Request for Advice : A.I. - can I make myself useful?

Might you be able to slightly retrain so as to become an expert on medium-term and long-term biosecurity risks? Biological engineering presents serious GCR risk over the next 50 years (and of course after that, as well), and very few people are trying to think through the issues on more than a 10-year time horizon. FHI, CSER, GiveWell, and perhaps others each have a decent chance of wanting to hire people into such research positions over the next few years. (GiveWell is looking to hire a biosecurity program manager right now, but I assume you can't acquire the requisite training and background immediately.)

Some relevant questions that come to my mind:

• To what extent do you want to be happy? Have a big impact? Pursue other goals? (The reason I ask is because I see trade-offs)
• If you really value having an impact, what are the fields you could enter where you'd be impactful? Neuroscience? Anti-aging? Cryonics? AI?
• How important do you think each field is? How much value do you think you'd add? How long would it take for you to be productive (although I don't think that's too important a question, assuming you're optimizing for the long-run)?
• How big of an impact do you think you could have in academia? Will you be slowed by bureaucracy? How happy will you be?
• Taking a step back, what are some alternatives to academia that would enable you to have a big impact? Earning to give is a popular option, but what else is there? Politics? Startups? Working on things that augment our ability to think? What sort of schemes could you think up?

My overall impression is that you seem like the kind of person whos happiness is largely tied to how big an impact you have, and who is rather altruistic. And so it seems important for you to find something that enables you to have a big impact. You also seem young enough where it seems worth thinking about the long-run. It seems worth taking the time to do a lot of research and make the right decision.

Most Americans spend too little time on higher-level actions, like being strategic - doing a quick analysis of what your goals are, and which Level 1 or Level 2 actions would best accomplish those goals. Witness the hordes of lawyers who spend thirty years on the Level 1 action of working at a law firm, three years on the Level 2 action of getting a law degree, and three minutes on the Level 3 action of deciding what to do after college.

• Levels of Action

Norman Borlaug is the poster child of how to use genetic manipulation for large-scale impact as an individual, so I don't think your degree is pointed in the wrong direction. But it is the nature of established institutions to fail at revolutionary thinking, so a survey of the 'heavyweights' in your field will tend to be disappointing.

I think a large part of my lack of enthusiasm comes from my belief that advances in artificial intelligence are going to make human-run biology irrelevant before long.

We have only crappy guesses about the completion date for the AGI project, and the success of FAI in particular is contingent on how well our civilization runs in the interim. For example, wartime research might involve risky choices in AGI development, because they have a more urgent need for rapid deployment- an arms race for the 'first' AGI would be terrible for our chances of FAI. Genomics won't help us build a mind, but it can help foster an environment where that research is more likely to go well (see Borlaug again). You might, say, investigate the regulatory networks surrounding genes correlated with sociability or IQ.

I think the ultimate problems we're tackling (predicting genotype from phenotype, reliable manipulation of biology, curing cancer/aging/death) are insoluble with our current methods - we need effective robots to do the experiments, and A.I. to interpret the results.

Do you believe that you can reliably distinguish 'problems that cannot be solved by humans' from 'problems that humans could solve in principle but haven't yet'? Personally, I'm very bad at this, especially when the solutions involve unexpected lateral thinking. While I do agree that AGI is more or less the last human invention, I doubt that it's the next one- we haven't run out of other things to invent, and I'd be surprised if that was the case in the narrower area of genomics.

It's probably worth pointing out that you are at the exact stage in your PhD that is most known for general burnout. This looks suspiciously like such an event, with an atypical LW filter. So, this: "I think a large part of my lack of enthusiasm comes from my belief that advances in artificial intelligence..." is likely to be false, since many of your colleagues are experiencing similar feelings at a similar time.

I don't see artificial intelligence improving in a way that suggest that it soon solves all our biological problems.

