Lifeboat Foundation

The researchers from the University of Southampton, working with colleagues in Canada and Italy, claim there is as much evidence for this theory as for traditional explanations for these irregularities.

A holographic universe, an idea first suggested in the 1990s, is one where all the information, which makes up our 3D ‘reality’is contained in a 2D surface on its boundaries.

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A discussion about Simulation theory, quantum mechanics and Super Mario!

Futurists Keith Comito, Gray Scott, Luis Arana, and Zach Waldman talk about the simulation theory as part of the #FuturistSessions at the Soho House New York. Discussions include quantum mechanics, mathematical realism vs mathematical fictionalism, the Matrix, Pacman, and Mario!

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In Brief Science fiction often serves as a curiosity catalyst for a lot of technological innovation. One such example is this Alcubierre Warp Drive, that would absolutely revolutionize the capability of humans to traverse the stars.

It’s always a welcome thing to learn that ideas that are commonplace in science fiction have a basis in science fact. Cryogenic freezers, laser guns, robots, silicate implants… and let’s not forget the warp drive! Believe it or not, this concept – alternately known as FTL (Faster-Than-Light) travel, Hyperspace, Lightspeed, etc. – actually has one foot in the world of real science.

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In Brief

• By mimicking the way neurons fire in the hippocampus during natural memory creation, a brain implant was used to successfully plant memories in the brains of rats.
• Though human implementation is far off, this breakthrough in cracking the hippocampus’ mathematical “memory code” has very important implications for health and research.

Memories are the faintest, most ethereal wisps of our neurophysiology — somehow, the firing of delicate synapses and the activation of neurons combine to produce the things we remember. The sum of our memories make us who we are; they are us, in every way, and without them we cease to be.

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What’s the future of education? How will students learn differently? What will the schools of the future look like? We asked TED-Ed Innovative Educators to share their ideas. Their answers are provocative, contradictory — and make for great conversation starters. Welcome to the “Choose Your Own Adventure” future of learning.

There will be more creativity in education. “Because that’s what careers will require. Education will be not just taking in information and sharing it back, but also figuring out what to do with that information in the real world.” —Josefino Rivera, Jr., educator in Buenos Aires, Argentina.

The classroom will be one big makerspace. “Technology like Evernote, Google, and Siri will be standard and will change what teachers value and test for. Basically, if you can ask Siri to answer a question, then you will not be evaluated on that. Instead, learning will be project based. Students will be evaluated on critical-thinking and problem-solving skills. Literature and math will still be taught, but they will be taught differently. Math will be taught as a way of learning how to solve problems and puzzles. In literature, students will be asked what a story means to them. Instead of taking tests, students will show learning through creative projects. The role of teachers will be to guide students in the areas where they need guidance as innovators. How do you get kids to be innovative? You let them. You get out of their way.” —Nicholas Provenzano, educator in Michigan, United States.

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Concerned that scientific views are not being properly represented in Washington, a new nonprofit group wants to get more scientists elected. 314 Action, named after the first three digits of pi, wants scientists to embrace the political process, running for all levels of government. The group’s aim is to get as many scientists elected as possible in the 2018 elections.

314 Action sees particular urgency for its work due to the rise of anti-science rhetoric on the Hill, especially from the right. The current Republican standard bearer President Trump has questioned the idea that climate change is caused by humans and seemingly encouraged debunked anti-vaccination opinions. With the appointments Trump made so far, it’s hard to believe his administration will advance scientific causes.

The 314 Action group describes its members as people who come from the STEM community whose goals are to increase communication between STEM community and elected officials, to actually elect STEM-trained candidates to public office, to increase presence of STEM ideas through the media, and to prevent the U.S. from falling further and further behind the rest of the world in math and science education.

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In Brief

• Princeton University researchers have developed the world’s first integrated silicon photonic neuromorphic chip, which contains 49 circular nodes etched into semiconductive silicon.
• The chip could complete a math equation 1,960 times more quickly than a typical central processing unit, a speed that would make it ideal for use in future neural networks.

As developments are made in neural computing, we can continue to push artificial intelligence further. A fairly recent technology, neural networks have been taking over the world of data processing, giving machines advanced capabilities such as object recognition, face recognition, natural language processing, and machine translation.

These sound like simple things, but they were way out of reach for processors until scientists began to find way to make machines behave more like human brains in the way they learned and handled data. To do this, scientists have been focusing on building neuromorphic chips, circuits that operate in a similar fashion to neurons.

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The goal of roboticists has long been to make A.I. as efficient as the human brain, and researchers at the Massachusetts Institute of Technology just brought them one step closer.

In a recent paper, published in the journal Biology, scientists were able to successfully train a neural network to recognize faces at different angles by feeding it a set of different orientations for several face templates. Although this only initially gave the neural network the ability to roughly reach invariance — the ability to process data regardless of form — over time, the network taught itself to achieve full “mirror symmetry. Through mathematical algorithms, the neural network was able to mimic the human brain’s ability to understand objects are the same despite orientation or rotation.

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What if a simple algorithm were all it took to program tomorrow’s artificial intelligence to think like humans?

According to a paper published in the journal Frontiers in Systems Neuroscience, it may be that easy — or difficult. Are you a glass-half-full or half-empty kind of person?

Researchers behind the theory presented experimental evidence for the Theory of Connectivity — the theory that all of the brains processes are interconnected (massive oversimplification alert) — “that a simple mathematical logic underlies brain computation.” Simply put, an algorithm could map how the brain processes information. The painfully-long research paper describes groups of similar neurons forming multiple attachments meant to handle basic ideas or information. These groupings form what researchers call “functional connectivity motifs” (FCM), which are responsible for every possible combination of ideas.

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