Is It AI or Machine Intelligence?

Artificial Intelligence (AI) and machine intelligence are commonly used synonymously, but there is a nuanced difference — AI is more properly intended to focus on simulating the features of the human mind, while machine intelligence can also include intelligent information processing that is distinct from or beyond the capabilities of the human mind. Machine intelligence is also sometimes used to refer to machine learning (ML), a subset of machine intelligence — and AI as well, depending on how you interpret the terms AI and machine intelligence. This informal paper will endeavor to distinguish AI and machine intelligence.

Whether discussing humans or machines, we have three related concepts:

  1. Physical activity. Movement, observation, manipulation, and communication.
  2. Intelligence. Processing of information and acquiring and applying knowledge. Perception and recognition of objects, scenes, and qualities from the environment, reasoning, generating new information, learning, and deciding, planning, initiating, and guiding physical activity.
  3. Learning. Acquisition of information and knowledge. This is part of intelligence, but worth calling out specially. Recognition of patterns, rules, objects, and qualities from information acquired from the environment, development of concepts and principles from observations, reasoning, and existing knowledge.

Again, learning is properly part of intelligence but worth calling out since learning commonly occurs distinctly from applying learned knowledge. As discussed later, learning occurs on an ongoing basis in daily life — so-called little learning — while more complex learning of concepts, principles, and methods — so-called big learning — tends to require a more specialized and more dedicated effort. The main reason for calling out learning here is since we have this specialized field of AI and machine intelligence called machine learning.

Back to those larger concepts, for people we have:

  1. Human physical activity.
  2. Human intelligence.
  3. Human learning.

And for machines we have:

  1. Robotics or machine activity.
  2. Artificial intelligence or machine intelligence.
  3. Machine learning.

There is no requirement that machines directly parallel or directly mimic people, but that is a common interest and noteworthy endeavor. It is worth calling out the distinction:

  1. Mimicking or paralleling human activity, intelligence, and learning. More commonly referred to as AI.
  2. Activity, intelligence, and learning for machines which may be distinct from that of humans. Whether is is still proper to call this AI or whether it is distinctly machine intelligence is a matter of debate.

A purist would argue that AI should only be used when the intelligence is directly comparable to human intelligence.

A different class of purist could assert that everything a machine does is artificial, so that any purported machine intelligence is by definition artificial intelligence.

Another purist could also assert that any intelligence of a machine is by definition machine intelligence, so that even AI which directly mimics human intelligence should be called machine intelligence even if another purist calls it machine intelligence.

The reader is free to take their pick. My job is simply to highlight the issues and the distinctions.

Context is important as well. It depends on whether the emphasis is on how well a machine can mimic a person or whether the emphasis is on what the machine can do better than a person.

Just to recap the related concept areas:

  1. Human physical activity. Things people do. Mostly manual tasks, guided by intelligence.
  2. Human intelligence. Human mental activity. Things people do in their minds. Thinking, reasoning, calculating, planning, imagining, learning, communicating, and guiding physical activity.
  3. Human learning. Little learning, such as names and faces of people we meet, places we go, things we observe, and activities we engage in, as well as big learning, such as school and other forms of education, studying, reading, and research to learn and develop concepts, principles, and methods.
  4. Robotics. Physical activity of machines.
  5. Artificial intelligence. Machines simulating human intelligence.
  6. Machine intelligence. May be artificial intelligence, a subset of artificial or human intelligence, or forms of reasoning, calculating, planning, imagining, learning, communicating, guiding machine activity, which are distinct from the mental activities of people.
  7. Machine learning. Specialized forms of pattern and rule recognition that either mimic analogous human recognition, or are distinct from human recognition. Generally, a very limited subset or minimal approximation of human recognition, or fairly distinct from human recognition. More advanced forms can develop concepts, principles, and methods, but that is generally beyond the abilities of most current machines.

