Lifeboat Foundation

So why not ask the neurons what they want to see?

Read: The human remembering machine

That was the idea behind XDREAM, an algorithm dreamed up by a Harvard student named Will Xiao. Sets of those gray, formless images, 40 in all, were shown to watching monkeys, and the algorithm tweaked and shuffled those that provoked the strongest responses in chosen neurons to create a new generation of pics. Xiao had previously trained XDREAM using 1.4 million real-world photos so that it would generate synthetic images with the properties of natural ones. Over 250 such generations, the synthetic images became more and more effective, until they were exciting their target neurons far more intensely than any natural image. “It was exciting to finally let a cell tell us what it’s encoding instead of having to guess,” says Ponce, who is now at Washington University in St. Louis.

Continue reading “AI Evolved These Creepy Images to Please a Monkey’s Brain” »

This paper published in Nature on 26th February 2015, describes a DeepRL system which combines Deep Neural Networks with Reinforcement Learning at scale for the first time, and is able to master a diverse range of Atari 2600 games to superhuman level with only the raw pixels and score as inputs.

For artificial agents to be considered truly intelligent they should excel at a wide variety of tasks that are considered challenging for humans. Until this point, it had only been possible to create individual algorithms capable of mastering a single specific domain. With our algorithm, we leveraged recent breakthroughs in training deep neural networks to show that a novel end-to-end reinforcement learning agent, termed a deep Q-network (DQN), was able to surpass the overall performance of a professional human reference player and all previous agents across a diverse range of 49 game scenarios.

With the possibility of millions or an infinite number of problems automating everything will cause all things to be solved digitally into a simple math problem. The problems could essentially be hacked by shores algorithm or maybe a theory of everything like m theory or Stephen Hawking’s theory of everything. Maybe it is just as simple as a basic formula like Einstein created E=mc². Also like some mathematicians have theorized maybe just one line of code that solves everything.

Automation is a game-changer for modern problem-solving – enabling not only visibility to real-time operations but the ability to effectively project the impact of potential solutions into the future. As problem-solvers become more comfortable using the new tools available to them, companies will be able to effectively isolate (and avoid) the impact of problems to their operations and focus their resources on solving the underlying issues and enabling long-term success. Learn More here.

Andrew Wiles’ proof of Fermat’s Last Theorem is a famous example. Pierre de Fermat claimed in 1637 – in the margin of a copy of “Arithmetica,” no less – to have solved the Diophantine equation xⁿ + yⁿ = zⁿ, but offered no justification. When Wiles proved it over 300 years later, mathematicians immediately took notice. If Wiles had developed a new idea that could solve Fermat, then what else could that idea do? Number theorists raced to understand Wiles’ methods, generalizing them and finding new consequences.

No single method exists that can solve all Diophantine equations. Instead, mathematicians cultivate various techniques, each suited for certain types of Diophantine problems but not others. So mathematicians classify these problems by their features or complexity, much like biologists might classify species by taxonomy.

Not everything about glass is clear. How its atoms are arranged and behave, in particular, is startlingly opaque.

The problem is that glass is an amorphous solid, a class of materials that lies in the mysterious realm between solid and liquid. Glassy materials also include polymers, or commonly used plastics. While it might appear to be stable and static, glass’ atoms are constantly shuffling in a frustratingly futile search for equilibrium. This shifty behavior has made the physics of glass nearly impossible for researchers to pin down.

Now a multi-institutional team including Northwestern University, North Dakota State University and the National Institute of Standards and Technology (NIST) has designed an algorithm with the goal of giving polymeric glasses a little more clarity. The algorithm makes it possible for researchers to create coarse-grained models to design materials with dynamic properties and predict their continually changing behaviors. Called the “energy renormalization algorithm,” it is the first to accurately predict glass’ mechanical behavior at different temperatures and could result in the fast discovery of new materials, designed with optimal properties.

Continue reading “New approach predicts glass’ always-evolving behaviors at different temperatures” »

Artificial intelligence has been showing us many ish tricks as apers of human-created art, and now a team of researchers have impressed AI watchers with PaintBot. They have managed to unleash their AI as a capable mimic of the old masters.

AI can deliver a Van Gogh–ish, Vermeer–ish, Turner–ish painting. The team, from the University of Maryland, the ByteDance AI Lab and Adobe Research, turned an algorithm into a mimic of the old masters.

“Through a coarse-to-fine refinement process our agent can paint arbitrarily complex images in the desired style.”

Continue reading “PaintBot: A deep learning student that trains then mimics old masters” »

In a focus section published in the journal Seismological Research Letters, researchers describe how they are using machine learning methods to hone predictions of seismic activity, identify earthquake centers, characterize different types of seismic waves and distinguish seismic activity from other kinds of ground “noise.”

Machine learning refers to a set of algorithms and models that allow computers to identify and extract patterns of information from large data sets. Machine learning methods often discover these patterns from the data themselves, without reference to the real-world, physical mechanisms represented by the data. The methods have been used successfully on problems such as digital image and speech recognition, among other applications.

More seismologists are using the methods, driven by “the increasing size of seismic data sets, improvements in computational power, new algorithms and architecture and the availability of easy-to-use open source machine learning frameworks,” write focus section editors Karianne Bergen of Harvard University, Ting Cheng of Los Alamos National Laboratory, and Zefeng Li of Caltech.

This particular version of Dadabots has been trained on real death metal band Archspire, and Carr and Zukowski have previously trained the neural network on other real bands like Room For A Ghost, Meshuggah, and Krallice. In the past, they’ve released albums made by these algorithms for free on Dadabots’ Bandcamp — but having a 24/7 algorithmic death metal livestream is something new.

Carr and Zukowski published an abstract about their work in 2017, explaining that “most style-specific generative music experiments have explored artists commonly found in harmony textbooks,” meaning mostly classical music, and have largely ignored smaller genres like black metal. In the paper, the duo said the goal was to have the AI “achieve a realistic recreation” of the audio fed into it, but it ultimately gave them something perfectly imperfect. “Solo vocalists become a lush choir of ghostly voices,” they write. “Rock bands become crunchy cubist-jazz, and cross-breeds of multiple recordings become a surrealist chimera of sound.”

Continue reading “This live stream plays endless death metal produced by an AI” »

The spoken word is a powerful tool, but not all of us have the ability to use it, either due to biology or circumstances. In such cases, technology can bridge the gap — and now that gap is looking shorter than ever, with a new algorithm that turns messages meant for your muscles into legible sounds.

Converting the complex mix of information sent from the brain to the orchestra of body parts required to transform a puff of air into meaningful sound is by no means a simple feat.

Continue reading “Scientists Unveil a ‘Brain Decoder’ That Turns Neural Activity Into Speech” »