Is Machine Learning Really AI

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One of the downsides to the recent revival and popularity of Artificial Intelligence (AI) is that we see a lot of vendors, professional services firms, and end users jumping on the AI bandwagon labeling their technologies, products, service offerings, and projects as AI products, projects, or offerings without necessarily being the case. On the other hand, there isn’t a well-accepted delineation between what is definitely AI and what is definitely not AI. This is because there isn’t a well-accepted and standard definition of what is artificial intelligence. Indeed, there isn’t a standard definition of intelligence, period.

Perhaps it is best to start with the overall goals of what we’re trying to achieve with AI, rather than definitions of what AI is or isn’t. Since the beginning of the AI in the 1950s, the goals of intelligent systems are those that mimic human cognitive abilities. This means the ability to perceive and understand its surroundings, learn from training and its own experiences, make decisions based on reasoning and thought processes, and the development of “intuition” in situations that are vague and imprecise; basically the world in which we live in. From a delineation perspective, it’s easy to classify the movements towards Artificial General Intelligence (AGI) as AI initiatives. After all, AGI systems are attempting to create systems that have all the cognitive capabilities of humans, and then some. Therefore certainly all AGI initiatives as AI initiatives.

On the flip side, simply automating things doesn’t make them intelligent. It may take time and effort to train a computer to understand the difference between an image of a cat and an image of a horse or even between different species of dogs, but that doesn’t mean that the system can understand what it is looking at, learn from its own experiences, and make decisions based on that understanding. Similarly, a voice assistant can process your speech when you ask it “What weigh more: a ton of carrots or a ton of peas?”, but that doesn’t mean that the assistant understands what you are actually talking about or the meaning of your words. So, can we really argue that these systems are intelligent?

In a recent interview with MIT Professor Luis Perez- Breva, he argues that while these various complicated training and data-intensive learning systems are most definitely Machine Learning (ML) capabilities, that does not make them AI capabilities. In fact, he argues, most of what is currently being branded as AI in the market and media is not AI at all, but rather just different versions of ML where the systems are being trained to do a specific, narrow task, using different approaches to ML, of which Deep Learning is currently the most popular. He argues that if you’re trying to get a computer to recognize an image just feed it enough data and with the magic of math, statistics and neural nets that weigh different connections more or less over time, you’ll get the results you would expect. But what you’re really doing is using the human’s understanding of what the image is to create a large data set that can then be mathematically matched against inputs to verify what the human understands.Recommended For You

How Does Machine Learning relate to AI?

The view espoused by Professor Perez-Breva is not isolated or outlandish. In fact, when you dig deeper into these arguments, it’s hard to argue that the narrower the ML task, the less AI it in fact is. However, does that mean that ML doesn’t play a role at all in AI?  Or, at what point can you say that a particular machine learning project is an AI effort in the way we discussed above? If you read the Wikipedia entry on AI, it will tell you that, as of 2017, the industry generally accepts that “successfully understanding human speech, competing at the highest level in strategic game systems, autonomous cars, intelligent routing in content delivery network and military simulations” can be classified as AI systems.

As published by Forbes