Here in Massachusetts, we find ourselves surrounded by beautiful old mill buildings that act as a constant reminder of how our nation’s history is built on manufacturing. In the early days of the industrial revolution, water powered the mills that produced the products the young nation needed to move forward.
Even today, with tech companies all around us, we know that machines continue to power the world. They move us from place to place, whether that’s a car, train, airplane, or ship. They provide the power we need and till the soil to provide the food we eat.
You can hear that line in dozens of movies and it's a clear indication that a lead character spends a lot of time at a particular diner. Of course the long-time waitress (and it's almost always a waitress) knows exactly how the main character likes his eggs (and yes, it's often a "him").
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.
How many times have you switched your mobile phone service provider when the service or support was poor? How hard did that service provider work to keep you? It’s likely they didn’t try very hard. They have many customers, so losing one isn’t that big of a deal. But for companies that provide complex products like those in manufacturing, aerospace or oil and gas, a high-quality customer support program is critical. The question is, what does a quality customer support program look like?
As we’ve explained in many blogs and 5-minute guides, a cognitive search platform should combine AI technologies such as natural language processing, machine learning, and knowledge graphing to deliver a contextualized search and discovery experience without compromising security. Those technologies can turn ordinary search into something much more powerful and transformative for any organization.
Today’s business users don’t search for documents that may have the information they seek buried within, instead they ask their systems for answers. This shift in attitude is a key driver behind the move to cognitive search.
Hurricane Harvey hit Houston hard. Harder than many expected, including a number of the oil and gas companies located in the area. Some evacuated early and had no idea when they would reopen for business.
Attivio 5.5—the latest release of our market leading Cognitive Search and Insight Platform—has a lot going for it. If you just want a quick summary of all its new features, the launch press release is a good place to start.
But I’m going to focus on one—the platform’s use of machine learning to improve relevancy. After all, relevancy is the heart of cognitive search.
Some days, it seems that new machine learningapplications are popping up everywhere you look in the news. In this author’s opinion, the search market seems to have anointed machine learning as the new hotness. This is a fascinating realization, because anyone who has spent more than a couple years deploying search in the enterprise knows that machine learning has been used and applied in exciting and unique ways for years and years.