November 17, 2016

It’s a Marketing Mess! Artificial Intelligence vs Machine Learning

Artificial intelligence is a thing. No matter where you turn, technology companies are selling AI as the secret sauce in their cybersecurity platforms, their decision support systems, their network analytics tools, even their email marketing software. You name it, it’s got “AI Inside.” You’ll see that acronym AI often, as companies refer to artificial intelligence that way – which in itself is pretty vague, as you’d expect for a term that’s been bandied about for many decades and has a great number of representative branches. In our current context, AI generally refers to hardware or software that thinks, learns, and cognitively processes data the same way a human would, although presumably faster and more accurately: Think about Commander Data from Star Trek as a human-shaped role model for what AI could become someday.

The latest marketing discovery of AI as a cybersecurity product term only exacerbates an already complex landscape of jingoisms with like muddled understanding. A raft of these associated terms, such as big data, smart data, heuristics (which can be a branch of AI), behavioral analytics, statistics, data science, machine learning and deep learning. Few experts agree on exactly what those terms mean, so how can consumers of the solutions that sport these fancy features properly understand what those things are?

From an InfoSec perspective, Igor Baikalov, Chief Scientist of Securonix, describes Big Data as a marketing term that combines existing technologies and architecture to achieve some specific goal, and evolves as the market gets saturated. Vendors can sell Big Data as 3 V’s (Volume, Velocity, Variety), cross-sell it as 4 V’s (+ Veracity), up-sell it as 5 V’s (+ Value), and – if they are really late to the market and desperate – down-sell it as 7 V’s (+ Variability and Visualization).

Not only is the definition of Big Data fuzzy, “but the problem with Big Data is that it is only that, data. The true value of the data comes with some analysis or other learning techniques applied to it,” says Baikalov.