UFO'S of UAP'S, ASTRONOMIE, RUIMTEVAART, ARCHEOLOGIE, OUDHEIDKUNDE, SF-SNUFJES EN ANDERE ESOTERISCHE WETENSCHAPPEN

IN BRIEF

The time for machines to take over most of humanity's work is rapidly approaching. The world is woefully unprepared to deal with the implications that automation will have over the coming decades. Universal basic income is just beginning to be discussed, and automation has the potential to displace much of the world's workforce. Many decisions have to be made, and quickly, if we hope to keep pace with innovation.

On December 2nd, 1942, a team of scientists led by Enrico Fermi came back from lunch and watched as humanity created the first self-sustaining nuclear reaction inside a pile of bricks and wood underneath a football field at the University of Chicago. Known to history as Chicago Pile-1, it was celebrated in silence with a single bottle of Chianti, for those who were there understood exactly what it meant for humankind, without any need for words.

Now, something new has occurred that, again, quietly changed the world forever. Like a whispered word in a foreign language, it was quiet in that you may have heard it, but its full meaning may not have been comprehended. However, it’s vital we understand this new language, and what it’s increasingly telling us, for the ramifications are set to alter everything we take for granted about the way our globalized economy functions, and the ways in which we as humans exist within it.

The language is a new class of machine learning known as deep learning, and the “whispered word” was a computer’s use of it to seemingly out of nowhere defeat three-time European Go champion Fan Hui, not once but five times in a row without defeat. Many who read this news, considered that as impressive, but in no way comparable to a match against Lee Se-dol instead, who many consider to be one of the world’s best living Go players, if not the best. Imagining such a grand duel of man versus machine, China’s top Go player predicted that Lee would not lose a single game, and Lee himself confidently expected to possibly lose one at the most.

What actually ended up happening when they faced off? Lee went on to lose all but one of their match’s five games. An AI named AlphaGo is now a better Go player than any human and has been granted the “divine” rank of 9 dan. In other words, its level of play borders on godlike. Go has officially fallen to machines, just as Jeopardy did before it to Watson, and chess before that to Deep Blue.

“AlphaGo’s historic victory is a clear signal that we’ve gone from linear to parabolic.”

So, what is Go? Very simply, think of Go as Super Ultra Mega Chess. This may still sound like a small accomplishment, another feather in the cap of machines as they continue to prove themselves superior in the fun games we play, but it is no small accomplishment, and what’s happening is no game.

AlphaGo’s historic victory is a clear signal that we’ve gone from linear to parabolic. Advances in technology are now so visibly exponential in nature that we can expect to see a lot more milestones being crossed long before we would otherwise expect. These exponential advances, most notably in forms of artificial intelligence limited to specific tasks, we are entirely unprepared for as long as we continue to insist upon employment as our primary source of income.

This may all sound like exaggeration, so let’s take a few decade steps back, and look at what computer technology has been actively doing to human employment so far:

Let the above chart sink in. Do not be fooled into thinking this conversation about the automation of labor is set in the future. It’s already here. Computer technology is already eating jobs and has been since 1990.

ROUTINE WORK

All work can be divided into four types: routine and nonroutine, cognitive and manual. Routine work is the same stuff day in and day out, while nonroutine work varies. Within these two varieties, is the work that requires mostly our brains (cognitive) and the work that requires mostly our bodies (manual). Where once all four types saw growth, the stuff that is routine stagnated back in 1990. This happened because routine labor is easiest for technology to shoulder. Rules can be written for work that doesn’t change, and that work can be better handled by machines.

Distressingly, it’s exactly routine work that once formed the basis of the American middle class. It’s routine manual work that Henry Ford transformed by paying people middle class wages to perform, and it’s routine cognitive work that once filled US office spaces. Such jobs are now increasingly unavailable, leaving only two kinds of jobs with rosy outlooks: jobs that require so little thought, we pay people little to do them, and jobs that require so much thought, we pay people well to do them.

If we can now imagine our economy as a plane with four engines, where it can still fly on only two of them as long as they both keep roaring, we can avoid concerning ourselves with crashing. But what happens when our two remaining engines also fail? That’s what the advancing fields of robotics and AI represent to those final two engines, because for the first time, we are successfully teaching machines to learn.

NEURAL NETWORKS

I’m a writer at heart, but my educational background happens to be in psychology and physics. I’m fascinated by both of them so my undergraduate focus ended up being in the physics of the human brain, otherwise known as cognitive neuroscience. I think once you start to look into how the human brain works, how our mass of interconnected neurons somehow results in what we describe as the mind, everything changes. At least it did for me.

