Lifeboat Foundation

The abstract basis of modern computation is the formal description of a finite state machine, the Universal Turing Machine, based on manipulation of integers and logic symbols. In this contribution to the discourse on the computer-brain analogy, we discuss the extent to which analog computing, as performed by the mammalian brain, is like and unlike the digital computing of Universal Turing Machines. We begin with ordinary reality being a permanent dialog between continuous and discontinuous worlds. So it is with computing, which can be analog or digital, and is often mixed. The theory behind computers is essentially digital, but efficient simulations of phenomena can be performed by analog devices; indeed, any physical calculation requires implementation in the physical world and is therefore analog to some extent, despite being based on abstract logic and arithmetic. The mammalian brain, comprised of neuronal networks, functions as an analog device and has given rise to artificial neural networks that are implemented as digital algorithms but function as analog models would. Analog constructs compute with the implementation of a variety of feedback and feedforward loops. In contrast, digital algorithms allow the implementation of recursive processes that enable them to generate unparalleled emergent properties. We briefly illustrate how the cortical organization of neurons can integrate signals and make predictions analogically. While we conclude that brains are not digital computers, we speculate on the recent implementation of human writing in the brain as a possible digital path that slowly evolves the brain into a genuine (slow) Turing machine.

The present essay explores key similarities and differences in the process of computation by the brains of animals and by digital computing, by anchoring the exploration on the essential properties of a Universal Turning Machine, the abstract foundation of modern digital computing. In this context, we try to explicitly distance XVIIIth century mechanical automata from modern machines, understanding that when computation allows recursion, it changes the consequences of determinism. A mechanical device is usually both deterministic and predictable, while computation involving recursion is deterministic but not necessarily predictable. For example, while it is possible to design an algorithm that computes the decimal digits of π, the value of any finite sequence following the nth digit, cannot (yet) be computed, hence predicted, with n sufficiently large.

Non-scientific versions of the answer have invoked many gods and have been the basis of all religions and most philosophy since the beginning of recorded time.

Now a team of mathematicians from Canada and Egypt have used cutting edge scientific theory and a mind-boggling set of equations to work out what preceded the universe in which we live.

In (very) simple terms they applied the theories of the very small – the world of quantum mechanics – to the whole universe – explained by general theory of relativity, and discovered the universe basically goes though four different phases.

This technique involves having participants place their finger over the camera and flash of a smartphone, which uses a deep-learning algorithm to decipher the blood oxygen levels from the blood flow patterns in the resulting video.

Conditions like asthma or COVID-19 make it harder for bodies to absorb oxygen from the lungs. This leads to oxygen saturation percentages dropping to 90% or below, indicating that medical attention is needed.

In a clinic, doctors monitor oxygen saturation using pulse oximeters — those clips you put over your fingertip or ear. But monitoring oxygen saturation at home multiple times a day could help patients keep an eye on COVID symptoms, for example.

Continue reading “A smartphone’s camera and flash could help people measure blood oxygen levels at home” »

Richard Gott, co author with Neil De Grasse Tyson of “Welcome to The Universe” argues the key to understanding the origin of the universe may be the concept of closed time like curves. These are solutions to Einstein’s theory that may allow time travel into the past. in this film, Richard Gott of Princeton University explains the model he developed with LIxin Li. Gott explores the possibility of a closed time like curve forming in the early universe and how this might lead to the amazing property of the universe being able to create itself. Gott is one of the leading experts in time travel solution to Einstein’s equations and is author of the book “Time Travel In Einstein’s Universe”.
This film is part of a series of films exploring competing models of th early universe with the creators of those models. We have interviewed Stephen Hawking, Roger Penrose, Alan Guth and many other leaders of the field. To see other episodes, click on the link below:
https://www.youtube.com/playlist?list=PLJ4zAUPI-qqqj2D8eSk7yoa4hnojoCR4m.

