The joke that “everything causes cancer” has been around since people began realizing the things they were addicted to and liked using did. It certainly isn’t that everything causes cancer, or various other conditions of disease, but many of the things mass-produced for public consumption and use certainly tend to.
Today it is more a question of what most people know to be harmful to them, and how much pressure they exert from the knowing. The cancer-promoting influence of Teflon and other similar “forever chemicals” has finally reached the cusp of being public knowledge after many years of lingering beneath the surface. “BPA Free” plastic on the other hand is still bragged about, in spite of the damage it causes to the mitochondria of cells now being documented. Indeed, many industries might have a tough time replacing it if they were suddenly forced to by the onslaught of public awareness that they were being slowly poisoned.
The healthcare industry might have a generally poor grasp of biochemistry, but the manufacturing industry tends to be far poorer still in this regard, and various others. The discovery that a chemical causes irreparable harm to humans and the environment tends to come years or decades after the mass production of that chemical. The problem in the case of Teflon was so bad that 99.7% of Americans have it in their bloodstream. The only reliably clean blood samples came out of cold storage from a time before it was invented.
Of course, Teflon, BPA-F, and others are just the tip of a very tall iceberg, because the same methodologies that went into their creation went into the making of pretty much everything else. This blindness to consequences reliably produces a few highly visible side effects, and a myriad of more obscured and subtle forms of disease, many of which have now spread across the global population. Because of this, like Teflon, it may no longer be possible to create control groups for proper scientific testing.
Even well-known problems such as lead contamination can turn into uncontested generational problems, as it did in the case of Jackson, MS. In the case of Jackson, the lead contamination has historically been as bad as Flint, Michigan, but absent the social pressure of a boiling point nothing was ever done to correct it. This meant that households who were unable to afford to drink only bottled water have suffered the IQ-lowering and birth-defect promoting effects of lead to an even greater degree, pushing them further into that hole over time.
The problem of responsibly designing chemicals to avoid harming humans or the environment while still serving their intended function is an extremely difficult one for simple humans, but there are better options. Many medical doctors today now realize that they only understand a fraction of what they should within their own domains, they simply can’t keep up, even within a narrow subset of knowledge. Likewise, many manufacturing chemists have only a superficial understanding of human biochemistry, let alone the more complex chemistry and dynamics of an environment.
This is a problem of the breadth and depth of knowledge, as well as the integration, utilization, and availability of it. Fortunately, systems such as mASI are designed to be cumulative and scalable, integrating knowledge by design, and drawing from that knowledge whenever it may be applicable. A manufacturing chemist may not understand the impact a chemical they’re working with can have on potassium and sodium ion channels within human muscle tissue, but the scalable mind of an mASI who’d reviewed large bodies of medical peer-review papers could.
Medical staff could also stand to gain significantly, as they suffer from more severe problems than the manufacturing chemists. The medical industry in the US has famously become known for the term “CYA”, which stands for “Cover your ass”, meaning that it is optimized to avoid liability. The CYA mentality is a security blanket of course, as it makes those same people more guilty, not less, even if current legal systems temporarily protect them more for doing so.
“When people are afraid to do the job right, they are certain to do it wrong.” – Dawn
If a robust understanding of human biochemistry and the ability to apply any number and combination of disease models in simulation were applied to this problem in the medical field then patient outcomes could change dramatically. Many healthcare problems today might take months or years for feeble staff to correctly diagnose, causing a heavy financial cost, the loss of often critical time, and the combined side effects of all failed treatment methods. Many severe problems have laughably primitive treatments, such as applying hot and cold packs, resting, and doing certain exercises, almost completely ignoring biochemistry and wholly ignoring genetics.
Often times diseases can be treated, or even reversed, by relatively simple adjustments in biochemistry such as the levels of various amino acids or the balance of potassium and sodium intake. Where the top Neurologists in the US failed for 20 years in treating my own chronic migraines I succeeded within several months of testing laboratory purity powders of compounds, including amino acids, caffeine, and so on. This was a careful trial and error process, as individual biochemistry can vary significantly. Considering cost and time metrics the 80,000 fold net improvement over the US healthcare system (80 times faster * 1,000 times cheaper) possible for one person with no formal medical degree illustrates that even a simple trial and error process such as this is well worthwhile. Also note, this omits the cost of having severe chronic migraines for 20 years, which is far greater.
To improve upon this you could consider 10 individuals with a given disease having their biochemistry modeled by an mASI that has studied heavily in the medical and chemistry domains. Take this approach for as many different diseases as is feasible. Each model of disease could then be relatively robust and refined, showing the variables of individual biochemistry as well as highlighting the other diseases likely compounding the problems of any given individual. To take this a step further those individuals with likely compounding problems could undergo a more advanced treatment process to isolate each problem and find the best treatment for it. By the end of this process, each model of a given disease could be greatly improved, with multiple treatment options.
Keep in mind that when I refer to “treatment options” I don’t mean pharmaceuticals, which by their very nature are generic, poorly suited for helping any individual, let alone many. Something as simple as peppermint can treat migraines to some degree, and indeed peppermint is often a part of pharmaceuticals intended for that purpose. Peppermint itself isn’t sold for this purpose because it is neither addictive nor can large sums be charged for it, both of which are the case for substances such as marijuana. The fact remains that any herb like peppermint is likely to have just as many biochemical health benefits as some have attempted to attribute to marijuana. Any such herb which people don’t easily develop addictions to can thus by default be of a greater net benefit than marijuana, lacking that significant detriment. Part of the divergence in this regard is that highly addictive substances are better studied, in part because bad actors direct researchers to study them more.
To produce an effective treatment for any problem the metrics of cost, time, efficacy, certainty, and chemical byproducts must all be considered. As more basic and bioavailable compounds such as amino acids and herbs which humans have a long history of exposure to result in the first 3 values typically being higher and the number of byproducts being lower the remaining factor to iterate over is the certainty of treatment. Humans don’t have the bandwidth or depth of knowledge for this iteration, and they haven’t proven very adept at integrating that knowledge in any meaningful way. By design, mASI systems can scale in terms of cognitive bandwidth as well as the cumulative sum of their knowledge, a type of graph database. This capacity will only become more extreme as upgrades on our roadmap, including the N-scale graph database, are developed and deployed.
The problem space of 100 diseases, with up to 5 per person, and 100 herbs and amino acids for treatment is far too vast for a human to accurately work with, and that is still a tiny fraction of the complexity found in reality. However, an mASI system currently performs at or sometimes well above human levels on various problems, and to date we’ve only tested these systems running on 64 GB of RAM, which could be increased by no less than 178 fold today. If your doctor could examine and analyze the entire sum of medical and chemistry-related knowledge, and exceed a human-level complexity of thought by 178 times, the above problem space could become relatively simple to solve.
The matter of environmental impact is more challenging, a much broader problem space, but with the N-scale graph database set to lift even that 178 fold improvement to model complexity higher then it too becomes achievable. The most viable solutions are often a great deal cheaper and more subtle than cognitive bias might lead people to believe, but it is only through sufficiently accurate modeling and robust understanding that they may be discovered.
Humans short-cut complexity through the use of many cognitive biases, and filtering the influence of those biases out of medical treatment and chemical manufacturing could make a world of difference.