Why Quantum Computers Prove Physics is the Future of Coding

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Neural networks are incredibly complex, and in virtual terms, very large. With the need for computers to make massive mathematical models, who better than your friendly neighborhood physicist to help plan out the future of AI.

You might be surprised where you find a physicist these days. But, coding? Yep, they’re everywhere; the astounding leaps in modern technology are thanks in no small part to their efforts.

Physicists helped bring about the earliest computers, and even the C programming language. At the base of every part of your IoT experience is an understanding of the laws of physics that allow you to power your phone, store data, and program any app you can dream of.

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It’s no surprise, then, that the companies on the bleeding edge of computer science and machine learning are hiring physicists to help us characterize Industry 4.0.

Why use Physicists When you Have Perfectly Well Coding Programmers?

Physicists are good at thinking abstractly to organize large amounts of data, and that fits perfectly with the current trend of machine learning.

Big Data software systems handle significant quantities of data, as their name implies, and for physicists, that means that the neural networks that machine learning runs on are a natural playground for their ability to organize data.

Neural networks are designed to take data from thousands of spaces. While coders can create something for each space, it takes a physicist to thread them all together. Ultimately, it comes down to the knowledge of mathematics, something that is key to an understanding of advanced physics.

Coding as a discipline began as a specialization in math at universities. Mathematical models help computer scientists to break down and improve structures, and machine learning follows this pattern by refining information until it produces an accurate result. Particle physics follows much the same methodology, processing every possible scenario to create mathematical models to explain how the world around us works.

Neural networks enable machines to follow a pattern based on mathematical models which allow them to make judgments and learn by amassing and analyzing large amounts of data. Neural networks, then, run on the same kind of math that physicists study, making them a perfect fit to aid computer scientists in advancing artificial intelligence.

Don’t Worry, Coders. You’re Still Needed

Computer science isn’t being pushed aside, of course. An interdisciplinary approach is necessary to ensure access to the best information to support the most accurate conclusions in neural networks.

Physicists are wonderful at the kind of linear algebra and probability theory that helps organize the data. Coding is what enables neural networks to perform the math in the first place.

In a surprising twist, physicists made the building blocks for coders, who then went on to make another set of building blocks for physicists. We’re scaling up our knowledge of computing to create advanced machine learning. Who knows what will be built with this new set of legos?

What we do know is this: Industry 4.0 is going to be characterized by interdisciplinary approaches. The world’s best example is the state of neural networks and the kinds of researchers necessary to advance them.

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