Human Learning vs Machine Learning
If machines can be taught to learn, human beings should outdo machines, by being human.
Learning as per the dictionary definition has the following aspects:
Definition of learning
1: knowledge or skill acquired by instruction or study
2: modification of a behavioral tendency by experience
All of us now know that the first part can be mimicked by machines and AI/ML is aiming for the second part through the first one.
But what are the key steps in this process and where are humans better than the machines?
We now have machines that can translate languages, compose music, write novels and operate vehicles.
The primary goal of AI research may be to teach machines how to learn, thereby automating some of the tasks that complicate our everyday lives, but brain scientists are saying it goes both ways: We now know more about human learning as a result of machine learning, and it has some exciting implications for the classroom.
Saga Briggs, Managing Editor of InformED in her article, What Machine Learning Is Teaching Us About Human Learning mentions four especially intriguing insights from the field:
- Our bodies aid our memories
- Metaphors are powerful learning tools
- There is no substitute for “Learning by doing”
- Good teaching draws on Shared Experience
They emphasize the point “Google” cannot replace “Gurus” and learning can be an effective tool for differentiation to careers. With the advent of DTOUCH 3.0 program of DesiCrew, it may be instructive to understand more about the Learning and Retention process. The following graphic illustrates the importance of practical application of classroom learnings in retention.
“Bloom’s Taxanomy’ provides a hierarchical model for the cognitive procedures and goals of learning divided into 6 levels where level 1 is the most basic level for teaching knowledge acquisition and level 6 the top with the highest education requirements to meet the goals of a specific educational program. Mastering a specific level is a prerequisite to move to the next higher level. The levels are defined as follows:
- Remembering/Memorizing defined as the knowing of previously learned material or retrieving, recognizing and recalling relevant knowledge.
- Understanding defined as being able to comprehend facts by comparing and interpreting main ideas within the learned material
- Applying defined as the ability to use learned material in a new unprompted way of abstraction and to solve a newly defined problem.
- Analyzing defined as the ability to examine a problem area and identify the various components ( breaking the problem down).
- Evaluating defined as the ability to make judgements based on criteria or standards or to combine parts to form a new concept or idea.
- Creating defined as the ability to integrate learning from different areas into a plan for solving a problem and to propose alternative solutions.
The findings of Peter Rudin in his recent essay on Thoughts on Human vs Machine Learning, published in Singularity 2030 can be summarized as below:
As stated above, machines can clearly outscore humans where the power of computing and speed of processing BIG DATA will tilt the balance. Even here, since learning is a cognitive process, adaptability for each student comes into the picture. Machine learning can be used to account for adaptability. Researchers are postulating that the result of each assessment of the student can be recorded in order to determine the probability of success in the next assessments.
It is pertinenent to note that MOOCs have succeeded in a big way in the use of audio-visuals in making the teaching more efficient,
the team work and motivation to learn (which cannot be replicated by machines) still remain the greatest assets in the learning process.
Happy Learning to everyone, both machines and humans!