AI from Rote Learning to Meaningful Learning, Understanding is what True AI requires?

AI/ML as Rote Learning (RL) Mnemonic AI Models.

All ML/AI modes are RL/AI mnemonic models involving mechanical learning disparaged as stochastic parroting, regurgitation, cramming, or mugging.

The RL/AI machine is programmed to keep a history of calculations and compare new input against its history of inputs and outputs, retrieving the stored output if present. This pattern requires that the RL machine can be modeled as a pure function — always producing same output for same input — formally described as follows:

f( 𝑥1,𝑥2,...,𝑥𝑛) → ( 𝑦1,𝑦2,...,𝑦𝑝) → store (( 𝑥1,𝑥2,...,𝑥𝑛),( 𝑦1,𝑦2,...,𝑦𝑝))

Rote learning memorization techniques as recall of facts is widely used in the learning of elementary/foundational knowledge: the alphabet, phonics reading, the periodic table in chemistry, elementary arithmetic, numbers, multiplication charts, times tables, anatomy in medicine, cases/statutes in law, basic formulae in any science, basic facts, etc.

RL/AI refers to a form of memorization where its model simply memorizes information without truly understanding its content and context.

RL/AI system stores and reproduces data or patterns without the ability to generalize and apply knowledge effectively.

RL/AI learns by memorizing specific examples, as a large dataset of text or images without understanding the content and context.

RL/AI systems struggle to generalize their knowledge to new, unseen situations. They may perform well on tasks similar to what they've memorized but poorly on tasks outside their narrow scope.

RL/AI systems typically do not adapt well to changes in data or environment because they lack the ability to reason or adapt their knowledge.

RL/AI systems are not effective at problem-solving or making decisions based on the information they've memorized.

By training generative models on vast amounts of social media data, such as video or audio, images and text, generative AI can generate the media data tailored to specific user preferences and trends, without any understanding its content or context.

Meaningful Learning AI is True AI.

Meaningful learning AI (ML/AI) involves understanding how all the pieces of an entire whole, system, concept, information or data fit together. The knowledge gained through meaningful learning could be transferred to new learning situations.

Meaningful learning AI emphasize the importance of deep understanding over the recalling of facts or statistical memorization.

Meaningful learning refers to the act of higher order thinking and development through intellectual engagement that uses pattern recognition and concept association.

It can include—but is not limited to—critical and creative thinking, inquiry, problem solving, critical discourse, and metacognitive skills.

The concept and theory of meaningful learning is that learned information is completely understood and can now be used to make connections with other previously known knowledge aiding in further understanding.

Since information is stored in a network of connections, it can be accessed from multiple starting points depending on the context of recall.

Meaningful learning is often contrasted with rote learning, a method in which information is memorized sometimes without elements of understanding or relation to other objects or situations.

A real-world example of a concept the learner has learned is an instance of meaningful learning. Utilization of meaningful learning may trigger further learning, as the relation of a concept to a real-world situation may be encouraging to the learner. It may encourage the learner to understand the information presented and will assist with active learning techniques to aid their understanding.

Although it takes longer than rote memorization information, it is typically retained for a longer period of time.

In all, ML/AI involves

Problem-Solving Learning involving critical thinking and creativity.

Inductive learning of general principles from specific examples, foundational in mathematics and science, where hypotheses are formed through observation and experimentation.

Explanation-based learning comprehending the underlying principles behind phenomena or concepts. Instead of memorizing isolated facts, it learns the rationale and mechanisms to apply learned knowledge in various contexts.

Discovery learning centering on self-directed exploration and experimentation.

Analogical learning of making parallels between familiar concepts and new, unfamiliar ones.

Learning by taking advice from the experiences and insights of experts or mentors.

What true AI requires..

We have to see differences among Data, Information, Knowledge and Understanding.

Data in their simplest form consist of raw alphanumeric values, or facts and statistics.

Data are observable and measurable properties that are represented in signs or symbols, such as numbers or letters, representing stimuli or signals. Data is raw facts or observations, which are unorganized and unprocessed and therefore have no meaning or value due to lack of context and interpretation.

Information is created when data are processed, organized, or structured to provide context and meaning. Information is essentially processed data, providing answers to "who", "what", "where", and "when" questions.

Knowledge is what we know, application of data and information; answers "how" questions.

Knowledge is the accumulation of facts, information, or skills obtained through education or experience or learning and reasoning.

Understanding is the ability to grasp or comprehend knowledge. Knowledge is the accumulation of information and skills, while understanding involves comprehension and application of that knowledge.

Understanding provides a deeper level of insight than knowledge and can be more difficult to measure or acquire. It answers "why" questions enabling wisdom, which integrates knowledge and understanding for intelligent decisions and interactions.

We have to mention here Bloom's taxonomy, cognitive, knowledge based domain levels, dividing comprehension from knowledge, where "knowledge involves the recall of specifics and universals, the recall of methods and processes, or the recall of a pattern, structure, or setting":

The Data-Information-Knowledge-Understanding-Wisdom-Interaction-Environment chain/hierarchy/framework/continuum, or the Intelligence Pyramid, could be partly presented as a Model Mnemonic of a flow diagram of the data, information, knowledge, understanding, wisdom with feedback loops and control relationships.

The Organisation for Economic Co-operation and Development AI Principles define an AI system as "a machine-based system that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments. Different AI systems vary in their levels of autonomy and adaptiveness after deployment".

AI system lifecycle phases involve: i) ‘design, data and models’; which is a context-dependent sequence encompassing planning and design, data collection and processing, as well as model building;

ii) ‘verification and validation’;

iii) ‘deployment’; and

iv) ‘operation and monitoring’.

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