What is the difference between knowledge and learning in human and artificial intelligence?

Knowledge vs. Learning

The primary difference between the two is that learning is a process whereas knowledge is the result, outcome or product or effect of the learning by experience, study, education or training data.

“Learning is the process of acquiring new understanding, knowledge, behaviors, skills, values, attitudes, and preferences”.

Learning is a system/method/algorithm according to which you learn things from your parents, a peer, a street, school or college or an institution or a book or the internet. So learning is to acquire skill or knowledge from practical experiences in life by trials and errors or from study, observation, instruction, etc.

We learn the facts, information, data, truths, principles, beliefs or bad habits.

We know what we learn and learn to know.

Human and machine knowledge and its learning is led by scientific activities, as theoretical, experimental, computational or data analytic. Science is the sum of universal knowledge about the world of reality, humanity and technology. And real-world learning and knowledge could be reached only at the transdisciplinary level, where all boundaries between and among knowledge fields and technical domains are transgressed by a holistic, systematic and transdisciplinary learning and knowledge, as pictured above.

Again, human knowledge is largely specialized and fragmented into narrow domains and fields, what arrests our capacity to solve global change problems, as climate change, pandemics or other planetary risks and threats.

There is one fundamental causal learning. But in the behavioristic psychology, learning is divided into the following groups: passive or active, non-associative, associative or active learning (the process by which a person or animal or machine learns an association between two stimuli or events). Add up here informal and formal learning, e-learning, meaningful or rote learning, interactive or incidental or episodic learning, enculturation and socialization.

In ML/DL philosophy, learning is a change of behavior caused by interacting with the environment to form new patterns of responses to stimuli/inputs, internal or external, conscious or subconscious, voluntary or involuntary.

Knowledge is all what a humanity, society, mind or machine knows, as learnt by experience or education. Knowledge involves intuition and experience, information and understanding, values, attitudes and beliefs, awareness and familiarity. Unlike machines, human minds are able to one-short learning, transfer learning,

Humans, animals and plants and machines could demonstrate learnt behaviors.

Machine Learning as a Rote Learning

But the today senseless narrow/weak AI, going as ML/DL, is unable to know or to learn by its meaningless statistic nature. It is a wishful thinking or something worse to claim “Machine Learning is the study of computer algorithms that improve automatically through experience”. Applications range from datamining programs that discover general rules in large data sets, to information filtering systems that automatically learn users' interests.

It is claimed that “Machine learning algorithms build a model based on sample data, training data, to make predictions or decisions without being explicitly programmed”. It just builds a mathematical model from input data, without any learning from data, making predictions or decisions.

ML is the rote learning, “learning by repetition, based on the idea that a learner can recall the material exactly (but not its meaning) if the information is repeatedly processed. Rote learning is used in diverse areas, from mathematics to music to religion” as well as in machine learning practice.

What ML is lacking by its design is the transfer of learning “the application of knowledge or understanding or skills to resolve a novel problem or situation”.


Overall, the world’s learning development cycle follows the complex causal loops:

the world>

changing environment (CE) >

data >

learning >

knowledge >

thinking >

decision-making >

actions/behaviors >

CE >

New data/Information/knowledge >…

All in all, intelligence comes from knowledge and learning. Real intelligence is derived from world’s knowledge and scientific learning.

Machine intelligence comes from world’s information/data and causal learning algorithms.


EIS has Created the First Trans-AI Model for Narrow AI, ML, DL, and Human Intelligence

Machine Learning, Tom Mitchell, McGraw Hill, 1997.

Chapter Outline: (or see the detailed table of contents (postscript))

  • 1. Introduction
  • 2. Concept Learning and the General-to-Specific Ordering
  • 3. Decision Tree Learning
  • 4. Artificial Neural Networks
  • 5. Evaluating Hypotheses
  • 6. Bayesian Learning
  • 7. Computational Learning Theory
  • 8. Instance-Based Learning
  • 9. Genetic Algorithms
  • 10. Learning Sets of Rules
  • 11. Analytical Learning
  • 12. Combining Inductive and Analytical Learning
  • 13. Reinforcement Learning