As a quick primer in the way our brains function, they’re a giant network of interconnected cells. Some of these connections are short, and some are long. Some cells are only connected to one other, and some are connected to many. Electrical signals then pass through these connections, at various rates, and subsequent neural firings happen in turn. It’s all kind of like falling dominoes, but far faster, larger, and more complex. The result amazingly is us, and what we’ve been learning about how we work, we’ve now begun applying to the way machines work.

One of these applications is the creation of deep neural networks – kind of like pared-down virtual brains. They provide an avenue to machine learning that’s made incredible leaps that were previously thought to be much further down the road, if even possible at all. How? It’s not just the obvious growing capability of our computers and our expanding knowledge in the neurosciences, but the vastly growing expanse of our collective data, aka big data.

BIG DATA

Big data isn’t just some buzzword. It’s information, and when it comes to information, we’re creating more and more of it every day. In fact we’re creating so much that a 2013 report by SINTEF estimated that 90% of all information in the world had been created in the prior two years. This incredible rate of data creation is even doubling every 1.5 years thanks to the Internet, where in 2015 every minute we were liking 4.2 million things on Facebook, uploading 300 hours of video to YouTube, and sending 350,000 tweets. Everything we do is generating data like never before, and lots of data is exactly what machines need in order to learn to learn. Why?

Imagine programming a computer to recognize a chair. You’d need to enter a ton of instructions, and the result would still be a program detecting chairs that aren’t, and not detecting chairs that are. So how did we learn to detect chairs? Our parents pointed at a chair and said, “chair.” Then we thought we had that whole chair thing all figured out, so we pointed at a table and said “chair”, which is when our parents told us that was “table.” This is called reinforcement learning. The label “chair” gets connected to every chair we see, such that certain neural pathways are weighted and others aren’t. For “chair” to fire in our brains, what we perceive has to be close enough to our previous chair encounters. Essentially, our lives are big data filtered through our brains.

DEEP LEARNING

The power of deep learning is that it’s a way of using massive amounts of data to get machines to operate more like we do without giving them explicit instructions. Instead of describing “chairness” to a computer, we instead just plug it into the Internet and feed it millions of pictures of chairs. It can then have a general idea of “chairness.” Next we test it with even more images. Where it’s wrong, we correct it, which further improves its “chairness” detection. Repetition of this process results in a computer that knows what a chair is when it sees it, for the most part as well as we can. The important difference though is that unlike us, it can then sort through millions of images within a matter of seconds.

This combination of deep learning and big data has resulted in astounding accomplishments just in the past year. Aside from the incredible accomplishment of AlphaGo, Google’s DeepMind AI learned how to read and comprehend what it read through hundreds of thousands of annotated news articles. DeepMind also taught itself to play dozens of Atari 2600 video games better than humans, just by looking at the screen and its score, and playing games repeatedly. An AI named Giraffe taught itself how to play chess in a similar manner using a dataset of 175 million chess positions, attaining International Master level status in just 72 hours by repeatedly playing itself. In 2015, an AI even passed a visual Turing test by learning to learn in a way that enabled it to be shown an unknown character in a fictional alphabet, then instantly reproduce that letter in a way that was entirely indistinguishable from a human given the same task. These are all major milestones in AI.

However, despite all these milestones, when asked to estimate when a computer would defeat a prominent Go player, the answer even just months prior to the announcement by Google of AlphaGo’s victory, was by experts essentially, “Maybe in another ten years.” A decade was considered a fair guess because Go is a game so complex I’ll just let Ken Jennings of Jeopardy fame, another former champion human defeated by AI, describe it:

Go is famously a more complex game than chess, with its larger board, longer games, and many more pieces. Google’s DeepMind artificial intelligence team likes to say that there are more possible Go boards than atoms in the known universe, but that vastly understates the computational problem. There are about 10¹⁷⁰ board positions in Go, and only 10⁸⁰ atoms in the universe. That means that if there were as many parallel universes as there are atoms in our universe (!), then the total number of atoms in all those universes combined would be close to the possibilities on a single Go board.

Such confounding complexity makes impossible any brute-force approach to scan every possible move to determine the next best move. But deep neural networks get around that barrier in the same way our own minds do, by learning to estimate what feels like the best move. We do this through observation and practice, and so did AlphaGo, by analyzing millions of professional games and playing itself millions of times. So the answer to when the game of Go would fall to machines wasn’t even close to ten years. The correct answer ended up being, “Any time now.”

