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subjects jacqu valet notable computer scientist author and ethologist delves into the intersections
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of artificial intelligence and The Unexplained throughout his career jacqu has witnessed the evolution of AI and UAP
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research from ai's humble beginnings to its current state he has seen it grow
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and transform into a powerful tool with both exciting possibilities and really concerning implications today we'll
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delve into Val's thoughts on the current state of AI and uaps exploring what excites him but also
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concerns him about their present and future State join us as we get rebelliously
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curious jacqu thank you so much for joining me on rebelliously Curious again it is such a pleasure to have you back
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and also thank you for contributing your recent article to the debrief I know that it has done extremely well it is called non-human intelligence at the
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threshold if people have not read it please go to the deb.org and take a look but again thank thank you so much jock
Chapter 2: Early AI development
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for being here today thank you to start off can you walk me through your first experiences at Northwestern working with
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AI so um I worked at Northwestern um in
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the early days of uh northwestern's computer science development in fact I I
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saw the the new computation Center being built on the lake field uh it was a very
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ambitious project for for Northwestern and very imaginative and uh it was the first
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building that was that was built on the land that was uh um filled over the the
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beach over the lake and so it was very exciting and we had a a new computer and
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we were using it for astrophysics I was working on the team of Dr heinik um and it wasn't a coincidence I
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mean we had wanted to combine the data we had about UFOs but most of my work at
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the computation Center was while I was doing my PhD was with astrophysics and
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with the medical school medical school was down in downtown Chicago
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and had uh very high demands to move all their records into digital form this was
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again uh 1965 1966 um and uh those were you know very
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busy days for the computer computer technology one of the things I had done
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for the observatory was uh computerized in
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the the the the catalog of bright stars uh Northwestern had specialized in the
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the bright star cataloges there are you know roughly let's say 10,000 Bright Stars you can only see 5,000 at a time
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because there are two hemispheres and uh it uh so it sounds like a small number the problem is that
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they are very complicated because because we know everything about those stars because they are very bright so we can see there are double Stars we can
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see clusters we can see you know all kinds of different different things at that at that level of of light that
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level of Luminosity so it it ends up being a large catalog for the days uh
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you know computers were not nothing like you know what we can do now and um astronomers who worked with the
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catalog had to punch CS uh to for their program when they want wanted to
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retrieve data and do comparisons of different types of stars over the entire
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sky so uh it was a lengthy process and uh in those days uh the the program
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would be performed would be compiled overnight and you'll have your results
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the next the next day for my um for my
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PhD I I I looked at different kinds of possibilities I done a lot of work with
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um the medical center so I could use I had access to medical data that were very very interesting and very
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challenging but uh you know my first love was astronomy and uh I thought if I
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could I wondered if I could um accelerate the process of giving answers to the
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astronomers if I could take English questions about the catalog and a a
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program uh you know AI system that would give answers right away so they wouldn't
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have to compile their own program and do it overnight and I learned a lot from that the first thing I learned is that
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my uh my advisor in computer science didn't think it could be done in
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those days there had only been about four um demonstrations of AI that were
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there were a couple at Harvard and MIT and there was one at Stanford the one at
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Stanford was really the only science program it was a program developed to study complex
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molecules and um it had amazed the scientific community that you could you
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could do that with artificial intelligence it was a program called dandr d n d r a l which was really the
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first demonstration of AI in real science in in in in chemistry and uh
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so um my my advisor said well it uh it probably cannot be done but you should
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try anyway which was you know a challenge so uh I thought
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that was that was fair and um it turned out I could do it and we learned it it
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took you know a number of months of programming and reprogramming and testing and s but I learned I learned a
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lot from it and we all learned a lot from it including the astronomers who found out that they didn't have to write
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a program every time they were looking for a particular type of statistics and
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astrophysics the the program I developed would take and that relates to today you
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know today talk about you can interrogate database in English or you
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can ask um philosophical questions and get complex answers um in in full
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English uh however you know people should not be
Chapter 3: Challenges in AI
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too enthusiastic too quickly and there are articles coming out now saying wait a minute those are demonstration
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programs but the reality is going to be a little bit more difficult because there are questions machine
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cannot answer so the uh my program was with a limited