Generally, machine intelligence refers to one of the following, sometimes dependent on context and usage:

  1. Artificial intelligence (AI). Exact synonym.
  2. Machine learning. Exact synonym. A subset of AI.
  3. AI for mechanical and electromechanical machines, as distinct from AI for artificial biological life forms.
  4. Intelligence beyond that of a human being. Superintelligence. Includes Kurzweil’s Singularity.
  5. Intelligence distinct from that of a human being. Such as processing of sensors and forms of data beyond or very different from the five human senses.
  6. Information processing involving complex data patterns and relationships for which the algorithms can be comprehended by humans but carrying out the volume or complexity of operations is impractical for a mere mortal. Includes big data.

A more full list of the nuanced meanings of machine intelligence:

  1. Any degree of approximation of human mental capabilities, even if not advanced AI.
  2. Exact synonym for artificial intelligence.
  3. Artificial intelligence specific to electronic digital machines (computers), as opposed to, say, an artificial biological system.
  4. Synonym for machine learning.
  5. Artificial intelligence that may may have elements of intelligence that have no counterpart in humans.
  6. Subset of human intelligence that machines excel at. Such as working with very large amounts of data or performing complex calculations very quickly.
  7. Any complex or sophisticated algorithm that accomplishes some task that impresses a human as being a task that humans are good at and that seems like it would be difficult for a machine.
  8. Intelligence beyond that of human beings (or animals.) Ranging from a modest improvement to true superintelligence.
  9. Forms of intelligence that machines are capable of but are beyond, different from, or difficult for humans to accomplish.
  10. Intelligence that is merely different from human intelligence. Such as processing data from a wider range of sensors or more specialized sensors than the five human senses.
  11. Intelligence needed for a robot to navigate and engage in activities in the real world.
  12. Some nuance or difference from AI that will have to be determined from the context of usage.
  13. Software that is beyond mere automation of rote mechanical operations and basic numerical and information processing.

The remainder of this paper will focus on specific aspects of machine intelligence and machine learning. The final summary offers some questions that will help the reader decide whether any given piece of technology is better classified as AI or machine intelligence — or machine learning.

Additional detail can be found in these related papers:

  1. Untangling the Definitions of Artificial Intelligence, Machine Intelligence, and Machine Learning
  2. Is It Really AI or Just Automation?

And for a more brief, light introduction to AI, see What Is AI (Artificial Intelligence)?

Tasks beyond what people and animals traditionally performed

The label machine intelligence makes most sense when considering tasks which are not traditionally associated with human (or animal) mental or physical activity, including:

  1. High volumes of data.
  2. Data that doesn’t correspond to the traditional five human senses.
  3. Relatively complex processing of data which does correspond to the traditional five human senses.
  4. Relatively intelligent processing needed to accomplish robotic activity. Dealing with the details and nuances of electrical, electronic, and mechanical systems — in a fairly intelligent manner.

Big learning and little learning

The former (big learning) is the difficult form of learning where complex concepts, principles, and methods are learned with great effort. This is the world of school and other forms of education, studying, tests, research, and struggling. It is usually a distinct activity from daily life.

The latter (little learning) is the learning we do in everyday daily life, like meeting new people, learning their names and interests, and remembering their faces. Or facts we learn as we travel and engage in activities in unfamiliar places. Not as much effort or as much of a struggle as big learning. No new concepts, principles, or methods involved.

In between we have a hybrid, focused more on categorical distinctions and nuances. The world of patterns and rules.


A lot of what passes for machine learning is focused on a combination of the latter three subsets of learning — little learning, hybrid of little and big learning, and training. Sometimes the machine can learn completely on its own, but very commonly some degree of human intervention and so-called training is required.

Deep learning and guided learning

Then, the machine can begin to discover patterns and infer rules for that focused domain.

The machine may indeed succeed at ferreting out what is happening (what the patterns and rules are), but not why the activity is happening or what the objective of the activity is. Very limited learning.

So, big learning focuses on deep concepts, broad principles, complex methods, and the bigger picture context, while little learning is much more superficial, either being relatively trivial or requiring hard-wired a priori knowledge (including human genetically-encoded abilities or hard-coded knowledge for a machine) or a limited ability to discover patterns and rules.

