Big data and machine learning – is the glass half empty?

Artificial intelligence is currently making a resurgence since the 1990s. Today, the focus is on machine learning and statistical algorithms. This shift has served AI well. Since machine learning and statistics provide effective algorithm solutions to certain kinds of problems, such as board games, spam detection, voice and image recognition, etc.

How is AI different today from 20 years ago? AI 20 years ago was focused on what is known as logic based AI or Knowledge Representation (KR). As any emerging technology, it became overhyped and over promised. The tools and frameworks to make KR successful never really materialized until recently.

As a technology decision maker, all the vocabulary of artificial intelligence might be a bit overwhelming. In Figure 1, starting from the bottom going up illustrates knowledge acquisition capabilities from a data usage perspective. By no means does this represent all the approaches to achieving an AI solution, but rather it illustrates how big data fits into the AI picture.

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Figure 1 Capabilities of AI in context by data types

Machine learning is represented by the right side of the above diagram, labeled, “Statistical Reasoning.” There are two types of machine learning, unsupervised and supervised. When big data vendors speak of machine learning, they are usually speaking of supervised machine learning that has existed since the 1950s.

Subsequently, supervised machine learning requires all the data to be annotated (that is metadata tagged), it is an ideal solution for problems that use structured data and its columns. Whereas, unsupervised learning doesn’t require the data to be annotated, but rather uses features of the data (that is patterns). Unsupervised learning excels in areas as image, voice, and hand written recognition, where any data features can be identified.

Both supervised and unsupervised machine learning uses a parallel distributed processing framework called neural networks. Neural networks are responsible for a number of recent breakthroughs in board games, audio and image recognition.

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In Pulling the plug on the AI hype, I made the case that the current incarnation of AI using machine learning doesn’t handle every day commonsense rational challenges, like reading a news headline.;

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