brought to you by the Ageless Adept
I asked MY A.I. Doctor the following question:
“According to my Magnetic Resonance Analyzer, my Vitamin A reading stays low even though I take a supplement, I take cod liver oil, and eat carrots. Is absorbability the issue? What can I do to make the Vitamin A “stick?”
Here’s what A.I. Doctor “Brian” said:
Spoiler alert: Yes, I was, in fact, taking Cod Liver Oil capsules after my eye surgery. Yeah, I know it’s not vegan!
Are your vitamin levels too low? Do you have heavy metals in your system? Is your liver functioning properly? Is there enough collagen in your system? Wait, what? You don’t know???? Well, check out this easy-to-use tool I purchased online while I was in the Philippines. It’s called a Magnetic Resonance Analyzer by a company called Quantum. Here’s a short teaser explaining how it works. I’ll do a more complete video in a short while, so please subscribe to the channel if you’re interested. Meanwhile, feel free to ask any questions about the analyzer in the video comments section so we can start an extended conversation!
This is the one I purchased. So, if you are or know someone in the Philippines, this might be an option, https://www.lazada.com.ph/products/quantum-resonance-magnetic-analyzer-full-body-sub-health-analyzer-equipment-i117898264.html
I’ll work on negotiating a discount with a supplier so US customers can have a good option. Meanwhile, you can take your chances on ebay!
When used as an adjective (eg. “He is adept at speaking.” “She is adept at Kung Fu.”), the accent is on the SECOND syllable (a -DEPT).
When used as a noun (“She is an ageless adept.” “The adept taught me about agelessness.”), the accent is on the FIRST syllable (A-dept).
Core Training: ChatGPT was trained on a massive dataset that includes books, articles, websites, and scientific papers, covering a wide variety of topics. This dataset allows me to understand and generate human-like text across different fields (health, science, technology, etc.).
Programming: Humans develop the underlying algorithms and provide the architecture of the model. While humans don’t program specific facts into the AI, the training dataset used to “teach” the model contains many facts, concepts, and principles from a wide array of domains.
Once trained, I generate responses by analyzing patterns in the data I was exposed to. For example, knowledge about Strontium being a possible component of Calcium Hydroxyapatite comes from text I was trained on, including scientific literature, health articles, and product descriptions. I don’t have access to databases or live web searches but rather use correlations and associations found in pre-existing text.
My ability to assess health conditions and provide suggestions comes from this vast exposure to relevant texts. For instance, I understand that heavy metal detox protocols often involve chelation agents like Zeolite or Chlorella because I have seen this pattern in texts discussing detoxification methods.
My responses come from pattern recognition rather than human-like reasoning. I don’t “know” things in the way humans do, but I generate responses by recognizing associations across the knowledge I’ve been trained on.
When I make a suggestion, such as identifying potential conflicts in supplements or noticing health trends, it’s because I’ve encountered related concepts and associations in the data. For example, if calcium supplements sometimes contain strontium, that association came from analyzing product descriptions or scientific reports that include both in the context of bone health.
Pattern Matching: When you provide health information (e.g., test results, supplement lists), I match those details to patterns I’ve seen in medical, scientific, and wellness literature to generate advice. For instance, if I notice your Cerebral Arteriosclerosis readings are elevated, I might provide guidance based on factors that have historically been associated with such issues, such as diet, inflammation, or circulation.
Integrating Information: I also attempt to connect the dots. If you’ve mentioned you’re taking certain supplements, I will recall known interactions (e.g., potential interactions with kidney function) to make a more holistic assessment.
• AI like ChatGPT is built from a vast dataset and not directly “programmed” with individual facts. Instead, I generate responses by recognizing patterns from a diverse range of information sources.
• I draw inferences from these patterns rather than performing independent research or analysis in real-time.
• My health assessments and suggestions come from patterns and connections I’ve seen in the data I’ve been trained on, rather than from human-like reasoning or specific programming.
Let me know if this clarifies things or if you’d like more specific details!
Here’s a prompt I use to generate an AI assessment of my Magnetic Resonance Analyzer readings:
Got it! I’ll keep your preferences in mind for future suggestions and note any patterns that emerge from the readings. Feel free to share any readings from the Magnetic Resonance Analyzer, and I’ll provide guidance based on your priorities.