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vocabulary of astronomy so it was much better defined because astronomers know
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precise terms you know frequency or binary star you know the the color of
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the star the Spectrum quality the speed of the star those are well defined so
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that makes my my job easier you know but remember it was
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1967 so not 19 97 and certainly not you
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know 2000 and something so um I found that
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the they were asking two types of questions how many stars in this large catalog have certain characteristics in
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color in speed you know in displacement uh in in combination of parameters and
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so that was fairly well defined and there was a well defined vocabulary so I could I could translate
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English into a formula they could have apply to a form of the catalog that the
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system would regenerate for that formula I couldn't apply to the catalog as it was but I
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could re transform the catalog and the question so they would meet and get an answer the other type of question which
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was more complex and more challenging and and more interesting was um what is
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the proportion of spectroscopic binary Stars amongst styles that have this kind
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of color this kind of speed so uh we we had styles that were very complex stars that were triple
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stars stars that were triple binary stars in other words three double Stars
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moving around each other you know that exist also so in among the bright stars
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you cannot see that kind of detail with the you know the the faint stars in the Galaxy but you certainly can see it in
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the environment around our son so that was that was very interesting and uh
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people were surprised that it could be done and um I expanded on the technology
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you know for my for my PhD for my doctor's uh
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dissertation but what I I gained some I know I I I shouldn't say that it
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wasn't that hard but once you had done the basic work it wasn't that hard to
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give answers to the astronomers that saved them you know 24 hours for every
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program sure so that was considerable you know difference where they didn't have to write programs anymore
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what I found is that the the the difficulty wasn't to give and this
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applies to today you know surprisingly if if you give me a clear question well formed in the English
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language and I have the data for that question as a programmer I should be
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able to give you an exact answer or or an answer that helps you in your
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research um I I could do it by developing a program but I should be able to do it by understanding your
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question and responding to your question in a logical way the problem is if your question is
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ambiguous and that's what we see now you know with chat GDP and those other questions are the the the era is so
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Broad know that people are going to ask vague questions that don't really have a
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precise answer like I did you know in astronomy so my
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job what I spend the most time in programming you know in the craft of
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programming my program once it worked was to catch the questions that I should not
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answer if you ask me how many stars are there you could ask that question and I
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could generate a formula and the formula could go through the entire catalog and in those days it would take several
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minutes of wasted time because I already know the answer I already know how many stars there are in the catalog so I
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should I should intercept your question and I should not not generate a formula
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for to get the answer because I already know the answer I should give you the clear answer you know to your clear
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question there were other questions that could be um that would not have an answer because
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they were not well formed you know um how many stars have no color well uh
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you know that's not again that's not a question that is relevant in in astrophysics so I would
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um uh I I should not I should be able to detect questions like that and give you
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a polite answer like could you could you reframe it in terms of you know the the
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colors that are uh recognized for on the Spectrum on the spectral uh differences
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of the Stars so uh we can then engage in this dialogue until there is a the
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question is fit something that that makes sense in science and that the program can answer that's where I spend
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the most time that this ambiguation you know of of the English question and that's today that's a
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problem that you know the the new the the the the the new languages are going to have uh that's where they will be
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tested because I I should not if I give you an answer that
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looks right to a question that wasn't right we I'm going to lead you into
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mistakes would you be able to break down how AI has evolved over the years the
Chapter 4: AI in UAP research
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well it's understandable that you know it's a new toy so everybody wants to use it and
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uh but that's good because we're going to find out what it's really good for and where we going to need a lot more
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work in the in the structure in the language in the disambiguation which is a horrible word
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u but uh you find that in other applications of AI you know after that
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uh as as you know I've worked I've done a lot of work with cataloges of UFOs starting in the
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1960s since I had brought um the French catalogues uh both of the French Air
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Force and and private cataloges we had developed and merged them with the cataloges that I developed with Dr
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heinik from the Air Force files and from other American fles so when we started
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doing that uh in those days uh the the Air Force had developed a
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questionnaire you know that you could yeah the witnesses would have to fill out it took 10 pages it was
