Lifeboat Foundation

I’ve posted a number of times about artificial intelligence, mind uploading, and various related topics. There are a number of things that can come up in the resulting discussions, one of them being Kurt Gödel’s incompleteness theorems.

The typical line of arguments goes something like this: Gödel implies that there are solutions that no algorithmic system can accomplish but that humans can accomplish, therefore the computational theory of mind is wrong, artificial general intelligence is impossible, and animal, or at least human minds require some as of yet unknown physics, most likely having something to do with the quantum wave function collapse (since that remains an intractable mystery in physics).

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Artificial Intelligence and Deep learning have brought about some great advancements in the field of technology. They are enabling robots to perform activities that were previously thought to be limited to human intelligence. AI is changing the way humans approach problems and bringing revolutionary transformations and solutions to almost every industry. Teaching machines to learn from massive amounts of data and make decisions or predictions based on that learning is the basic idea behind AI. Its application in scientific endeavors has given rise to some amazing tools that are gaining massive popularity in the AI community.

In Artificial Intelligence, Symbolic Regression has been playing an important role in the subtleties of scientific research. It basically focuses on algorithms that allow machines to interpret complicated patterns and correlations found in datasets by automating the search for analytic expressions. Scientists and researchers have been putting in efforts to explore the possible uses of Symbolic Regression.

Diving into the field of Symbolic Regression, a team of researchers has recently introduced Φ-SO, a Physical Symbolic Optimization framework. This method navigates the complexities of physics, where the presence of units is crucial. It automates the process of finding analytic expressions fitting complex datasets.

The tests assessed the use of AI-based navigation sensors and enhanced algorithms for autonomous formation flight.

Airbus.

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Open-source supercomputer algorithm predicts patterning and dynamics of living materials and enables studying their behavior in space and time.

Biological materials are made of individual components, including tiny motors that convert fuel into motion. This creates patterns of movement, and the material shapes itself with coherent flows by constant consumption of energy. Such continuously driven materials are called “active matter.” The mechanics of cells and tissues can be described by active matter theory, a scientific framework to understand shape, flows, and form of living materials. The active matter theory consists of many challenging mathematical equations.

Scientists from the Max Planck Institute of Molecular Cell.

A research team from the Hefei Institutes of Physical Science of the Chinese Academy of Sciences (CAS) has developed an analysis service platform called CRISPRimmunity, which was an interactive web server for identifying important molecular events related to CRISPR and regulators of genome editing systems. The study is published in Nucleic Acids Research.

The new CRISPRimmunity platform was designed for integrated analysis and prediction of CRISPR-Cas and anti-CRISPR systems. It includes customized databases with annotations for known anti-CRISPR proteins, anti-CRISPR-associated proteins, class II CRISPR-Cas systems, CRISPR array types, HTH structural domains and mobile genetic elements. These resources allow the study of molecular events in the co-evolution of CRISPR-Cas and anti-CRISPR systems.

To improve prediction accuracy, the researchers used strategies such as homology analysis, association analysis and self-targeting in prophage regions to predict anti-CRISPR proteins. When tested on data from 99 experimentally validated Acrs and 676 non-Acrs, CRISPRimmunity achieved an accuracy of 0.997 for anti-CRISPR protein prediction.

Microbial sequence databases contain a wealth of information about enzymes and other molecules that could be adapted for biotechnology. But these databases have grown so large in recent years that they’ve become difficult to search efficiently for enzymes of interest.

Now, scientists at the Broad Institute of MIT and Harvard, the McGovern Institute for Brain Research at MIT, and the National Center for Biotechnology Information (NCBI) at the National Institutes of Health have developed a new search algorithm that has identified 188 kinds of new rare CRISPR systems in bacterial genomes, encompassing thousands of individual systems. The work appears in Science.

The algorithm, which comes from the lab of CRISPR pioneer Feng Zhang, uses big-data clustering approaches to rapidly search massive amounts of genomic data. The team used their algorithm, called Fast Locality-Sensitive Hashing-based clustering (FLSHclust) to mine three major public databases that contain data from a wide range of unusual bacteria, including ones found in coal mines, breweries, Antarctic lakes, and dog saliva.

Quantum advantage is the milestone the field of quantum computing is fervently working toward, where a quantum computer can solve problems that are beyond the reach of the most powerful non-quantum, or classical, computers.

Quantum refers to the scale of atoms and molecules where the laws of physics as we experience them break down and a different, counterintuitive set of laws apply. Quantum computers take advantage of these strange behaviors to solve problems.

Continue reading “What Is Quantum Advantage? A Quantum Computing Scientist Explains An Approaching Milestone Marking The Arrival Of Extremely Powerful Computers” »

Why it matters: While AI algorithms are seemingly everywhere, processing on the most popular platforms require powerful server GPUs to provide customers with their generative services. Arm is introducing a new dedicated chip design, set to provide AI acceleration even in the most affordable IoT devices starting next year.

The Arm Cortex-M52 is the smallest and most cost-efficient processor designed for AI acceleration applications, according to the company. This latest design from the UK-based fabless firm promises to deliver “enhanced” AI capabilities to Internet of Things (IoT) devices, as Arm states, without the need for a separate computing unit.

Paul Williamson, Arm’s SVP and general manager for the company’s IoT business, emphasized the need to bring machine learning optimized processing to “even the smallest and lowest-power” endpoint devices to fully realize the potential of AI in IoT. Despite AI’s ubiquity, Williamson noted, harnessing the “intelligence” from the vast amounts of data flowing through digital devices requires IoT appliances that are smarter and more capable.

An algorithm that can analyse hundreds of millions of genetic sequences has identified DNA-cutting genes and enzymes that are extremely rare in nature.