Advanced machine learning

  1. Processing large volumes of data.
  2. Operating in dangerous physical environments.
  3. Operating for extended lengths of time which would tax even the most patient human.
  4. Discovering extremely complex patterns and relationships.
  5. Discovering patterns, rules, and relationships which require performing extremely complex mathematical calculations and modeling.

As usual, a hybrid is possible, so that the best of both worlds, man and machine, can be combined in a synergistic manner.


Adding a column of numbers or sorting a list of names does require some intelligence, but we normally don’t consider arithmetic and basic information processing to be AI or even machine intelligence.

Some forms of automation that are in a gray area where it’s a fielder’s choice whether to consider it just automation or true machine intelligence include:

  1. Optimization.
  2. Scheduling.
  3. Data analytics.
  4. Business intelligence.
  5. Simulation.
  6. Scientific and engineering calculation and modeling.

A key question is whether there is some higher-order intellectual capacity required.

Or, is it just automating relatively rote, mechanical processing.

See a companion paper, Is It Really AI or Just Automation?, for more on this distinction.


  1. Detecting or recognizing that a certain sequence or arrangement of data conforms to a known pattern. Finding instances of a known pattern.
  2. Discovering a new pattern, an abstract pattern. Deciding that an otherwise random, chaotic, or even regular sequence or arrangement of data should be considered to be an instance of a new pattern. Further, generalizing from that instance or a collection of similar instances to a more general or abstract form of pattern that can recognize a broader class of instances of the abstracted pattern.
  3. Recognizing or discovering the significance of a newly discovered pattern. Its cause. Its consequences. Its relationship to other patterns.
  4. Discerning the semantic significance of a newly discovered pattern. The concepts or principles that involve the abstract pattern. Connecting to human-level concepts and principles. Or, possibly even transcending even human knowledge.

For the purposes of this paper, AI would relate to patterns that a typical person could recognize, like names, faces, objects, and concepts, while machine intelligence would related to patterns more easily recognized by machines than people, as well as the simpler forms of patterns that even an average person can recognize quite quickly.

Recognizing concepts and principles is generally beyond the common machine intelligence methods of today, and only relevant to AI in relatively limited, niche, and specialized domains at this time.

Both AI and machine intelligence will gradually and sometimes dramatically move up the complexity curve in the coming years and decades.

Right now, people are generally pleasantly surprised whenever machines are able to recognize even relatively simple patterns.

Granted, people are amazed by specialized cases such as DNA and fingerprint matching, facial recognition, voice recognition, chess, Go, ping pong, and Jeopardy. But, that’s the point here, that each of those is very specialized and requires careful and complex hard-wiring of basic knowledge.

It may be another few years or more before people begin to be wowed and blown away by machines recognizing concepts and principles.

Computer vision

In truth, most people would not consider visual recognition of objects, scenes, and qualities to require much in the way of intelligence — even small children can do it. Or even animals, for that matter.

Computer vision can legitimately be considered AI as well as machine intelligence. The reader is welcome to choose for themselves.

Personally, I’d consider computer vision more associated with machine intelligence than AI.

Generally, perception and recognition are inputs to intelligence rather than exemplifying intelligence itself — reasoning, working with concepts and principles, planning, guiding activity, etc.

A lot of applications of computer vision encompass types of objects, scenes, and qualities that an average person might not have much interest or particular skill in. For example, recognizing heat signatures from infrared light or detecting defects in objects, or small movements or small changes in objects and scenes.

For sure, there are plenty of applications for human-like vision, whether for robots in the home or driverless vehicles, but there are whole other broad categories for computer vision that are more distinct from or beyond the vision that people generally possess. I would classify the latter under machine intelligence, although technically some would insist that this is still AI.

Internet of Things (IoT)

This can include detecting patterns in data from individual devices as well as patterns in data across many devices, as well as patterns across many different types of devices.

