In response to your comment about new medication development.
1. https://www.scientificamerican.com/article/organs-on-a-chip/
"But researchers are working on a new technique to help bridge that gap:
microchips that simulate the activities and mechanics of entire organs and
organ systems. These “organs on a chip,” as they are called, are typically
glass slides coated with human cells that have been configured to mimic a
particular tissue or interface between tissues. Developers hope they could
bring drugs to market more quickly and, in some circumstances, perhaps even
eliminate the need for animal testing."
2.
https://www.scientificamerican.com/article/new-type-of-stem-cell-could-make-it-easier-to-grow-human-organs/
"A newly discovered type of stem cell could help provide a model for early
human development—and, eventually, allow human organs to be grown in large
animals such as pigs or cows for research or therapeutic purposes."
Jevan.
On Tue, 8 Oct 2019 13:36:00 +1100
Kim Holburn wrote:
> https://www.newyorker.com/tech/annals-of-technology/the-hidden-costs-of-automated-thinking
>
> > ike many medications, the wakefulness drug modafinil, which is marketed
> > under the trade name Provigil, comes with a small, tightly folded paper
> > pamphlet. For the most part, its contents—lists of instructions and
> > precautions, a diagram of the drug’s molecular structure—make for anodyne
> > reading. The subsection called “Mechanism of Action,” however, contains a
> > sentence that might induce sleeplessness by itself: “The mechanism(s)
> > through which modafinil promotes wakefulness is unknown.”
> >
> > Provigil isn’t uniquely mysterious. Many drugs receive regulatory approval,
> > and are widely prescribed, even though no one knows exactly how they work.
> > This mystery is built into the process of drug discovery, which often
> > proceeds by trial and error. Each year, any number of new substances are
> > tested in cultured cells or animals; the best and safest of those are tried
> > out in people. In some cases, the success of a drug promptly inspires new
> > research that ends up explaining how it works—but not always. Aspirin was
> > discovered in 1897, and yet no one convincingly explained how it worked
> > until 1995. The same phenomenon exists elsewhere in medicine. Deep-brain
> > stimulation involves the implantation of electrodes in the brains of people
> > who suffer from specific movement disorders, such as Parkinson’s disease;
> > it’s been in widespread use for more than twenty years, and some think it
> > should be employed for other purposes, including general cognitive
> > enhancement. No one can!
say how
it works.
> >
> > This approach to discovery—answers first, explanations later—accrues what I
> > call intellectual debt. It’s possible to discover what works without
> > knowing why it works, and then to put that insight to use immediately,
> > assuming that the underlying mechanism will be figured out later. In some
> > cases, we pay off this intellectual debt quickly. But, in others, we let it
> > compound, relying, for decades, on knowledge that’s not fully known.
> >
> > In the past, intellectual debt has been confined to a few areas amenable to
> > trial-and-error discovery, such as medicine. But that may be changing, as
> > new techniques in artificial intelligence—specifically, machine
> > learning—increase our collective intellectual credit line. Machine-learning
> > systems work by identifying patterns in oceans of data. Using those
> > patterns, they hazard answers to fuzzy, open-ended questions. Provide a
> > neural network with labelled pictures of cats and other, non-feline
> > objects, and it will learn to distinguish cats from everything else; give
> > it access to medical records, and it can attempt to predict a new hospital
> > patient’s likelihood of dying. And yet, most machine-learning systems don’t
> > uncover causal mechanisms. They are statistical-correlation engines. They
> > can’t explain why they think some patients are more likely to die, because
> > they don’t “think” in any colloquial sense of the word—they only answer. As
> > we begin to integra!
te their
insights into our lives, we will, collectively, begin to rack up more and more
intellectual debt.
> >
> > Theory-free advances in pharmaceuticals show us that, in some cases,
> > intellectual debt can be indispensable. Millions of lives have been saved
> > on the basis of interventions that we fundamentally do not understand, and
> > we are the better for it. Few would refuse to take a life-saving drug—or,
> > for that matter, aspirin—simply because no one knows how it works. But the
> > accrual of intellectual debt has downsides. As drugs with unknown
> > mechanisms of action proliferate, the number of tests required to uncover
> > untoward interactions must scale exponentially. (If the principles by which
> > the drugs worked were
