Differences Between AI and Machine Learning and Why it Matters

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Data Driven Investor | Roberto Iriondo | Oct 15, 2019

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Why do tech companies tend to use AI and ML interchangeably?

Unfortunately, some tech organizations are deceiving customers by proclaiming using AI on their technologies while not being clear about their products’ limits

The term “artificial intelligence” came to inception in 1956 by a group of researchers including Allen Newell and Herbert A. Simon [9], AI’s industry has gone through many fluctuations. In the early decades, there was a lot of hype surrounding the industry, and many scientists concurred that human-level AI was just around the corner. However, undelivered assertions caused a general disenchantment with the industry along the public and led to the AI winter, a period where funding and interest in the field subsided considerably.

Afterwards, organizations attempted to separate themselves with the term AI, which had become synonymous with unsubstantiated hype, and utilized different terms to refer to their work. For instance, IBM described Deep Blue as a supercomputer and explicitly stated that it did not use artificial intelligence [10], while it actually did.

See: 

During this period, a variety of other terms such as big data, predictive analytics and machine learning started gaining traction and popularity. In 2012, machine learning, deep learning and neural networks made great strides and started being utilized in a growing number of fields. Organizations suddenly started to use the terms machine learning and deep learning to advertise their products.

Deep learning started to perform tasks that were impossible to do with classic rule-based programming. Fields such as speech and face recognition, image classification and natural language processing, which were at early stages, suddenly took great leaps.

Hence, to the momentum, we are seeing a gearshift back to AI. 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. Machine learning and deep learning engineers are earning skyward salaries [11], even when they are working at non-profit organizations, which speaks to how hot the field is.

All these elements have helped reignite the excitement and hype surrounding artificial intelligence. Therefore, many organizations find it more profitable to use the vague term AI, which has a lot of baggage and exudes a mystic aura, instead of being more specific about what kind of technologies they employ. This helps them oversell, redesign or re-market their products’ capabilities without being clear about its limits.

In the meantime, the “advanced artificial intelligence” that these organizations claim to use, is usually a variant of machine learning or some other known technology.

Sadly, this is something that tech publications often report without profound examination, and frequently go along AI articles with pictures of crystal balls, and other supernatural portrayals. Such deception helps those companies generate hype around their offerings. Yet, down the road, as they fail to meet the expectations, these organizations are forced to hire humans to make up for the shortcomings of their so-called AI [12]. In the end, they might end up causing mistrust in the field and trigger another AI winter for the sake of short-term gains.

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