Many articles written about artificial intelligence (AI) actually focus on machine learning (ML) and authors often use the two terms interchangeably. Chris Meserole (@chrismeserole), a fellow in Foreign Policy at the Brookings Institution, takes no umbrage with the practice. He believes AI and ML are so interconnected that trying to separate them is a distinction without a difference. He writes, “Machine learning is now so popular that it has effectively become synonymous with artificial intelligence itself. As a result, it’s not possible to tease out the implications of AI without understanding how machine learning works — as well as how it doesn’t.” Nicole Martin, owner of NR Digital Consulting, believes it does matter. She writes, “When it comes to Big Data, these computer science terms are often used interchangeably, but they are not the same thing.” She asserts machine learning is basically a self-explanatory term. “The important thing to remember with ML,” she writes, “is that it can only output what is input based on the large sets of data it is given. It can only check from what knowledge it has been ‘taught.’ If that information is not available, it cannot create an outcome on its own.” On the other hand, she explains, “AI can create outcomes on its own and do things that only a human could do. ML is a part of what helps AI by taking the data that it has been learned and then the AI takes that information along with past experiences and changes behavior accordingly.” She adds, “They are both crucial to the future of technology.” For business leaders who want to make the most of cognitive technologies, understanding the differences does make a difference.
Artificial Intelligence vs Machine Learning
Artificial intelligence has been defined in a number of ways. Mike Colagrossi observes, “A lot of the times we use the term Artificial intelligence as an all-encompassing umbrella term that covers everything. That’s not exactly the case. A.I., machine learning, deep learning, and robotics are all fascinating and separate topics. They all serve as an integral piece of the greater future of our tech. Many of these categories tend to overlap and complement one another.”
For years, artificial intelligence was divided into two categories: weak and strong. Strong AI was defined as artificial general intelligence and weak AI was everything else. As the artificial intelligence field has matured, those definitions have become outdated. As I see it, there are currently three levels of AI being developed. They are:
- Weak AI: Wikipedia states: “Weak artificial intelligence (weak AI), also known as narrow AI, is artificial intelligence that is focused on one narrow task.” In other words, weak AI was developed to handle/manage a small and specific data set to answer a single question. Its perspective is singular, resulting in tunnel vision.
- Strong AI: As noted above, strong AI originally referred to Artificial General Intelligence (i.e., a machine with consciousness, sentience and mind), “with the ability to apply intelligence to any problem, rather than just one specific problem.” Today, however, there are cognitive systems that fall short of AGI but far surpass weak AI. These systems were developed to handle/manage large and varied data sets to answer a multitude of questions in a variety of categories. This is the category into which cognitive computing falls. Cognitive AI can deal with ambiguous situations whereas weak AI cannot.
- General AI: The AGI Society notes the ultimate goal of AGI researchers is to develop “thinking machines” (i.e., “general-purpose systems with intelligence comparable to that of the human mind”). The development of these potential thinking machines keeps some scientists and many science fiction writers up at night.
In the near-term, weak and strong AI systems will dominate conversations and provide the most benefit to businesses. Cognitive systems should prove particularly useful since they can function in ambiguous situations — the kind of situations in which individuals and companies often find themselves.
Colagrossi notes, “At its foundation, machine learning is a subset and way of achieving true AI. It was a term coined by Arthur Samuel in 1959, where he stated: ‘The ability to learn without being explicitly programmed.’ The idea is to get the algorithm to learn or be trained to do something without being specifically hardcoded with a set of particular directions. It is the machine learning that paves way for artificial intelligence.” He goes on to note, “Over the years, machine learning developed into a number of different methods.” Those methods are:
1. Supervised learning. “In a supervised setting, a computer program would be given labeled data and then be asked to assign a sorting parameter to them. This could be pictures of different animals and then it would guess and learn accordingly while it trained.”
2. Semi-supervised. “Semi-supervised would only label a few of the images. After that, the computer program would have to use its algorithm to figure out the unlabeled images by using its past data.”
3. Unsupervised. “Unsupervised machine learning doesn’t involve any preliminary labeled data. It would be thrown into the database and have to sort for itself different classes of animals. It could do this based on grouping similar objects together due to how they look and then creating rules on the similarities it finds along the way.”
4. Reinforcement. “Reinforcement learning is a little bit different than all of these subsets of machine learning. A great example would be the game of Chess. It knows a set amount of rules and bases its progress on the end result of either winning or losing.”
Alan J. Porter (@alanjporter), Head of Strategic Services for [A], observes some scientists aren’t enthralled with machine learning because they believe “ML is great at recognizing patterns but not much else” and “ML assumes tomorrow is going to be the same as today.” Although ML has limitations, it remains a very useful tool in a business’ kit. Porter explains, “Machine learning can greatly reduce the workload and automate the process of recognizing patterns of behavior in large sets of customer data, but it is not a magic panacea for developing an understanding of why customers do what they do.”
Why knowing the difference matters
Kevin Gardner, a business consultant for InnovateBTS, writes, “The primary aim of AI is to increase the probability of success and not accuracy. Naturally, human beings are not accurate. This explains why they use different technological devices such as calculators or personal computers to aid in enhancing accuracy. This means that, if they fit a system with the same knowledge and intelligence that they possess, they will only be increasing the chances of success but not accuracy.” Cognitive technologies are often described as decision support systems. “On the other hand,” Gardner writes, “the primary role and aim of ML are to improve efficiency through experience. ML is focused on ensuring that a machine that performs a particular task can perform it correctly without concern for success.”
Terence Mills (@terence_mills), CEO of AI.io and Moonshot, concludes, “Both AI and ML can have valuable business applications. Determining which one is best for your company depends on what your needs are. These systems have many great applications to offer, but ML has gotten much more publicity lately, so many companies have focused on that source of solutions. However, AI can also be useful for many simpler applications that don’t require ongoing learning.” Business executives need to know, however, that AI and ML capabilities are not an either/or situation. By leveraging cognitive computing platforms, like the Enterra Enterprise Cognitive System™ (AILA®) — a system that can Sense, Think, Act and Learn®, you can have both capabilities to use as needed.
 Chris Meserole, “What is machine learning?” The Brookings Institution, 4 October 2018.
 Nicole Martin, “Machine Learning And AI Are Not The Same: Here’s The Difference,” Forbes, 19 March 2019.
 Mike Colagrossi, “What’s the difference between A.I., machine learning, and robotics?” Big Think, 28 May 2018.
 Alan J. Porter, “Machine Learning Isn’t Rocket Science,” CMS Wire, 9 September 2019.
 Kevin Gardner, “Clearing the Confusion: Artificial Intelligence and Machine Learning,” BBN Times, 6 September 2019.
 Terence Mills, “Machine Learning Vs. Artificial Intelligence: How Are They Different?” Forbes, 11 July 2018.