Making MMI so Responsive it Can't Be Ignored

After roughly 30 years of intense focus on MMI development, I have to ask, “What is it that keeps the technology from being noticed by mainstream high tech businesses?”

I have developed substantial intellectual property and theory allowing an increase in effect size (ES) and information rate (rate of obtaining error-free information) by orders of magnitude beyond other researchers. Peak ES of about 50% and information rate up to 1 bit/sec have been observed. However, the typical ES for the average user in “real-world” testing is only about 1-2% with some practice, and the information rate is more like 0.01 bits/sec. These levels are high enough for trained users to obtain information of significant value. But, such an application would require significant motivation and investment, which has not been forthcoming.

My conclusion is that Mind-Enabled technology or applied MMI systems are still too hard to use and not dramatic enough to convince more than a handful of already interested followers. So, what is the one thing that could make the technology go viral in a big way? That would seemingly be a significant increase in responsivity; meaning, the effect size must increase by about an order of magnitude to 10-20% for the average user in real applications.

Theoretical analysis strongly suggests that no continuous entropy source can be used to achieve the big boost necessary. Note, continuous sources are any kind that produce an analog signal that is periodically sampled, such as thermal noise in a resistor and shot noise from a Zener diode. It doesn’t matter if the source is considered quantum mechanical or classical.

I recently began developing a few different approaches for a “Zero-Energy Switch” random generator. That is, a generator that takes zero or exceeding small amounts of energy to switch a bit to its intended state. All these approaches measure a discrete signal from an entropy source. “Discrete” means each measurement stands alone and is inherently either a 1 or a 0 at the instant it is measured. Two examples of such discrete entropy measurements are timing of nuclear decay and single-photon detection at the output of a beam splitter.

To be sure, either of these approaches presents a challenging engineering task. I am presently working on the nuclear decay timing method. The first step is to get a usable signal from a decay source. I use a 0.8-1.0 micro-Curie (uCi) Americium-241 from a smoke detector. 1uCi is defined as a source that produces 37,000 disintegrations per second. Am-241 primarily produces alpha particles when it disintegrates. Note, an alpha “ray” or particle is a positively charged particle consisting of two protons and two neutrons. Given that alpha radiation is emitted in all directions randomly, only about 10,000 of those particles can be detected. I use a photodiode without an enclosure or package, because alpha particles are blocked even by a sheet of paper. I found it impractical to use a scintillator that is meant to convert radiation into flashes of light that can be detected by a photodiode. Only about 10% of the emitted particles produce enough light from a ZnS scintillator sheet to be detected by the photodiode.

The alpha particles produce about 1-2 nA current pulses in the photodiode, lasting about 1 us. This tiny pulse must be converted to a voltage and amplified enough to be converted by a high-speed comparator to a logic pulse for subsequent processing. There are two ways (I know of) to produce a random output binary sequence from these pulses. Each method must produce a signal that represents the average time between pulses. In method 1, the duration from the previous pulse to the current pulse will be either less than the average producing a “1” output, or greater than the average, producing a “0” output. This generates outputs at uneven intervals at the average rate of the number of detected disintegrations. Method 2 checks if there is a detection during each period equal to the average (detected) disintegration time. If yes, output a “1,” else output a “0.” This is perhaps a little simpler to implement, but the statistics of the decay may cause some autocorrelation in output bits, which is highly undesirable. One goal is to produce an output that needs no statistical correction prior to being used. Therefore, the output sequence must have very low bias and autocorrelation.

I will provide updates as the development progresses.

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That sounds amazing. Really looking forward to hearing more updates. Thank you for your continuous effort in posting so much valuable info on this forum.

Merry Xmas!

I have sense that belief is the greatest obstacle. Further, the audience that receives the information about MMI needs to be a receptive group to the possibility that the mind can indeed control our physical environment. To that end, biofeedback professionals especially QEEG practitioners have been receptive to the mind and mental intention. My thought on improving a MMI device’s responsiveness to mental intention does not lie as much on how fast or how much data is produced but the pattern (form) this data takes in time frames that are consistent with detection and function. For example, if a functional MMI application works at let’s say 1-2 seconds, opening and closing a mechanical hand, then the useful pattern (i.g., time in phase entrainment or amount of coherence, low rate of change) would require a 10-20 millisecond update.

I have suggestion to try scalped SRAM memory +UV diode. SRAM memory consists of loads of bistable cells. So, diode flash must scramble their states. Memory cell, which is array of photosensitive JK triggers might work better, but there aren’t such chips available to buy.

Anyway, I think, that “just shove more entropy to process” might be dead end. Possibly, different entropy sources has different receptivity to mind influence.

That’s a good “outside the box” thought. Unfortunately it takes significant time (minutes) for the UV light to change the state of the memory cells, at least for the UV-erasable EPROM I tested. I considered using an SRAM as a source of randomness by lowering the operating voltage to the threshold where data can be held. However, I didn’t pursue this approach because the results are not consistent and vary a lot between devices (and also with time and temperature).

