Josiah Zayner, a biochemist who once worked for NASA, became the first person known to have edited his own genes (…) During a lecture about human genetic engineering that was streamed live on Facebook, Zayner whipped out a vial and a syringe, then injected himself. Now, following in his footsteps, other biohackers are getting ready to take the plunge and tinker with their own genes. Zayner’s experiment was intended to boost his strength by removing the gene for myostatin, which regulates muscle growth. A similar experiment in 2015 showed that this works in beagles whose genomes were edited at the embryo stage. He injected himself (…) to remove the gene. (Biohackers are using CRISPR on their DNA and we can’t stop it)
We found that the long-finned pilot whale neocortex has approximately 37.2 Ã— 109 neurons, which is almost twice as many as humans, and 127 Ã— 109 glial cells. Thus, the absolute number of neurons in the human neocortex is not correlated with the superior cognitive abilities of humans (at least compared to cetaceans) as has previously been hypothesized.
We can make old mice smarter by tweaking just one gene according to an article published in Nature:
This study demonstrates for we believe the first time in vivo that 6 months after a single injection of s-KL into the central nervous system, long-lasting and quantifiable enhancement of learning and memory capabilities are found. More importantly, cognitive improvement is also observable in 18-month-old mice treated once, at 12 months of age.
I stumbled on an older post (2015) by Marcel Weiher about his views on where software is headed:
(…) for most performance critical tasks, predictability is more important than average speed (…) Alas the idea that writing high-level code without any concessions to performance (often justified by misinterpreting or simply just misquoting Knuth) and then letting a sufficiently smart compiler fix it lives on. I don’t think this approach to performance is viable, more predictability is needed and a language with a hybrid nature and the ability for the programmer to specify behavior-preserving transformations that alter the performance characteristics of code is probably the way to go for high-performance, high-productivity systems.
He is arguing that engineers have made it hard to reason about performance, and to design software for the needed performance. When we are stuck with unacceptable performance, we are often stuck… unable to know how to fix the problems.