Dear Ismail Abbas M. Abbas,

Deep Learning and Machine Learning are integral subsets of Artificial Intelligence (AI) rather than separate from it. AI is a broad field encompassing various approaches to creating intelligent systems, and Machine Learning (ML) is one such approach that enables systems to learn patterns from data. Deep Learning (DL), a subset of ML, leverages artificial neural networks to perform complex tasks such as image recognition, natural language processing, and autonomous decision-making. The claim that ML and DL now fall under "Stored Intelligence (SI)" is not widely recognized in the AI research community. While ML and DL models store learned patterns and statistical relationships, they still exhibit adaptive learning capabilities, making them distinct from static knowledge repositories. AI continues to evolve, and while new terminologies like SI may emerge, ML and DL remain core methodologies within AI rather than being separate from it.

Dear Ismail Abbas M. Abbas ,

Machine learning and deep learning are both types of AI. In short, machine learning is AI that can automatically adapt with minimal human interference. Deep learning is a subset of machine learning that uses artificial neural networks (ANNs) to mimic the learning process of the human brain.

https://www.coursera.org/articles/ai-vs-deep-learning-vs-machine-learning-beginners-guide

It is simple: because ML is only a contributing branch of AI. If we follow Norvig and Russell book -and other authors-, saying that machine learning is equivalent to artificial intelligence is grossly misleading. ML is a contributing discipline of AI, just like search, reasoning, planning, decision making, natural language processing, vision, and robotics.

https://oilgains.medium.com/why-machine-learning-is-not-artificial-intelligence-61b174a3c9a2

Regards,

Shafagat

AI is the broad field of machines mimicking human intelligence. Machine learning (ML) is a subset of AI where machines learn from data without explicit programming. Deep learning (DL) is a further subset of ML, utilizing deep neural networks for complex data analysis. So, DL is a type of ML, and ML is a type of AI. They are nested, not separate.

Misunderstanding the hierarchical relationship between these two concepts. Artificial intelligence is a broad field that includes any technology that enables machines to mimic human-like cognitive functions, such as learning, reasoning, problem solving, natural language understanding, and perception. Within this broad field, machine learning stands out as a critical subset that focuses specifically on developing algorithms that allow computers to learn from data and improve their performance over time without being explicitly programmed for each task. Deep learning and machine learning are fundamental building blocks that enhance the capabilities of AI as a whole.

If you don't understand artificial intelligence, ask yourself if I'm right because others don't understand it either.

There is no generally applicable definition of artificial intelligence [1].

The field is so vast that its actors will always have to develop a more precise definition of their own objectives in their context.

Artificial intelligence is broadly defined as the simulation of human intelligence by machines programmed to learn, reason, and solve problems.

Therefore, AI encompasses several subfields, including machine learning (ML), deep learning (DL), robots, natural language processing (NLP), and computer vision, etc., each with distinct applications and limitations.

And yet, AI can be classified into two broad categories:

i-Narrowly defined AI (concrete definition of AI)

Which is simply defined as an automated algorithm operating on the basis of statistical transition matrices, which allows it to increase the amount of existing information.

ii- General AI (broad definition of AI):

such as machine learning (ML), deep AI (DAI), image and voice recognition, etc.

We believe that the term AI should be reserved for software capable of increasing the amount of information that is the function of only humans (and much less other creatures) and statistical transition matrices.

A hypothetical AI with human-like cognitive abilities, including reasoning, writing and reading rules, pronunciation, specific tasks, such as facial recognition, speech processing, and more.

These systems operate within predefined parameters and many input data cannot be generalized beyond their designated functions.

These systems obviously cannot increase the amount of existing information and therefore cannot be called artificial intelligence.

We can conveniently call these systems storage intelligence SI systems rather than artificial intelligence systems.

Concrete and narrow AI remains a theoretical and practical construct with many existing examples in physics and mathematics as well as in real daily life.

1-Theory and Practice of artificial intelligence, RG,RSIJST review, Feb 2025.

We assume that Western theory and practice of artificial intelligence are poorly defined and, consequently, poorly applied and sometimes misleading.

Figure 1 below illustrates how this theory is poorly defined and poorly applied.