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deves and so on and they would have questions like um you know did what um what what was
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the shape of it well if it's a light that you saw at night you might might not have seen a shape so you might not
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be able to answer those questions so whether the the the witness was seeing
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something during the day or at night or in Cloudy conditions and so on you would if you were face Toof face with the
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person you would then go into a different type of question well an intelligent program can do that the
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program already knows it was 2:00 in the morning so it's not going to expect that you saw the kind of detail that for
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example it's not going to ask did you see a shadow because it's 2:00 in the morning there isn't going to be any
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Shadow and it knows where the moon was and so on so it it can save you a lot of
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time and have a much more friendly attitude to the poor witness
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and trying to give you the details of what they saw so um I worked on that and
Chapter 5: Industrial AI applications
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that was a fun uh you know development uh I showed it recently to um uh the son
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of Dr heinik and we had a good time you know looking at that that little program
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um later uh as you know I worked in I was recruited to to work in venture
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capital and I worked with a number of companies that were using AI not the
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linguistic AI but we're using AI to guide machines or to replace humans in
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factories so on so um one one of those was a very a very interesting company
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that had developed a a system for to use hard
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x-rays for brain tumors to to to to treat brain tumors that were not
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operable so now you could operate on patients who before you know would have
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died from from that tumor because the a surgeon would not have access to it with a normal a normal device and the
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alternative was was radiation very hard radiation which was uh very very complex and very
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difficult both for the doctors and for the patients and was not very successful in the radiation surgery and um the the
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team was at Stanford University and uh I finance a team uh and then brought in
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other other investors uh the company eventually went public at a billion and a half valuation
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W so that was that was a success for artificial intelligence because the the
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machine would have a map of the brain of that particular patient and a map of the
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tumor and the the machine would develop a set of
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radiation rays that could essentially uh treat the tumor it might
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take several several modes and then eliminate
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the tumor but the the patient could move a little bit so it was a lot more comfortable because the machine would
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detect motions of the head and would remap its program to deliver to to find
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the best way to deliver the same dose of radiation at the same tubic millimeter inside the brain after the patient had
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moved nobody had done that before and that was a proof of AI but that was 25
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years ago and people don't realize that we were able to do that you know we were
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able to um already to use some some of
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the principles of AI and in machines you know General Electric was already using
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it 20 years ago in locomotives you know locomotives that would cost you know between 20 and $70
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million but would recognize when something was about to fail you don't
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want the locomotive to fail with a train in the middle of the desert you know where you have to send know equipment to
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get the to to to get the train out of there and then fix the locomotive Lo locomotive nose can
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detect things that are about to fail in its own internal you know thousands of
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devices inside the locomotive and then it can direct itself to a repair repair
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center where you can substitute another locomotive so those are things that people don't know about but that uh you
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know devices that are intelligent to to the point where they can communicate with a small team of humans who would
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just control the process but the locomotive essentially will tell you what's wrong with it and and what parts
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it's going to need and you can you can help direct it to uh a a repair center
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that has a particular parts now and that translates into a lot of money right and
Chapter 6: Dangers of AI
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you're bridging me into the next question I want to ask you is about the dangers within AI right now because
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we're seeing that if we have you know the maybe the wrong data that's put in or again we're asking the wrong
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questions and we're not phrasing the right way you know from the past to now to the present what are the dangers
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right now with artificial intelligence that you can see so the um one of the
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major areas of application is in defense because humans can do certain things in
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analysis but it takes too long you know in in today's military environment whether that's on the battlefield or
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whether that's you know in space um or in in in combat in air combat so there
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is a a lot of development that would take place there which is not uh the the
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you know dialogue with the machine it it's a perception of a very complex space with things moving within it with
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different signatures so it's it's a problem of recognizing signatures and of course you know with UFOs we have that
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problem do we is is it just a balloon uh or is it some is it something that's
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going to accelerate and surprise us with uh the way the way it's going to behave
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and how should we react to it so uh and if it's a balloon is it our balloon or is it something that shouldn't be there
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that belongs to another country so all those programs are being developed now and being
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refined and uh they can be applied of course to uh Aviation to civil aviation
