“We are drowning in information and starving for knowledge.” — John Naisbitt.

The Era of Big Data

We have entered the era of big data. For example, there are about 60 trillion web pages; 300 hours of video are uploaded to YouTube every minute, amounting to 10 years of content every day; the genomes of 10000s of people, each of which has a length of 3.8 × 109 base pairs, have been sequenced by various labs; Walmart handles more than 1M transactions per hour and has databases containing more than 2.5 petabytes (2.5 × 1015) of information [1]; We are currently creating around five exabytes a day roughly equivalent to 500 million songs — the amount of data available today is giving machines the possibility to become super-intelligent.

What is ML?

This deluge of data calls for automated methods of data analysis, which is what machine learning provides. In particular, machine learning (ML) is defined as a set of methods that can automatically detect patterns in data, and then use the uncovered patterns to predict future data, or to perform other kinds of decision making under uncertainty (such as planning how to collect more data) [2]. A more technical definition of ML is “a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation” [3].

AI vs ML?

It’s worth noticing that often ML is used with artificial intelligence interchangeably. Although artificial intelligence is technically broader than ML and they are different, for the purpose of this note, we treat them the same.

Why Now?

Now, you might ask, how old is this stuff and why is it getting a lot of attention now? Artificial intelligence was founded as an academic discipline in 1956. It because most of ML methods work well when you have a large amount of data (to be precise it really depends on the problem you are trying to solve) and as noted we have entered into an era that there is no shortage of data, yay! Another interesting thing recently happed; our computers got way stronger and faster than before. Analyzing and extracting information from an enormous amount of data is not trivial. It needs a lot of computational power and the introduction of graphics processing units (GPUs), which were developed to speed up the graphics in our everyday computers and especially in the gaming world, to ML world changed the game.

What are Some of the Applications of ML?

What are some of the applications of AI/ML? Good question. Although you might not notice it, ML already is a big part of our lives. Amazon makes its recommendations based on a set of ML algorithms called “Recommender systems” specifically a hybrid of “Collaborative filtering” and “Content-based filtering”. Google uses ML to detect spam emails. Facebook uses ML to show you the content that you would find interesting. Tesla is using AI/ML to make self-driving cars. Intelligence services are using ML to detect criminals in crowds and …

ML and AI are already here. The question is how to align your business to benefit from it rather than getting killed by it.

“Cukier K., The Economist, Data, data everywhere: A special report on managing information, 2010, February 25, Retrieved from http://www.economist. com/node/15557443”
“Kevin P. Murphy , Machine Learning: A Probabilistic Perspective Hardcover — Aug 24 2012”
“Kaplan, A. and M. Haenlein (2019) Siri, Siri in my Hand, who’s the Fairest in the Land? On the Interpretations, Illustrations and Implications of Artificial Intelligence, Business Horizons, 62(1)”.