I would definitely classify such intelligence as machine intelligence, especially since it is highly unlikely that many people would engage in such intelligent activity themselves. In fact, people are likely to be unable to engage in such activity even if they wanted to.

Again, technically, this could still be considered AI, but you gain no conceptual benefit from labelling it AI rather than what it really is — machine intelligence.

Still, if the processing directly parallels human intellectual activity, then by all means label it as AI as well.


But at some stage, the types of data, its volume, its complexity, and the relationships within and between the data begins to take on a character that is distinctly beyond what even a highly-motivated technical specialist could muster. Enter the world of machine intelligence.

Again, technically, it can still be considered AI, but it begins to look so different from what any normal person would do that it just makes a lot more sense to label it what it really is — machine intelligence.

That said, I wouldn’t want to go so far as to label all forms of automated cybersecurity as machine intelligence. I’d prefer to reserve the term for cases such as discovery of new patterns of data, new patterns of activity and behavior, and new threats, rather than detecting instances of data, activity, behavior, and threats which are already known and manually hard-coded by skilled human operators.

Generally, processing in cybersecurity will fall into one of four general categories.

  1. Basic processing. But just vast amounts of data.
  2. Automation. Of fairly mundane tasks.
  3. AI. Automation that parallels or mimics sophisticated human thought.
  4. Machine intelligence. Complex, intelligent processing that doesn’t have a direct parallel in normal human thought. Or does have a parallel, but the complexity is beyond that of a human or even a team of people.

The point here for machine intelligence is to emphasize the brain of the computer, not just its brawn.

As usual, a hybrid is possible and welcome as well — man and machine, each contributing their own strengths in intelligence.

Data analytics and business intelligence

But any time that the machine can discover new, previously unknown patterns, the label of machine intelligence can become warranted.

That said, if the machine capabilities are simply aiding a human user in their own detection of patterns, I’d be more reluctant to trot out the label of machine intelligence.

And if the types of patterns recognized look very little like the patterns that a human would normally recognize, I’d be more cautious about labeling it AI.

Again, I lean towards labeling an automated activity as AI when it has a fairly direct analog to human mental activity.

Otherwise, let’s just call it what it is — machine intelligence.

Scientific and engineering calculation and modelling

But if the calculations endeavor to automatically recognize new patterns, rules, concepts and principles, or any other activity normally associated with higher-order intellectual activity, then AI and machine intelligence might become appropriate labels. That is not commonly the case.

Usually it is up to the researcher to use their own mind to recognize patterns, rules, concepts, and principles from the data output from the calculations or modelling process.

Very complex data patterns and connections within data

  1. Graphs.
  2. Networks.
  3. Time series.
  4. Complex database joins.
  5. Relationships.
  6. Complex relationships.

The mere complexity would not automatically confer the label of machine intelligence, but to the degree that the algorithms working with such data are discovering new patterns, rules, or even concepts, principles, and methods, it becomes more appropriate to label such processing as machine intelligence.

Although merely labeling such processing machine intelligence can also bring it under the larger umbrella of AI, I’d opt to stick with labelling it machine intelligence rather than labelling it AI unless there are distinctly human patterns, rules, concepts, and principles involved.

If the machine is doing something that a human would traditionally have done (before computers were invented), then it could well be considered AI proper.


  1. Is it simple, basic information processing? Then it’s probably just automation.
  2. Is it a task that humans have traditionally done? If not, it’s probably machine intelligence.
  3. Is it something comparable to working with the five human senses? It’s probably AI.
  4. Is it working with sensor data that is very unlike the five human senses? It’s probably machine intelligence.
  5. Does it involve complex data relationships that no normal human would personally relate to? Then it’s probably machine intelligence.
  6. Does it involve learning that is rather different from the learning of humans? Then it’s machine intelligence. Or, more specifically, machine learning.

When in doubt, unless it’s basic automation, it’s probably AI unless it’s processing data that no mere mortal would relate to.

For more of my writings on artificial intelligence, see List of My Artificial Intelligence (AI) Papers.

Freelance Consultant