Lifeboat Foundation

In 1,832, Charles Darwin witnessed hundreds of ballooning spiders landing on the HMS Beagle while some 60 miles offshore. Ballooning is a phenomenon that’s been known since at least the days of Aristotle—and immortalized in E.B. White’s children’s classic Charlotte’s Web—but scientists have only recently made progress in gaining a better understanding of its underlying physics.

Now, physicists have developed a new mathematical model incorporating all the various forces at play as well as the effects of multiple threads, according to a recent paper published in the journal Physical Review E. Authors M. Khalid Jawed (UCLA) and Charbel Habchi (Notre Dame University-Louaize) based their new model on a computer graphics algorithm used to model fur and hair in such blockbuster films as The Hobbit and Planet of the Apes. The work could one day contribute to the design of new types of ballooning sensors for explorations of the atmosphere.

There are competing hypotheses for how ballooning spiders are able to float off into the air. For instance, one proposal posits that, as the air warms with the rising sun, the silk threads the spiders emit to spin their “parachutes” catch the rising convection currents (the updraft) that are caused by thermal gradients. A second hypothesis holds that the threads have a static electric charge that interacts with the weak vertical electric field in the atmosphere.

Psilocybin is illegal in Canada, but people can apply for exemptions for end-of-life anxiety.

AI algorithms prompt robot to interrogate, select, decision-make to create a painting.

AI algorithms prompt robot to interrogate, select, and decision-make to create a painting.

The study also developed an automated diagnostic pipeline to streamline the genomic data— including the millions of variants present in each genome—for clinical interpretation. Variants unlikely to contribute to the presenting disease are removed, potentially causative variants are identified, and the most likely candidates prioritized. For its pipeline, the researchers and clinicians used Exomiser, a software tool that Robinson co-developed in 2014. To assist with the diagnostic process, Exomiser uses a phenotype matching algorithm to identify and prioritize gene variants revealed through sequencing. It thus automates the process of finding rare, segregating and predicted pathogenic variants in genes in which the patient phenotypes match previously referenced knowledge from human disease or model organism databases. The use of Exomiser was noted in the paper as having greatly increased the number of successful diagnoses made.

The genomic future.

Not surprisingly, the paper concludes that the findings from the pilot study support the case for using whole genome sequencing for diagnosing rare disease patients. Indeed, in patients with specific disorders such as intellectual disability, genome sequencing is now the first-line test within the NHS. The paper also emphasizes the importance of using the HPO to establish a standardized, computable clinical vocabulary, which provides a solid foundation for all genomics-based diagnoses, not just those for rare disease. As the 100,000 Genomes Project continues its work, the HPO will continue to be an essential part of improving patient prognoses through genomics.

Summary: Using algebraic topography, researchers have created an algorithm that requires only a few examples to generate a large number of unique cells.

Source: EPFL

EPFL’s Blue Brain Project has found a way to use only mathematics to automatically draw neurons in 3D, meaning we are getting closer to being able to build digital twins of brains.

This effect looks extremely realistic thanks to the correct equations behind it.

The battle between artificial intelligence and human intelligence has been going on for a while not and AI is clearly coming very close to beating humans in many areas as of now. Partially due to improvements in neural network hardware and also improvements in machine learning algorithms. This video goes over whether and how humans could soon be surpassed by artificial general intelligence.
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TIMESTAMPS:
00:00 Is AGI actually possible?
01:11 What is Artificial General Intelligence?
03:34 What are the problems with AGI?
05:43 The Ethics behind Artificial Intelligence.
08:03 Last Words.
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#ai #agi #robots

We study the question of how to decompose Hilbert space into a preferred tensor-product factorization without any pre-existing structure other than a Hamiltonian operator, in particular the case of a bipartite decomposition into “system” and “environment.” Such a decomposition can be defined by looking for subsystems that exhibit quasi-classical behavior. The correct decomposition is one in which pointer states of the system are relatively robust against environmental monitoring (their entanglement with the environment does not continually and dramatically increase) and remain localized around approximately-classical trajectories. We present an in-principle algorithm for finding such a decomposition by minimizing a combination of entanglement growth and internal spreading of the system. Both of these properties are related to locality in different ways.

Put your coding skills to the test as you work your way through multiple rounds of algorithmic coding puzzles for the title of Code Jam Champ and 15,000 USD.