This article describes an end-to-end learning framework that allows a novice to create models from data easily by helping structure the model building process and capturing extended aspects of domain knowledge. By treating the whole modeling process interactively and exploiting high-level knowledge in the form of an ontology, the framework is able to aid the user in a number of ways, including in helping to avoid pitfalls such as data dredging. We describe how the framework automatically exploits structured knowledge in an ontology to identify relevant concepts, and how a data extraction component can make use of online data sources to find measurements of those concepts so that their relevance can be evaluated. Prediction error on unseen examples of these models show that our framework, making use of the ontology, helps to improve model generalization.

This article describes a challenging, real-world planning problem within the context of a NASA mission called LADEE (Lunar Atmospheric and Dust Environment Explorer). One key aspect of this approach is the design of the activity planning process based on principles of problem decomposition and planning abstraction levels. The second key aspect is the mixed-initiative system developed for this task, called LASS (LADEE Activity Scheduling System). The primary challenge for LASS was representing and managing the science constraints that were tied to key points in the spacecraft's orbit, given their dynamic nature due to the continually updated orbit determination solution.

The 28th International Workshop on Qualitative Reasoning (QR-15) presented advances toward reasoning tractably with massive qualitative and quantitative models, automatically learning and reasoning about continuous processes, and representing knowledge about space, causation, and uncertainty.

Civil unrest events (protests, strikes, and “occupy” events) are common occurrences in both democracies and authoritarian regimes. The study of civil unrest is a key topic for political scientists as it helps capture an important mechanism by which citizenry express themselves. In countries where civil unrest is lawful, qualitative analysis has revealed that more than 75 percent of the protests are planned, organized, or announced in advance; therefore detecting references to future planned events in relevant news and social media is a direct way to develop a protest forecasting system. We report on a system for doing that in this article. It uses a combination of keyphrase learning to identify what to look for, probabilistic soft logic to reason about location occurrences in extracted results, and time normalization to resolve future time mentions. We illustrate the application of our system to 10 countries in Latin America: Argentina, Brazil, Chile, Colombia, Ecuador, El Salvador, Mexico, Paraguay, Uruguay, and Venezuela. Results demonstrate our successes in capturing significant societal unrest in these countries with an average lead time of 4.08 days. We also study the selective superiorities of news media versus social media (Twitter, Facebook) to identify relevant trade-offs.

Realize this is an older thread, but need to correct the explanation from Ram, as it is not correct. I noticed this using the Wine data set available here. The resulting plot is attached. The way to interpret the plot is that each class of V1 (1, 2, or 3) corresponds to a different color in the plot. Each square is a node.

Matters of machine intelligence are topics of great interest these days. It reminds me of a statement I read a couple years back from renowned technology writer and author Kevin Kelly: "The business plans of the next 10,000 startups are easy to forecast: Take X and add AI." Although that prediction proved to be a bit off the mark (or perhaps it's still too early for its time), the idea is certainly compelling. Nearly every day I hear of a new startup announcing some new intelligence offering. It's an exciting time to be in the industry.

Alpha: 'AI who beats Human Pilot in Tests' and in these lengthy tests; as usual the human subject is prone to getting tired. The Computer AI known as ALPHA does not get tired and is one of recent AI developments that reveal just how good the Artificial intelligence is getting. I have written on AI for some time now and it is evident the next stage of development that men will shoot for is AI use in many different areas. But areas where these AI robots, and instruments can work with humans as an aid. This is all fine and good until the Robot or AI instrument starts learning on it's own.

Employing online tools to settle routine legal disputes can improve access to justice for people who can t afford to hire a lawyer, while freeing up court dockets for more complex cases, enthusiasts say. And citizen expectations are being driven by the private sector, Rule says. Courts and government agencies that adopt the technology stand the best chance of keeping their constituents satisfied, he says.

Google notes in their patent overview that "Baby cribs are routinely purchased on the basis of safety and aesthetic features. Typically a mattress for the crib is separately purchased for similar reasons. Many users separately select a baby monitor that includes a camera and/or microphone. More sophisticated monitors may have an infrared camera and/or a speaker. The monitor may include a camera that can be placed in a position that overlooks the baby crib.

Intro for this event: Come see examples of how today's large data collections are being tackled by data science and machine learning methods, thereby empowering a data-driven transformation in organizations and industries that is bringing about greater competitive intelligence, insights, and innovation. Speaker Bio: Dr. Kirk Borne is the Principal Data Scientist for NextGen Analytics and Data Science in the Strategic Innovation Group at Booz Allen Hamilton. He previously spent 12 years as Professor at George Mason University in the Computational and Data Sciences program. Before that, he worked 18 years on various NASA contracts, as research scientist and as manager on large data systems. He has a PhD in Astrophysics from Caltech.