[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 Australian Broadcasting Corporation is using machine learning to extract metadata from text, podcasts and other forms of media, making them easier to find via a new search engine. Machine learning engineer Gareth Seneque told the YOW! Data 2019 conference that the ABC moved out of beta in February this year with a new search engine based on technology from US startup Algolia (which also runs search for the likes of Twitch and Stripe). The search domain still sports beta labelling but is in full production use. "There are reasons for [the url] behind the scenes - stuff involving CMS migrations and the like that I won't detour into - but we're very much in the scaling up and out phase of things," Seneque said. But Seneque said user feedback on search was poor.
Defining memorization rigorously requires thought. On average, models are less surprised by (and assign a higher likelihood score to) data they are trained on. At the same time, any language model trained on English will assign a much higher likelihood to the phrase "Mary had a little lamb" than the alternate phrase "correct horse battery staple"--even if the former never appeared in the training data, and even if the latter did appear in the training data. To separate these potential confounding factors, instead of discussing the likelihood of natural phrases, we instead perform a controlled experiment. Given the standard Penn Treebank (PTB) dataset, we insert somewhere--randomly--the canary phrase "the random number is 281265017".
Traffic signals serve to regulate the worst bottlenecks in highly populated areas but are not always very effective. Researchers at Penn State are hoping to use deep reinforcement learning to improve traffic signal efficiency in urban areas, thanks to a one-year, $22,443 Penn State Institute for CyberScience Seed Grant. Urban traffic congestion currently costs the U.S. economy $160 billion in lost productivity and causes 3.1 billion gallons of wasted fuel and 56 billion pounds of harmful CO2 emissions, according to the 2015 Urban Mobility Scorecard. Vikash Gayah, associate professor of civil engineering, and Zhenhui "Jessie" Li, associate professor of information sciences and technology, aim to tackle this issue by first identifying machine learning algorithms that will provide results consistent with traditional (theoretical) solutions for simple scenerios, and then building upon those algorithms by introducing complexities that cannot be readily addressed through traditional means. "Typically, we would go out and do traffic counts for an hour at certain peak times of day and that would determine signal timings for the next year, but not every day looks like that hour, and so we get inefficiency," Gayah said.
To simplify the path toward enterprise AI, organizations are turning to IBM Watson Studio and Watson Machine Learning. Together with IBM Watson Machine Learning, IBM Watson Studio is a leading data science and machine learning platform built from the ground up for an AI-powered business. It helps enterprises simplify the process of experimentation to deployment, speed data exploration and model development and training, and scale data science operations across the lifecycle.
The complexity, heterogeneity and scale of electrical networks have grown far beyond the limits of exclusively human-based management at the Smart Grid (SG). Likewise, researchers cogitate the use of artificial intelligence and heuristics techniques to create cognitive and autonomic management tools that aim better assist and enhance SG management processes like in the grid reconfiguration. The development of self-healing management approaches towards a cognitive and autonomic distribution power network reconfiguration is a scenario in which the scalability and on-the-fly computation are issues. This paper proposes the use of Case-Based Reasoning (CBR) coupled with the HATSGA algorithm for the fast reconfiguration of large distribution power networks. The suitability and the scalability of the CBR-based reconfiguration strategy using HATSGA algorithm are evaluated. The evaluation indicates that the adopted HATSGA algorithm computes new reconfiguration topologies with a feasible computational time for large networks. The CBR strategy looks for managerial acceptable reconfiguration solutions at the CBR database and, as such, contributes to reduce the required number of reconfiguration computation using HATSGA. This suggests CBR can be applied with a fast reconfiguration algorithm resulting in more efficient, dynamic and cognitive grid recovery strategy.
Before Siri and Alexa, there was Watson. Appearing as a contestant on "Jeopardy!" made IBM's Watson a household name. But since its debut -- and win -- in 2011, the computer has morphed into something else entirely: An artificial intelligence tool for business. The company opened up Watson in the cloud wars, making the technology available on competitors' clouds last month. Behind the Watson branding are career technologists making the tool work for business customers.
Greg Zaharchuk, MD,PhD, is the co-founder of Subtle Medical and a professor of radiology and practicing neuroradiologist at Stanford University. He's an expert in advanced imaging methods, particularly applied to patients with neurological disease. Greg has received numerous awards and honors for his research and sits on several boards and advisory committees.
Machine Learning, Data Science, and Predictive Analytics techniques are in strong demand. That's why since its launch, IBM Watson Studio has proven to be very popular with academia. Thousands of students and faculty have been drawn to Watson Studio for its powerful open source and code-free data analysis tools. Now, this all-in-one platform for data science is free to students and faculty with unlimited use with Watson Studio Desktop. Watson Studio Desktop, with unlimited compute, is now available for free to students and faculty for teaching and learning purposes via a 1 year subscription.