Yes, using more entropy can produce better response, but there seem to be limits. At some point the extra true randomness seems to balance the increase in effect size, preventing further benefit. I have tested up to about a trillion bits of entropy per output bit. Effect size increases at about the square root of the number of bits of entropy used, but this curve fell off when peak effect size got to about 50-60%. This number is based on years of testing, and the average subject without long-term practice got more like 5-10%.

I have studied the possibilities of better types of entropy sources and measurements. So far I conclude continuous entropy sources are not suitable for getting the highest response. “Continuous” means an analog source that runs continuously over time. On the other hand, sources with discrete outputs, and perhaps pure quantum sources, seem to be more responsive. The measurements made by Helmut Schmide using radioactive decay timing – a discrete, quantum source – seemed to be up to 50 times as responsive (producing 50 time the effect size) as the continuous sources used and designed by PEAR lab – and just about everyone else. This difference existed even though many of PEAR generators’ entropy source was a reverse biased base-emitter junction, potentially almost pure quantum shot noise, but still a continuous noise source.

Do you have some equation or theoretical relationship between the 10-20ms update and the 1-2 seconds of producing a functional result? By “update,” do you mean a single measurement of your MMI sensing device (what we usually call an entropy source).

It appears from your numbers that it takes about 100 updates or samples to accomplish the stated task. This would seem to be constrained by the responsivness of the sensor and the processing algorithm.

how about to feed Sram with unstable voltage? to connect it with stabilitron?
Also, I suggested to scalp sram and radiate it with UV. Any semiconductor device is photosensitive to some extent. Which could be used for random generation.

That might be very important observation. It means, that we have limits, how much bits could be altered with our intention, until they’ll be overwhelmed with noise.

After much research into resistance to anything Psi, I now believe there is no functional level of operation that will ever make MMI acceptable to either scientists or to most people. It is not a belief barrier, but rather a conscious or unconscious fear about having one’s secrets revealed or being affected in ways that are not in their “control.”

If I had known this at the beginning I would have used very different terminology and carefully changed my presentation to something more widely acceptable. I will have to consider this carefully, but a very different approach is probably what is needed.

I will appreciate other’s thoughts on these ideas. It’s too easy to build up an erroneous belief system without good feedback from (preferably many) other people.

I have this idea regarding the belief that most have a fear about one’s secrets revealed or being effected in ways that are not in their “control”. Consider that any MMI application must have two components: 1. and application that the person becomes aware that he/she “controlled” the application. 2. That the application is engaging and fun for the long term. No big changes in an intention-effect metric will be dynamic if the application is not fun and engaging for the long term. Or, the application is critical to one’s function as a MMI prosthetic hand or a communication board for quadriplegics. Bob Plotke

I agree, a game application must be engaging and fun to keep people’s attention. Or, the application must be useful, valuable or even irreplaceable in other ways. However, achieving these requirements does not in any way deal with an inherent fear of the underlying operating principle of the applications. I have had visitors or investors achieve good MMI results themselves, and their response has been, “that’s very interesting,” and never heard from them again. I suspect you have experienced this as well.

Rational ideas or presentations have little to no effect on emotion-based responses. A counter example is: it’s likely a fact that artificial intelligence (AI) applications are much more likely to be a real threat to both an individual’s secrets and making them susceptible to external influence on their lives than any type of MMI application. But, there doesn’t seem to be an innate fear of computer programs or AI specifically, even though very few people actually understand what AI is.

I have invented and commercialized pulse oximeters, OCR and check scanners, and true random number generators – enough to have an impact on the culture. Each took about 2-3 years to make profitable. Between us we have about 50 years of working to commercialize MMI-type tech and applications. That’s a stark indication of how hard it is.

I am in firm agreement that moving MMI technology forward is very challenging. I agree with your assessment that the application must be useful, valuable or even irreplaceable in other ways. With that in mind I have two thoughts. I have noticed the interest in the value of MMI technology with those that meditate, do yoga and tai chi with the goal to achieve higher levels of consciousness. I presented MMI technology to meditation instructors with the emphasis that an application that one has certainty that they are controlling the application with their mind, would accelerate their goal to achieve higher levels of consciousness. Seems like a good place to start.; mental feedback to promote certainty.

I realize that there are two applications that may be in the category of irreplaceable, mental control of prosthetic hands and mental control of communication boards for quadriplegics or other fully involved neurologic persons.

