watson has been rolled out for general commercial use, most notably to aid doctors in making diagnoses.


IBM expands Watson Data Platform

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IBM has announced a significant expansion of its Watson Data Platform, designed to help data scientists and developers make the most of of data cataloging and data refining features. These updates aim to improve enterprises' ability to analyze and leverage data in artificial intelligence, a field growing rapidly in the business world. Furthermore, improved data security policy capabilities, along with better data visibility, will help customers connect with and share data across both public and private cloud environments. IDC research indicates that 75 percent of software developers will include AI functionality in their programs. Developers, however, often have limited experience with AI, and the increasing complexity of data, along with the difficulty of handling data that can reside across several platforms, will present challenges.


Jetstar looks to AI for smarter customer service

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Jetstar is looking to artificial intelligence to boost its ability to provide "smart service" to customers and differentiate in Asia's increasingly congested low-cost carrier market. A demonstration at Salesforce's annual Dreamforce conference in San Francisco revealed a smart service platform powered by Salesforce and IBM, using both vendors' respective AI technologies Einstein and Watson. There is some ambiguity over how much of what was shown in the presentation is in production use. Salesforce's managing director for global go-to-market strategy and execution in the vendor's travel, transportation and hospitality segments, Farhan Mohammad, called it "a demonstration of how Jetstar is using Salesforce". Jetstar's head of digital Cathryn Arnold indicated to iTnews following the presentation that the IBM Watson tie-ins were "aspirational" and not yet in production.


Try Watson Knowledge Studio for free - Watson

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Create and deploy custom annotation models without writing a single line of code. With our new IBM Watson Knowledge Studio free plan, developers can create custom annotator components and five machine learning projects using 5GBs of storage. And there's no time restriction to do so. Use both machine learning and rule-based approaches to create custom language models using a cloud-based application. The rule-based approach (currently experimental) gets results fast, while the machine learning approach helps the model scale.


What Powerful CEOs Think About AI Access AI

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An elite group of powerful CEOs are obsessing about it. Sundar Pichai (Google), Tim Cook (Apple), Satya Nadella (Microsoft), Mark Zuckerberg (Facebook), and Akio Toyoda (Toyota); they are all making huge bets on Artificial Intelligence. It may come to determine the future power and wealth of their corporations. Power and resources go hand in hand. The Oxford dictionary defines power as "the capacity or ability to direct or influence the behaviour of others or the course of events".


IBM at the me Convention 2017: Internet of Things Blog

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A mere 20 minutes from this year's IAA event in Frankfurt, a new convention is born – one that will run in parallel with the famous international motor show. This event is the me Convention: a festival-style feast of creativity, design and technology served up through panel sessions, keynotes, workshops and art installations, running from 15th-17th September. It certainly feels different from other conventions. The vibe is almost hip – probably unsurprising when you consider that it's the brainchild of Mercedes-Benz and Texas' South by Southwest (SXSW) festival, where folk from the interactive, film and music industries flock each spring to shake things up with like-minded colleagues. Besides the expected speeches and powwows with industry powerhouses, there are evening events taking place throughout Frankfurt's Bahnhofsviertel district, to continue the spirit of exploration and creativity into the night.


Easily add AI-managed content to marketing campaigns

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At IBM we continue to "walk the walk" when it comes to AI. The most recent example is the new IBM Watson Content Hub (WCH) Marketing Connector – a Google Chrome plug-in for IBM Watson Campaign Automation (WCA) that integrates IBM Watson Content Hub directly within the WCA user experience – making it easy to access and use rich assets when creating emails, dynamic email options and landing pages. In addition to taming content sprawl with cognitive content/asset tagging inside WCH for web, mobile and other digital channels, the same content can now be more effectively reused in an organization's supporting digital marketing campaigns – ensuring consistency critical for varied customer digital engagement entry points.


Shenzhen-listed tech major eyes medical sector after AI robot success

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The Shenzhen-listed iFlyTek said on Thursday its intelligent doctor's assistant, which works similarly to IBM's Watson, has become the first AI robot to pass the exam taken by medical students training to become licensed doctors in China. "We have leapfrogged IBM's Watson in becoming the first AI [robot] to qualify as a human doctor. Watson hasn't passed such a licensing exam in the United States," said Liu Qingfeng, the chairman of iFlyTek, which is headquartered in central China's Anhui province. The robot, called the iFlyTek Smart Doctor Assistant, achieved a score of 456, higher than the mark of 360 required to pass the Clinical Practitioner Examination. After showing AI's power in the exam, Liu said the company was going to use the technology, which allows machines to talk and even think like humans, to "empower the world" by starting to change the education, medical care and law industries.


Machine learning use cases for health

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Machine learning (ML) and artificial intelligence (AI) have quickly rocketed to the top of the industry's buzzword list, driven partly by heightened interest in big data analytics amongst healthcare providers and vendors The allure of intelligent algorithms to mine masses of structured and unstructured data for innovative insights get's health planners pretty excited. However, a fragmented health ICT landscape and sluggish analytics development have thus far kept that Holy Grail beyond reach. Regardless, ML is already making a difference. ML can supplement the skills of human radiologists by identifying subtler changes in imaging scans more quickly and potentially leading to earlier and more accurate diagnoses. At Stanford University, ML tools performed better than human pathologists when distinguishing between two types of lung cancer.


Artificial Intelligence vs. Machine Learning - DATAVERSITY

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Currently, Artificial Intelligence (AI) and Machine Learning are being used, not only as personal assistants for internet activities, but also to answer phones, drive vehicles, provide insights through Predictive and Prescriptive Analytics, and so much more. Artificial Intelligence can be broken down into two categories: Strong (also known as General or Broad) AI and Weak (Applied or Narrow) AI. According to a recent DATAVERSITY interview with Adrian Bowles, the lead analyst at Aragon Research, Strong AI is the goal of achieving intelligence equal to a human's, and continues to evolve in that direction. The debate on the differences between Artificial Intelligence vs. Machine Learning are more about the particulars of use cases and implementations of the technologies, than actual real differences – they are allied technologies that work together, with AI being the larger concept that Machine Learning is a part of. Deep Learning also fits into this debate and is a more distinct usage of Machine Learning.