We would like to thank the following who helped us are this movie:
Animations:
Morn 1415
David Yates.
NASA
ESA
M Buser, E Kajari, and WP Schleich.
Storyblocks.
Nina McCurdy, Anthony Aguirre, Joel Primack, Nancy Abrams.
Pixabay.
Ziri Younsi.

Continue reading “Before the Big Bang 6: Can the Universe Create Itself?” »

A reinforcement learning approach based on AlphaZero is used to discover efficient and provably correct algorithms for matrix multiplication, finding faster algorithms for a variety of matrix sizes.

The end of classical Computer Science is coming, and most of us are dinosaurs waiting for the meteor to hit.

I came of age in the 1980s, programming personal computers like the Commodore VIC-20 and Apple ][e at home. Going on to study Computer Science in college and ultimately getting a PhD at Berkeley, the bulk of my professional training was rooted in what I will call “classical” CS: programming, algorithms, data structures, systems, programming languages. In Classical Computer Science, the ultimate goal is to reduce an idea to a program written by a human — source code in a language like Java or C++ or Python. Every idea in Classical CS — no matter how complex or sophisticated — from a database join algorithm to the mind-bogglingly obtuse Paxos consensus protocol — can be expressed as a human-readable, human-comprehendible program.

When I was in college in the early ’90s, we were still in the depth of the AI Winter, and AI as a field was likewise dominated by classical algorithms. My first research job at Cornell was working with Dan Huttenlocher, a leader in the field of computer vision (and now Dean of the MIT School of Computing). In Dan’s PhD-level computer vision course in 1995 or so, we never once discussed anything resembling deep learning or neural networks—it was all classical algorithms like Canny edge detection, optical flow, and Hausdorff distances. Deep learning was in its infancy, not yet considered mainstream AI, let alone mainstream CS.

Tiny particles are interconnected despite sometimes being thousands of kilometers apart—Albert Einstein called this “spooky action at a distance.” Something that would be inexplicable by the laws of classical physics is a fundamental part of quantum physics. Entanglement like this can occur between multiple quantum particles, meaning that certain properties of the particles are intimately linked with each other.

Entangled systems containing multiple quantum particles offer significant benefits in implementing quantum algorithms, which have the potential to be used in communications, data security or quantum computing. Researchers from Paderborn University have been working with colleagues from Ulm University to develop the first programmable optical quantum memory. The study was published as an “Editor’s suggestion” in the Physical Review Letters journal.

Algorithms have helped mathematicians perform fundamental operations for thousands of years. The ancient Egyptians created an algorithm to multiply two numbers without requiring a multiplication table, and Greek mathematician Euclid described an algorithm to compute the greatest common divisor, which is still in use today.

During the Islamic Golden Age, Persian mathematician Muhammad ibn Musa al-Khwarizmi designed new algorithms to solve linear and quadratic equations. In fact, al-Khwarizmi’s name, translated into Latin as Algoritmi, led to the term algorithm. But, despite the familiarity with algorithms today – used throughout society from classroom algebra to cutting edge scientific research – the process of discovering new algorithms is incredibly difficult, and an example of the amazing reasoning abilities of the human mind.

In our paper, published today in Nature, we introduce AlphaTensor, the first artificial intelligence (AI) system for discovering novel, efficient, and provably correct algorithms for fundamental tasks such as matrix multiplication. This sheds light on a 50-year-old open question in mathematics about finding the fastest way to multiply two matrices.

The rise of quantum computing and its implications for current encryption standards are well known. But why exactly should quantum computers be especially adept at breaking encryption? The answer is a nifty bit of mathematical juggling called Shor’s algorithm. The question that still leaves is: What is it that this algorithm does that causes quantum computers to be so much better at cracking encryption? In this video, YouTuber minutephysics explains it in his traditional whiteboard cartoon style.

“Quantum computation has the potential to make it super, super easy to access encrypted data — like having a lightsaber you can use to cut through any lock or barrier, no matter how strong,” minutephysics says. “Shor’s algorithm is that lightsaber.”

Continue reading “How Quantum Physics Leads to Decrypting Common Algorithms” »