[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)
"The system could potentially be an aid for doctors in the ICU, which is a high-stress, high-demand environment," says PhD student Harini Suresh, lead author on the paper about ICU Intervene. "The goal is to leverage data from medical records to improve health care and predict actionable interventions." Another team developed an approach called "EHR Model Transfer" that can facilitate the application of predictive models on an electronic health record (EHR) system, despite being trained on data from a different EHR system. "Much of the previous work in clinical decision-making has focused on outcomes such as mortality (likelihood of death), while this work predicts actionable treatments," Suresh says.
"The goal is to leverage data from medical records to improve health care and predict actionable interventions." Another team developed an approach called "EHR Model Transfer" that can facilitate the application of predictive models on an electronic health record (EHR) system, despite being trained on data from a different EHR system. EHR Model Transfer was found to outperform baseline approaches and demonstrated better transfer of predictive models across EHR versions compared to using EHR-specific events alone. In the future, the EHR Model Transfer team plans to evaluate the system on data and EHR systems from other hospitals and care settings.
As defensive technologies based on machine learning become increasingly numerous, so will offensive ones – whether wielded by attackers or pentesters. We guarantee that you'll be either writing machine learning hacking tools next year, or desperately attempting to defend against them," the researchers concluded. At the same conference, Hyrum Anderson, Technical Director of Data Science at Endgame, explained how an AI agent trained through reinforcement learning to modify malware can successfully evade machine learning malware detection. As DeepHack, Anderson's AI agent was able to "learn" by playing thousands of "games."
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.
Snapchat debuted its camera-equipped Spectacles this fall. Snapchat is adding ways to optimize campaign performance with the help of machine learning. The option, available to marketers buying ads through Snapchat's API, uses machine learning to know which users are most likely to swipe a certain type of ad. Here's how it works: With goal-based bidding, advertisers can inform Snapchat of when their main goal is increasing swipe-ups--perhaps for app installs, web views or movie trailers--instead of focusing solely on impressions.