more sightings, less pictures
More UFOs Than Ever Before
51–58 of 58 posts
Re: More UFOs Than Ever Before
#52I have a more dark and cynical outlook on UFOs and their continued presence in our culture. I watch Joe Rogan and I saw most of his interaction with Lazar as well as his even weirder interaction with Blink-182 guitarist Tom DeLonge. In both cases I got the impression that they were dupes. Tom had these crazy stories about high-level military officers bouncing him around between each other. They all seemed to be givin…
> I have a suspicion that there is an old-boys-club of high-level military guys pushing UFO conspiracies for some purpose. At the very least, muddying the waters can help mask any civilian reports of potential classified aircraft. At the worst it can act as a lightening rod to redirect public thirst for unearthing military secrets.
This is the thesis of the documentary "Mirage Men".
Re: More UFOs Than Ever Before
#53Earlier quoted context omitted.
> the complexity of growing e.g. a new leg because you somehow need it is again unthinkable with our current computing power (we can't even model that properly with reinforcement learning on fastest supercomputers). On the contrary, the complexity is so trivial it’s something I did in 30 minutes, on a laptop, in 2008, as a “prove it to myself” response to a young-Earth Baptist Creationist. What we can’t do is simulat…
What do you mean? You trivially simulated the process of figuring out that an animal needs to grow a new leg/organ in response to some environmental situation or to gain "an upper leg" against its adversaries? Are you serious?
Genetic algorithms are a very easy to implement form of AI. The hard part is to create a fitness function which does what you actually want — In the wild, reality and competitors are your fitness function, and none needs to be created; conversely, in a simulation the fitness function is necessarily artificial, and can be made to reach any goal you want, from “compose a song” to “write the rules for an AI for this video game so I don’t have to” to “discover a new superconductor” to “design a clock” to “learn to walk”… etc., and all of those are real examples.
Separately, as an algorithm, evolution also has easily characterisable failure modes (local minima), and we do observe those failure modes in organic examples.
But, to emphasise, the fact artificial evolution requires us to write our own fitness functions is merely an implementation detail; these algorithms work because evolution works. They produce novel solutions without the authors of the programs creating the solutions.
Re: More UFOs Than Ever Before
#54Earlier quoted context omitted.
What do you mean? You trivially simulated the process of figuring out that an animal needs to grow a new leg/organ in response to some environmental situation or to gain "an upper leg" against its adversaries? Are you serious?
Genetic algorithms are a very easy to implement form of AI. The hard part is to create a fitness function which does what you actually want — In the wild, reality and competitors are your fitness function, and none needs to be created; conversely, in a simulation the fitness function is necessarily artificial, and can be made to reach any goal you want, from “compose a song” to “write the rules for an AI for this vid…
Humans have a history of occasionally choosing bad fitness functions that predates computers, for example look at the awful things we've done to persian cats or chihuahua dogs.
Re: More UFOs Than Ever Before
#55Earlier quoted context omitted.
What do you mean? You trivially simulated the process of figuring out that an animal needs to grow a new leg/organ in response to some environmental situation or to gain "an upper leg" against its adversaries? Are you serious?
Genetic algorithms are a very easy to implement form of AI. The hard part is to create a fitness function which does what you actually want — In the wild, reality and competitors are your fitness function, and none needs to be created; conversely, in a simulation the fitness function is necessarily artificial, and can be made to reach any goal you want, from “compose a song” to “write the rules for an AI for this vid…
Genetic/memetic algorithms are just a rough approximation of a certain selection mechanism we see in the nature, like what Deep Learning is to brain. Making a conclusion that some trivial algorithm with even complicated custom cross-over function can be translated to real-world is as much fantasy as thinking one could upload consciousness to TensorFlow. I never understood how anyone calling themselves a scientist could make such a leap, but obviously it's popular and people made a "scientific" career doing just that...
Re: More UFOs Than Ever Before
#56> Some suggest it was the first atomic bomb [that] sent a shock wave across the universe. It registered in distant solar systems as deep-sea earthquakes register with us. It was a clarion call. It said: DANGER! It meant the previously harmless human race had gotten ahold of matches that could immolate all of creation. ...or all of planet Earth, whichever comes first. Hint: it's not the former.
Assuming that UFO's are being controlled by an alien species and that they are in fact more frequent since the event of nuclear explosions on earth, then I think the safest assumption we can make is that the nuclear event sent a strong enough signal to notify them of our existence. If that's the case, I don't think we should speculate too quickly on how they would interpret the signal. However it does seem like there…
> it's pretty insane.
All of this is. You can either live in something resembling the real world, or LARP your life away in a weird fantasy. If I were going to do the latter, I wouldn't pick this particular fantasy, it's boring and unoriginal. Try reading some good science fiction instead.
Re: More UFOs Than Ever Before
#57Earlier quoted context omitted.
Genetic algorithms are a very easy to implement form of AI. The hard part is to create a fitness function which does what you actually want — In the wild, reality and competitors are your fitness function, and none needs to be created; conversely, in a simulation the fitness function is necessarily artificial, and can be made to reach any goal you want, from “compose a song” to “write the rules for an AI for this vid…
Humans have a history of occasionally choosing bad fitness functions that predates computers, for example look at the awful things we've done to persian cats or chihuahua dogs.
Well yes. Also, the fact that brains (not even just human ones) influence the real world’s organic evolutionary fitness functions does make it somewhat impossible to perfectly replicate in a computer.
Fortunately my point has no dependency on any specific fitness function, natural or programmed — my argument has been that the original claim “We can observe that cells/DNA are programmed to adapt to local changes; we have no idea how they make sudden jumps for a more efficient design;” totally misrepresented evolution, that in fact evolution totally can make sudden-looking jumps (a point which in retrospect I don’t think I properly justified, even if my list of examples was intended that way), and that it’s trivial to demonstrate this by writing a genetic algorithm.
Re: More UFOs Than Ever Before
#58Earlier quoted context omitted.
Genetic algorithms are a very easy to implement form of AI. The hard part is to create a fitness function which does what you actually want — In the wild, reality and competitors are your fitness function, and none needs to be created; conversely, in a simulation the fitness function is necessarily artificial, and can be made to reach any goal you want, from “compose a song” to “write the rules for an AI for this vid…
Genetic/memetic algorithms are just a rough approximation of a certain selection mechanism we see in the nature, like what Deep Learning is to brain. Making a conclusion that some trivial algorithm with even complicated custom cross-over function can be translated to real-world is as much fantasy as thinking one could upload consciousness to TensorFlow. I never understood how anyone calling themselves a scientist cou…
> Genetic/memetic algorithms are just a rough approximation of a certain selection mechanism we see in the nature
This is irrelevant to my argument. If the simplified version is capable of doing what you have said is unexplained, it is unreasonable to assume that the more complicated version will fail to be capable of the same.
> I never understood how anyone calling themselves a scientist could make such a leap, but obviously it's popular and people made a "scientific" career doing just that...
Occam’s Razor. Start with the simplest possible model, make a prediction, look carefully at reality to see if you were wrong (an act which is easier the simpler the model), and only update the model when reality disagrees with it.
Me coding a genetic algorithm can easily reproduce the entirety of any specific human’s genome if I pick the correct fitness function. Pretty pointless to do so beyond proof of concept, but proof of concept is enough to make the point in this case.