Lifeboat Foundation

The news: In a fresh spin on manufactured pop, OpenAI has released a neural network called Jukebox that can generate catchy songs in a variety of different styles, from teenybop and country to hip-hop and heavy metal. It even sings—sort of.

How it works: Give it a genre, an artist, and lyrics, and Jukebox will produce a passable pastiche in the style of well-known performers, such as Katy Perry, Elvis Presley or Nas. You can also give it the first few seconds of a song and it will autocomplete the rest.

Rice University researchers have discovered a hidden symmetry in the chemical kinetic equations scientists have long used to model and study many of the chemical processes essential for life.

The find has implications for drug design, genetics and biomedical research and is described in a study published this month in the Proceedings of the National Academy of Sciences. To illustrate the biological ramifications, study co-authors Oleg Igoshin, Anatoly Kolomeisky and Joel Mallory of Rice’s Center for Theoretical Biological Physics (CTBP) used three wide-ranging examples: protein folding, enzyme catalysis and motor protein efficiency.

In each case, the researchers demonstrated that a simple mathematical ratio shows that the likelihood of errors is controlled by kinetics rather than thermodynamics.

The Newtonian laws of physics explain the behavior of objects in the everyday physical world, such as an apple falling from a tree. For hundreds of years Newton provided a complete answer until the work of Einstein introduced the concept of relativity. The discovery of relativity did not suddenly prove Newton wrong, relativistic corrections are only required at speeds above about 67 million mph. Instead, improving technology allowed both more detailed observations and techniques for analysis that then required explanation. While most of the consequences of a Newtonian model are intuitive, much of relativity is not and is only approachable though complex equations, modeling, and highly simplified examples.

In this issue, Korman et al.¹ provide data from a model of the second gas effect on arterial partial pressures of volatile anesthetic agents. Most readers might wonder what this information adds, some will struggle to remember what the second gas effect is, and others will query the value of modeling rather than “real data.” This editorial attempts to address these questions.

The second gas effect² is a consequence of the concentration effect³ where a “first gas” that is soluble in plasma, such as nitrous oxide, moves rapidly from the lungs to plasma. This increases the alveolar concentration and hence rate of uptake into plasma of the “second gas.” The second gas is typically a volatile anesthetic, but oxygen also behaves as a second gas.⁴ Although we frequently talk of inhalational kinetics as a single process, there are multiple steps between dialing up a concentration and the consequent change in effect. The key steps are transfer from the breathing circuit to alveolar gas, from the alveoli to plasma, and then from plasma to the “effect-site.” Separating the two steps between breathing circuit and plasma helps us understand both the second gas effect and the message underlying the paper by Korman et al.¹

An exact solution of the Einstein—Maxwell equations yields a general relativistic picture of the tachyonic phenomenon, suggesting a hypothesis on the tachyon creation. The hypothesis says that the tachyon is produced when a neutral and very heavy (over 75 GeV/c^2) subatomic particle is placed in electric and magnetic fields that are perpendicular, very strong (over 6.9 × 10¹⁷ esu/cm^2 or oersted), and the squared ratio of their strength lies in the interval (1,5]. Such conditions can occur when nonpositive subatomic particles of high energy strike atomic nuclei other than the proton. The kinematical relations for the produced tachyon are given. Previous searches for tachyons in air showers and some possible causes of their negative results are discussed.

Can we study AI the same way we study lab rats? Researchers at DeepMind and Harvard University seem to think so. They built an AI-powered virtual rat that can carry out multiple complex tasks. Then, they used neuroscience techniques to understand how its artificial “brain” controls its movements.

Today’s most advanced AI is powered by artificial neural networks —machine learning algorithms made up of layers of interconnected components called “neurons” that are loosely inspired by the structure of the brain. While they operate in very different ways, a growing number of researchers believe drawing parallels between the two could both improve our understanding of neuroscience and make smarter AI.

Continue reading “AI-Powered Rat Could Be a Valuable New Tool for Neuroscience” »

The main idea of artificial neural networks (ANN) is to build up representations for complicated functions using compositions of relatively simple functions called layers.

A deep neural network is one that has many layers, or many functions composed together.

Although layers are typically simple functions(e.g. relu(Wx + b)) in general they could be any differentiable functions.

Quantitative biologists David McCandlish and Juannan Zhou at Cold Spring Harbor Laboratory have developed an algorithm with predictive power, giving scientists the ability to see how specific genetic mutations can combine to make critical proteins change over the course of a species’ evolution.

Described in Nature Communications, the algorithm called “minimum epistasis interpolation” results in a visualization of how a protein could evolve to either become highly effective or not effective at all. They compared the functionality of thousands of versions of the protein, finding patterns in how mutations cause the protein to evolve from one functional form to another.

Continue reading “Algorithm Developed to Predict the Evolution of Genetic Mutations” »

Robots could soon assist humans in a variety of fields, including in manufacturing and industrial settings. A robotic system that can automatically assemble customized products may be particularly desirable for manufacturers, as it could significantly decrease the time and effort necessary to produce a variety of products.

To work most effectively, such a robot should integrate an assembly planner, a component that plans the sequence of movements and actions that a robot should perform to manufacture a specific product. Developing an assembly planner that can rapidly plan the sequences of movements necessary to produce different customized products, however, has so far proved to be highly challenging.

Continue reading “An algorithm to enhance the robotic assembly of customized products” »

Stephen Wolfram is a cult figure in programming and mathematics. He is the brains behind Wolfram Alpha, a website that tries to answer questions by using algorithms to sift through a massive database of information. He is also responsible for Mathematica, a computer system used by scientists the world over.

Last week, Wolfram launched a new venture: the Wolfram Physics Project, an ambitious attempt to develop a new physics of our Universe.

The new physics, he declares, is computational. The guiding idea is that everything can be boiled down to the application of simple rules to fundamental building blocks.