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uh and they can be applied to of course research on UFOs which is where where they become very very uh challenging and
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interesting now where do you see the similarities then between UAP and AI so there are
Chapter 7: AI and UAP parallels
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similarities in the technology because we share
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networks and we have to discriminate about among object among
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the real world so the we we have an an AI system if you will if you will want to think of a robot you know it could be
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a robot could be a humanoid robot but it most of the time is going to be a set of
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devices maybe distributed devices collaborating to make a decision or to
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propose a decision to humans you know like the locomotive but so uh it's encouraging to
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know that locomotives have been doing that for 20 years and people are not aware of it but the it's also
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interesting because of the the challenges that they that they pose you know
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the there is a real concern among the the general population
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about the use of AI you know obviously will AI replace replace me in my job I
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mean that's a concern that you know many people will have if they have a a
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mechanical that's always it's already happened in electronics it's happened in electronics
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because it came to a point where even if you wanted to use human beings you couldn't anymore you know in the in in
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in a device like this you know you know there's a memory there you know megabytes thousands or millions of
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megabytes of information and uh millions of btes of information and the the
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structure is too small for human beings to do it so we become used to having robots assembling these devices now the
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question is you know is it going to replace complex jobs
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that humans take pride in accomplishing is it if it it's okay if it just makes
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it faster to do something or easier to do something or less less dangerous to
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do something but um what if it can replace a a human worker
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completely uh so the the question also is which you know leaders in AI have
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been asking and it's a real question can it get to the point where we are not going to understand the decisions that
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these systems make and the scale how do we stop them to stop them we have to
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understand what they do and it could come to a point that we can see with today's
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problems where we don't understand what they do one of the one of the problems we have with the medical company was
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that the um you know in in the US you have to
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prove that a program will always do what it's supposed to do well in
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AI you there may be situations that you were not able to test so we for a long time we could not
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get clearance for our device and it was going to be a robot that would cost $2 million for the hospital now it it saved
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money for the hospital because there was no hospitalization needed and you didn't
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need an environment that was radioactive anymore which was you know a great
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progress for for the doctors but we couldn't prove to the FDA that the robot
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30 minutes, 26 seconds
would always do the same thing because it wouldn't do always the same thing it would adapt to the patient
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position which saved having to screw in a metal helmet to immobilize a patient
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which led to hospitalization led to other costs and so on so the the
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benefits meant that we had to trust the robot oh yeah they this was a large
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robber that was going to go around you you know and essentially zap you
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with hard hard rays that uh were not
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31 minutes, 11 seconds
radioactive but still with damaging the tissue so uh that we almost ran out of
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money because of that we could not get the clearance for you know as a medical device from the
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FDA uh we we managed to get over that by proving on uh extreme cases you know and
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uh human you know humanitarian cases that that the device did work large quantities of of patients but we are
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going to run into the same problem again in the future applications of AI so you know we we had that experience with this
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uh this medical company we're going to have the same problem with UFOs you know
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um the if if you apply AI to recognize enomy airplanes well we know what enomy
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airplanes look like you know through intelligence and through uh you know what observation and so on
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and with UFOs we don't know that we that's what we're looking for we're looking for an
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unknown configuration answer if you remember um you know the the the nits
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incident over the Pacific there is still a question of what was the exact shape
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you know of that of that white cylinder object of that Tic Tac right um
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well how many Tes are there and do they all always have the same size you know those are questions that people are
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33 minutes
going to want to ask so um the the similarities are there between you know
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what uh what what we've done with with General Ai and and what's going to
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happen the the other question is what are the dangers you know what what are the
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dangers in terms of UFOs you know uh where where how far can we go in recognizing
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33 minutes, 30 seconds
these and what is it a threat so we're going to ask to at some point we're going to ask a system to characterize
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33 minutes, 40 seconds
something as a threat but you see in medicine we knew everything about the device we had we knew how it could
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reposition itself if the patient moved which was unheard of you know because again we're looking at 1 cubic
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33 minutes, 58 seconds
millimeter inside somebody's brain and we have to be able to track that in an
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unpredictable environment well we're going to have the same similar thing with UFOs except we don't know what UFOs
