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

If you thought the only place in the universe where you have to worry about predator capitalists is on Earth … think again. Two professors in Germany have written a new book detailing possib

le outcomes of a human encounter with an intelligent alien species and in it they warn that our SETI messages sent into space may be attracting the wrong kind of extraterrestrials – namely, the capitalistic kind who will see only the financial gains to be had by exploiting (or worse) the physically, technologically and intellectually weaker species they encounter on this wet blue planet. Does this scenario sound vaguely familiar? Even scarier, human capitalistic space explorers could find alien technology that gives them an advantage over other humans and use it against their fellow Earthlings. Cue the Twilight Zone music.

In an interview with Bild, Michael Schetsche, a Research Coordinator at the Freiburg Institute for Frontier Areas of Psychology and Mental Health and an adjunct professor at the Institute for Sociology of the Albert-Ludwigs-University Freiburg, discussed the book “The Alien Society — Introduction to Exosociology” which he co-authored with his scientific assistant, Andreas Anton. He sees the second scenario – humans finding alien technology, whether current or an artifact – as the more dangerous one, because it could happen on the Moon or one of the asteroids we’re already visiting.

“Maybe it’s space debris that a strange probe has left on the moon or in the asteroid belt. But imagine, the artifact is still functional. Maybe it is a drive segment intended for free space. Of course we humans want to explore that, we bring it to earth, experiment with it, activate it with overflowing curiosity. And then it rips away a quarter of the planet’s surface.”

On the other hand, the discovery of this technology may not destroy humanity on its own but instead be used as a tool by greedy states or corporations. Even the quest to get it and bring it back could be deadly.

“(It could happen) that an artifact discovered somewhere in the asteroid belt provides the reason for warlike conflict on Earth.”

Schetsche recommends immediately setting up an international agreement to “subordinate extraterrestrial finds to the supervision of the United Nations” and let the U.N. determine how found alien technology is handled and when, if at all, it should be brought back to Earth. (A U.N. agreement on alien technology to prevent one nation or corporation from becoming almighty? Good luck with that.)

It’s the first scenario – that of contact with an extraterrestrial species with human capitalistic tendencies – that conspiracy thoerists like Alex Jones picked up on and screamed “GERMAN SCIENTISTS URGE TO STOP SENDING SIGNALS INTO SPACE TO ‘CAPITALIST ALIENS’.” (His all-caps headline.) Schetsche compares it to the European ‘discovery’ of America and the conquest of Africa. As Native Americans and Africans found out, overwhelming power changes everything.

“All action of nation states is based on power. The entire structure is obsolete in the moment in which an overpowering extraterrestrial actor appears in the sky. It would be a struggle between Bambi and Godzilla, and we would be Bambi in the case.”

Take me to your money.

Even if the aliens are benevolent, human response to no longer being superior to everything could cause chaos.

“Even if people do not kill each other, direct contact can destroy the social, economic, political and religious structures of countries.”

Based on these speculations, Schetsche obviously thinks it’s better to be the discoverer, not the discoveree, and recommends that we stop sending signals to superior aliens, especially ones that might have the same conquer and capitalize tendencies as their Earth counterparts once and most likely still have (sorry Trekkies).

It won’t be like this.

Should we stop sending signals and start sending more humans and exploration robots into space? That’s already happening. Should we fear contact with superior life forms? Look in the mirror. Should we trust those in power to do the right thing should alien technology be found? Look in the mirror again.

Either way, it looks like most of us will probably end up like Bambi.

I’ve often wondered whether future varieties of artificial intelligence might not help humans discover incredible new things about reality. Perhaps machines will even unveil little nuances that have been hidden beneath our noses all along, but which required the kind of critiques that only a computer mind could offer.

But while we are thinking about the ways ever-watchful A.I. may help us critique reality in the future, another question comes to mind which mirrors the old idea, “quis custodiet ipsos custodes?” proposed by Roman satirist Decimus Junius Juvenalis: in a world where we’re learning from the machines we’ve built, who fact-checks those machines, and more importantly, how do we know that what we learn from them even be trusted?

This might sound like a silly proposition, due in part to the sorts of stigmas we have about artificial intelligence and machine learning. On the one hand, many would ascribe to notions like, “of course we shouldn’t trust machines completely… they aren’t human!” In direct contrast to this, advocates of machine learning and A.I. might argue that this is precisely the reason they should be trusted: machines are, and will continue to be, capable of processing and synthesizing information in ways that often far exceed what the human brain can do.

Genevera Allen, a Rice University statistician recently made the argument that until machine learning systems can be designed in ways that allow for objective critiques of the information they provide, their trustworthiness remains flawed.

Allen spoke earlier in February at an Annual Meeting of the American Association for the Advancement of Science, where she addressed this problem as part of her lecture:

“The question is, ‘Can we really trust the discoveries that are currently being made using machine-learning techniques applied to large data sets?’ The answer in many situations is probably, ‘Not without checking,’ but work is underway on next-generation machine-learning systems that will assess the uncertainty and reproducibility of their predictions.”

Fundamental to the problem is the predictive nature of many (if not most) computational systems. Allen argues that because making a prediction about information is what these computers are designed to do, they are unlikely not to find data when tasked with doing so… even if a human observing the same data could easily discern that, in some instances, none exists.

“[Machines] never come back with ‘I don’t know,’ or ‘I didn’t discover anything,’ because they aren’t made to,” Allen said in a Rice University press release. However, the concerns Allen raises aren’t merely prospective: some instances already exist where it appears that questionable information may have been gleaned, at least in part, from studies that incorporated computational data that remained uncorroborated with relation to cancer research.

Allen explains:

“[T]here are cases where discoveries aren’t reproducible; the clusters discovered in one study are completely different than the clusters found in another… Because most machine-learning techniques today always say, ‘I found a group.’ Sometimes, it would be far more useful if they said, ‘I think some of these are really grouped together, but I’m uncertain about these others.'”

I recall a discussion I had with a futurist a few months ago, where we discussed the question over dangers stemming from interpretive issues that may arise from the way machine learning differs from our own. The example he gave was relevant here, although more in line with the “Hollywood” conception of potential problems we may one day have with A.I. (we might call this the “Terminator” model, which I think needs no further explanation here).

Imagine, he said, if we told A.I. to find a way to destroy a particular human disease. We input the information, and the computer outputs the following solution: “destroy all carriers of the disease.” In other words, rather than finding a cure, the machine interprets the problem in simple terms of its elimination… whereby the machine makes no distinction between “solving” the problem and committing murder.

This is a dramatic example, but it works analogically in comparison with what we already see in studies where machine learning is involved. Computers, in other words, function and respond to data sets in ways that differ vastly from human reason. Thus, we do need to be aware of these kinds of issues as more and more computational systems are affecting the knowledge science is acquiring and working with in the future.