I have been giving much consideration and doing a lot of research about how to proactively make MMI tech a reality. One aspect is defining objective goals for performance. There are two fundamental types of systems – one with user-initiated trials and the other continuous or hands-free operation. Therefore, there are two performance definitions:

  1. User-initiated trials shall produce statistically significant results (p <or= 0.05) within one (or two) minutes. Cognitive efforts (mental efforts) may typically take about 1 second each, or at least 50 efforts in one minute. The hit rate to achieve significance in 50 efforts is 0.64. (from the Binomial distribution: 32/50=0.64, ES=28%, with 97% accuracy) For two minutes, 59/100=0.59, ES=18%, with 96% accuracy.
  2. For continuous (non-initiated) applications, such as hands-free control of devices, the standard is significantly higher. While the number of trials may be as high as 5 per second, a result will be expected in no more than 5 seconds. The utility of controlling a device with mental efforts depends both on the accuracy and on getting the desired result almost immediately. Assuming 25 trials per result, the single-trial hit rate is 18/25=0.72, ES=44%, with 98% accuracy. That’s 3-4 times the ES for initiated-trial methods. Producing more than 5 trials per second will not likely result in increased overall ES (optimization will be required), and user-feedback will still be needed. To achieve such high ES will require innovative approaches, which could include a large array of sensing hardware, at least 10-20 times initiated-trial approaches, and sophisticated processing and/or error-correction methods.

These performance levels are not necessarily the only and final numbers, but they are reasonable to shoot for. Plus, they will provide highly useful devices with “self-evident” functionality. To achieve these levels will take significant advances over current technology. I will provide multiple hardware designs in another thread.

Please give your thoughts and feedback. Thanks.

I just finished this paper on using the number of bits to reach a bound in a random walk bias amplifier as an indication of the strength of mental influence on the sequence of bits.

I’m a new member to this forum and I’m highly interested in the work that you do. I’m still learning about the forum’s dynamics, so please bear with me if I make any mistakes in etiquette.

I am the Principal Data Scientist at HeartMath and am leading the technical design of the Global Consciousness Project 2.0, inheriting from Dr. Nelson at PEAR labs. My background is a Ph.D. in Computational Physics at Stanford. We just launched a first version of the website and already have distributed some of our RNG’s globally.

I resonate strongly with the title of this thread; before joining this forum I had been excited by the prospect of high sampling rate RNG’s to be so responsive that their effects cannot be ignored. It sounds like you may have already accomplished this technically (impressive!) and perhaps it is a matter of “marketing” and how to get people interested.

I am a scientist too, not a marketer. Yet, being a relative newcomer at HeartMath having joined just 2 years ago, I have noticed the company is unique in having built trust over many decades with a large community around the science of psi-adjacent material. People who were not interested in psi consider HeartMath to be producing legit science and technology even if the subjects are “out there.”

I wonder whether we could collaborate to get this technology out to the greater public, whether via the Global Consciousness Project 2.0 that is already gaining momentum or otherwise.

Hi Nachum, I’m very glad to welcome you here.

While I have increased effect size two orders of magnitude beyond PEAR’s, I believe another factor of 10 will be required to accomplish the title of this thread. Plus, it is essential that the tech be presented and accepted by the greater public, as it has little effect if it’s only in a laboratory setting.

My only job now is sharing what I have learned and bringing the tech to that next level. I believe I know how to do that, i.e., with a combination of even more responsive entropy sources and more advanced signal processing. I will be releasing a paper in the next day or so as a step in the process, plus the paper on weighting factors for anomalous cognition from random walk bias amplifiers from a week ago (see in this thread).

I would be very pleased to collaborate in any beneficial way. I can answer almost any question in the fields of random number generation and randomness theory, anomalous cognition and effects or mind-matter interaction. I can provide various types of hardware designed specifically for detecting and measuring an influence of mind.

Though I got virtually no acknowledgement, I provided the hardware and data analysis showing the effects of fan emotions during Red Sox games (2007) in the Joyofsox movie (Rick Leskowitz): The Joy of Sox and my analysis of my generator output: The Joy of Sox Movie: Finally, some data. This is now ancient technology, but still pretty dramatic results.

Hi, welcome to the forum! I’m Simon and I’m the forum admin. It’s great to see GCP taking off again. Is HMI working in collaboration with IONS at all on the endeavor?

Is it completely being built and designed from the ground up?

I used to be one of the developers in Randonautica, as are some of the other members on here. That’s how we got to meet Scott and setup this forum a couple of years ago.

I’m really excited to see where this goes.

Welcome again and it’s a pleasure to have you here.

Also I’d like to ask how you found out about this forum?

Hi Simon,

Scott invited me to the forum after I reached out to ask him about his technology, as I find the prospect of high sampling rate and bias amplification intriguing.

Yes, HMI is leading this endeavor in collaboration with IONS. Our RNG’s have been custom-built by the team, who are cryptographers well-versed in the tech of random number generation and newer in the space of mind-matter interactions.

Some more technical details can be found on our GCP2 website FAQ.

I am looking forward to further engagement with this forum’s community.

Awesome. Thanks for the details.

Are there plans to have an app this time around for GCP 2? I know IONS were working on a similar app but haven’t seen any updates for a while.

I’d love to host one of the RNGs if possible. Is there a signup process?

Ah, I found the signup page.