A word cloud of theScinder’s output for 2016, made with wordle.net
This subject includes throwbacks to 2015, when I did most of my writing about CRISPR/Cas9. That’s not to say 2016 didn’t contain any major genetic engineering news. In particular scientists are continue to move ahead with the genetic modification of human embryos.
If you feel like I did before I engaged in some deeper background reading, you can catch up with my notes on the basics. I used the protein structures for existing gene-editing techniques to highlight the differences between the old-school gene editing techniques and editing with cas9. I also compared the effort it takes to modify a genome with cas9 to how difficult it was using zinc-finger nucleases, the previous state-of-the-art (spoiler: it amounts to days of difference).
TLDR: The advantage of genetic engineering with Cas9 over previous methods is the difference between writing out a sequence of letters and solving complex molecular binding problems.
aLIGO and the detection of gravitational waves
Among the most impressive scientific breakthroughs of the previous hundred years or so, a bunch of clever people with very sensitive machines announced they’ve detected the squidge-squodging of space. A lot of the LIGO data is available from the LIGO Open Science Center, and this is a great way to learn signal processing techniques in Python. I synchronized the sound of gravitational wave chirp GW150914 to a simulated visualization (from SXS) of a corresponding black hole inspiral and the result is the following video. You can read my notes about the process here. I also modified the chirp to play the first few notes of the “Super Mario Brothers” theme.
I’ve just started an intensive study of the subject, but machine learning continues to dip its toes into everything to do with modern human life. We have a lot of experience with meat-based learning programs, which should give us some insight into how to avoid common pitfalls. The related renewed interest in artificial intelligence should make the next few years interesting. If we do end up with a “hard” general artificial intelligence sometime soon, it might make competition a bit tough, if you could call it competition at all.
Devote a few seconds of thought to the twin issues of privacy and data ownership.
2016 also marked a renewed interest in manned space exploration, largely because of the announcement from space enthusiast Elon Musk that he’s really stoked to send a few people to Mars. NASA is still interested in Mars as well, and might be a good partner to temper Musk’s enthusiasm. In the Q&A at about 1:21 in the video below, Musk seems to suggest a willingness to die as the primary prerequisite for his first batch of settlers. There’s some known unavoidable and unknown unknowable dangers in the venture, but de-prioritizing survivability as a mission constraint runs a better chance of delaying manned exploration as long as it remains as expensive as Musk optimistically expects.
It doesn’t grab the headlines with such vigor, but Jeff Bezo’s Blue Origins had an impressive year: retiring their first rocket after five flights and exceeding the mission design in a final test of a launch escape system.
Blue Origin is also working on an orbital launch system called New Glenn, in honor of the first astronaut from the USA to orbit the earth.
In that case, where are we headed?
The previous year provided some exciting moments to really trip the synapses, but we had some worrying turns as well. The biggest challenges of the next few decades will all have technical components, and understanding them doesn’t come for free. Humanity is learning more about biology at more fundamental levels, and medicine won’t look the same in ten years. A lot of people seem unconcerned that we probably won’t make the 2 degrees Celsius threshold for limiting climate change, although not worrying about something doesn’t mean it won’t kill anyone. Scientists and engineers have been clever enough to develop machine learners to assist our curiosity, and it’s exciting to think that resurgent interest in AI might give us someone to talk to soon. Hopefully they’ll be better conversationalists than the currently available chatbots, and a second opinion on the nature of the universe could be useful. It’s not going to be easy to keep up with improving automation, and humans will have to think about what working means to them.
Take some time to really dig into these subjects. You probably already have some thoughts and opinions on some of them, so try to read a contrary take. If you can’t think of evidence that might change your mind, you don’t deserve your conclusions.
Remember that science, technological development, and innovation have a much larger long-term effect on humans and our place in the universe than the petty machinations of human fractionation. So keep learning, figure out something new, and remember that if you possess general intelligence you can approach any subject. On the other hand, autogenous annihilation is one of the most plausible solutions to the Fermi Paradox. This is no time to get Kehoed