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Differences Between AI and Machine Learning, and Why it Matters

October 15, 2018, by Roberto Iriondo — Last updated: May 24, 2019 Recently, a report was released regarding the misuse from companies claiming to use artificial intelligence [29] [30] on their products and services.

Last year, TechTalks, also stumbled upon such misuse by companies claiming to use machine learning and advanced artificial intelligence to gather and examine thousands of users’ data to enhance user experience in their products and services [2] [33].

Often the terms are being used as synonyms, in other cases, these are being used as discrete, parallel advancements, while others are taking advantage of the trend to create hype and excitement, as to increase sales and revenue [2] [31] [32] [45].

For instance, if you provide a machine learning model with a lot of songs that you enjoy, along their corresponding audio statistics (dance-ability, instrumentality, tempo or genre), it will be able to automate (depending of the supervised machine learning model used) and generate a recommender system [43] as to suggest you with music in the future that (with a high percentage of probability rate) you’ll enjoy, similarly as to what Netflix, Spotify, and other companies do [20] [21] [22].

In a simple example, if you load a machine learning program with a considerable large data-set of x-ray pictures along with their description (symptoms, items to consider, etc.), it will have the capacity to assist (or perhaps automatize) the data analysis of x-ray pictures later on.

The type of machine learning from our previous example is called “supervised learning,” where supervised learning algorithms try to model relationship and dependencies between the target prediction output and the input features, such that we can predict the output values for new data based on those relationships, which it has learned from previous data-sets [15] fed.

According to Andrew Moore [6] [36] [47], Former-Dean of the School of Computer Science at Carnegie Mellon University, “Artificial intelligence is the science and engineering of making computers behave in ways that, until recently, we thought required human intelligence.” That is a great way to define AI in a single sentence;

Prior works of AI utilized different techniques, for instance, Deep Blue, the AI that defeated the world’s chess champion in 1997, used a method called tree search algorithms [8] to evaluate millions of moves at every turn [2] [37] [52] [53].

Fields such as speech and face recognition, image classification and natural language processing, which were at early stages, suddenly took great leaps [2] [24] [49], and on March 2019–three the most recognized deep learning pioneers won a Turing award thanks to their contributions and breakthroughs that have made deep neural networks a critical component to nowadays computing [42].

For those who had been used to the limits of old-fashioned software, the effects of deep learning almost seemed like “magic” [16], especially since a fraction of the fields that neural networks and deep learning are entering were considered off limits for computers.

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