If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Almost a quarter century ago, a book was written about how organizations would focus on share of customer as opposed to share of market, building a personalized collaboration driven by big data. Instead of watching as non-banking organizations or fintech start-ups set expectations, the banking industry can now offer individualized engagement, integrating advanced analytics, artificial intelligence, machine learning, robotics and even blockchains to build a cognitive bank. The banking industry continues to be challenged be a low interest rate environment, intense competition from new market entrants, and heightened consumer experience expectations set by highly digital non-bank organizations. It is also proposed that cognitive systems can continually build knowledge and learning, providing the insight needed to increase efficiency and effectiveness throughout the organization.
Companies in the vanguard of developing and deploying machine learning and AI are now starting to talk openly about ethical challenges raised by their increasingly smart creations. "We're here at an inflection point for AI," said Eric Horvitz, managing director of Microsoft Research, at MIT Technology Review's EmTech conference this week. Maya Gupta, a researcher at Google, called for the industry to work harder on developing processes to ensure data used to train algorithms isn't skewed. In the past year, many efforts to research the ethical challenges of machine learning and AI have sprung up in academia and industry.
After attending this year's IBM World of Watson event in Las Vegas a few weeks ago, I was both stunned and overwhelmed by the advancements in artificial intelligence and cognitive. I have written about IBM and Watson several times over the last few years, but this year seems to be it's coming out party. I remember when Watson was a contestant on the TV game show "Jeopardy." It has grown leap years in the last five years. What we think we know about AI mostly comes from the movies, TV and sci-fi, and it has been with us for quite a while.
When you throw an event hoping to draw 400 people but an audience of 29,000 shows up, do you think it's a good sign indicating you're onto something? That incredible interest is what happened in Beijing the week of 28 November 2016 at the International Summit on Machine Learning and Industry Application. The event's 20 speakers gathered from across industries and academia to offer their insights about machine learning trends and new directions. For the keynote address, Dinesh Nirmal, vice president, analytics development, at IBM, teamed up with Kent Ting, vice president, IBM Analytics Global Consulting Group, at IBM. Nirmal and Ting talked about the IBM focus on machine learning and the company's efforts to enable developers in China and elsewhere. Of particular interest to the audience was their demo of IBM Watson Machine Learning, a full-service IBM Bluemix platform offering.
IBM Chief Executive Officer Ginni Rometty said she plans to hire about 25,000 people in the U.S. and invest $1 billion over the next four years, laying out her vision for filling technology jobs in America on the eve of a meeting of industry leaders with President-elect Donald Trump. Rometty, who is on Trump's advisory panel of business leaders, will join Facebook Inc.'s Sheryl Sandberg, Amazon.com Inc.'s Jeff Bezos and Alphabet Inc.'s Larry Page and Eric Schmidt at a summit with Trump Wednesday in New York that is said to focus on jobs. During the run-up to the election, Trump made employment issues a mainstay of his campaign, promising to scrap trade deals he viewed as draining jobs from the country and impose tariffs on imports if necessary. He has since claimed credit for preventing thousands of manufacturing jobs from moving overseas and used state incentives to strike a deal with Carrier, a unit of United Technologies Corp., to pull back on its plans to move some operations to Mexico.
While many technology players--large and small--are investing in developing cognitive computing or artificial intelligence (AI) solutions, as of March 2015, IBM is the only company marketing a cognitive computing platform that's specifically designed to support the development of a broad range of enterprise solutions. Deloitte has joined IBM in investing time, money, and people toward applying Watson technologies to help solve our client's business problems. IBM Watson combines natural language processing, machine learning, and real-time computing power to sift through massive amounts of unstructured data--documents, emails, journals, social posts, and more-- to answer questions fast. Like humans, Watson learns from experience and instruction. Cognitive computing is still in its infancy.
This story was delivered to BI Intelligence "Fintech Briefing" subscribers. To learn more and subscribe, please click here. IBM launched its IBM Watson for Cyber Security program in beta on Tuesday, and announced that it already has 40 clients signed up, including global leaders in the banking and insurance industries. Companies like Sun Financial and Sumitomo Mitsui Banking Corporation will test the ability of Watson -- IBM's artificial intelligence (AI) -- to identify and fight cyberattacks. Watson will help them more easily identify specific malware programs and provide background on known cybercrime campaigns, as well as more accurately pinpoint suspicious behavior.
An IBM's executive Deborah DiSanzo just announced a collaboration with a pharmaceutical giant Pfizer to speed up anticancer drug discovery. This is yet another sign of a technological transformation unfolding in pharmaceutical industry. The newly formed partnership will bring the power of IBM's supercomputer Watson and its artificial intelligence system to help researchers at Pfizer advance "immuno-oncology", a potentially promising area for cancer research. Pfizer will use Watson's capabilities of machine learning, natural language processing, and other cognitive reasoning technologies to improve analysis of massive volumes of public and private datasets, including more than 30 million sources of laboratory and data reports, research articles, patents, and other medical literature. It is supposed to assist in testing research hypotheses and identify new promising therapeutic targets.
Intelligence, defined as the ability to acquire knowledge and skills. Intelligence for the longest time possible is associated with the human brain. Artificial intelligence is basically defined as intelligence that is originating from machines. Most computer applications only make existing processes and functions faster and maybe more efficiently but cannot create new duties altogether. However, artificial intelligence has already challenged this notion.
As healthcare moves to a model of any-time, any-place, continuous and personalized care, it is important to identify the key technologies that will enable this transition and work toward their implementation into different care settings. Frost & Sullivan's Visionary Healthcare research has identified several technologies that are most likely to impact healthcare paradigms by 2025. It is interesting to note that technological advances in the fields of computing, machine learning, nanotechnology and electronics are all playing a role in helping reshape the industry. The figure below provides an overview of the top technologies that will change this industry dramatically, and an analysis of the time frame for their commercialization and maturation. We are now beginning to see larger data sets in healthcare research and delivery to analyze and make sense of entire genome sequences; impact of environmental, behavioral and hereditary factors on health; population health data; patient generated health data; etc.