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Neutrino mysteries, fuzzballs, almost quantum theory, and testing free will with AI: New Podcast!
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FQXi Administrator Zeeya Merali wrote on Aug. 27, 2018 @ 17:21 GMT
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IceCube/NASA |
Neutrinos have been helping physicists solve some long-standing puzzles, over the past couple of months, while raising whole new ones. On the latest edition of the
podcast, we chat with astrophysicist
Azadeh Keivani, of Columbia University, in New York, about her work with the IceCube collaboration, at tracking the source of a high-energy neutrino to its source—a distant galaxy, 4 billion light years away—for the first time. (The image on the right is an artistic rendering, showing neutrinos and gamma rays being emitted that could be detected by the IceCube Neutrino Observatory as well as by other telescopes on Earth and in space. Credit: IceCube/NASA.)
That’s one mystery solved, but then we talk with particle physicist Richard Van de Water, of Los Alamos National Laboratory about his work on the MiniBoone experiment at Fermilab, where they may have found signs of a new type of neutrino—the sterile neutrino—backing up an old claimed discovery of this particle from the 1990s. If it’s not a sterile neutrino, it still shows that something weird and new is going on. What might it be? Another particle? Extra dimensions?
Free Podcast
High-energy neutrino traced back to a distant galaxy, with Azadeh Keivani; possible discovery of sterile neutrinos, with Richard Van de Water; black holes replaced by fuzzballs, with Samir Mathur; finding an almost-quantum theory, with Miguel Navascues; and testing free will with AI, with Avi Loeb.

LISTEN:
Go to full podcast
Next up is FQXi’s
Samir Mathur, of The Ohio State University, who has been making headlines around the world thanks to his idea that black holes should be replaced by fuzzballs, tangled balls of stringy matter. He talks to Sophie Hebden about how this would solve the
black hole information paradox, and re-write the big bang, possibly even doing away with the need for inflation. You can find out more about his work in Sophie’s article, “
Fuzzballs vs Black Holes.” So, should we give up on the idea that black holes are cosmic monsters that suck everything that falls past their surface, or event horizon, to their infinitely dense core? And instead embrace this fuzzier picture?
Miguel Navascues, of IQOQI, Vienna, meanwhile, has been searching for an “almost quantum” theory that will usurp our current standard version of quantum mechanics. He explains to Colin Stuart just why he thinks it is only a matter of time before experiments reveal a flaw in the current quantum framework, and how he is preparing for that eventuality. Colin has also written an article, “
Usurping Quantum Theory” about this quest.
And finally, Harvard’s
Avi Loeb muses about whether developments in artificial intelligence and machine learning will enable us to test whether free will is real, emergent, or an illusion, with reporter John Farrell.
So what do you think? Do we have free will? Do you think that machines could potentially exhibit it too? And if free will is an illusion, do you agree with Loeb that it may be better if people never learn this?
Lorraine Ford wrote on Aug. 28, 2018 @ 16:30 GMT
“Machine learning” is just words that some rather silly people take far too seriously. Only silly people believe that machines have “intelligence”, or that machines “learn from experience and educate themselves”, or that machines have “will”. Physicist Avi Loeb spouts an awful lot of nonsense.
So how do we actually “shape reality”? The necessary logic of such a system is clear:
1. If all outcomes in a system have been 100% determined by a fixed set of rules interacting with the system environment,
then there are no outcomes that have any distinguishing features, i.e. there are absolutely no outcomes that can be labelled as “free will”.
2. The only way to “shape reality” in such a system is to add a
new rule to the system e.g. add a new number assignment rule that re-initialises a system variable, or e.g. add a new algorithm to the system (that analyses and collates data, and determines outcome numbers for the variables).
3. The trouble with physicists, e.g. Loeb, is that they don’t seem to notice that
it is necessary to account for where the rules and algorithms that determine system outcomes are coming from.
4. The “we” that “shape reality” is the “we” that creates the rules.
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Lorraine Ford replied on Aug. 29, 2018 @ 00:28 GMT
“Intelligence” is “military or political information” and “the collection of information of military or political value”[1]. I.e. in more general terms, “intelligence” is knowledge that we have the ability to gather of a situation that we find ourselves in.
What a lot of people (including Loeb) seemingly find very hard to grasp is the fact that machines can only operate because they process
symbolic representations of this information/ knowledge/ military intelligence. A symbolic representation of a person, e.g. a stick figure, is not the same as the person. Symbolic representations of knowledge (e.g. words in a book; or binary digital representations inside a computer) are not the same as knowledge. Knowledge ≡ the experience of knowledge.
1. https://en.oxforddictionaries.com/definition/intelligence
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Lorraine Ford wrote on Aug. 31, 2018 @ 01:11 GMT
A system, e.g. the universe-system, is not just a set of distinct rules [1].
It is necessary that something somehow knows about/ can distinguish these distinct rules; and it is necessary that something somehow creates these distinct rules [1]. The knowledge/ creativity aspects are necessary aspects underlying any system.
Any system cannot be understood unless the knowledge and creativity aspects are recognized/ conceded/ admitted to.
When it comes to our universe, the only conclusion that one can logically make, is that matter really matters in our universe: that what we call “matter” is the necessary part of the system that is conscious of relationships and creates relationships [1]. (And clearly, creativity is the same as “free will”.)
1. We symbolically represent these rules/relationships as equations, number assignments and algorithms.
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