Historical Origin of Quantum Mechanics

In this section, we will try to look at the historical origin of Quantum Mechanics, which is usually presented succinctly using scary looking mathematical formulas. The role of mathematics in physics, as Richard Feynman explains (in his lectures on QED given in Auckland, New Zealand in 1979, available on YouTube, but as poor quality recordings) is purely utilitarian.
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Quantum Mechanics

Quantum Mechanics (QM) is the physics of small things. How do they behave and how do they interact with each other? Conspicuously absent from this framework of QM is why. Why small things do what they do is a question QM leaves alone. And, if you are to make any headway into this subject, your best bet is to curb your urge to ask why. Nature is what she is. Our job is to understand the rules by which she plays the game of reality, and do our best to make use of those rules to our advantage in experiments and technologies. Ours is not to reason why. Really.

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Classical Physics

The main difficulty in describing particle physics to general public is the fact that it is built on modern physics. Even if you are physics aficionado and did extremely well in your high school physics, what you have learned and loved is classical physics. The difference between classical physics and modern physics is not just more physics, but a completely new way of looking at the reality around us.
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Reductionism

In all our scientific endeavors, we use similar high-level techniques to understand and study things. The most common technique is reductionism. It is based on the belief that the behavior, properties and structure of large and complex objects can be understood in terms of their simpler constituents. In other words, we try to understand the whole (the universe, for instance) in terms of smaller, reduced constituents (such as particles).

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Particles and Interactions

Recently, I gave a talk on particles and interactions to my daughter’s classmates who were planning on a trip to DESY, Germany and wanted to have an idea of what it was all about. As my first talk of this kind, I was a bit nervous because I didn’t know what level, and background, I should peg the talk at. I didn’t want to make it too basic, which I thought would be a waste of time. Nor did I want to make it too technical, which also would make it useless in a different way.

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Sensory and Physical Worlds

Animals have different sensory capabilities compared to us humans. Cats, for instance, can hear up to 60kHz, while the highest note we have ever heard was about 20kHz. Apparently, we could hear that high a note only in our childhood. So, if we are trying to pull a fast one on a cat with the best hifi multi-channel, Dolby-whatever recording of a mouse, we will fail pathetically. It won’t be fooled because it lives in a different sensory world, while sharing the same physical world as ours. There is a humongous difference between the sensory and physical worlds.

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Problem of Free Will

Free will is a problem. If all of us are physical machines, obeying laws of physics, then all our movements and mental states are caused by events that took place earlier. What is caused is fully determined by the cause. So whatever we do now and in the next minute is all pre-ordained by antecedent events and causes, and we have no control over it. How can we then have free will? The fact that I am writing this note on free will — is it totally and completely determined by the events from time immemorial? That doesn’t sound right.

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Data to Wisdom

When it comes to the amount of intelligence and experience required, we have a clear hierarchy from data to information to knowledge to wisdom. What we get from raw observation are just data points. We apply some techniques of aggregation, reporting charting etc. to arrive at information. Further higher level processing in revealing interconnections and relationships will give us condensed and actionable information, which we can consider knowledge. But to arrive at wisdom, we need a keen mind and years of experience, because what we mean by wisdom itself is far from obvious. Rather, it is obvious, but not easily described, and so not easily delegated to a computer. At least, so I thought. How could machines bridge the gap from data to wisdom?

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