Many roles in today’s enterprises are metrics-driven and metrics-motivated. The search architects and search platform administrators we work with are no different; they’re hungry for feedback and insights about how users are searching (user analytics) and what content is performing best. With Attivio’s new Search Analytics capabilities (available today), we give search architects the information they desire, in real-time and right at their fingertips.
Search Analytics enables you to better understand the performance of your search platform by delivering insights into:
The concept of Garbage In, Garbage Out (GIGO) is almost as old as computing itself. Its origins have been traced back to the 1950s and basically means that if you start with bad information, you get faulty results. It’s a pretty simple concept that remains at the core of computing.
As machine learning takes off in the marketplace, the GIGO issues have become even more pronounced. In truth, the garbage is everywhere and we need to be careful about training our systems to emulate the wrong human behaviors. This is at the heart of a story in Wired Magazine that points out how photos were helping some machines learn sexist behaviors. A pair of researchers began to notice that some images, like those of kitchens, were more associated with women than with men. As they looked deeper they realized the problem wasn’t in the algorithm or in the core of machine learning, but in the images used as the base datasets designed to train the system.
In case you missed it, here’s a recap of last week’s webinar, New Modules in the Attivio Platform, presented by Attivio’s Director of Solution Architecture, Brian Flynn.
During the session, Brian reviewed two new modules that are included in the Attivio platform: Query Frames and the Search UI Toolkit, or “SUIT” for short.
As you may know, the Attivio platform includes a patented capability called Query-time JOIN. This combines indexed structured & unstructured data dynamically to answer questions like “What are the customers in my region saying about the new products they bought in the past 90 days?”
Even as the press is filled with stories of artificial intelligence and cognitive technologies, the market isn’t entirely sure what to make of these advances. As with any emerging technology, the question arises of whether it’s all hype or if it’s truly transformative.
In a recent post, we looked at the reasons why so many cognitive computing initiatives fail. And that leads to the next obvious question, “So, how do you avoid failure and plan for success?”
At Attivio, we think of cognitive computing as a set of building blocks with AI capabilities such as machine learning, NLP, text analytics, and so on. Cognitive search uses many of the same building blocks, which makes it a good place to start any cognitive computing project.
We used to think of knowledge workers as a few librarian-like employees responsible for managing all kinds of information. But information has become like currency: the more you have, the more you can do with it. And that means everyone has the potential to leverage information to perform their jobs better and increase the quality – and speed - of their decisions.
At the 2107 Sohn Conference, Social Capital CEO and founder Chamath Palihapitiya declared, “Watson is a joke, just to be completely honest.” Of course, this quote got a lot of play. But another quote from the same interview is actually more revealing about why so many cognitive computing initiatives eventually circle the drain — and why they take so many resources with them. Palihapitiya noted, "I think what IBM is excellent at is using their sales and marketing infrastructure to convince people who have asymmetrically less knowledge to pay for something."
In terms of cognitive search, IBM has the lion's share of failures at this point, so it's easy to throw stones at them. And, since they approach cognitive search as a services engagement, the reasons behind the failures are instructive.