Machines would look at data, understand, reason over it, and they continue to learn: understand, reason and learn, not program, in my simple definition. There would be two big differences between business and consumer AI. It leads me to the second big difference between consumer and business AI. That gives you a long, long, long answer, but this is why I'm so positive this world will have more really tough problems solved with AI.
MUMBAI: American technology company IBM has said that more than 1,000 Indian companies, including startups, are using the Watson Internet of Things, its data analytics and cognitive artificial-intelligence (AI) platform, to draw insights from enormous amount of data collected through sensors placed on machines and devices. IBM counts Tata Steel, Reliance Group, Tech Mahindra, Maruti-Suzuki, Mahindra & Mahindra, KPIT, Arrow Electronics, Kone, Acculi Labs, Avanijal Agri Automation and Schneider Electric India among its customers and partners. We are working with big brands, medium-sized companies and a phenomenal number of startups, particularly in agriculture and healthcare," said Harriet Green, general manager, Watson Internet of Things, customer engagement and education at IBM. IBM combines its cognitive AI platform called Watson to analyse data collected through various internetof-things (IoT) sensors placed by customers.
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With the IBM Watson Visual Recognition service, creating mobile apps that can accurately detect and analyze objects in images is easier than ever. Lastly, add your Visual Recognition service's API key to the strings.xml The Watson Java SDK exposes all the features the Visual Recognition service offers through the VisualRecognition class. You can take a picture by simply calling the CameraHelper object's dispatchTakePictureIntent() method, so add the following code inside the event handler: The above method uses the device's default camera app to take the picture. Here's a sample face analysis result: The Watson Visual Recognition service makes it extremely easy for you to create apps that are smart and aware of their surroundings.
Today, there are multiple large companies offering cloud services for AI and NLP. Google has its Cloud Machine Learning Engine; Microsoft has a series of Cognitive services APIs through Azure; IBM's Watson Ecosystem has spread out into various sectors, and it's Natural Language Processing services have been at the forefront; and Amazon, the biggest cloud player, has many Machine Learning tools offered through AWS such as Rekognition, Polly and Lex. From the perspective of NLP, there are several services available to engineers based on the level of customization you're interested in. If, however, you're looking to build something quick to test your hypothesis, you can pick from services like API.AI, Watson Conversation, Amazon Lex or MS Cognitive Services.
And then, around 2012, mostly due to the advance of computing power within graphic cards, which enable programmers to make massively parallel linear algebra, we had a number of breakthroughs which now mean that computers have become better than humans at pattern recognition. But then came a new technology called'convolutional neural networks', around 2011, which showed incredibly accurate results: error rates lower than 1%. The ones that Tim showed IBM Watson Work Service's natural language processing. One of the fascinating developments of last year was the perfecting of Google Deep Mind's system to such a point that it could beat Lee Sedol, one of the best Go players in the world.
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In a nutshell, the easier IBM can make it for companies to move data into Watson Data Platform and make it consumable the faster it can be a data science operating system. One big wild card for a Watson data science OS will be importing enterprise data. Gartner has IBM as a leader in data science platforms and noted strengths included a strong customer base, commitment to open source technologies, strong model management and governance and support for a broad range of data types. IBM's data science platform hurdles include overcoming confusion about its product offerings, confusing roadmaps and customer support, according to Gartner, which noted that IBM's Data Science Experience (DSx) could address shortcomings.
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