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are we don't know what they can do we don't know what guidance system they have we don't know what intelligence
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they have is there someone somebody inside is it robot what kind of robot how can it how can we de you know
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develop that knowledge so that's the next that's a frontier and that's where
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we meet you know the people who develop um uh Chad GPD and and the other the
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other systems in Silicon Valley so it's going to be a very interesting uh very interesting day 100%
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34 minutes, 54 seconds
And I like the fact that you said about we we have to trust AI because it's predicting right now we're not we have
35:01
35 minutes, 1 second
some data that we're putting in but realistically we are asking potentially for predictions so we do have to put a
35:09
35 minutes, 9 seconds
lot of trust into that why do you think that UFOs uaps and AI are on the same rise at the
Chapter 8: Historical perspective
35:18
35 minutes, 18 seconds
same time like why do you think this has happening simultaneously well
35:25
35 minutes, 25 seconds
they you know UFOs have always been there that's one argument I would I
35:32
35 minutes, 32 seconds
would say you know from my uh my work with ancient records of of UFOs again
35:41
35 minutes, 41 seconds
here AI can help us and characterizing what has changed and what hasn't changed
35:48
35 minutes, 48 seconds
when we go back to you know the 18th century when uh we have descriptions of UFOs by astronomers you know right what
35:56
35 minutes, 56 seconds
a concept you know um so we we can we can track that and it's not a new thing
36:04
36 minutes, 4 seconds
it's again you know uh my my program could help the the Air Force you know
36:12
36 minutes, 12 seconds
deal with UF reports back in the 1980s this was you know before people knew uh
36:19
36 minutes, 19 seconds
before people could use the internet you know so the it it it's not new it but it's
36:30
36 minutes, 30 seconds
a the the tool makes it convenient to to to look for Solutions in both
36:38
36 minutes, 38 seconds
areas and both areas can learn something from each other because in in both areas
36:46
36 minutes, 46 seconds
you have a people describing something that is not recognized you have you have
36:55
36 minutes, 55 seconds
the problem of apply recognition to a particular physical or linguistic situation so there is a
37:03
37 minutes, 3 seconds
similarity there and we're going to make the same mistakes in both cases and that's that's
37:12
37 minutes, 12 seconds
how you learn I mean you learn from mistakes from things that don't work so
37:19
37 minutes, 19 seconds
um the the the other thing is that we are going to be in in a ling istic
37:27
37 minutes, 27 seconds
environment where certainly in in interviewing a witness of a UFO case we
37:35
37 minutes, 35 seconds
can use all the lessons that we we can get now from you know from the the big
37:44
37 minutes, 44 seconds
uh natural language AI systems that are coming to you know to this
37:50
37 minutes, 50 seconds
computer um so we we can begin to facilitate a process it was really
37:58
37 minutes, 58 seconds
complex and difficult before you had to transcribe you had to do all those things uh a lot of that can be done
38:07
38 minutes, 7 seconds
faster and um hopefully better but again we have to build those
38:14
38 minutes, 14 seconds
guidelines around what the system does I think for a long time people will have the
38:22
38 minutes, 22 seconds
impression that you know AI is Magic because it it is magic it seems that it
38:29
38 minutes, 29 seconds
it understands you you know one of the very early programs in AI was uh before my program
38:39
38 minutes, 39 seconds
you know somebody at uh MIT had developed a psychiatrist an automatic psychiatric
38:48
38 minutes, 48 seconds
interview uh you know of a of a crazy patient and the patient was paranoid and
38:55
38 minutes, 55 seconds
they uh would actually stop the conversation if he didn't like what the doctor was saying and that was very
39:04
39 minutes, 4 seconds
funny I mean uh a lot of students spent hours you know experimenting with the
39:12
39 minutes, 12 seconds
program and you you got to be the doctor and the program was was a paranoid program and uh so we're coming back to
39:22
39 minutes, 22 seconds
that with you know three generations of system now a much greater sophistication but it it will be used in
39:31
39 minutes, 31 seconds
in medical interviews uh it it will be used in a number of situations it will be used in it's already used in law
39:41
39 minutes, 41 seconds
where the the the place where I look forward to it being used with UFOs is
39:48
39 minutes, 48 seconds
that people you know ethologists have theories they think that UFOs turn blue when they are
39:56
39 minutes, 56 seconds
accelerate well yeah we have some cases where that's true is it always
40:05
40 minutes, 5 seconds
true you we have a catalog as you know that I contributed to
40:11
40 minutes, 11 seconds
designing uh which is still classified but um people you know it's been
40:18
40 minutes, 18 seconds
published that that cataloged 260,000 cases of selected UFO cases around the
40:27
40 minutes, 27 seconds
world all in English with the same structure well in among that catalog there are
40:35
40 minutes, 35 seconds
thousands of cases where people have seen a UFO that accelerated did it always turn blue or were there cases
40:44
40 minutes, 44 seconds
where it didn't have any light were there cases where it turned red and when
40:50
40 minutes, 50 seconds
it accelerated so we're going to be able to answer those questions much better to
40:57
40 minutes, 57 seconds
uh it's important to to to raise a question but now we'll have a tool that
41:06
41 minutes, 6 seconds
will enable us to to to to Really test it and then we can sell building theories physical theories that will
41:16
41 minutes, 16 seconds
tell us something about the the phenomena so that's know it's kind of application that I look forward to that
41:25
41 minutes, 25 seconds
uh I'd like to see being developed when we start to use it in that application it'll be really
Chapter 9: The role of mathematics
41:31
41 minutes, 31 seconds
exciting uh looking at qar right now they're right now qar is being built and developed how do you think the
41:40
41 minutes, 40 seconds
application of qar because it's based around mathematics mathematical Theory how will that affect the research how
41:47
41 minutes, 47 seconds
will that affect UAP research um I I see it as you know just a normal step in the
41:55
41 minutes, 55 seconds
the evolution of the the programs it doesn't change the you know the basic uh
42:02
42 minutes, 2 seconds
applications of the of the technology we're going to see I think the inspiration now is going
42:10
42 minutes, 10 seconds
to come from applications that's only what you know what happened historically to uh to AI
42:20
42 minutes, 20 seconds
[Music]
42:22
42 minutes, 22 seconds
the I think qar is is you know is one is broadening the the
42:31
42 minutes, 31 seconds
field of application and and refining it but I think the inspiration is going to
42:38
42 minutes, 38 seconds
come from people subscribing to these systems and complaining that it it
42:47
42 minutes, 47 seconds
doesn't quite do what they want in different fields you know we already have the the lawyers saying wait a
42:55
42 minutes, 55 seconds
minute you know you your systems are are fine but they are too primitive to
43:02
43 minutes, 2 seconds
really understand what I do in the courtroom okay so yes they can they can
43:09
43 minutes, 9 seconds
quickly look at the the you know the judicial literature to find a case law
43:19
43 minutes, 19 seconds
that could apply to this particular situation but it it's still takes a lot of human
43:29
43 minutes, 29 seconds
development before it will mean something with those particular plaintiffs in that particular
43:37
43 minutes, 37 seconds
courtroom so the the AI doesn't know about that it just knows that there is a text that matches a particular situation
43:46
43 minutes, 46 seconds
in law that doesn't mean you should use it it doesn't mean it's completely irrelevant and so uh so the
43:56
43 minutes, 56 seconds
I think we're going to see a the normal process that we've always seen in in the
44:03
44 minutes, 3 seconds
development of computers you know the applications themselves inform the next
44:10
44 minutes, 10 seconds
generation of Hardware uh should the computer see should the computer hear
44:18
44 minutes, 18 seconds
