[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)
Buy Now Pay Later (BNPL) services have increased in popularity in recent years and are ready to become a popular mode of financing. Experts claim that demand for BNPL has been accelerating in India for the past three to four years. Further, COVID-19 has boosted its demand. BNPL has now established itself as a more comfortable payment option, reducing borrowers' financial stress by providing no-cost EMIs. Uni Cards, which recently secured $18.5 million in financing, has launched its Uni Pay 1/3rd card. The product aims to enhance the customer experience in the credit card business.
Today, I continue my top-AI-stocks video series. If you are new to this series, it covers my top 12 artificial intelligence stocks focused on growth and disruptive innovation. I have done my best to find the highest-growth companies in a variety of sectors with disruptive growth trends. Last time, I shared my favorite chatbot stock. In today's video, I am covering an unknown business that Square (NYSE:SQ) acquired in 2020 that focuses on artificial intelligence and machine learning.
Counterfactual explanations focus on "actionable knowledge" to help end-users understand how a machine learning outcome could be changed to a more desirable outcome. For this purpose a counterfactual explainer needs to discover input dependencies that relate to outcome changes. Identifying the minimum subset of feature changes needed to action an output change in the decision is an interesting challenge for counterfactual explainers. The DisCERN algorithm introduced in this paper is a case-based counter-factual explainer. Here counterfactuals are formed by replacing feature values from a nearest unlike neighbour (NUN) until an actionable change is observed. We show how widely adopted feature relevance-based explainers (i.e. LIME, SHAP), can inform DisCERN to identify the minimum subset of "actionable features". We demonstrate our DisCERN algorithm on five datasets in a comparative study with the widely used optimisation-based counterfactual approach DiCE. Our results demonstrate that DisCERN is an effective strategy to minimise actionable changes necessary to create good counterfactual explanations.
Some say that artificial intelligence (AI) will radically change healthcare in the future. But that prediction overlooks an important detail: AI is already significantly changing healthcare. IBM (NYSE:IBM) Watson Health general manager Deborah DiSanzo spoke at the annual J. P. Morgan Healthcare Conference on Wednesday. She provided an update on the progress that IBM Watson, the AI system famous for beating Jeopardy! DiSanzo highlighted four areas where AI is making a big difference today.
IBM launched an update to IBM Watson Assistant that includes an integration with communications platform-as-a-service provider IntelePeer to set up virtual agents quickly and work better with human agents. Under the collaboration with IntelePeer, IBM added Watson Assistant to IntelePeer Atmosphere Communications PaaS to set up voice tools and a new phone number for a virtual agent in minutes. If a company already has a contact center system, IntelePeer can connect Watson Assistant and the existing phone system. IBM also said that Watson Assistant can connect with most contact center platforms without code in as little as an hour. With the Watson Assistant updates, IBM is looking to integrate its natural language processing technology with contact centers.
Ian Watson, Rosina O Weber, David Leake Case-based reasoning is reasoning from experience, solving new problems and interpreting new situations by retrieving and adapting prior cases. The Twenty-Eight International Conference on Case-Based Reasoning (ICCBR2020) was held from June 8-12, 2020, with program chairs Ian Watson and Rosina Weber. The conference was originally scheduled for Salamanca, Spain, a World Heritage site, under the auspices of local chair Juan Manuel Corchado and the University of Salamanca. Its theme, "CBR Across Bridges", reflected the goal of bringing together researchers and practitioners with relevant work across various AI areas. Before the conference, the pandemic struck, with tragic effects. The conference chairs resolved to continue with a safe alternative: the first virtual ICCBR. With researchers unable to travel, the virtual conference not only bridged AI areas but geographic ones: 141 conference attendees participated from 23 countries.
In general, one vulnerability with chatbot development frameworks is the turn-around time to go from development to testing. Changes are made to the chatbot application, and a lengthy process needs to be followed to save, deploy, restart the testing environment, and start a test conversation. And obviously this is an iterative process. This is especially debilitating when troubleshooting and much time is lost during this process. Another challenge, in general, with testing chatbots is that the test environment and development environments are really separated and not integrated.
When venturing into the field of chatbots and Conversational AI, usually the process starts with a search of what frameworks are available. Invariably this leads you to one of the big cloud Chatbot service providers. Most probably you will end up using IBM Watson Assistant, Microsoft LUIS/Bot Framework, Google Dialog Flow etc. There are advantages…these environments offer easy entry in terms of cost and a low-code or no-code approach. However, one big impediment you often run into with these environments, is the lack of diversity when it comes to language options. This changed 17 June 2021 when IBM introduced the Universal language model.