Hardly an industry today can escape disruption. New technologies and business models compel CTOs and IT leaders to seek innovative ways to stay ahead of the digital transformation curve. But they face challenges understanding the role and impact of cognitive technologies such as artificial intelligence (AI) and machine learning when integrating them into their existing infrastructure. An increasing number of those infrastructures include mainframe systems, and enterprises are looking to leverage the compute power and secure data protection mainframes offer for running cognitive applications. Cognitive technologies applied to transactional data are pivotal in capturing keen insights, building client relevancy, implementing cost-effective business models and more.
As if active portfolio managers didn't have enough challenges from computer-driven passive investing strategies, now machines are directly horning in on their territory. San Francisco-based EquBot LLC is launching the first ever exchange-traded fund to use artificial intelligence, according to a company statement on Tuesday. Employing International Business Machines Corp.'s Watson platform, the AI Powered Equity ETF, ticker AIEQ, will attempt to mimic an army of equity research analysts working around the clock, according to Art Amador, co-founder of EquBot. "There has been an explosion of information," Amador said by phone. "AI provides a more informed way of investing."
There's no doubt in my mind that machine learning (ML) as part of a data science strategy can help revolutionize many aspects of everyday life. Below I highlight a few examples of how different industries are able to leverage machine learning for competitive differentiation and customer benefit. There are tens of thousands of daily published journals and papers across the world. It is impractical for every clinician to read and absorb these. ML can help identify patterns and correlations that humans alone would otherwise miss -- possibly resulting in diagnosis and treatment plans that are suboptimal.
New AI technologies like machine learning and deep learning are fitting ever more snugly into the shifting enterprise landscape. Deep learning in particular is being adopted by an increasing number of enterprises for expanded insights and with the aim to better serving their clients. Thanks to more powerful systems and graphics processing units (GPUs), we are able to train complex AI models that enable these insights. IBM has long been one of the leaders in analytics and over the last year or two introduced two key new products, Data Science Experience and IBM PowerAI, designed to enable enterprises to more easily start using advanced AI technologies. Today we're unveiling that we are bringing these two key software tools for data scientists together.
IBM CEO Ginni Rometty: "Look, we really think this is about man and machine, not man vs. Well, one person who cares very deeply about the rapid emergence of AI is Microsoft CEO Satya Nadella--and if you're wondering about Nadella's chops in AI, consider that as he's boosted Microsoft's market cap by $250 billion during his 3-1/2 years as CEO, he's also created a global AI team of more than 5,000 computer scientists and engineers. And if you're still wondering if AI's just a fad, consider this comment made by Nadella at the same event: "To me, AI can in fact bring more human empowerment, bring more inclusion to that people can fully participate in our community and society." An excellent interview last month with IBM CEO Ginni Rometty by Bloomberg Businessweek under the compelling headline Ginni Rometty: The End of Programming reveals her compelling views on AI and several other subjects.
With the new Science for Social Good program, IBM is using the rise of AI to help solve some of the most significant problems facing the world today. Today's AI is capable of performing research, drawing from previous experience, and even using context as a guide rather than simply tackling a problem based on a specific set of restrictive parameters. Teams consisting of postdoctoral fellows, researchers, and nonprofit organizations are working with Watson, IBM's signature AI whose namesake is the company's founder, to tackle issues covered by the United Nation's Sustainable Development Goals. Directives numbers one and two respectively on the UN's list of Sustainable Development Goals, Science for Social Good is partnering with St. John's Bread & Life to take on both these challenges.
Artificial intelligence's ability to transform creative working practices has been thrust into the spotlight of late. For instance, IBM has plugged its "Cognitive Bid Optimiser" technology into its demand-side platform for its paid-media buys, to optimise bidding on ad groups. In addition, the IBM marketing team is now experimenting with its own cognitive bot to enhance the customer experience online. Linda Hill, a professor at Harvard Business School who specialises in innovation and talent management and is co-author of Collective Genius: the Art and Practice of Leading Innovation, believes AI platforms can be hugely powerful tools for collaboration and creativity.
Sachin Lulla 61 views Delivering personalized experiences with IBM Digital Experiences - Duration: 4:36. IBM Watson 1,248,434 views IBM Digital Analytics: Creating a Segment Compare Report - Duration: 5:59. IBM Watson Marketing 152 views IBM Digital Commerce: Creating externalized customization (xC) extensions - Duration: 7:28. IBM Digital Analytics: Creating a Segment Compare Report - Duration: 5:59.
IBM has long been one of the leaders in analytics and over the last year or two introduced two key new products, Data Science Experience and IBM PowerAI, designed to enable enterprises to more easily start using advanced AI technologies. We are integrating PowerAI deep learning enterprise software distribution into the Data Science Experience. The Data Science Experience is a collaborative workspace designed for data scientists to develop machine learning models and manage their data and trained models. Enabling such capabilities through the Data Science Experience brings accelerated deep learning to DSX's collaborative workspace environment.