Lifeboat Foundation

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” »

For years, post traumatic stress disorder (PTSD) has been one of the most challenging disorders to diagnose. Traditional methods, like one-on-one clinical interviews, can be inaccurate due to the clinician’s subjectivity, or if the patient is holding back their symptoms.

Now, researchers at New York University say they’ve taken the guesswork out of diagnosing PTSD in veterans by using artificial intelligence to objectively detect PTSD by listening to the sound of someone’s voice. Their research, conducted alongside SRI International — the research institute responsible for bringing Siri to iPhones— was published Monday in the journal Depression and Anxiety.

According to The New York Times, SRI and NYU spent five years developing a voice analysis program that understands human speech, but also can detect PTSD signifiers and emotions. As the NYT reports, this is the same process that teaches automated customer service programs how to deal with angry callers: By listening for minor variables and auditory markers that would be imperceptible to the human ear, the researchers say the algorithm can diagnose PTSD with 89% accuracy.

Continue reading “Artificial Intelligence Can Detect PTSD in Your Voice” »

The multiplication of integers is a problem that has kept mathematicians busy since Antiquity. The “Babylonian” method we learn at school requires us to multiply each digit of the first number by each digit of the second one. But when both numbers have a billion digits each, that means a billion times a billion or 10¹⁸ operations.

At a rate of a billion operations per second, it would take a computer a little over 30 years to finish the job. In 1971, the mathematicians Schönhage and Strassen discovered a quicker way, cutting calculation time down to about 30 seconds on a modern laptop. In their article, they also predicted that another algorithm—yet to be found—could do an even faster job. Joris van der Hoeven, a CNRS researcher from the École Polytechnique Computer Science Laboratory LIX, and David Harvey from the University of New South Wales (Australia) have found that algorithm.

They present their work in a new article that is available to the scientific community through the online HAL archive. But one problem raised by Schönhage et Strassen remains to be solved: proving that no quicker method exists. This poses a new challenge for theoretical computer science.

Continue reading “A faster method for multiplying very big numbers” »