3D Cellular Automata

This is a project that ressembles the classic Game of Life, but implemented in a 3D world. The project needs OpenGL 4.5 or greater to render, and currently only compiles under Linux (although it would be relatively easy to change CMakeLists to compile for Windows/macOS*(apple discontinued OpenGL support in favor of Metal)).

Cellular Automaton 3D

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Source:  https://github.com/w84death/cellular-automaton

Machine Learning’s ‘Amazing’ Ability to Predict Chaos

In new computer experiments, artificial-intelligence algorithms can tell the future of chaotic systems.
Gif illustration for "Machine Learning’s ‘Amazing’ Ability to Predict Chaos"

Researchers have used machine learning to predict the chaotic evolution of a model flame front.

Half a century ago, the pioneers of chaos theory discovered that the “butterfly effect” makes long-term prediction impossible. Even the smallest perturbation to a complex system (like the weather, the economy or just about anything else) can touch off a concatenation of events that leads to a dramatically divergent future. Unable to pin down the state of these systems precisely enough to predict how they’ll play out, we live under a veil of uncertainty.

In a series of results reported in the journals Physical Review Letters and Chaos, scientists have used machine learning — the same computational technique behind recent successes in artificial intelligence — to predict the future evolution of chaotic systems out to stunningly distant horizons. The approach is being lauded by outside experts as groundbreaking and likely to find wide application.

“I find it really amazing how far into the future they predict” a system’s chaotic evolution, said Herbert Jaeger, a professor of computational science at Jacobs University in Bremen, Germany  …. <more>

Graphic illustration depicting the charts of training computers to predict chaos.

Full pages:

https://www.wired.com/story/machine-learnings-amazing-ability-to-predict-chaos/

https://www.quantamagazine.org/machine-learnings-amazing-ability-to-predict-chaos-20180418/

 

 

A Novel Algorithm Enables Statistical Analysis of Time Series Data

MIT scientists have developed a novel approach to analyzing time series data sets using a new algorithm, termed state-space multitaper time-frequency analysis (SS-MT). SS-MT gives a structure to dissect time arrangement information progressively, empowering analysts to work in a more educated manner with extensive arrangements of information that are nonstationary, i.e. at the point when their qualities develop after some time.

A Novel Algorithm Enables Statistical Analysis of Time Series Data

Using a novel analytical method they have developed, MIT researchers analyzed raw brain activity data (B). The spectrogram shows decreased noise and increased frequency resolution, or contrast (E and F) compared to standard spectral analysis methods (C and D). Image courtesy of Seong-Eun Kim et al.

It is important to measure time while every task such as tracking brain activity in the operating room, seismic vibrations during an earthquake, or biodiversity in a single ecosystem over a million years. Measuring the recurrence of an event over some stretch of time is a major information investigation errand that yields basic knowledge in numerous logical fields.

This newly developed approach enables analysts to measure the moving properties of information as well as make formal factual correlations between discretionary sections of the information.

Emery Brown, the Edward Hood Taplin Professor of Medical Engineering and Computational Neuroscience said“The algorithm functions similarly to the way a GPS calculates your route when driving. If you stray away from your predicted route, the GPS triggers the recalculation to incorporate the new information.”

“This allows you to use what you have already computed to get a more accurate estimate of what you’re about to calculate in the next time period. Current approaches to analyses of long, nonstationary time series ignore what you have already calculated in the previous interval leading to an enormous information loss.”  …… 

Full post: https://www.techexplorist.com/novel-algorithm-enables-statistical-analysis-time-series-data/

Abstract: http://www.pnas.org/content/early/2017/12/15/1702877115

AutoML for large scale image classification and object detection

A few months ago, we introduced our AutoML project, an approach that automates the design of machine learning models. While we found that AutoML can design small neural networks that perform on par with neural networks designed by human experts, these results were constrained to small academic datasets like CIFAR-10, and Penn Treebank. We became curious how this method would perform on larger more challenging datasets, such as ImageNet image classification and COCO object detection. Many state-of-the-art machine learning architectures have been invented by humans to tackle these datasets in academic competitions.

In Learning Transferable Architectures for Scalable Image Recognition, we apply AutoML to the ImageNet image classification and COCO object detection dataset — two of the most respected large scale academic datasets in computer vision. These two datasets prove a great challenge for us because they are orders of magnitude larger than CIFAR-10 and Penn Treebank datasets. For instance, naively applying AutoML directly to ImageNet would require many months of training our method.

To be able to apply our method to ImageNet we have altered the AutoML approach to be more tractable to large-scale datasets:

  • We redesigned the search space so that AutoML could find the best layer which can then be stacked many times in a flexible manner to create a final network.
  • We performed architecture search on CIFAR-10 and transferred the best learned architecture to ImageNet image classification and COCO object detection.

With this method, AutoML was able to find the best layers that work well on CIFAR-10 but work well on ImageNet classification and COCO object detection. These two layers are combined to form a novel architecture, which we called “NASNet”.

Our NASNet architecture is composed of two types of layers: Normal Layer (left), and Reduction Layer (right). These two layers are designed by AutoML.

 

source: https://research.googleblog.com/2017/11/automl-for-large-scale-image.html