Many people use search engines to find online guidance to solve computer or mobile device problems. Users frequently encounter challenges in identifying effective solutions from search results, often wasting time trying ineffective solutions that seem relevant yet fail to solve real problems. This paper introduces a novel approach to improving the accuracy and relevance of online technical support search results through automated search results verification and reranking. Taking "How-to" queries specific to on-device execution as a starting point, we developed the first solution that allows an AI agent to interpret and execute step-by-step instructions in the search results in a controlled Android environment. We further integrated the agent's findings into a reranking mechanism that orders search results based on the success indicators of the tested solutions. The paper details the architecture of our solution and a comprehensive evaluation of the system through a series of tests across various application domains. The results demonstrate a significant improvement in the quality and reliability of the top-ranked results. Our findings suggest a paradigm shift in how search engine ranking for online technical support help can be optimized, offering a scalable and automated solution to the pervasive challenge of finding effective and reliable online help.

Businesses are often sitting on mounds of data that are not utilized. John Kelly, IBM's "father of Watson," says that 80% of data is "untouched," meaning it's never actually used to make improvements or changes deemed necessary by the customer. You might have state-of-the-art martech tools sitting in your martech stack- but how can you use all these data sources to see the bigger picture, and unlock the full potential of your marketing and media investments? Instead of keeping your customer data siloed, with different data sets spread out across different ecosystems, businesses need to start recognizing the value of connecting these data sources. Having one integrated customer intelligence platform that helps businesses understand their customers' conversations from all data sources can be a real game-changer.

Unless you've been living under a rock, you are sure to have heard of artificial intelligence (AI) and machine learning (ML): it is the current buzzword, and every technology company worth its salt talks about how AI is going to transform everything. The strange thing is that most business leaders, especially in consumer businesses, claim to be excited about AI, but they will be hard-pressed to give a tangible example of how AI has been used in their business, or what difference it has made to business performance. So, is this all just hype, or is there some real impact that AI is going to have on businesses? If you think about your everyday life, it is almost certain that AI-based technologies are an integral part of your everyday experience. See content and posts that you like on social media?

Creating and training machine-learning models is more complex on distributed systems, but there are lots of frameworks for abstracting that complexity. There are more options now than ever from proven open source projects for doing distributed analytics, with Python and R become increasingly popular. In this post, you'll learn the options for setting up a simple read-eval-print (REPL) environment with Python and R within the Cloudera QuickStart VM using APIs for two of the most popular cluster computing frameworks: Apache Spark (with MLlib) and H2O (from the company with the same name). To compare these approaches, you'll train a linear regression against a data set with known coefficients. Spark includes PySpark (supported by Cloudera), the Python API for Spark.