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Current brain-computer interface (BCI) research helps people who have lost the ability to affect their environment in ways many of us take for granted. Future BCIs may go beyond motor function, perhaps aiding with memory recall, decision-making, and other cognitive functions.

Have you ever studied a foreign language and wished you could upload the vocabulary lists directly into your brain so that you could retain them? Would you like to do mental math with the speed and accuracy of a calculator? Do you want a literal photographic memory? Well, these dreams are still the stuff of science fiction, but the brave new world of brain-computer interfaces, or BCI, is well on its way to making technological miracles of this sort a reality.

The story of BCI begins with the discovery of electrical signals emitted by the brain. In 1924, German scientist Hans Berger recorded the first electroencephalogram, or EEG, by placing electrodes under a person’s scalp. Although his research was at first met with derision, a whole new way to study the brain was born from his work. It is now well accepted that the human brain emits electric signals at a variety of frequencies currently known as brainwaves.

Continue reading “Brain Meets Machine: The Art and Science of Brain-Computer Interfaces” »

https://www.youtube.com/watch?v=n4YtQ-N_t84

When listening to world science festival’s latest episode on youtube, Pondering the Imponderables: The Biggest Questions of Cosmology, I found myself to be most in line with George F.R. Ellis’ line of thinking overall.

Big Bang cosmology, chemical and biological evolutionary theory, and associated sciences have been extraordinarily successful in revealing and enabling us to understand the development of the.

Continue reading “On the Nature of Causality in Complex Systems, George F.R. Ellis” »

By translating a key human physical dynamic skill — maintaining whole-body balance — into a mathematical equation, the team was able to use the numerical formula to program their robot Mercury, which was built and tested over the course of six years. They calculated the margin of error necessary for the average person to lose one’s balance and fall when walking to be a simple figure — 2 centimeters.

“Essentially, we have developed a technique to teach autonomous robots how to maintain balance even when they are hit unexpectedly, or a force is applied without warning,” Sentis said. “This is a particularly valuable skill we as humans frequently use when navigating through large crowds.”

Sentis said their technique has been successful in dynamically balancing both bipeds without ankle control and full humanoid robots.

Physicists face the same hard problem as neuroscientists do: the problem of bridging objective description and subjective experience. Physics has encountered consciousness. Quantum theory says an object remains in a superposition of possibilities until observed. We can consider a quantum state as being about our knowledge rather than a direct description of physical reality. The physics of information just may be that bridging of quantum-to-digital reality of subjective experience. We are now at the historic juncture when quantum computing could reveal quantum information processing underpinnings of subjectivity. Quantum mechanics is a spectacularly successful theory of fundamental physics that allows us to make probabilistic predictions derived from its mathematical formalism, but the theory doesn’t tell us precisely how these probabilities should be interpreted in regards to phenomenology, i.e. our experiential reality. There are basically three main interpretive camps within quantum mechanics from which stem at least a dozen further interpretations.

By Alex Vikoulov.

Continue reading “The Physics of Information: Quantum Potentiality to Classical Actuality of Your Experiential Reality” »

In a world increasingly driven by industries that rely on advanced technical learning and innovation, fluency in STEM fields (science, technology, engineering and math) becomes more vital every day. Yet our education system isn’t keeping up. Five years ago, a Business-Higher Education Forum study found that 80% of high school students either lacked interest or proficiency in STEM subjects. Meanwhile, a college and career readiness organization known as ACT reported last year that the number of students pursuing STEM careers is growing at less than 1% annually.

The Amgen Foundation is doing something about it. As the principal philanthropic arm of Amgen, the largest independent biotechnology company, the Amgen Foundation has been committed to inspiring the next generation of scientists and innovators by making immersive science education a focus of its social investments for almost 30 years. While Amgen has reached millions of patients around the world with biotechnology medicines to combat serious illnesses, such as cardiovascular disease, cancer and migraines, the Amgen Foundation has reached more than 4 million students globally—and it is poised to launch a new program called LabXchange with the potential to reach millions more.

“As a scientist, it’s clear to me that the most effective way to learn science is by doing it,” says David Reese, executive vice president of Research and Development at Amgen and member of the Amgen Foundation board of directors. “It’s time to transform the science learning experience. We need to move from information acquisition to application and exploration, from students as passive listeners to active participants in the learning process, from teachers as knowledge transmitters to facilitators and coaches.”

Continue reading “Philanthropy Assignment: Inspire Tomorrow’s Leaders With Science” »

Not the end, but interesting… Also, note that hupercanes are possible products of some mathematical instability, where the speed start to grow almost unlimited after some threshold. Buts Cat 6 is not a hypercane, as in the hypercane winds will be 500 mph.

There is no such thing as a category 6 hurricane or tropical storm — yet. But a combination of warmer oceans and more water in the atmosphere could make the devastation of 2017 pale in comparison .

Realistic climate simulations require huge reserves of computational power. An LMU study now shows that new algorithms allow interactions in the atmosphere to be modeled more rapidly without loss of reliability.

Forecasting global and local climates requires the construction and testing of mathematical climate models. Since such models must incorporate a plethora of physical processes and interactions, climate simulations require enormous amounts of computational power. And even the best models inevitably have limitations, since the phenomena involved can never be modeled in sufficient detail. In a project carried out in the context of the DFG-funded Collaborative Research Center “Waves to Weather”, Stephan Rasp of the Institute of Theoretical Meteorology at LMU (Director: Professor George Craig) has now looked at the question of whether the application of artificial intelligence can improve the efficacy of climate modelling. The study, which was performed in collaboration with Professor Mike Pritchard of the University of California at Irvine und Pierre Gentine of Columbia University in New York, appears in the journal PNAS.

General circulation models typically simulate the global behavior of the atmosphere on grids whose cells have dimensions of around 50 km. Even using state-of-the-art supercomputers the relevant physical processes that take place in the atmosphere are simply too complex to be modelled at the necessary level of detail. One prominent example concerns the modelling of clouds which have a crucial influence on climate. They transport heat and moisture, produce precipitation, as well as absorb and reflect solar radiation, for instance. Many clouds extend over distances of only a few hundred meters, much smaller than the grid cells typically used in simulations – and they are highly dynamic. Both features make them extremely difficult to model realistically. Hence today’s climate models lack at least one vital ingredient, and in this respect, only provide an approximate description of the Earth system.

The nineteenth-century discovery of numbers called “quaternions” gave mathematicians a way to describe rotations in space, forever changing physics and math.