Perhaps the most important concept in physics is that of emergence: higher-level theories describing macro-phenomena mat

This soon blew up, and Oringer expanded his services by adding on contributors beyond himself. This was all bootstrapped with Oringer raising a small round only once (mainly to help scale his management team) and still maintains a close to 50% ownership of the company by himself.

When the bridge was built, we simply did not have a good enough theory about the aerodynamical forces that a bridge is exposed to ‘in production’. Nowadays, with CFD simulations we can stress-test bridges before they are being built, and prevent failures like that of the Tacoma Narrows bridge from happening again. Theory enabled us to build better bridges.

Last year, Microsoft partnered with Qualcomm and gunned for the always-connected, ultra-light crown with the Surface Pro X, a $999 2in1 device whose promises don’t sound all that different from Apple’s. All-day battery life with unrivaled performance in a svelte package? It sounded like a tech enthusiast’s dream come to life. The catch? Compatibility.

Today Shuttlestock is worth over 2.5 billion dollars since IPOing in 2012. That surely is an impressive run for a solo founder whilst bootstrapping his service. One of the main reasons he never had to raise a round was that he achieved product-market fit relatively early on, where demand exceeded his own photo supply.

One candidate for such an emergent theory is energy-based learning, a theoretical framework promoted by Yann LeCun and others. The framework introduces a scalar field that describes the energy of a combined system. For a datapoint consisting of features and a label, for instance, this scalar energy is minimized if the label is the correct one. LeCun shows that any machine learning problem can be reformulated in this way as an energy-minimization problem. In physics terms, this framework views energy as the emergent property of the machine learning system.

Although working as a coder was of interest, one other thing interested him even more, and that was making money. Oringer was always creating new applications and marketing to his own email list a variety of products to buy. He did this while working on his master’s degree but soon realized that products sold better if you attached photos with them.

Which brings us back to the topic. Today’s machine learning practice is much like 1940’s bridge-building. Deep neural networks seems to be working astonishingly well, but we lack a thorough mathematical understanding of why they do. Even worse, deep learning systems can sometimes fail spectacularly, and we don’t really understand why, either.

Machine learning research today is almost entirely empirical. We need a better theory to sharpen our intuition on where to look next, and to distinguish real signal from fluke discoveries. Perhaps the success of physics can be a guide.

However, photos were super expensive to use, so he decided to take his own photos with a Canon he bought. This soon prompted him to realize that other entrepreneurs on the web might want stock photos as he did. With this, he put together the odd 30 000 photos he had already took and paywalled it behind a website with a $49 monthly subscription for unlimited use.

Apple’s delivery of the capabilities of its devices has changed little over the past decade. But these numbers are impressive, even by Apple standards. The new M1 chip promises performance and endurance hitherto unheard of in the computing space. The power of a workhorse in a device that lasts as long as a smartphone? 20 hours of battery life on a family of devices superior to its predecessors? I wouldn’t be surprised if viewers left Apple’s snazzy keynote with equal parts hope and skepticism.

What we need, then, is an emergent theory of deep learning: a theory that goes beyond describing what individual neurons do, and explains the emergent behavior of the entire network.

Right off the bat, Apple bets big on its ARM-powered future with not one but three powerful additions to its Mac lineup, from the diminutive Macbook Air to the desktop-class Mac Mini. With a sprinkling of terms like unified memory architecture, neural engine, and industry-leading 5nm technology, there’s no doubt that the M1 is a powerhouse that packs a punch. Comparing one of its four cores to the performance of its current Intel-powered dual-core Macbook Air set the stage for the next generation of Macbooks. But performance isn’t what I’m worried about.

The story of the Tacoma Narrows bridge illustrates vividly why it is so important to have a good theory. After being opened for traffic in July 1940, the bridge started swaying wildly in the wind, until it spectacularly collapsed just 4 months later. One dog died.

Machine learning research today is almost entirely empirical. We need a better theory to sharpen our intuition on where to look next, and to distinguish real signal from fluke discoveries. Perhaps the success of physics can be a guide.

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