Haha yeah I figured you might like that. :-)

Do you have any recommendations on the most efficient way to become knowledgeable about biology, especially in the way that would be useful to longevity research?

Would I have to go through a full college degree on the topic, or is there a way to bypass a lot of the noise and focus on learning the key parts that matter? I have a long history of rapidly learning new things. I like to start with a problem and work my way backwards towards the solution, learning and leveraging different technologies as I iterate toward a solution.

For example, when I was 13, I was approached by a company that wanted a software system that would let them have a communal inbox for their support staff, and a way for individual team members to pick up an email and start responding to it without stepping on someone else’s toes. So I repurposed a Matt’s Script Archive forum perl script, taught myself the basics of the perl language, and then molded it into a support ticket system that met their needs. I did that in a matter of weeks, at the age of 13, with a language I didn’t even know.

That was a long time ago, sure, but I have since learned many other languages and built many other solutions for companies over the years. For example l, I learned Python and got a job working with ai in education, specifically because I knew that Python was big in the machine learning world, and I wanted to move my career in that general direction.

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Thank you for citing my own figure to refute me!

You can't be in the "superman" or bioinformatician areas without having an understanding of biology - that's how Venn diagrams work.

https://i.imgur.com/vX5hSEX.jpg

Draw a circle around the intersection of Data Scientist, Programmer, Superman, and Bioinformatician.

That’s basically my career target

I hacked the front page.

You hacked a site that anyone can edit? Mazal tov?

See https://wikipediasucks.co/forum/viewtopic.php?f=19&t=2143

Feel free to join the crowd of people who are trying to do that.

I've spent the last year talking with people in this space, and all of the big pharmaceutical companies are now saying they won't work with AI-based companies because their algorithms don't work on complex biology data. Too many people have made the claim that they could use machine learning to mine patterns out of biology data sets and failed.

It's not a knock on ML or AI. How would your algorithm know that the data it's working on is unreliable and that biology data often has 50% false positive rates on yeast-2-hybrid screens, or a given SNP may be a miscall that has propagated through 10 generations of reference genomes? Or that the assay that generated the data you're looking at used a promiscuous antibody that's triggered on a related protein that happens to express in the lab culture you're working on? If the data you're working on isn't clean, how are you planning on getting a clean signal out?

Rubik's cubes are child's play compared to the networks that Recursion is working on.

I don't get where you're coming from. Is it the combining with the world's datasets piece? They're probably using either publicly available datasets or have specific agreements with companies to make use of their datasets.

HIPAA concerns patient identity mainly, so if the dataset is anonymized or fictionalized then it's likely fine. Or if it can't be anonymized then they'll just add some extra paperwork before sharing.

Don't think that HIPAA means your data isn't shared with other companies. It just means the companies will sign some paperwork first.

Edit: also the rubber was on the road 9 years ago apparently because they've been doing this since 2013

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To simplify: you let the AI do the brainstorming at the cellular level but you test the most successful of those predictions using traditional methods.

Yes, I can 100% design a machine that will iteratively develop an algorithm that can solve Rubik’s cubes, without ever knowing exactly how to solve them myself.

What's this about wiki corruption? If Wikipedia is gone then who am I to turn to?

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Well, it is not my job to stop you from trying. I just wanted to explain the issue, to save you some pain and surprise.

Please prove me wrong - that’s how it’s done in this field. Let me know when you have solved the problems that have stumped us for the last 60-odd years without a deep understanding of what those problems are.

Edit: can you design a rubiks cube solver without understanding how to solve a Rubik’s cube?

Who is the sexiest member of your senior staff and why is it Mason Victors?

I seriously doubt that the biology field is wholly saturated with ai engineers and that the only way to be helpful is to have a deep knowledge of biology.

I’m a generally intelligent person, and I learn and adapt to new problems reasonably quickly. The future will be full of the need for human-guided thinking machines of all levels of complexity.

It’s not being “stuck up” - it’s just that the field doesn’t really lack for your skill set. There are plenty of people who know what you know who also know the biology side.

I’m not trying to gate keep, or tell you that your skills are useless. I am just trying to tell you the harsh reality that the skill sets that make you so valuable in your own field aren’t sufficient for you to solve difficult problems in my field.

Do you know the Dunning -Kruger curve? It basically translates to people having way more confidence in their own skills than warranted when venturing into areas they know little about, and are usually faced with a shocking “wake up call” when they start learning the complexity of the problems.

I’m not saying you can’t contribute, but I am saying that the low hanging “let’s apply AI to all these problems!” Days are already here for biology. What’s needed isn’t more programmers, it’s programmers who understand the complexity of the data they’re working on.

I’ve watched two decades of programmers volunteer to solve biology’s greatest problems all fall short. That shouldn’t stop you from trying, but keep in mind that your skills are necessary - but not sufficient - to accomplish your goals.

First of all, I greatly appreciate your time in writing a response!

If I understand you correctly, you believe that I would not have much to offer in longevity research without first going deep into training about biology.

If so, maybe that’s what I need to do next. I’m not opposed to going to school or being an apprentice somewhere while I transition into a helpful contributor.

With that said, I do feel like I could be a helpful contributor right now, as it is, even if I don’t have a degree in biology. I’m not too fond of wasting years of my life in college while the world goes by and all my other skills atrophy and become dated.

My thoughts on how to get around the time constraints revolve around multiplying the efficiency of our time by building and leveraging AI-powered data processing pipelines and ai workflows to analyze, summarize, and filter data and train new iterations or models. You say you want more insights and more time. That’s what I’m talking about—leveraging AI automation. I don’t have to know about biology to build those types of systems or to help you come up with out-of-the-box solutions to various problems.

I don’t have to spend years mastering how to solve Rubik’s cubes. I can use an app on my phone to solve any scrambled cube in less than a minute.

I think the life and longevity science fields could use a few more “ethical max scientists” and “ethical biohackers” to help them think outside of the box instead of being so stuck up in academia and clinically focused on minutiae

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So are you going to use AI to help people get more affordable healthcare or just exploit people in need to get rich?

If DNA is just the source code of living creatures, why can't we make an "emulator" to run it?

finally a question i understand.

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