Archive for the ‘biological’ category: Page 133

May 4, 2017

“Glowing Mushroom” Discovery Could Revolutionize Biology

Posted by in categories: biological, innovation

  • Of the estimated 100,000 mushroom species on Earth, only about 80 glow. Scientists uncovered the explanation behind two of these glowing mushrooms.
  • Studying the mushrooms allowed researchers to make chemicals that glow various colors that may help us make more scientific breakthroughs.

Mushrooms are our favorite fungi. From savory dishes to surprising video game power-ups — we can’t seem to get enough of the little things. What’s more interesting, however, is the fact that mushrooms can be far more relevant to our own progress as a society than we imagined. The Neonothopanus gardneri and Neonothopanus nambi are two distinct species of glow-in-the-dark mushroom found in Brazil and Vietnam respectively, that have reshaped our perspective on bioluminescence permanently.

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Apr 7, 2017

OpenAI Just Beat Google DeepMind at Atari With an Algorithm From the 80s

Posted by in categories: biological, Elon Musk, information science, robotics/AI

OpenAI vs. Deepmind in river raid ATARI.

AI research has a long history of repurposing old ideas that have gone out of style. Now researchers at Elon Musk’s open source AI project have revisited “neuroevolution,” a field that has been around since the 1980s, and achieved state-of-the-art results.

The group, led by OpenAI’s research director Ilya Sutskever, has been exploring the use of a subset of algorithms from this field, called “evolution strategies,” which are aimed at solving optimization problems.

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Apr 6, 2017

New partnership set to transform the engineering of biology

Posted by in categories: biological, engineering

News from Cambridge businesses. Network members upload news here about their products, services and achievements.

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Apr 6, 2017

Towards an Artificial Brain

Posted by in categories: biological, ethics, information science, neuroscience, robotics/AI

The fast-advancing fields of neuroscience and computer science are on a collision course. David Cox, Assistant Professor of Molecular and Cellular Biology and Computer Science at Harvard, explains how his lab is working with others to reverse engineer how brains learn, starting with rats. By shedding light on what our machine learning algorithms are currently missing, this work promises to improve the capabilities of robots – with implications for jobs, laws and ethics.

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Apr 6, 2017

Electronic synapses that can learn : towards an artificial brain?

Posted by in categories: biological, particle physics, robotics/AI

© Sören Boyn / CNRS/Thales physics joint research unit.

Artist’s impression of the electronic synapse: the particles represent electrons circulating through oxide, by analogy with neurotransmitters in biological synapses. The flow of electrons depends on the oxide’s ferroelectric domain structure, which is controlled by electric voltage pulses.

Download the press release : PR Synapses

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Apr 4, 2017

Futurist Speaker Gerd Leonhard at SAP Executive Summit 2017: Exponential technological ®evolutions

Posted by in categories: biological, economics

Thanks for your interest!

Thanks to SAP Italia for making this video available, the original video is at You can download all my slides at; direct link to the slides used here is

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Apr 2, 2017

A Journey Into Transhumanism

Posted by in categories: biological, transhumanism

Nice article in Financial Times on #transhumanism via book To Be a Machine:

A lively portrait of the secular religion seeking to overcome biology.

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Mar 28, 2017

Elon Musk launches Neuralink, a venture to merge the human brain with AI

Posted by in categories: biological, Elon Musk, robotics/AI

SpaceX and Tesla CEO Elon Musk is backing a brain-computer interface venture called Neuralink, according to The Wall Street Journal. The company, which is still in the earliest stages of existence and has no public presence whatsoever, is centered on creating devices that can be implanted in the human brain, with the eventual purpose of helping human beings merge with software and keep pace with advancements in artificial intelligence. These enhancements could improve memory or allow for more direct interfacing with computing devices.

Musk has hinted at the existence of Neuralink a few times over the last six months or so. More recently, Musk told a crowd in Dubai, “Over time I think we will probably see a closer merger of biological intelligence and digital intelligence.” He added that “it’s mostly about the bandwidth, the speed of the connection between your brain and the digital version of yourself, particularly output.” On Twitter, Musk has responded to inquiring fans about his progress on a so-called “neural lace,” which is sci-fi shorthand for a brain-computer interface humans could use to improve themselves.

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Mar 21, 2017

Researchers Seek Guidelines for Embryo-Like Entities Created in Labs

Posted by in category: biological

A group of prominent researchers is calling for changes to scientific-research guidelines to address a range of new biological entities created in labs that may share similar characteristics to embryos.

These entities, created through a variety of techniques, have been studied at only the earliest stages of development. In some cases, scientists have taken cells from an embryo and manipulated them to generate another embryo-like…

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Mar 19, 2017

New Artificial Synapse Bridges the Gap to Brain-Like Computers

Posted by in categories: biological, robotics/AI

From AlphaGo’s historic victory against world champion Lee Sedol to DeepStack’s sweeping win against professional poker players, artificial intelligence is clearly on a roll.

Part of the momentum comes from breakthroughs in artificial neural networks, which loosely mimic the multi-layer structure of the human brain. But that’s where the similarity ends. While the brain can hum along on energy only enough to power a light bulb, AlphaGo’s neural network runs on a whopping 1,920 CPUs and 280 GPUs, with a total power consumption of roughly one million watts—50,000 times more than its biological counterpart.

Extrapolate those numbers, and it’s easy to see that artificial neural networks have a serious problem—even if scientists design powerfully intelligent machines, they may demand too much energy to be practical for everyday use.

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