[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)
As part of the update to Google Forms, one of the improvements included is intelligent response validation, and from time to time (whenever it's possible to do so) Google Forms will make a suggestion to users to validate a response that was issued by the person filling out a Google Form based on the questions that are asked by the form's creator. Also in the presence of saving time for users, Google Forms will now allow you to set up pre-configured preferences for future forms that you create so you don't have to choose certain elements each time you set up a new form, such as the option for always collecting email addresses or making questions required. Google has set limits on the file uploads, which starts at just 1GB, but there's also an option to increase the limit to 1TB if it's needed. So, when creating a new Form, if you want to provide the recipients with the ability to select multiple options for a single question, the Checkbox Grid would be the one to pick.
In threat trapping, passive technologies identify malware using models of bad behavior like signatures. Unfortunately, developing accurate malware detection products based on good behavior modeling is not easy. But no company has enough human resources to manually evaluate a large number of alerts about possible security threats. When AI applies both bad and good behavior models, it reduces the number of false positives to a manageable amount.
Campus Technology reports that a team of educators developed a writing-to-learn tool called M-Write, which uses automated text analysis (ATA) to identify the strengths of a writing submission. A report from the EDUCAUSE Center for Analysis and Research explores how the University of Central Florida piloted adaptive learning in large, introductory courses like General Psychology. General Psychology is a general education course with many sections that can often be taught by adjuncts. "As with any new tool, adaptive learning provides a new set of capabilities and insights -- and a lot of very useful data -- that can be used to explore ways to increase students learning and success," ECAR reports.
Whether its neural networks, machine learning, fuzzy logic, case-based reasoning or expert systems, AI has the potential to transform the industry. This type of technology was brought into the fortune 500 earlier this week when Intel acquired Nervana Systems, an Indian-American San Diego based startup, who was using this technology to increase operational efficiency in oil exploration. On a macro scale, deep machine learning can help to increase the awareness of macroeconomic trends to drive investment decisions in exploration and production (E&P). Although the adoption of new hard technology such as directional drilling and hydraulic fracturing brought on fracking, the O&G industry needs to continue this trend in today's low-price market to survive.
For example, Google offers APIs such as its Cloud Vision API, Cloud Speech API, Cloud Jobs API, Cloud Translation API, Cloud Video Intelligence API, and the Cloud Natural Language API. SEE: Why machine learning and data analysis are critical to Google's success in the cloud The two most relevant APIs for customer service are the Cloud Speech API and the Natural Language API. However, if a company needs a more unique solution, they would have to build a custom system using TensorFlow and the Cloud Machine Learning engine. However, they then built a custom solution with Cloud Machine Learning and TensorFlow to get more detailed filtering.
For the learning phase, Levinson used a combination of rote learning as in case-based reasoning, restructuring to derive significant generalizations, a similarity measure based on the generalizations, and a method of back propagation to estimate the value of any case that occurred in a game. Every position that occurred in a game was stored in a generalization hierarchy, such as those used in definitional networks. At the end of each game, the system used back propagation to adjust the estimated values of each position that led to the win, loss, or draw. When playing a game, the system would examine all legal moves from a given position, search for similar positions in the hierarchy, and choose the move that led to a position whose closest match had the best predicted value.
The department's research in Machine Learning contributes to the state-of-the-art of individual methods and algorithms as well as combinations of methods targeting particular tasks, for example, combining data-intensive methods with knowledge-based methods to produce user explanations for decision support.
A content vector, that is an array of values that represents the information content suitable for the Vector space model matching, as explained in 4.; with paired justifications (They express the provenience of that element, if from initial interview or the current active stereotype, or feedback, etc.) This elements are dinamically updated, inserted or deleted after the user feedback, not like the content vector's elements that are build once for all and only changes in weigthing are possible. A content vector, that is an array of values that represents the information content suitable for the Vector space model matching, as explained in 4.; with paired justifications (They express the provenience of that element, if from initial interview or the current active stereotype, or feedback, etc.) This elements are dinamically updated, inserted or deleted after the user feedback, not like the content vector's elements that are build once for all and only changes in weigthing are possible.
In just few years, case-based reasoning has evolved from a research topic studied at a small number of specialized academic labs into an industrial-strength technology applied in various fields. The INRECA methodology presented in detail in this monograph provides a data analysis framework for developing case-based reasoning solutions for successful applications in real-world industrial contexts. The book provides a self-contained introduction to case-based reasoning applications that address both R&D professionals and general IT managers interested in this powerful new technology. In this second edition, improvements and updates have been incorporated throughout the text.
Another topic of my interest is the use of Machine Learning techniques to reason and learn about musical processes like expressive music generation. Currently focused on the design of CBR systems with introspective capabilities for autonomously improving the retrieval and adaptation steps (see NEXT-CBR project). The Phd was devoted to the design and implementation of the Noos representation language (Advisor Enric Plaza). Learning methods were introduced as reasoning methods with introspection capabilities able to improve/modify the knowledge of the system.