Hi and Multiplay

Hi Im Theo…

(Hi Soliax thanks for the invite). I’ve been reading up on the forum… reading Scott’s material. This kind of expertise is rare and truely impressive.

I’ve only fooled around with MMI stuff myself… I have grand ambitions for the future but for now finding “real uses” for MMI that “feel right” to me… isn’t easy.

Long-term, I wanna make some kinda temporal-AI. But my goals are pretty wild and out there… I might never make it. Ideally I wanna make computer-games more “Dream-like”… respond to your emotional energy.

Anyhow… I made this app multiplay http://klinkerhoppen.website

its basically a music-player. You can put subliminal-texts into the player, to play them, underneath the main audio.

My app multiplay uses temporal-randomness (timing jitter), to randomise the order of the sentances spoken. Not very impressive maybe… however I do feel the app is “less annoying” now! lol.

Basically the app “just feels” a bit less robotic and more organic now… that it is temporally randomised.

Instead of getting a fixed list of sentances spoken at you… now you get sentances in any order, often repeated… sometimes for a strong effect. it feels more like “its trying to tell you something”. More than that, sometimes when it repeats itself it seems like “its trying to hit you with a message to make you feel something.”

Again, im no MMI-master. I just fooled around with stuff. But my fooling is quite good i think. I made this thing GitHub - gamblevore/temporal: Generate physically-based randomness using rdtsc

it does a lot of stuff, generates randomness temporally, and also visually respresents the randomness as pictures in an HTML file. Also it “Scores” The output for randomness… Also it has a “Temporal randomness live-viewer” app which displays the “raw output” colourised, so you can “kinda see what your CPU is doing”.

Some of the temporal-generators look really nice. Like artistic glitch-based art.

Thanks for having me.

thats the kind of output it creates. It’s also got “more highly” random generators that achieve almost perfect random scores, but they aren’t so visually appealing lol.!

my chaotic generator is highly random…

what I do is have a “jump table”.

So like lets say I have 8 “temporal generation” approaches. I then put these approaches into a table. So if I have a number from 0-7, I can choose which approach to use.

Basically, the timing value from one temporally generated sample, is used to select the next “generation approach” for the next sample.

So each approach decides the next approach. That makes it more chaotic… I know chaos isn’t true “quantum randomness”, but I think in this case the chaos it could also make it more… psy-sensitive, in theory.

Like let’s say you are playing a song… you can choose to use various instruments. And whatever “note” you play on one instrument, lets you choose the next instrument to play. Seems like we just increased the available choices right?

Thanks for sharing your ideas and programs. I like your visuals. I want to emphasize how important it is for MMI development to have a group of interested dreamers, programmers, mathematicians and every sort of imagineer joining together to help create what can be a truly amazing future. People, together, reaching for the impossible can make it happen.

The PCQNG is a software-enabled TRNG that uses jitter in a CPU’s core oscillator (high speed phase locked loop) to extract true entropy in PCs. I don’t remember if it uses RDTSC, but it’s related. You can find a download for it on the ComScire website downloads page. There is a limited time trial license, but I would be happy to provide you a license for your development work. I just need your email address for the license generator.

At the moment it only works on PCs, but it could conceivably be ported to other platforms if access to the necessary Intel-type CPU commands is available. I am not really a programmer, except for Mathematica, so I don’t know if it’s possible for any particular platform. The primary limitation of this type of generator is it can only extract about 4 Kbps of true entropy, and the raw sampled data has to be postprocessed to correct for statistical defects. I know how to extend or increase the number of bits in the context of MMI generation, but that is a little complex and off-topic for this message. These approaches will work for any type of true random generator for MMI purposes.

We are all potentially MMI Masters – either in its development or use, or both.