A growing body of research runs human subject evaluations to study whether providing users with explanations of machine learning models can help them with practical real-world use cases. However, running user studies is challenging and costly, and consequently each study typically only evaluates a limited number of different settings, e.g., studies often only evaluate a few arbitrarily selected model explanation methods. To address these challenges and aid user study design, we introduce Simulated Evaluations (SimEvals). SimEvals involve training algorithmic agents that take as input the information content (such as model explanations) that would be presented to the user, to predict answers to the use case of interest. The algorithmic agent's test set accuracy provides a measure of the predictiveness of the information content for the downstream use case. We run a comprehensive evaluation on three real-world use cases (forward simulation, model debugging, and counterfactual reasoning) to demonstrate that SimEvals can effectively identify which explanation methods will help humans for each use case. These results provide evidence that \simevals{} can be used to efficiently screen an important set of user study design decisions, e.g., selecting which explanations should be presented to the user, before running a potentially costly user study.

A growing body of research runs human subject evaluations to study whether providing users with explanations of machine learning models can help them with practical real-world use cases. However, running user studies is challenging and costly, and consequently each study typically only evaluates a limited number of different settings, e.g., studies often only evaluate a few arbitrarily selected model explanation methods. To address these challenges and aid user study design, we introduce Simulated Evaluations (SimEvals). SimEvals involve training algorithmic agents that take as input the information content (such as model explanations) that would be presented to the user, to predict answers to the use case of interest. The algorithmic agent's test set accuracy provides a measure of the predictiveness of the information content for the downstream use case.

We present KNOW--the Knowledge Navigator Ontology for the World--the first ontology designed to capture everyday knowledge to augment large language models (LLMs) in real-world generative AI use cases such as personal AI assistants. Our domain is human life, both its everyday concerns and its major milestones. We have limited the initial scope of the modeled concepts to only established human universals: spacetime (places, events) plus social (people, groups, organizations). The inclusion criteria for modeled concepts are pragmatic, beginning with universality and utility. We compare and contrast previous work such as Schema.org and Cyc--as well as attempts at a synthesis of knowledge graphs and language models--noting how LLMs already encode internally much of the commonsense tacit knowledge that took decades to capture in the Cyc project. We also make available code-generated software libraries for the 12 most popular programming languages, enabling the direct use of ontology concepts in software engineering. We emphasize simplicity and developer experience in promoting AI interoperability.

The relationship between AI and big data is a two-way street, to be sure: Artificial intelligence success depends largely on high-quality data, and lots of it. Managing massive amounts of data and deriving value from it, meanwhile, increasingly depends upon technologies such as machine learning (ML) or natural language processing (NLP) to solve problems that would be too burdensome for humans to contend with on their own. It's a "virtuous cycle," as Anexinet senior digital strategist Glenn Gruber told us recently. Whereas the "big" in big data once might have been seen more as a challenge than an opportunity, this is changing as organizations begin rolling out enterprise uses of machine learning and other AI disciplines. "Today, we want as much [data] as we can get – not only to drive better insight into business problems we're trying to solve, but because the more data we put through the machine learning models, the better they get," Gruber explained.

Learn how applying the concept of machine learning to capacity management can make the process more effective and efficient. Machine learning involves computers assimilating information and then drawing conclusions from that data without being explicitly programmed to do so. This technology has significant positive implications for businesses. Yet, machine learning can be improved even further. The answer lies in data integration.

There are many resources, free and paid, for learning data science. However, most of them are not really complete, and they have the wrong focus. The modern data scientist needs to be able to combine various skills, which not many courses take into account. First of all, a complete data scientist needs to know both machine learning and statistics. Also, familiarity with at least either R or Python (ideally both) is a must.

WIRE)--Jul 30, 2018--MapR Technologies, Inc., provider of the industry's leading data platform for AI and Analytics, today announced a new, free introductory course from MapR Academy on Artificial Intelligence (AI) and Machine Learning (ML). This on-demand course provides insights into how businesses can leverage AI and ML to improve operations through real-world use cases. This course is ideal for developers just starting out in ML, as well as higher-level business decision makers. "Machine learning, a trending topic in big data, is not fully understood. This course provides a foundation for anyone curious about ML/AI and how it works in practical applications," said Suzanne Ferry, vice president, global training and enablement, MapR.

Artificial intelligence (AI) is key to how consumer internet companies operate today, allowing them to roll out hyper-personalized services by following an "AI first" strategy. The rest of the market is still catching up on adopting AI and has yet to fully understand the value of AI, including the breadth and depth of use cases, the technology choices surrounding AI, and the implementation strategies for AI. Compared to a few years ago, the AI market is starting to solidify around real-world applications with the pace of change being faster than it has ever been before, as startups and technology providers rush to create platforms and targeted niche solutions for solving specific enterprise problems. Tractica's AI market forecast is aimed at quantifying the software, hardware, and services opportunity, building on a database of real-world use cases and applications. In sizing and forecasting the total global AI market, Tractica has created a taxonomy of 266 real-world use cases for AI, distributed across 29 different industry sectors and corresponding with six major technology categories, plus multiple combinations of technologies.

Artificial intelligence (AI) is key to how consumer internet companies operate today, allowing them to roll out hyper-personalized services by following an "AI first" strategy. The rest of the market is still catching up on adopting AI and has yet to fully understand the value of AI, including the b...

Spark's popularity means the field is expanding in terms of both use and capability. Faster than Hadoop and MapReduce, but compatible with Java, Scala, Python, and R, this open source clustering framework is becoming a must-have skill. Spark: Big Data Cluster Computing in Production goes beyond the basics to show you how to bring Spark to real-world production environments. With expert instruction, real-life use cases, and frank discussion, this guide helps you move past the challenges and bring proof-of-concept Spark applications live.