Amazon launches QuickSight business intelligence service

     

As companies generate more and more data, one of the key things they need to do is find some way to make it all useful from a business standpoint. 

Business intelligence software has long been a way to help with that, and Amazon threw its hat into the ring Wednesday with a service called QuickSight, provided through its Amazon Web Services division. It was announced at the company's AWS Re:Invent conference in Las Vegas. 

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Amazon

An example of AWS QuickSight's ability to graph data.

http://www.infoworld.com/article/2989989/analytics/amazon-launches-quicksight-business-intelligence-service.html

Why artificial intelligence is succeeding: Then and now

     

Artificial intelligence has a checkered past. It has gone through multiple waves of huge expectations followed by incredible disappointments. We have seen the rise and fall of expert systems, neural networks, logic (hard and fuzzy) and the use of statistical models for determining reasoning.

We seem to be, once again, in an era of heightened expectations regarding A.I. We now have Siri, IBM Watson, self-driving cars and the proliferation of machine learning, data mining and predictive systems that promise an unprecedented, even frightening, level of machine intelligence.

But how is this current rise of A.I. different from what we have experienced before? What has changed to make us believe that the technology will make good on its countless promises?

I would argue that, ironically, the core technologies of A.I. have not changed drastically and today’s A.I. engines are, in most ways, similar to years’ past. The techniques of yesteryear fell short, not due to inadequate design, but because the required foundation and environment weren’t built yet. In short, the biggest difference between A.I. then and now is that the necessary computational capacity, raw volumes of data, and processing speed are readily available so the technology can really shine.

For example, IBM Watson leverages the idea that facts are expressed in multiple forms and that each match against each possible form equals evidence of the answer. The technology first analyzes the language input to extract the elements and relationships needed to determine what you might be looking for, and then uses thousands of patterns made up of the words in the original query to find matches in massive corpora of text. Each match provides a single piece of evidence, and each piece of evidence is added up to provide Watson with a number associated with each answer. While an exceptional system, there is not a lot of new A.I. technology at play in Watson.Watson tends to converge on the right answer because of the sheer volume of the different matches against the truth as it is expressed in the text.

If it only has a small number of documents to examine, however, the odds of it finding the information it needs, expressed in a way that it can understand, is small. In parallel, the odds that faulty information is going to get in the way of finding the truth is reduced as the corpus size grows. So, in much the same way that search results are improved as the data sets are expanded, the raw volume of text available to Watson directly correlates with the probability that the system will get to the right answer.
http://www.computerworld.com/article/2982482/emerging-technology/why-artificial-intelligence-is-succeeding-then-and-now.html

IBM Launches Cognitive Computing Consulting Practice

IBM Cognitive Business Solutions will help clients accelerate their time to value on big data and advanced analytics solutions.

While advanced analytics and big data have gained momentum over the last few years, there's often still a bit of a disconnect when it comes to getting business value out of these systems. Organizations realize there's a lot of potential, but they're not sure how to tap it.

Looking to help close that gap, IBM announced the creation of IBM Cognitive Business Solutions, a consulting organization under the umbrella of IBM Global Business Services to help clients accelerate getting the value out of cognitive computing solutions such as IBM's Watson.

http://www.informationweek.com/big-data/big-data-analytics/ibm-launches-cognitive-computing-consulting-practice-/d/d-id/1322517?

Apple Buys VocalIQ To Supercharge Siri

The purchase could give Apple the software it needs to take Siri, and potentially its automotive project, to the next level.

Apple has reportedly bought UK startup VocalIQ, according to a new report from the Financial Times.

VocalIQ builds speech-processing technology. Its software is designed to facilitate more natural communication between humans and computers, as explained by sources close to the deal.

In a blog post from the company, VocalIQ explains how the challenge of the Internet of Things is in combining human and artificial intelligence to create a continuous stream of new knowledge. It believes we need more than automated speech recognition (ASR) to improve the conversation between people and machines.

http://www.informationweek.com/mobile/mobile-applications/apple-buys-vocaliq-to-supercharge-siri/d/d-id/1322471?

7 Ways To Avoid Information Governance Pitfalls

Information governance practices must be updated as laws, technologies, and business models change. Here are seven ways to make sure you're governing your data effectively.

The governance of information and data isn't a subject that only regulated companies need to worry about. Businesses, regardless of their size or the industry they're in, need to understand how they store and use data, and whether it's adhering to their own privacy policies or complying with a regulatory mandate. Without formal data governance, companies are managing the associated risks by default.

http://www.informationweek.com/big-data/big-data-analytics/7-ways-to-avoid-information-governance-pitfalls/d/d-id/1322334?