Machine Learning to Impact Business Analytics in 2017

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Although we are far away from the ‘Artificial Intelligence enabled co-worker, the maiden implementations of machine learning capabilities are finding their way into everyday data-analysis tools used by businesses of all types, writes Brian Wheeler from Morpheus Data writes in the new track.io.

Cognitive Assistance

Cognitive assistance promises to reshape business processes, but only if app development and deployment tools are adapted to support machine learning, he writes. While it has become fashionable to hype AI as the next game-changing technology promising to have an impact greater than either mobile or cloud, the reality is that machine learning will be a long time coming to everyday business analytics, he pointed out.

“As with any sea change, cognition is likely to sneak its way into applications and processes in drips and drops. It looks like 2017 could be the year many businesses get their first hands-on experience with cognitive-learning business apps”, Wheeler asserts.

For example, IBM’s Watson elicited plenty of “oohs” and “aahs” when it beat the Jeopardy champions, but the AI-based platform drew praise of another sort with the introduction of business solutions at the recent World of Watson event, quoting NewsFactor, he pointed out. Watson’s professional series applies cognitive learning to the analysis of large datasets; it works in tandem with enhancements to IBM’s DB2 for transactional processing in analytical databases.

IBM may have gotten a bit of a jump in the field of vendors racing to bring machine-learning capabilities to business processes, but the contest has just begun. The real winners are line managers, who stand to benefit the most from AI-enabled business applications.

Business Tackles Cognitive Implementation Challenges

The three cornerstones of cognitive technology are machine learning, natural-language processing (NLP), and speech recognition. In an article on Open Source For U, systems architect Sanghamitra Mitra writes that machine cognition is intended to imitate human reasoning to automate judgment-based components of business processes. The goal is to augment human activities to give people more time to focus on the really tough problems, like where to hold the holiday party.

The primary obstacle to implementation of cognitive systems is dealing with their inherent complexity. This fact is reflected in the cost of packaged machine-learning systems sold by vendors, as well as in the extensive infrastructure needed to support the systems. Several open-source alternatives have surfaced, providing enterprises with a quick, simple, and inexpensive way to dip their toe in the cognitive-computing water.

Here’s a quick look at popular open-source cognitive-learning tools:

The R language and environment for statistical analysis are highly extensible and offer linear and non-linear regression, traditional statistics tests, time-series analysis, classification, clustering, and other statistical functions in addition to graphical features.

Python is a high-level language that is popular with scientists and features machine learning implementations that fit well with the language’s agile and iterative approach.

Apache Mahout serves as a useful environment for quick creation of scalable machine-learning applications. The H2O parallel-processing engine is used by data scientists and developers requiring fast, scalable machine-learning apps.

The RapidMiner platform provides an end-to-end environment for implementing machine learning predictive-analytics models via a wizard interface.

Vendors Collaborate on AI Best Practices

There seems to be an inverse relationship between how much a new technology is hyped, and how well the technology is understood by would-be practitioners. In an attempt to remove some of the question marks surrounding machine learning and encourage adoption of the technology, Amazon, Facebook, Google, IBM, and Microsoft have joined to create the Partnership in AI program. The goals of the initiative are to support best practices, educate the public about AI’s potential benefits and costs, and “create an open platform for discussion and engagement”, he concluded.

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