Can Artificial Intelligence Exist w/o Machine Learning?

Artificial intelligence has been postulated since the time of the Greeks, and it started to become feasible during the 1950’s. This subfield of computer science focuses on the proper method of teaching computers to behave as though they were humans. Innovations in this field have been ongoing for the last several decades, and current developments have produced the burgeoning discipline of machine learning, i.e. the art in which computers are programmed to learn from exposure to datasets.

So the terms artificial intelligence and machine learning are often used alongside one another because the latter is a subset of the former.

And this leads the casual listener to wonder if artificial intelligence can be separated from machine learning.

Artificial intelligence can exist without machine learning, but this hampers its potential. A program which possesses machine learning capabilities may improve itself of its own accord; one without may not. Artificial intelligence without machine learning does not adapt to new information.

Machine learning is a subset of artificial intelligence (AI), and it where the former is, the latter it as well, although many AI’s are without machine learning capabilities.

A Video Tutorial Explaining The Difference

The video presented here concisely explains the difference between AI and machine learning. It also touches upon the related concept of deep learning, which can be thought of as an advanced version of the latter.

A Common AI Without Machine Learning

The most frequently seen AI without machine learning is probably the chatbot. If you’ve ever called a tech support hotline, then you’ve interacted with one of these.

A chatbot is a type of AI which contains a list of preprogrammed responses and instructions which dictate how the AI will use them in any given scenario. These responses never change, and the rules which an AI uses to select them are unchanging as well.

A chatbot without machine learning can often be identified by its strange response to outlandish statements. Oftentimes, when one speaks to an AI of this kind, the AI is programmed by people who have a rough expectation of how someone might converse with it. These programmers write rules which command the bot to respond in appropriate ways to statements which they believe the bot will frequently encounter.

However, they also include rules which dictate how the AI will respond if it is told something unpredictable. These rules often order the AI to state something such as,

“I’m sorry, can you please repeat that?”

If you give an AI strange inputs, i.e., if you say weird things to it, and if it responds in a manner similar to what has been described above, then the bot in question probably lacks machine learning capabilities.

Common AI’s With Machine Learning Capabilities

The prevalence of video games in the modern era has exposed most people to AI with machine learning capabilities.

Most video games have bosses, and bosses are programmed with an AI meant to make them difficult to overcome. Certain bosses are able to be encountered multiple times in the same setting. So in order to prevent the battle from becoming predictable, repetitive, and, therefore, boring, game producers will often incorporate two types of gimmicks into the enemy AI’s behavior.

These two common gimmicks are: (1) randomness and (2) machine learning.

A boss that contains an AI with randomness coded into it will behave, get this, randomly, and the fight against it is saved from monotony because it changes slightly every time.

However, although the battle retains novelty, it does not become more difficult. Bosses with random behavior patterns may not always act in their best interests, so these fights change without experiencing an increase in difficulty. So the sense of accomplishment which comes from defeating one is not preserved.

Therefore, programmers work to preserve the novelty and the sense of accomplishment which comes from defeating a difficult boss. They accomplish this by inserting machine learning algorithms into the boss AI. These codes allow the enemy to improve after every battle by taking into account those methods which had been previously used to defeat it and adjusting its behavior so that they will lose effectiveness.

How Is Machine Learning Incorporated Into An AI?

An AI learns in the same way that a human does. It observes information and searches for patterns and noteworthy features. The AI then commits what it finds to memory and changes its behaviors accordingly.

However, machines do not do this of their own accord. Engineers must instruct them to search for patterns and identify valuable information. This task is accomplished by the creation of algorithms, i.e., sequences of steps in a code, which inform the AI in question that patterns of data exist and enable it to discern what those patterns may be.

The role of the machine learning engineer is to develop these algorithms which teach the AI both what and how to learn. The engineer does this by gathering sets of data from which the machine can learn. Then he models the data in a way so that it becomes coherent. Afterward, he trains the AI to understand the patterns found therein and search for them in new environs.

The machine learning engineer requires large stores of data to exist in order to train an AI. Therefore, machine learning belongs to the field of data science. Machine learning engineers often hold employment in other areas pertaining to computer science before they begin teaching AI’s to think.

Why Aren’t All AI’s Trained With Machine Learning?

Machine learning is a highly specialized skill. It also requires large volumes of data to be processed in order for it to be implemented. Moreover, the field is still quite young, and the supply of competent and relevant engineers is still low. Consequently, many devices are created without ML because to do otherwise would draw scarce resources away from projects of greater importance.

Moreover, many programs exist solely to serve a limited and simple function, so little value would be added to them if they were taught how to think.

A Brief Summary Of The Differences Between AI And Machine Learning

The following list summarizes eight key differences between machine learning and AI.

  1. Machine learning is a subset of AI.
  2. An AI can exist without the ability to learn.
  3. Both depend upon data science.
  4. Machine learning makes use of algorithms which respond to and process data.
  5. Algorithms are created to simulate different models.
  6. Many video games already contain AI’s which can learn.
  7. Simple AI will not become obsolete as machine learning becomes more common.
  8. Machine learning is a young field, and engineers are in short supply.

Wrapping Up

So, now you should understand the difference between AI and machine learning. If you’re interested in developing your expertise in one of these fields, then you should begin by exploring data science. If not, then keep an eye out for AI’s in the world around you, and see if you can spot which ones have been taught how to learn.



Gene Botkin

Gene is a graduate student in cybersecurity and AI at the Missouri University of Science and Technology. Ongoing philosophy and theology student.

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