NONROUTINE AUTOMATION

Any time now. That’s the new go-to response in the 21st century for any question involving something new machines can do better than humans, and we need to try to wrap our heads around it.

We need to recognize what it means for exponential technological change to be entering the labor market space for nonroutine jobs for the first time ever. Machines that can learn mean nothing humans do as a job is uniquely safe anymore. From hamburgers to healthcare, machines can be created to successfully perform such tasks with no need or less need for humans, and at lower costs than humans.

Amelia is just one AI out there currently being beta-tested in companies right now. Created by IPsoft over the past 16 years, she’s learned how to perform the work of call center employees. She can learn in seconds what takes us months, and she can do it in 20 languages. Because she’s able to learn, she’s able to do more over time. In one company putting her through the paces, she successfully handled one of every ten calls in the first week, and by the end of the second month, she could resolve six of ten calls. Because of this, it’s been estimated that she can put 250 million people out of a job, worldwide.

Viv is an AI coming soon from the creators of Siri who’ll be our own personal assistant. She’ll perform tasks online for us, and even function as a Facebook News Feed on steroids by suggesting we consume the media she’ll know we’ll like best. In doing all of this for us, we’ll see far fewer ads, and that means the entire advertising industry — that industry the entire Internet is built upon — stands to be hugely disrupted.

A world with Amelia and Viv — and the countless other AI counterparts coming online soon — in combination with robots like Boston Dynamics’ next generation Atlas portends, is a world where machines can do all four types of jobs and that means serious societal reconsiderations. If a machine can do a job instead of a human, should any human be forced at the threat of destitution to perform that job? Should income itself remain coupled to employment, such that having a job is the only way to obtain income, when jobs for many are entirely unobtainable? If machines are performing an increasing percentage of our jobs for us, and not getting paid to do them, where does that money go instead? And what does it no longer buy? Is it even possible that many of the jobs we’re creating don’t need to exist at all, and only do because of the incomes they provide? These are questions we need to start asking, and fast.

DECOUPLING INCOME FROM WORK

Fortunately, people are beginning to ask these questions, and there’s an answer that’s building up momentum. The idea is to put machines to work for us, but empower ourselves to seek out the forms of remaining work we as humans find most valuable, by simply providing everyone a monthly paycheck independent of work. This paycheck would be granted to all citizens unconditionally, and its name is universal basic income. By adopting UBI, aside from immunizing against the negative effects of automation, we’d also be decreasing the risks inherent in entrepreneurship, and the sizes of bureaucracies necessary to boost incomes. It’s for these reasons, it has cross-partisan support, and is even now in the beginning stages of possible implementation in countries like Switzerland, Finland, the Netherlands, and Canada.

The future is a place of accelerating changes. It seems unwise to continue looking at the future as if it were the past, where just because new jobs have historically appeared, they always will. The WEF started 2016 off by estimating the creation by 2020 of 2 million new jobs alongside the elimination of 7 million. That’s a net loss, not a net gain of 5 million jobs. In a frequently cited paper, an Oxford study estimated the automation of about half of all existing jobs by 2033. Meanwhile self-driving vehicles, again thanks to machine learning, have the capability of drastically impacting all economies — especially the US economy as I wrote last year about automating truck driving — by eliminating millions of jobs within a short span of time.

And now even the White House, in a stunning report to Congress, has put the probability at 83 percent that a worker making less than $20 an hour in 2010 will eventually lose their job to a machine. Even workers making as much as $40 an hour face odds of 31 percent. To ignore odds like these is tantamount to our now laughable “duck and cover” strategies for avoiding nuclear blasts during the Cold War.

All of this is why it’s those most knowledgeable in the AI field who are now actively sounding the alarm for basic income. During a panel discussion at the end of 2015 at Singularity University, prominent data scientist Jeremy Howard asked “Do you want half of people to starve because they literally can’t add economic value, or not?” before going on to suggest, ”If the answer is not, then the smartest way to distribute the wealth is by implementing a universal basic income.”

AI pioneer Chris Eliasmith, director of the Centre for Theoretical Neuroscience, warned about the immediate impacts of AI on society in an interview with Futurism, “AI is already having a big impact on our economies… My suspicion is that more countries will have to follow Finland’s lead in exploring basic income guarantees for people.”

