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Let’s be honest, we have our fair share of problems on planet Earth: war, floods, disease, poverty, environmental destruction, Justin Bieber (the list goes on and on, really). But we also have a lot of things going for us: the Alcubierre Warp Drive, invisibility cloaks, the Mars rover missions, the discovery of the Higgs (the list goes on and on, really).

How can we weigh all the exciting and inspiring scientific discoveries against all the destruction and chaos? We have an ever expanding list of catastrophes that is coupled with (indeed, that parallels) our unrelenting march towards technological perfection. With such a coupling of unimaginable horrors and magnificent advancements, how can we possibly measure our status as a civilization?

One of the easiest ways to answer this question is to form a scale that will allow us to scientifically measure our technological *abilities* against the technological *possibilities.* Or in layman’s terms, something that will allow us to measure our awesomeness against the total possible awesomeness. Fortunately, there are several ways of conducting such measurements.

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The solar revolution.


Tesla CEO Elon Musk said the solar roof that will be sold under a combined Tesla-SolarCity will likely cost less than a normal roof to install.

Tesla and SolarCity shareholders voted in favour of the US$2 billion deal Thursday. In late October, Musk unveiled a new solar roof product to show his vision for a combined company with SolarCity, but did not provide specifics on how much it would cost.

On Thursday after the shareholder vote, Musk said its solar roof will likely cost less than a normal roof:

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A world where DNA can be rewritten to fix deadly diseases has moved a step closer after scientists announced they had genetically-edited the cells of a human for the first time using a groundbreaking technique.

A man in China was injected with modified immune cells which had been engineered to fight his lung cancer. Larger trials are scheduled to take place next year in the US and Beijing, which scientists say could open up a new era of genetic medicine.

The technique used is called Crispr, which works like tiny molecular scissors snipping away genetic code and replacing it with new instructions to build better cells.

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“I will answer very simply, that the internet will disappear… It will be part of your presence all the time.”

That was Eric Schmidt talking on a panel about the future of the web earlier last year. He’s referencing a vision of the future which, not too long ago would have felt very much a work of science fiction.

But today, we inch closer to this idea of architecturally integrated, living, breathing, dynamic spaces. The internet becomes something that’s omnipresent, instead of just something you click on.

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The subject of Nicotinamide Riboside (NR) has been in the new a lot lately but is all the hype justified? Find out in this scientific article on Longecity by researcher Sven Bulterijs.


Researcher Sven Bulterijs has published a scientific article about nicotinamide riboside, how it works and looking at the data behind this recently popular supplement. So does it live up to the hype? Why not check it out and decide yourself.

“The first article in my new science column takes a look at a special vitamin B3 derivative called nicotinamide riboside that was shown to slightly extend mouse lifespan.”

http://www.longecity.org/forum/blog/201/entry-3579-nicotinamide-riboside/

In Brief:

  • Researchers have created a heuristically trained neural network that outperformed conventional machine learning algorithms by 160 percent and its own training by 9 percent.
  • This new teaching method could enable AI to make correct classifications of data that’s previously unknown or unclassified, learning information beyond its data set.

Machine learning technology in neural networks has been pushing artificial intelligence (AI) development to new heights. Most AI systems learn to do things using a set of labelled data provided by their human programmers. Parham Aarabi and Wenzhi Guo, engineers from the University of Toronto, Canada have taken machine learning to a different level, developing an algorithm that can learn things on its own, going beyond its training.

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