Perry Marshall

Long post, because apparently many neither understand nor appreciate the intricacies of cancer research and think that pharmaceutical companies and regulators are holding back cures. Your immune system is constantly surveilling your body for both self and non-self recognition. It does this by checking the proteins expressed. If it finds something it doesn't recognize, it ramps up the inflammatory response and attacks. If it is actually non-self, great. If it is actual-self, that is autoimmune disease. Cancer occurs when cells acquire mutations that both 1) alter cell division and 2) cloak those cells from recognition by the immune system. If the second part doesn't occur, then the immune system will recognize that something is wrong and kill the tumor when it's just a few cells. At a high level, many modern cancer therapies are about getting the immune system to recognize the tumor and do all the work on killing it. And yes, it's quite easy to do this. But this is where the "safe and effective" line comes in. If you're treatment just cranks up the immune response in general, you start killing the tumor AND other things. If you give me a decent CAR-T at work, I have tools to boost it in ways that'll eliminate any realistic sized mouse tumor in 24-48 hours. The problem is that it's just a general immune overdrive and the cells start attacking everything. Okay, let's not send the immune system into a frenzy and just use the CAR-T, which is a T cell that has been edited with a protein that we know binds to a protein that the cancer expresses. The hope is, if the cancer cells express X and we edit the T cells to explain anti-X, then they'll go and attack the tumor. But this is where specificity and selectively come in. Your body expresses thousands of different proteins. Sometimes they look very much like each other even if they are different. Say there is a protein called XX expressed in your heart, it shares 99.9% homology (likeness) with X. We inject X-CAR-T cells, they go and kill the tumor, everything looks great. But a few binded to XX in the heart and started inflamming the heart wall. Not good to the point of unacceptability. This step gets especially hard when working in animals because mice and dogs or whatever have different proteins than humans! When we inject human tumors and human CAR-T cells into mice, they are NOT encountering the same proteins (or cytokines, hormones, other immune cells, etc.) that they would in an actual human body. This is just a brief explanation or some of the considerations that go into oncology. Here's another: we monitor experimental mice for max a few months. Even non-experimental mice have a lifespan of ~1.5-2 years. Meanwhile, you want your parent/spouse/child to be in remission for 5, 10, 15+ years! In fact, one of the main outcomes for assessing human cancer treatments is the "5-year survival rate." Mice and elderly dogs don't live for five years!! So yes, scientists and pharmaceutical companies have tools to easily kill tumors. What is hard is developing therapeutics that are BOTH safe AND effective in actual humans **relative** to current standards of care (e.g. a cutting edge treatment isn't "better" if it has a strong response in the first year but a lower 5-year overall survival, etc.) And this is where regulators come in. I expressed multiple times in the comments that I, generally, wish they weren't as risk averse and that I support "Right To Try" laws. But you need to appreciate that regulators **are** in a DIFFICULT position. An analogy: It's proven that nuclear energy is by far the safest form of energy per KwH energy produced. Yet a few high profile accidents, a couple of which didn't even kill anyone, have poisoned a large segment of the population and several nations against nuclear energy. Even though it's safe and a reliable form of carbon-free electricity!!! Now think about that relative to cutting edge medicines.

I asked myself this very question, when strategizing about it in the early 1990's. One problem was that the data (which were very good) made no contact with the dominant paradigm - molecular genetics. No one knew the source of the endogenous bioelectric patterns, and no one knew the targets downstream. Also, no one in the mainstream cared, because they thought they could ignore it - it made no impact on the pathways they were interested in (or so they thought). By 2002, we made the first molecular tools to allow visualizing patterns of Vmem in vivo over time, noninvasively, without having to poke each cell with an electrode, to manipulate the Vmem patterns via ion channel misexpression (so you knew exactly which cells were modified and in what way - very different from prior electric field applications), to find out which ion channels were responsible for the endogenous signals (inverse drug screens), and to identify which gene expression changes (of things like Sonic hedgehog, which everyone cared about) were downstream of the bioelectric signaling. We used these methods to integrate the bioelectric signals into the developmental pathways that the mainstream cared about, and now they needed to pay attention because it impacted on genes and transcriptional networks that were important to them. We used molecularly-tractable model species and released tools to everyone, which allowed mainstream labs to get into it. We developed computational tools (also open source) that enabled predictions integrating the molecular genetic signals with bioelectric changes over time - which were ultimately used to infer interventions for birth defects etc. We characterized transduction machinery - 6 molecular mechanisms by which bioelectric changes control cell behavior and gene expression, so now it was known *how* bioelectrics exerted their effects. Second, prior work focused on ion fluxes and electric fields. It was known that these could direct cell migration. We focused on resting potential patterns, which enabled mapping out at least part of the bioelectric code in inducing the creation of entire organs (ectopic eyes, first) - that had not been possible before. This in turn connected to deep ideas in neuroscience about high-level stimuli propagating through networks to control downstream mechanisms that we don't need to micromanage. There's more but this gives an idea of what was different - more powerful concepts, practical tools, and a commitment to making sure we anchored the work to endpoints that the mainstream cared about. It was (and still is) a slog, but the ice has broken. The pioneers of the past - Lund, Burr, Cone, Jaffe, Robinson, Nuccitelli, Borgens, etc. were inspirational - they had it very tough, but they saw the possibilities clearly (especially Burr, back in 1936) and fought tooth and nail so that others had access to the data showing why this is so important.

For many, the facility for continuous self-learning is a non-negotiable minimum requirement for any definition of true autonomous AGI. • Unappreciated though, is this expectation puts some intriguing constraints on how any true autonomous AGI can be both engineered and programmed. • That is, any system capable of continuous self-learning must be running its programming both interpretively and asynchronously. • First, to run code interpretively means there can be no reliance on code that must be compiled or trained-on externally. • The hardware impact of this requirement is that stack-based architectures are much more efficient at running interpreted code, as compared to register-based architectures, which are better for running compiled code. • And second, the necessity of running asynchronously, means there can be no overarching operating system involved either. • As it stands now, I’m not familiar with any R&D program, directed towards AGI, that is tracking either of these requirements.