[Sometimes called Case-Based Reasoning or CBR]
"At the highest level of generality, a general CBR cycle may be described by the following four processes: 1. RETRIEVE the most similar case or cases. 2. REUSE the information and knowledge in that case to solve the problem. 3. REVISE the proposed solution. 4. RETAIN the parts of this experience likely to be useful for future problem solving "– from Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. By A. Aamodt and E. Plaza. (1994)
BPO stands for "business process outsourcing." In short, it's a business practice we see implemented when an organization decides to outsource activities like payroll, human resources, billing and customer service. The best example of this is customer service because we all have experienced speaking with someone from a different country when we've called a bank or had an issue with a credit card and needed it resolved. We will not spend any more time discussing BPO, but our technology conversation in this article will be focused on improving customer service. Now, recall an incident when you called your credit card company.
IBM Watson Health is tapering off its Drug Discovery program, which uses "AI" software to help companies develop new pharmaceuticals, blaming poor sales. IBM spokesperson Ed Barbini told The Register: "We are not discontinuing our Watson for Drug Discovery offering, and we remain committed to its continued success for our clients currently using the technology. We are focusing our resources within Watson Health to double down on the adjacent field of clinical development where we see an even greater market need for our data and AI capabilities." In other words, it appears the product won't be sold to any new customers, however, organizations that want to continue using the system will still be supported. When we pressed Big Blue's spinners to clarify this, they tried to downplay the situation using these presumably Watson neural-network-generated words: The offering is staying on the market, and we'll work with clients who want to team with IBM in this area.
Machine learning takes artificial intelligence (AI) to the next level by allowing a system to learn without prior programming. Now, restaurants are starting to benefit from this technology. Simon Bocca, COO at Fourth, shared how his company is using machine learning. Fourth recently announced its end-to-end restaurant and hospitality platform and services. The company provides an all-in-one solution for purchase-to-pay, inventory and workforce management with advanced demand forecasting, predictive analytics and collaboration tools.
Golf fans who are planning to watch the Masters this weekend will have yet more ways to check out the action. For the first time at a golf tournament, practically every one of the more than 20,000 shots from the first major of the year will be available to view on the Masters website and app within five minutes of a player striking the ball. While these videos won't be live, you'll essentially be able to watch full rounds from the likes of Tiger Woods, Rory McIlroy and Jordan Speith without such trivial matters as watching them walk between shots. There is a caveat in that cameras might not capture shots in some instances, such as those from unusual lies, or if a group's tee shots end up in wildly different spots. The Masters attracts sports aficionados who might not typically watch golf as well as devotees, so it's a high-profile way to debut this technology after a few years of development.
In 2014, IBM opened swanky new headquarters for its artificial intelligence division, known as IBM Watson. Inside the glassy tower in lower Manhattan, IBMers can bring prospective clients and visiting journalists into the "immersion room," which resembles a miniature planetarium. There, in the darkened space, visitors sit on swiveling stools while fancy graphics flash around the curved screens covering the walls. It's the closest you can get, IBMers sometimes say, to being inside Watson's electronic brain. One dazzling 2014 demonstration of Watson's brainpower showed off its potential to transform medicine using AI--a goal that IBM CEO Virginia Rometty often calls the company's moon shot. In the demo, Watson took a bizarre collection of patient symptoms and came up with a list of possible diagnoses, each annotated with Watson's confidence level and links to supporting medical literature. Within the comfortable confines of the dome, Watson never failed to impress: Its memory banks held knowledge of every rare disease, and its processors weren't susceptible to the kind of cognitive bias that can throw off doctors. It could crack a tough case in mere seconds. If Watson could bring that instant expertise to hospitals and clinics all around the world, it seemed possible that the AI could reduce diagnosis errors, optimize treatments, and even alleviate doctor shortages--not by replacing doctors but by helping them do their jobs faster and better.
Customer experience management (CXM) programs are necessarily a quantitative endeavor, requiring CX professionals to decipher insights from a sea of customer data. In this post, I will illustrate how you can use IBM Watson Studio to analyze one source of customer data, customer survey responses, to answer two important questions about the health of your customer relationship: 1) what is the current level of satisfaction across the CX touch points and 2) which of these touch points is responsible for ensuring customers are loyal? Customer Experience Management (CXM) programs rely on different types of data that come from a variety of sources. The most popular source of customer feedback is surveys. These two questions will help you understand how well you are meeting the needs of your customers and, more importantly, understand what you need to do to improve customer loyalty.
Developers need to understand the intersectionality between DevOps technology and machine learning. Machine learning algorithms can significantly improve the effectiveness of DevOps applications. It is important to be aware of the different ways that machine learning can be applied to DevOps. Before you begin implementing DevOps practices, it is important to carefully define your objectives and recognize the biggest shortcoming of traditional DevOps environments. DevOps processes are invaluable for generating massive data sets for different applications.
There has been a lot of hand-wringing in certain circles that European businesses are not exploiting advanced technologies such as AI anything like as well as US or Chinese companies. It is true we haven't (yet) spawned global giants like Google or Baidu. But O think there's a more nuanced reality. Back in November 2018, I was delighted to be invited by IBM to be a judge at its European IBM Watson Challenge event. This was a "Dragon's Den" style event where 32 IBM business partners (from an initial submission of 155 prototypes) were each invited to present an innovative AI-based business solution and associated business plan to a panel of judges (the Dragons!) over two, exhausting and intensive (but exhilarating) days.