Moshe Vardi expressed the same sentiment after speaking at the 2016 annual meeting of the American Association for the Advancement of Science about the emergence of intelligent machines, “we need to rethink the very basic structure of our economic system… we may have to consider instituting a basic income guarantee.”

Even Baidu’s chief scientist and founder of Google’s “Google Brain” deep learning project, Andrew Ng, during an onstage interview at this year’s Deep Learning Summit, expressed the shared notion that basic income must be “seriously considered” by governments, citing “a high chance that AI will create massive labor displacement.”

When those building the tools begin warning about the implications of their use, shouldn’t those wishing to use those tools listen with the utmost attention, especially when it’s the very livelihoods of millions of people at stake? If not then, what about when Nobel prize winning economists begin agreeing with them in increasing numbers?

No nation is yet ready for the changes ahead. High labor force non-participation leads to social instability, and a lack of consumers within consumer economies leads to economic instability. So let’s ask ourselves, what’s the purpose of the technologies we’re creating? What’s the purpose of a car that can drive for us, or artificial intelligence that can shoulder 60% of our workload? Is it to allow us to work more hours for even less pay? Or is it to enable us to choose how we work, and to decline any pay/hours we deem insufficient because we’re already earning the incomes that machines aren’t?

What’s the big lesson to learn, in a century when machines can learn?

I offer it’s that jobs are for machines, and life is for people.

First came the suggestion that an “alien megastructure” had been observed around KIC 8462852, a.k.a. Tabby’s Star. Months later, people were talking about a signal seen by a Russian telescope that some thought was transmitted from the environs of a stellar cousin of the sun. And not long after that, the Cyclopean Arecibo antenna in Puerto Rico reported weird signals that seemed to come from the dwarf star Ross 128, a scant 11 light-years away.

This brisk cadence of celestial surprises might make it seem that we’re on the cusp of proving the existence of extraterrestrials. But just because the crow’s nest announces clouds on the horizon doesn’t mean you’re close to land.

These three claims purporting to show the existence of aliens haven’t panned out. But what happens if some future claim does? What preparations are in place to deal with the discovery of a radio signal or a laser flash that would prove beyond doubt that we have cosmic compeers? Does the government have a plan? Does anyone?

A lot of people think there is a plan. A secret one. A recent survey indicated that 55 percent of the population figure that the discovery of extraterrestrials would be squelched — deep-sixed to prevent widespread panic. Only 19 percent believe the feds would fess up to E.T.’s existence.

Such a cover-up would be virtually impossible to pull off. There’s no policy of secrecy, and verifying the signal would involve teams of scientists around the world. But leaving that aside, the fact that so many folks believe it's in the works attests to a discouraging lack of trust in both science and the public’s ability to handle the news.

So what’s the truth about what would happen if we discovered intelligent aliens? Back in 1989, when a now-defunct NASA program to search for extraterrestrial intelligence was gaining steam, protocols were drafted to spell out best practices in case the search proved successful. These were later updated and streamlined by the International Academy of Astronautics SETI Permanent Committee. (Click here to see the revised protocols.)

There are really only three important components to this two-page text. First, the detection of alien life should be carefully verified by repeated observations. Second, the discovery should be publicized. Third, no response should be sent without international consultation.

All that sounds both tame and sane. But there’s an implicit assumption here: namely, that picking up signals from another world will be a Hollywood moment. We assume that it will play out the way it so often does in the movies: stuporous scientists, settling in for another decade or two of fruitless search, are suddenly jolted into wild-eyed excitement as a signal lights up their equipment. Then they spend about 10 minutes turning knobs and shouting at one another, after which they presumably reach into a desk drawer and pull out the protocols.

Actually, they never take this last step in the movies. And they wouldn’t do it in real life either. In the many years of SETI efforts, there have been numerous false alarms in addition to the three noted at the top of this article. And what happens every time is that the media immediately start reporting the story. There is almost always a bit of sensationalism and a few garbled facts, but the news is out there long before the researchers have managed to verify the signal, as specified by the protocols.

That’s the truth of the matter. Really, it is. Sure, speaking of “protocols” has a certain gravitas, but these would only work for a Hollywood-style discovery.

But there’s a deeper question here — one that’s much harder to answer: what would be the long-term effect of learning we’re not alone? Would we give up religion? Would we stop waging war? Would we cower in the face of possible interstellar aggression?

