intelligence


New AI tools make BI smarter -- and more useful

#artificialintelligence

Companies looking to make good on the promise of machine learning for data analysis are turning to a somewhat unlikely old friend. Business intelligence systems, largely the domain for analyzing past performance, are being retrofitted with artificial intelligence to bring predictive features to their reporting capabilities. The Symphony Post Acute Network is one such organization. The health care company, which has 5,000 beds in 28 health care facilities in Illinois, Indiana and Wisconsin, wanted to use artificial intelligence and machine learning to improve care for up to 80,000 patients a year recovering from procedures like knee surgery, or receiving dialysis treatment. For example, buried deep in a patient's medical core could be an indication that a patient is particularly at risk for a dangerous fall and therefore requires extra precautions.


New AI tools make BI smarter -- and more useful

#artificialintelligence

Companies looking to make good on the promise of machine learning for data analysis are turning to a somewhat unlikely old friend. Business intelligence systems, largely the domain for analyzing past performance, are being retrofitted with artificial intelligence to bring predictive features to their reporting capabilities. The Symphony Post Acute Network is one such organization. The health care company, which has 5,000 beds in 28 health care facilities in Illinois, Indiana and Wisconsin, wanted to use artificial intelligence and machine learning to improve care for up to 80,000 patients a year recovering from procedures like knee surgery, or receiving dialysis treatment. For example, buried deep in a patient's medical core could be an indication that a patient is particularly at risk for a dangerous fall and therefore requires extra precautions.


Column Similarity: Metadata Intelligence for Curation and Consumption

@machinelearnbot

Ability to accurately label columns, attributes and fields is a critical requirement for both data discovery and data governance. However, organizations can have millions of datasets and hundreds of millions of columns/fields in various structured and semi-structured data sources, making it impossible to manually curate them one by one. Also, not all columns represent unique business concepts/data elements. A single data element, like a CUSTOMER ID or PRODUCT ID, can be a part of multiple datasets. Machine learning can help cluster these "instances" of data elements together based on data similarity.


Are we going from "Artificial Intelligence" to "Augmented Intelligence?"

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I can't think of a reference to the intellect that would feel more unauthentic and fake. It's no wonder people turn to The Terminator or The Matrix to fathom what it's all about. Fortunately, after 60 years of AI rumors fueled by academia and movies, we're finally starting to see signs that it means more than just robots taking over. Working in the tech industry, it's ironic that the AI lightbulb clicked not through an understanding of machine learning or engineering -- but through the human challenges we face in the technology world. While at Yahoo! and Apple, it was amazing to be part of technologies that not only helped enable the modern cloud today but continue to support its more than doubling in performance every year.


How artificial intelligence is transforming GEOINT -- GCN

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Artificial intelligence has improved by leaps and bounds since IBM's chess-playing Deep Blue defeated reigning world champion Garry Kasparov in 1997. But that early face-off illustrated machine learning in its nascence: A computer makes sense of data it is given, finding patterns and crafting solutions based on the presented scenarios. One major difference today is that modern ML systems have access to infinitely more data from which they can uncover relationships and predict outcomes without pre-existing empirical models. The new frontier in ML is turning geographic data into deep location intelligence. Enabling applications to understand relationships in geographic data is the key to addressing some of the most pressing threats facing the geopolitical world today.


Learning to include AI in UX : Part one of my journey

#artificialintelligence

Let's kill a few false ideas first. No, artificial intelligence won't take the control of the world. You can forget your dreams about Terminator, The Matrix, Johnny 5 and Wall-e (yes I know for the last one it's kind of sad). But you just have to watch some Alexa, Google home or any other AI fails on YouTube to understand that we are far away from that scenario. Now that we agree on that, let's talk more seriously.


Healthcare needs AI for improved business intelligence

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Artificial intelligence can help pharmaceutical companies to leverage improved data insights in many ways. These include reviewing and interpreting comprehensive datasets; running speedier development cycles; interpreting data in context; and other types of business intelligence. Most significantly, machine intelligence seems to be able to solve the problems that have perpetually caused road blocks with pharmaceutical development, notably time for drug discovery and the subsequent clinical trial success rate. These ideas are explored by Gunjan Bhardwaj is the founder and CEO of Innoplexus, writing for PharmaPhorum. He makes the point that, in terms of improved scrutiny of datasets, platforms that work more like "a window into the world of available information" as opposed to "high-priced collection of limited data", are required.


From BI to AI and From Automation to Augmentation

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By succeeding in making machines work in tandem with humans to collect and process data, analyze it, and make decisions, enterprises have benefited from a continuously rising productivity. Over the years, advances in what machines can do have resulted in new tools and methods for analyzing data. These advances have also been accompanied by new waves of excitement and anxiety about automation. Already at the dawn of the computer age, speedy calculations led to new approaches to data analysis such as simulations and Monte Carlo methods. At the same time, the excitement over these "thinking machines" or "giant brains" as they were popularly called at the time, led 26-year-old John Diebold to write a book titled automation, published in 1952.


Can AI help strike the right emotional tone for content?

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As the volume of digital content grows exponentially, marketers are spending more time than ever trying to understand what resonates with their customers. Over the next couple of years, artificial intelligence (AI) could boost these efforts by pinpointing the best emotional appeal, subject matter, style, tone and sentiment to focus on. U.S. marketers spent more than $10 billion on content in 2016, according to a Forrester estimate. For many marketers, their content marketing strategy now includes a wide range of tactics like social media posts, short- and long-form video, live video and sponsored articles. However, for most, content is delivered with a piecemeal approach.