should the computer deal with images is that more important than some other things and that defines where the
44:25
44 minutes, 25 seconds
technology is going to go I mean what kind of of circuit you're going to build so and we certainly see that with the AI
44:33
44 minutes, 33 seconds
with specialized circuits that will process AI faster um to again to to make
44:41
44 minutes, 41 seconds
it more convenient so I think we're we're going to see development that comes from you you and me and people who
44:50
44 minutes, 50 seconds
have real life you know real real world applications of they want to use it for
44:58
44 minutes, 58 seconds
it's not going to be reserved to you know very theoretical things in the
45:05
45 minutes, 5 seconds
classified domain and so on it's going to be everywhere does the concept of
45:11
45 minutes, 11 seconds
sentient AI change how we potentially might research UFOs in the future or how
45:18
45 minutes, 18 seconds
we relate to UFOs in the future and the concept of UAP absolutely even in in the the
Chapter 10: AI as a research tool
45:26
45 minutes, 26 seconds
interview with a patient you know if you have but that's I'm I'm glad you're asking that because that's been very important
45:35
45 minutes, 35 seconds
to me the the AI is not I'm not going to turn the case over to some sort of robot
45:45
45 minutes, 45 seconds
I'm going to be and it's much more enjoyable and much
45:51
45 minutes, 51 seconds
more fascinating to have an an AI companion that can guide the the
46:01
46 minutes, 1 second
interaction and remember the interaction better than I can can remind me of
46:07
46 minutes, 7 seconds
things to ask of you know when people investigate UFOs and I've seen that with
46:15
46 minutes, 15 seconds
the Air Force I've seen that with in France as you know I'm I'm on the from a French committee that from um from the
46:24
46 minutes, 24 seconds
the space agency that is going to go into the field and interview people the
46:33
46 minutes, 33 seconds
the the the the dialogue is made is made difficult because when when
46:41
46 minutes, 41 seconds
in the in the conversation or in the the interrogation if you want there
46:49
46 minutes, 49 seconds
is you can only think as a human of you know a dozen possibilities when you're
46:57
46 minutes, 57 seconds
in front of the of the person you know that and and you can you think of a particular set of questions to
47:06
47 minutes, 6 seconds
ask a a robot would look at 200 you know in in the system that I
47:12
47 minutes, 12 seconds
built there were over 200 possible things to look at that could explain that particular
47:21
47 minutes, 21 seconds
observation if I'm in the field with someone I'm only going to to have command of maybe 10 or 12 you know I'm
47:29
47 minutes, 29 seconds
not going to go through my brain isn't going to have isn't going to have present to my mind 200 possibilities at
47:37
47 minutes, 37 seconds
the same time right it's not humanly possible I can guide me you know you you
47:43
47 minutes, 43 seconds
forgot to ask you know um how the the if
47:50
47 minutes, 50 seconds
it was blurry at a particular point when the person saw it or was it always precise and
47:59
47 minutes, 59 seconds
clear well I might not remember to ask that question but it could be an
48:06
48 minutes, 6 seconds
important question at that particular Point yeah 100% And Its memory and data
48:14
48 minutes, 14 seconds
collection will be so much stronger than ours would would ever be it it doesn't necessarily mean that it's uh smarter
48:23
48 minutes, 23 seconds
than I am you know my pride is is reacts to what you say uh
48:31
48 minutes, 31 seconds
the it it simply means that I'm going to save time in going through all the
48:39
48 minutes, 39 seconds
hypothesis and I'll know that I haven't forgotten anything you know that I I I'm
48:46
48 minutes, 46 seconds
not going to go home and say oh you know I should have asked you know which which happens so so often so it it's really
48:54
48 minutes, 54 seconds
having a a trusted friend it's not going to be you know
49:01
49 minutes, 1 second
it's not going to do the interview it cannot do the interview because so much is in the human connection you know in
49:10
49 minutes, 10 seconds
the human to human reaction that I'm going to be
49:16
49 minutes, 16 seconds
guided by that person there may be silences or hesitations that are notice the that
49:26
49 minutes, 26 seconds
where I want to go back uh or I want to give a person time to remember something
49:34
49 minutes, 34 seconds
where a robot would just push push push push push through a list of situations
49:41
49 minutes, 41 seconds
okay I I will know when to let go of certain things and come back later and
49:49
49 minutes, 49 seconds
and so on uh it's not going to be mechanical but if the the robot at the
49:55
49 minutes, 55 seconds
same time can say don't forget to ask about you know how he disappeared you
50:02
50 minutes, 2 seconds
know uh or were there times where it wasn't visible and then became visible again okay that
50:10
50 minutes, 10 seconds
not that might not come to my my mind and it might not come to the mind of the
50:17
50 minutes, 17 seconds
witness to mention that I like that you brought that up because for example if an AI is a research or an interviewer to
50:27
50 minutes, 27 seconds
an experiencer that has seen and witnessed a UFO do you think the AI will be sentient sentient enough to have an idea on how
50:36
50 minutes, 36 seconds
to capture feelings and emotions from the experiencer witnessing the UFO incident do you think that we'll be able
50:45
50 minutes, 45 seconds
to you know play it as a as a partner for both of us where it will be a give and take relationship like you're saying but do you think that it will have more
50:53
50 minutes, 53 seconds
of a sentient feeling in and um and more human realistic feeling where it would know how to empathize with the
51:01
51 minutes, 1 second
experiencer witnessing a UFO sighting I can think of some movies uh you know
51:07
51 minutes, 7 seconds
where the robot lies to the interview and the question is to detect the LIE of
51:15
51 minutes, 15 seconds
the robot and I don't think we're there but uh we
51:22
51 minutes, 22 seconds
are certainly at the point where what I'm afraid of is that people will it it
51:28
51 minutes, 28 seconds
will look so slick you know and precise that people will
51:35
51 minutes, 35 seconds
overestimate the Mind behind a robot well there is no mind behind a robot you
51:42
51 minutes, 42 seconds
know there is a complex program that reorganizes itself kind of like well the Mind does
51:52
51 minutes, 52 seconds
but it's not it it it's not the brain it's not do you think we'll stop critically thinking then uh I think
52:01
52 minutes, 1 second
people will have to learn the hard way you know that it it can lead you to
52:08
52 minutes, 8 seconds
trust it in conditions where you should not be trusting it that makes
52:17
52 minutes, 17 seconds
sense the the problem is especially in English in the English language
52:27
52 minutes, 27 seconds
um know how do you how do you stop it how do you lead it to contradict itself
52:35
52 minutes, 35 seconds
the way you would with a with another human you know you know now you you telling me it was blue but earlier you
52:45
52 minutes, 45 seconds
said something that could imply it was red you know or can you can you explain
52:52
52 minutes, 52 seconds
that that's the kind of thing we do when when we talk to a person uh but there it's going to be for
53:02
53 minutes, 2 seconds
a while it's going to the robot is going to be so intimidating that because it's going to
53:09
53 minutes, 9 seconds
look very clever very very clear so you have a reaction of trusting it too
53:18
53 minutes, 18 seconds
much you'll have to be able to catch it um uh you know things that are mistakes things that are
53:27
53 minutes, 27 seconds
contradictory and and that's going to be that's going to be difficult to do when do you think we stop and pull the plug
Chapter 11: Future of AI interaction
53:34
53 minutes, 34 seconds
on AI then like what's your threshold in your mind I do I don't
53:42
53 minutes, 42 seconds
know there are lots of things we don't know what I can tell you is I see you know my friends around me in in Silicon
53:50
53 minutes, 50 seconds
Valley uh uh amazed with uh you know they said they
53:57
53 minutes, 57 seconds
asked what's the difference in the uh art of painting between Vango and you
54:05
54 minutes, 5 seconds
know some other and and they get a long dissertation from you know the AI
54:12
54 minutes, 12 seconds
explaining things by you know alluding to different paintings and different
54:19
54 minutes, 19 seconds
styles and so on I mean it looks perfect so it's the end of the story they when
