[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)
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.
Judah Cohen, director of seasonal forecasting at AER (Atmospheric and Environmental Research) and visiting scientist in MIT's Department of Civil and Environmental Engineering, and Ernest Fraenkel, professor of biological engineering at MIT, have won first place in three out of four temperature forecasting categories in the Sub-Seasonal Climate Forecast Rodeo competition, hosted by the National Oceanic and Atmospheric Administration and sponsored by the U.S. Bureau of Reclamation. The MIT researchers, who were joined by Stanford University PhD students Jessica Hwang and Paulo Orenstein and Microsoft researcher Lester Mackey, beat the operational long-range forecasting model used by the U.S. government. To be eligible for the competition, the teams were required to submit their climate predictions every two weeks between April 17, 2017 and April 18, 2018. The goal was to create a model that the western United States would be able to rely on weeks in advance to help manage water resources and prepare for wildfires and drought. The competition required that the models achieve a higher mean skill over all competitive forecasts, and two benchmarks submitted by the U.S. Government, which are unbiased versions of the physics-based U.S. Climate Forecasting System.
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.
Finding the best way to get around a busy city is no easy task. At MWC 2019, Seat and IBM announced Mobility Advisor, which uses Watson artificial intelligence (AI) to work out the best way to reach your destination – whether it's a train, ride-hailing service or an electric scooter. The tool's suggestions will take into account traffic reports, weather forecasts, and any events happening in the city that day, so you won't get caught in the rain riding a hire bike, or reach a train station at the same time as a crowd of sports fans. Mobility Advisor is currently in development, and is intended to run as a mobile app on 4G and 5G networks. Over time, it will learn your preferences and make personalized recommendations based on the way you like to travel.
With Watson Machine Learning Accelerator you can drive faster time to results and accuracy, running in special AI hardware in the Cloud on On-Premises. WML Accelerator comes with SnapML library. We have developed an effi cient, scalable machine-learning library that enables very fast training of generalized linear models. We have demonstrated that our library can remove the training time as a bottleneck for machine-learning workloads, paving the way to a range of new applications. For instance, it allows more agile development, faster and more fine-grained exploration of the hyper-parameter space, enables scaling to massive datasets and makes frequent retraining of models possible in order to adapt to events as they occur.
India could emerge as the third-largest market in the Asia-Pacific (APAC) region for IBM's artificial intelligence (AI)-powered workforce automation solution, launched in November last year. The Armonk-based software services giant expects large-sized and mid-sized enterprises from sectors such as banking, insurance and manufacturing to be among the first adopters of the solution. The solution, dubbed the Talent and Transformation suite of services, is one among several that have come out of IBM's global AI platform, Watson. "India is one of the largest markets for the solution in terms of opportunity after Australia and Singapore (in the APAC region)," Lula Mohanty, general manager for APAC at IBM Global Business Services, told TechCircle. "Only five per cent of chief executive officers (CEOs) think that they have embarked on a transformation journey, especially when it comes to human resources core functions and only 24% of CHROs (chief human resources officers) think that they have a lot of work to do in terms of improving their core functions. This is a positive change in terms of rising awareness in the country," she added.
Folks that don't do much of it are often astounded about how quickly costs escalate and how much the process can cost. While the trial itself can cost upwards of $50K, just getting to trial with all the back and forth between the attorneys can cost several times that. A general rule of thumb is that unless the judgment is reasonably likely to be over $100K and include attorney's fees, you'll probably end up in the hole even if you win. Litigation was one of the initial target industries for IBM's advanced artificial intelligence (AI) platform Watson because litigation was so well defined and well documented. The promise was a significant reduction in costs for those bringing or defending against lawsuits and a far better way of determining if it was economically viable to bring or defend against the action to begin with.
IBM Corp. is becoming more open-minded with a revenue-driving bid to "democratize" access to artificial intelligence. Big Blue will open its previously proprietary Watson AI platform to competing cloud computing services including rivals Amazon Web Services, Microsoft Azure and Google Cloud Platform. The IBM Watson Anywhere initiative will allow a new portable version of IBM's cognitive platform to run on any cloud -- whether it's private, public or a hybrid multi-cloud -- in addition to IBM Cloud, the company announced Tuesday. IBM did not announce a time frame for the Watson Anywhere rollout. "This will be the most open, scalable AI for business in the world," CEO Ginni Rometty said at IBM's Think 2019 conference in San Francisco.