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.
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MIT's motto "Mens et Manus" (Latin for mind and hand) echoes our values here at IBM, to leverage the talent we have and create real technology with impact. Together with our fellow scientists at MIT, we selected four key pillars for our collaboration: core algorithmic advancements that enable learning and reasoning to broaden what AI systems can do, computational innovations tailored to AI and achieved through a mastery of physics, applications of AI to important domains like healthcare and cybersecurity, and achieving shared prosperity through AI technology. While a significant amount of the work we plan to do together is focused on achieving fundamental scientific breakthroughs, we are also extremely committed to leading in the application of AI to solve crucial problems in healthcare and security. Some of the questions we'll address together include the creation of AI systems that can detect and mitigate human biases, building trustworthiness and explainability into AI systems, ensuring that AI systems complement worker skills that might be in short supply and exploring how productivity gains will be distributed across firms, workers and consumers.
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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IBM Big SQL is a hybrid SQL engine for Hadoop that enables you to easily query big data across the enterprise.Big SQL allows organizations to access data across Hadoop and relational databases, whether they're on the cloud, on premises, or both, using a single database connection. IBM Data Science Experience (DSX), together with Hortonworks Data Platform (HDP), accelerates adoption of data science, machine learning and artificial intelligence for businesses and enterprises. In addition, Hortonworks support for Power Systems means data scientists can easily tap into GPU-accelerated deep learning training at scale with the industry-leading IBM PowerAI solution. Ranging from a personalized recommendation engine to improved sales to next best action use cases that improve efficiencies of customer service, companies are deploying data science and machine learning to detect anomalies at scale and to efficiently manage fraud detection and cyber security.
This includes Big Data, machine learning and artificial intelligence companies, as well as startups where data is the "secret sauce" or the core competitive moat I very actively invest in the space through companies like ActionIQ, Dataiku, x.ai, Sense360 and HyperScience. I also blog extensively on those topics and run Data Driven NYC, a big community of 14,000 Big Data and AI enthusiasts. Sure, I'm told people internally at Salesforce freaked out a little bit when Marc Benioff made all those big claims about Einstein [Salesforce's new AI] last year, but part of it comes with the territory of big personalities. They're very active on the investment front through Salesforce Ventures, and Marc Benioff has personally invested in all these different startups that leverage machine learning.
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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.