54:25
54 minutes, 25 seconds
not at the point yet where they would there would be a dialogue where we would
54:34
54 minutes, 34 seconds
challenge the AI um you know and say wait a minute if
54:40
54 minutes, 40 seconds
you compare you know this pain this artist to this artist then you should
54:47
54 minutes, 47 seconds
also do it from the point of view of you know some other theory of Art and then
54:55
54 minutes, 55 seconds
when we begin to see dialogues like that that's when it's going to become really interesting can you can you get the
55:04
55 minutes, 4 seconds
robot to break down can you get it to a level of complexity where it uh
55:11
55 minutes, 11 seconds
contradicts itself I think we will um I'm I'm sure we will and so that's where
55:20
55 minutes, 20 seconds
that's a point where we we're really going to to be able to learn something now with UFOs we're going to have that
55:28
55 minutes, 28 seconds
right away because we don't know what UFOs are so we will be we have to disagree you
55:38
55 minutes, 38 seconds
know two people asking the same AI about the same case are going to get different
55:45
55 minutes, 45 seconds
answers from the way they ask a question and then there will be a
55:52
55 minutes, 52 seconds
Reconciliation you know between between the two and that's that's where as as a you know if
56:00
56 minutes
you still an AI programmer that's where I would like to be you know watching the dialogue
56:08
56 minutes, 8 seconds
and taking it home to to rework the the the to replay the the
56:16
56 minutes, 16 seconds
situation Ai and UFOs are both nonhuman right and they both have the
Chapter 12: Absurdity and UAP
56:24
56 minutes, 24 seconds
potential to control Humanity potentially right potentially so how would these two subjects
56:33
56 minutes, 33 seconds
influence society and each other in the future well I could imagine the
56:40
56 minutes, 40 seconds
situation where the UFO asks you know let me talk to your
56:45
56 minutes, 45 seconds
air oh my gosh you're right um the the situations I think
56:53
56 minutes, 53 seconds
people generally in you know my friends would disagree with me on that and they do but
57:02
57 minutes, 2 seconds
I I I think we're looking at the dialogue with UFOs the way we have
57:10
57 minutes, 10 seconds
started to look at dialogue with apes and uh you know their their higher level
57:18
57 minutes, 18 seconds
the whales and yers um and um the that's not going to
57:25
57 minutes, 25 seconds
work because the in most situations where there is a dialogue dialogue is in
57:32
57 minutes, 32 seconds
English in other words you know a witness confronts a a
57:40
57 minutes, 40 seconds
humanoid that has come out of the UFO or a humanid on board the
57:46
57 minutes, 46 seconds
UFO and um the human R is going to speak English there is no difference in
57:56
57 minutes, 56 seconds
language and but it may speak English in a way that's
58:03
58 minutes, 3 seconds
absurd and we have to deal with absurdity the way people will deal with
58:12
58 minutes, 12 seconds
um Zen you know what is the sound of one hand clapping well that's absurd but
58:21
58 minutes, 21 seconds
it's absurd in a particular way for a particular way of thinking and
58:29
58 minutes, 29 seconds
um the there there are a number of situations I've spent a lot of time looking at the dialogues in the field
58:39
58 minutes, 39 seconds
between a witness was surprised by encountering a UFO and encountering a um
58:47
58 minutes, 47 seconds
a A humanid or a voice that dialogues with it and very often the the the questions are
58:57
58 minutes, 57 seconds
absurd but when you analyze the absurdity you know absurdity is doesn't mean that it's
59:06
59 minutes, 6 seconds
meaningless you know absurdity can mean that the the meaning is at a higher
59:15
59 minutes, 15 seconds
level there is a case in France where a witness went through a wood at night he was a night Watchman was going to his
59:24
59 minutes, 24 seconds
job um in a clearing in the the wood he was going through there was a large
59:32
59 minutes, 32 seconds
disc there was a a humanoid in front of the the desk and the humanoid asked
59:40
59 minutes, 40 seconds
where am I um am I in Italy or
59:47
59 minutes, 47 seconds
Germany the witness said you're in alas you're in France then he said um what time is it
59:56
59 minutes, 56 seconds
and the witness said it's 2:30 in the morning and the minut said you li it's
1:00:03
1 hour, 3 seconds
4:00 that's absurd I mean it was 2:30 right and he was in France he wasn't not
1:00:11
1 hour, 11 seconds
in Germany or Italy and you know there's a whole of Switzerland between Germany and Italy so if he was lost he was
1:00:19
1 hour, 19 seconds
really completely lost so people didn't believe the witness when he told that so he said you
1:00:26
1 hour, 26 seconds
must have been dreaming you know you were still asleep and going to your job
1:00:33
1 hour, 33 seconds
in the morning but one question was absurd about time and one was about
1:00:40
1 hour, 40 seconds
space as if to say there is no time and there is no space and when when I talk
1:00:49
1 hour, 49 seconds
to Eric Davis who's a physicist and I've done lot of had the the pleasure of
1:00:56
1 hour, 56 seconds
doing a lot of work you know jointly with with groups in research um is
1:01:05
1 hour, 1 minute, 5 seconds
telling me that physics today says there is no space and there is no time those
1:01:12
1 hour, 1 minute, 12 seconds
are things that we experience as a result of being placed in a much more
1:01:20
1 hour, 1 minute, 20 seconds
complex physical situation that we perceive as you know that as we we behave within
1:01:29
1 hour, 1 minute, 29 seconds
that situation as if there was time and space well that night Watchman going through the forest that's essentially
1:01:38
1 hour, 1 minute, 38 seconds
what he was told in a way that was completely absurd in English or with French in in that particular case there
1:01:46
1 hour, 1 minute, 46 seconds
are cases like that in in English where the the the witness shmer uh in 19
1:01:55
1 hour, 1 minute, 55 seconds
65 there is a patrolman highway patrolman is Kansas in in in Kansas he's
1:02:01
1 hour, 2 minutes, 1 second
driving along he sees an object off the side of the road he thinks there may be
1:02:08
1 hour, 2 minutes, 8 seconds
an accident or there may be something it's at that night um he stops his patrol car goes towards
1:02:16
1 hour, 2 minutes, 16 seconds
it um again two humanoids take him inside the inside is much larger than
1:02:25
1 hour, 2 minutes, 25 seconds
the outside that was I think the first instance where I I noticed that and I
1:02:32
1 hour, 2 minutes, 32 seconds
published it you know in the 60s was the first time that U you know ethologist had to deal with the fact
1:02:41
1 hour, 2 minutes, 41 seconds
that UFOs were not crafts that as we understand the craft that they were
1:02:48
1 hour, 2 minutes, 48 seconds
manipulating the topology of space type now that's a different kind of thing you
1:02:54
1 hour, 2 minutes, 54 seconds
know it's not an aircraft it's not the spacecraft do you think that later on that AI will be able to look at those
1:03:04
1 hour, 3 minutes, 4 seconds
past cases well and have a better idea of what was going on it looks like these
1:03:12
1 hour, 3 minutes, 12 seconds
two one of these humanoids took him he was now in a very large you know hemisphere very
1:03:20
1 hour, 3 minutes, 20 seconds
large and there was a a way to go up to the top they took him up to the top and
1:03:28
1 hour, 3 minutes, 28 seconds
they told him Watchmen they called him Watchman not patrolman
1:03:35
1 hour, 3 minutes, 35 seconds
Watchman uh because I could tell what the C was um someday you will see the
1:03:45
1 hour, 3 minutes, 45 seconds
universe in English his he had he had the the phrase in English in his
1:03:52
1 hour, 3 minutes, 52 seconds
head there was no it's not like communicating with whales communicating with whales is fascinating we have
1:04:01
1 hour, 4 minutes, 1 second
everything to learn about it but this is not the same situation they they control
1:04:09
1 hour, 4 minutes, 9 seconds
their language correct but when we're looking no I I hear what you're saying jacqu you
1:04:16
1 hour, 4 minutes, 16 seconds
know when we're looking at those cases though right when we're looking at cases that deal with space and time right and
1:04:23
1 hour, 4 minutes, 23 seconds
we're talking about potentially Dimensions will we get to a point where AI is so sophisticated that it would be
1:04:31
1 hour, 4 minutes, 31 seconds
able to look at all the different cases that have dealt with let's say something to do with space and time and have a
1:04:38
1 hour, 4 minutes, 38 seconds
better conclusion of what's going on I I don't know uh the AI will
