221–230 of 282 posts
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I like to think that in life the code is also the runtime, unlike in computer technology where the hardware is the runtime.
The code is a serialized record of the hardware. It has to be translated from codons to amino acids before being assembled.
I dropped out of tech after a couple years to go study neuroscience. Even though I returned to software, I do not regret for a second that I went to study something so broad and deep and mysterious. I know that huge areas of neuroscience will remain opaque to science for decades to come. I am blessed to have had a few years contact with that complexity and mystery. If anyone reading this, at any age, wants to take bi…
Also to anyone reading this - don't feel compelled to drop out of anything to study something you find interesting. You can very easily find good resources online and do a bit of study everyday.
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The easiest and fastest and most financially preferable option would probably be to join the software engineering team of a fairly software-oriented biotech company and then, once you're established, let it be known that your personal career advancement aims are to move more in the direction of scientific work. That would be fine at many companies, assuming you can make them think of you as a valuable asset for your…
A lot of job descriptions I see are looking for people who already have the scientific formal education background and expect them to do the software engineering aspect too. I haven't really seen generic Software Engineer job descriptions where a science background is the secondary requirement. If you have any pointers I would greatly appreciate them!
https://boards.greenhouse.io/invitae/jobs/4196489?gh_jid=419...
https://www.recursion.com/careers?gh_jid=3992017 "A biology background is not necessary, but intellectual curiosity is a must!"
https://ekgn.fa.us6.oraclecloud.com/hcmUI/CandidateExperienc...
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>You are riding a bicycle. Explain Oh man, where do I even start? Sensory input from the inner ear to balance, the networks that handle feedback from afferent signals from the periphery, efferent pathways to control motor movement. I don't even know all the details but it's mind bogglingly complex. Do I explain the molecular basis of action potentials? The modulating effects of inhibitory feedback within the networks…
Hold my beer... Assume an experienced rider, as learning is different. Intention is set, requiring the basal ganglia and fore brain and either a notion of free will, determinism, or whatever you fancy. The area ahead is scanned and mapped for a clear path via retina- optic nerve - visual cortex and particularly the dorsal parietal pathway. Initial organised motor signal sequences originate in pre and pre pre motor ar…
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Sounds like learning a new (human) language vs. learning a new math concept. Every attempt I've made into learning a new language has been a failure. I've resigned myself that only through immersion will I ever learn a new language. I suppose there is no equivalent in the medical field. :-)
Funny enough my only bad grades in high school were with languages, I think your analogy is apt. I could understand bio and learn it but performing at the level needed to be a doctor was not going to happen. Although, I have no regrets very happy with where I ended up!
I had been putting off quantum physics and time was running out. I was worried forth semester physics at MIT was going to be tough, but then through some miracle of providence the EE department decided that a solid state chemistry chemistry class plus Into to Biology could be substituted for quantum physics. Because I had already taken a solid state chemistry class, all I had to do was take a basic introductory biolo…
The thing I found about biology in my limited study of it is that if a physical effect can happen, it's almost certainly used somewhere as a functional effect. I'm regular puzzled that people think that biology is tractable. It seems to me the best we can hope for towards a complete understanding is a computational generative model.
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I love it! Much like the "I type a URL into my browser and press enter. What happens?" tech interview question.
My finger gently depresses the black plastic key. As the machine begins to pull in data from the net I shift my weight back in the chair and look up over the top of screen, out the window at the lunch hour foot traffic passing silently by beyond the steam tinted cafe window. The overheavy graphics begin to render, but my gaze is caught by a momentary glimpse between the rushing cars of a woman in a red dress on the f…
One thing I don't understand after reading this and several comments, is I'll often hear stuff like "biology is so complex. Everytime you think you understand a piece you find out it's another abstraction on top of another complex system". And then, immediately people say stuff like one comment that I thought was pretty funny: "they are structurally like a cathedral built by a blind deranged architect.".
And also from the article:
> Biology is like this, just much, much worse, because living systems aren’t intentionally designed. It’s all a big slop of global mutable state.
My question is, all these comments describe how biology is an indescribable complexity, and then conclude its so complex because it's just a bunch of random coincidences that built up over time. Just because biology is one big glob of mutable global state, does that mean it wasn't intentionally designed? I know certain algorithms like video encoding/decoding and coding compilers may seem like random pieces of code when you first encounter it. You may also question whether the original authors were just writing "bad code" since it's all so interconnected. But then you learn a thing or two and realize that the process itself is very interconnected, and you can only cleanly separate so much of it, but you'll still be left with a hairy piece of code that's kind of messy but necessary.
Why can't it be the same way with life? We study these astronomically complex systems, and then we talk about how we're still so far away from understanding the system as a whole, then we conclude it's just a soupy mess that all came together randomly. How can we come to a conclusion like that without being able to understand all the intricacies as a whole? How much of biological complexity is necessary complexity, and how much is accidental complexity? And by the way, I am a believer in intelligent design which is why I raise these questions.
I just find it amazing that we can simultaneously speculate about the wonders of life, and then chalk it all up to a bunch of random coincidences haha.
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I don't even think we understand software, much less biology :-) We can only hope to understand the pieces that are most relevant to the business domain we're trying to solve (like curing a disease or expanding an online business). The complexity of both types of systems is just increasing exponentially over time, so there's little hope (or even need) to understand the whole thing. The challenge is, of course, to und…
There’s a difference between YOU understanding everything vs. ANYONE understanding everything though.
And the analogy with biology actually goes even further - just like in software, we "know" the code (DNA), but how does that translate to the behavior of the complex system (and business requirements in software), is lost to time and the sheer complexity of these systems.
I'd even claim that we don't really fully understand how computer systems work anymore. Let me explain. When someone creates a new system, we could argue they have a complete understanding of it, since they build everything from the "ground up". Although even then, they use a particular level of abstraction - not necessarily needing to understand how third-party libraries work, or how it all translates to machine cod…
You do not need to know every implementation detail down the stack, just the the general concepts behind it all. But in case you do need to know a specific part of the stack very thoroughly, you can just read the source code and come up with an understanding quite soon. That is not the case with biology, if you want to know a part of the stack thoroughly, you in essence have to come up with the "source code" your sel…
That's the problem though - code is not enough for understanding. Very well-written and documented code can be, but it's not a given. And the complexity grows exponentially the more of the code there's.
Unfortunately, many managers think the way you describe, while discounting the intangible knowledge that's only in the heads of engineers, and leaves with them. So they treat engineers as disposable units while thinking that possession of the code will guarantee transfer of knowledge.