Black Holes, Consciousness, and Deep Learning
By: Amelie Schreiber
Tags: Machine Learning, Artificial Intelligence, Quantum Computing, Physics, The Singularity Research

Deep Learning and Neural Networks

Deep neural networks have seen a recent explosion in applications to nearly every aspect of our lives. They model our financial markets with arbitrary accuracy, driving investments and algorithmic stock trading. They classify images, performing facial recognition on only partial images. They write their own code and improve ours. They create webpages And apps for us from hand drawn sketches.

There are AI agents that can see through walls and predict the position and movement of humans by observing the perturbations in WiFi signals.

They drive cars. They assist us in deciding what to watch on Netflix and YouTube. They serve as therapists and doctors to us, assisting in medical diagnoses, new drug discovery, and treatment analysis.

Google has shown that AI is outperforming human doctors in treatment and diagnosis.

They have conversations with us and serve as friends. They create music. They create anime. They paint.

They create false videos, images, and voice recordings of real people in deep fakes, allowing social engineering en mass, with both positive and negative outcomes.

Using the same technology they can clone our voices with only 5 seconds of audio to learn from, allowing those of us who are camera shy the ability to create flawless podcasts, YouTube videos, and lectures.

They play video games better than any human ever has. They perform sentiment analysis of our texts, comments, and the things we say and can even diagnose schizophrenic episodes and other mental illnesses from 2 minutes of smartphone video. AI can detect and understand human emotions. They track our spending and recommend products to us on Amazon. Neural networks predict traffic patterns and offer us the quickest route, improving our commutes and effecting the price of an Uber or Lyft. In fact, the DWave quantum computer was recently used to solve this problem in particular. They adapt what our search engines show us based on our personal data. They help in job placement and hiring protocols. They are used for national defense. They discover new materials and chemical compounds. They optimize anything that can be optimized and solve physics problems formerly thought intractable or impossible. Using automated theorem proving and natural language processing they even prove new theorems in mathematics and write academic papers. They learn how to correct errors in quantum computers.

AI aided us in capturing the first images of black holes.

This encounter between machine learning and black holes is not our primary focus however.

Here, we will be concerned with understanding deep neural networks in terms of some of the deepest and most fundamental physics known to humans, the physics of quantum gravity.

In this article we will discuss how layers of neural networks can be replaced with “tensor networks”, a tool well known to physicists since at least as far back as the early 1970s, when the famous mathematical physicist Sir Roger Penrose first used his graphical tensor notation to describe physics. This is a running theme in many of my articles, but for good reason, as we will see:

We all live in a giant quantum computer that is constantly running quantum machine learning algorithms.

Understanding how we can treat deep neural networks as tensor networks gives a direct connection to quantum computing and “The Holographic Principle,” a principle in the theory of quantum gravity and the AdS/CFT correspondence (which we will return to later on). This will not be a light read, but for the technically courageous, it will be a rewarding read that will help in understanding why deep learning is such an effective tool:

Neural Networks mimic quantum physics and the quantum machine learning algorithms running on the quantum computer we call the universe.

In this article we will also address the “black-box” issue so often spoken of in the machine learning community. We will explain what it is, and how it can be mitigated using theoretical physics fundamental to the understanding of black holes, gravity, and quantum physics.

We will also make direct connections to quantum computing, and the sexy new topic of quantum machine learning. Replacing layers of deep neural networks with tensor networks is only one step away from replacing those same layers with quantum circuits and the methods of quantum neural networks. To top it all off, we will provide references to Github tutorials and open source software that actually puts all of this theory into action. We’ll look at how you can train your own quantum neural networks, on real quantum computers. The takeaway: The black hole singularity and the technological singularity of sentient AI very closely related via their mathematical structure. Moreover, this is not technology of the future. It is available to the sufficiently curious and mathematically adept to use and explore now, on actual quantum computers.