1:04:46
1 hour, 4 minutes, 46 seconds
certainly be one of the instruments that we want to use we want to throw it at them and see how they can deal with
1:04:55
1 hour, 4 minutes, 55 seconds
it uh my guess would be that they going to deal with it very well but it would
1:05:02
1 hour, 5 minutes, 2 seconds
be interesting to to to see that they several of my dear friends in this
1:05:10
1 hour, 5 minutes, 10 seconds
research have given up because of the complexity of the
1:05:16
1 hour, 5 minutes, 16 seconds
situation because there is the if we have if we Face a technology can
1:05:24
1 hour, 5 minutes, 24 seconds
disguise itself to that point where it can manipulate the space of time and the
1:05:31
1 hour, 5 minutes, 31 seconds
language environment what else can it do I mean do we even when we see a Tic Tac
1:05:40
1 hour, 5 minutes, 40 seconds
when we see a sphere is it really a sphere or is it just an image that they can
1:05:48
1 hour, 5 minutes, 48 seconds
project an image of something that can become material when they want to but it can change shapes it can be
1:05:57
1 hour, 5 minutes, 57 seconds
something else it can be a lot bigger if they want to and and then uh what's the point of doing this research if it's
1:06:05
1 hour, 6 minutes, 5 seconds
well essentially helpless my friend m Michelle in France essentially gave up because he he
1:06:15
1 hour, 6 minutes, 15 seconds
was in situations where with Witnesses who were doctors who were uh scientists
1:06:24
1 hour, 6 minutes, 24 seconds
and so on who had seen something and had been completely
1:06:31
1 hour, 6 minutes, 31 seconds
confused with just the topology or with the with the interaction to the point where their science was essentially
1:06:40
1 hour, 6 minutes, 40 seconds
useless so if we come to that point maybe that will be the end of our interaction with it you know where we
1:06:49
1 hour, 6 minutes, 49 seconds
give up but the AI isn't going to give up so maybe the we we can you know just
1:06:56
1 hour, 6 minutes, 56 seconds
like you send a a robot to explode you know a bomb you don't send a human
1:07:03
1 hour, 7 minutes, 3 seconds
anymore to to you know to uh uh to make
1:07:09
1 hour, 7 minutes, 9 seconds
it know to make it uh idle and uh you
1:07:16
1 hour, 7 minutes, 16 seconds
know the AI will be one one more two um it it it will be an interesting in in
1:07:25
1 hour, 7 minutes, 25 seconds
the field I agree so my last question to you
Chapter 13: Government AI use
1:07:32
1 hour, 7 minutes, 32 seconds
jacqu is how could the government use AI now as a tool to research uaps we know that you've created a databas a database
1:07:41
1 hour, 7 minutes, 41 seconds
in the past but what could the government do now as a best practice using the AI technology that we have now
1:07:49
1 hour, 7 minutes, 49 seconds
when looking into the UFO topic well um I I think a AI again has many levels
1:07:59
1 hour, 7 minutes, 59 seconds
just like simple tools and complex tools uh in in a factory
1:08:06
1 hour, 8 minutes, 6 seconds
the the simple tools that they they should develop uh redevelop was my my
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1 hour, 8 minutes, 15 seconds
little robot that uh would help you in interviewing a witness you know to save
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1 hour, 8 minutes, 22 seconds
the time of the witness and to save your time and get a quality report you know so you're not you know asking questions
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1 hour, 8 minutes, 32 seconds
that are irrelevant just because you have to fill out a piece of paper the government is very good at getting you
1:08:39
1 hour, 8 minutes, 39 seconds
to fill questionaires we have to you know throw that into the garbage can and do it more you know in a way that's more
1:08:47
1 hour, 8 minutes, 47 seconds
intelligent the AI can do that today we again we have to start with a 200
1:08:55
1 hour, 8 minutes, 55 seconds
hypothesis now it doesn't have to be the government you know
1:09:01
1 hour, 9 minutes, 1 second
uh Harvard is you know the the project there is is doing that very well Galileo
1:09:09
1 hour, 9 minutes, 9 seconds
you know has already developed something like that so uh it's it's fun it's it's
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1 hour, 9 minutes, 16 seconds
fun to do it's fun to develop it's not supremely difficult because you know you know what instruments can do
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1 hour, 9 minutes, 25 seconds
so and and you're asking this of a a a human being or you're asking it of a
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1 hour, 9 minutes, 32 seconds
device that you control so again that's the the more complex one
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1 hour, 9 minutes, 40 seconds
is um when you you you have a a situation that
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1 hour, 9 minutes, 47 seconds
involves something like the limits where you have multiple observers you need to control control system and you you
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1 hour, 9 minutes, 55 seconds
cannot predict where the object is going to go and you have to
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control a large number of parameters and that is you know something that
1:10:11
1 hour, 10 minutes, 11 seconds
the should be developed um certainly by initially by the military because the
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1 hour, 10 minutes, 18 seconds
military has resources and also some of the more interesting Dev Es
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1 hour, 10 minutes, 25 seconds
are you know are secret are are classify so it it it will first happen in in the
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1 hour, 10 minutes, 33 seconds
classified domain and then it can be applied to in in eviation for example or
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1 hour, 10 minutes, 40 seconds
just to determine is that a threat or is it not a threat and determine fast enough so that people can begin to to
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1 hour, 10 minutes, 50 seconds
react and um that could save lives you know this could be could have rewards very
1:11:00
1 hour, 11 minutes
quickly system can do is acquire a lot more data that we can analyze later than
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1 hour, 11 minutes, 8 seconds
we would do with normal instruments in the field because it can require everything at the same time which again
1:11:16
1 hour, 11 minutes, 16 seconds
is is a principle uh you know in in Galileo I I think it's it's a wonderful
1:11:24
1 hour, 11 minutes, 24 seconds
application of of AI yeah I think that there's going to be multiple different applications in the
1:11:30
1 hour, 11 minutes, 30 seconds
future that will be exciting scary Innovative you know we'll be able
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1 hour, 11 minutes, 36 seconds
to look at UFO data and cross uh cross collaborate it with other data and come
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to maybe hopefully some better conclusions I plan to do uh with my data you know it's completely unclassified
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you know civilian data but from all over the world and uh you don't need you know
1:12:00
1 hour, 12 minutes
super sophisticated database to do that you can as as we see now with chat GPT
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1 hour, 12 minutes, 8 seconds
and so on we can do it with the English text we have now so I'm looking forward
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1 hour, 12 minutes, 16 seconds
to doing that just on my own right at the civilian databases that are being built right now using AI
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1 hour, 12 minutes, 24 seconds
is unbelievable and hopefully the civilians that are doing the work will use that data for for positive use
1:12:31
1 hour, 12 minutes, 31 seconds
rather than maybe negative and maybe not selling that data but I am excited that yourself and other people are creating
1:12:39
1 hour, 12 minutes, 39 seconds
those databases that will be hopefully you know public to to everyone to view and to open source so they could do their own research
1:12:48
1 hour, 12 minutes, 48 seconds
yes but the larger problems um we have to place at a higher level and we but we
1:12:57
1 hour, 12 minutes, 57 seconds
have to continue to analyze them and work on them it's true it's very true thank you so much jock for spending time
1:13:06
1 hour, 13 minutes, 6 seconds
with me today and thank you for being rebelliously curious byebye