Facing such questions, social scientists tend to look for historical analogies. For example, what were the consequences when Columbus discovered the American continent (or if you prefer, when the Vikings or Ice Age Asians did)? One problem here is that the analogy isn’t terribly apt. These folks weren’t doing exploration for its own sake. They found something new by accident.

A better analogy might be the discovery of Antarctica or the source of the Nile. These really were exploration efforts. But even these are poor guides to how we should prepare for the discovery of intelligent aliens or anticipate its effects.

Nineteenth-century explorers had no protocols other than to write up their experiences. Furthermore, the eventual consequences of their discoveries were completely incalculable. Do you think Fabian von Bellingshausen, who first saw the Antarctic continent in 1820, could have anticipated that less than 200 years later there would be a research base at the South Pole, or that cruise ships would be taking tourists to these forlorn latitudes?

There is little certainty about what the consequences of finding aliens might be, but there is this: We’ll immediately know something very important. We’ll know that we are neither unique nor special. But if you ask what the legacy of such a discovery will be hundreds or thousands of years from now, there’s simply no way to arrive at an answer that’s either useful or accurate.

Using satellite data, Brown researchers have for the first time detected widespread water within ancient explosive volcanic deposits on the Moon, suggesting that its interior contains substantial amounts of indigenous water.

First U.S. Human Embryo Gene Editing Experiment Successfully “Corrects” a Heart Condition

IN BRIEF

CORRECTING MUTANT GENES

A [CONTROVERSIAL] NEW ERA?

04-08-2017 om 00:24 geschreven door peter

Diamond Batteries Made of Nuclear Waste Can Generate Power For Thousands of Years

IN BRIEF

NUCLEAR DIAMONDS

POWERING THE FUTURE

04-08-2017 om 00:09 geschreven door peter

How Automation is Going to Redefine What it Means to Work

IN BRIEF

ROUTINE WORK

NEURAL NETWORKS

BIG DATA

DEEP LEARNING

NONROUTINE AUTOMATION

DECOUPLING INCOME FROM WORK

03-08-2017 om 23:58 geschreven door peter

“It looked like an alien invasion”: Dad captures stunning display of light pillars from the heavens

03-08-2017 om 23:28 geschreven door peter

The Racism Behind Alien Mummy Hoaxes

03-08-2017 om 23:14 geschreven door peter

What Happens Next If We Find Proof of Space Aliens?

Protocols for responding to the discovery of extraterrestrial intelligence have been created, but they might not be followed.

03-08-2017 om 22:41 geschreven door peter

NASA has a job opening for someone to defend Earth from aliens — and it pays a 6-figure salary

A rare and cosmically important position

Who qualifies as a candidate

NOW WATCH: Stephen Hawking warned us about contacting aliens, but this astronomer says it's 'too late'

03-08-2017 om 22:21 geschreven door peter

Spike In UFO Sightings Reported In Connecticut: Check Out The Full List

There have been 30 UFO sightings in Connecticut in 2017. Ten of those sightings were reported in July alone.

03-08-2017 om 22:00 geschreven door peter

Recent UFO reports in the region

03-08-2017 om 21:46 geschreven door peter

New survey shows nearly half of Americans believe in aliens

03-08-2017 om 21:36 geschreven door peter

Scientists spy new evidence of water in the Moon’s interior

03-08-2017 om 21:24 geschreven door peter

Binnenste van de maan bevat enorme hoeveelheden water. Maar waar komt het vandaan?

Veel natter

Grote vraag

Net zoveel

03-08-2017 om 21:14 geschreven door peter

03-08-2017 om 21:08 geschreven door peter

Nieuwe studie voorspelt hittegolven die mens niet kan overleven

03-08-2017 om 20:46 geschreven door peter

03-08-2017 om 19:59 geschreven door peter

03-08-2017 om 19:29 geschreven door peter

Strange giant beams appear to follow the sun from East to West across the US

03-08-2017 om 19:05 geschreven door peter

Thermal Camera Captures Invisible V-shaped UFO Anomaly over Bucharest, Romania

03-08-2017 om 18:51 geschreven door peter

Filer’s Files #31 – 2017 Dead Sea Scrolls and UFOs - PART I

Special Projects

Russell Crowe Films UFO

03-08-2017 om 00:54 geschreven door peter

Filer’s Files #31 – 2017 Dead Sea Scrolls and UFOs - PART II

UFO Sightings in the United States

Worldwide UFO Sightings

Support Earth Changing Research

Please Help

03-08-2017 om 00:39 geschreven door peter

MUFON’s New Social Network