As far as effective robots go, you could call high-throughput sequencing machines effective robots. Cheaper gene sequencing then produced a lot of new knowledge. Fortunately databases like uniprot allow access towards the produced knowledge that goes well beyond what scientific papers can provide. Imagine the mess if all the information about genetic sequences would be written in papers and textbooks.

We could have further success in biology if we manage to treat other biological problems the same we. Instead of focusing on insight, we focus on building better tools and infrastructure to organize the resulting knowledge in a useful fashion.

With smart watches that integrate constant heart rate monitoring, we have suddenly a lot of data but are still lacking in interpretation. Lack of use cases for data currently stalls development (see Apple). It might be possible to develop early warning signals for the illnesses like the flu with pick up signals before the patient is consciously aware of the flu.

Obesity is also an interesting subject. To me BMI feels like a stone age diagnostic tool. There's no way that the statistic is optimal but it still get's used because obesity researchers generally don't work on developing better scales for obesity but take the existing scores for granted and test interventions or go for molecular biology.

I think we should look more on how we structure biological knowledge then on testing individual molecular biological hypothesis.

First and foremost, don't bother with Java, it'll be dead in 5 years. (Okay, just kidding, sorta.)

Okay, so jokes aside: what do you want? As in, what do you hope that the world will accomplish before you die? Even if you aren't the one who makes the breakthrough, you still benefit. So, what do you hope that someone, anyone, it could be you, it could be some scientist somewhere else, what do you hope they will do, more than anything else?

You seem to point to things that revolve around life extension, and your thought that current methods aren't going to get it done. So, conceptually, what DO you see getting it done? You mention, effective robots to do experiments, and AI to interpret results, but what does that actually mean? What types of experiments? Why a robot instead of a human? As for AI: what results need to be interpreted? What answers are you hoping to find?

I have found that turning a more analytical eye towards your long-term goals, and changing them from purely conceptual to something more actionable, is a great first step for determining what you want to do with your life.

There are several organizations around tackling the problem of life extension, have you looked into those?

I'm just midway through a masters in bioinformatics, and am currently applying for jobs at deep learning startups, so I'm fairly familiar with AI and genomics.

A few suggestions:

• Have you considered cognitive genomics? This is very relevant to the future of intelligence in the absence of radically superhuman AI. Plomin and Steve Hsu (BGI) are the main relevant researchers in this area.
• Have you considered identifying pathogenic sequences? Companies that allow biological sequences to be ordered need to be able to accurately identify pathogenic sequences to reduce probabilities of disasters. Don't know much more about this but it's pretty future-relevant and close to your area, possibly even closer than biosecurity risks at large, which is also a good suggestion by Luke.
• If by AI, you're interested in machine learning research, then it would make more sense to start with Numpy (or Matlab if you have it), you can transition easily from R, and then C/C++/CUDA for the lower levels and Caffe or Torch for higher-level programming. Java and Hadoop seem more useful for scaling existing algorithms than for researching AI and AI safety.

The types of people who change the world through their research or at least make huge progress tend to be the type who naturally focus on and become outright obsessed with a particular unsolved problem, technology or idea.

I think a large part of my lack of enthusiasm comes from my belief that advances in artificial intelligence are going to make human-run biology irrelevant before long.

That may be true, but if so it can be viewed as an opportunity just as much as a threat. If you believe this then go learn some AI - your background in bio could give you a unique perspective. The modern world is all about dual/multi class.

You could probably find some overlap between your current area of expertise and FAI research by bridging with philosophy of psychology, performance psychology, behavioral economics, linguistics, philosophy of language, classification of mental disorders, treatment of mental disorders, positive psychology, happiness economics, organizational psychology, machine learning, decision theory, cognitive psychology, cognitive science or philosophy of science

Does computational neuroscience sound interesting to you?

Seems like a field that could use your stats skills, and understanding how the brain works seems likely to be important for AI.

I think a large part of my lack of enthusiasm comes from my belief that advances in artificial intelligence are going to make human-run biology irrelevant before long.

I suspect that's the issue, and I suspect AI will not be the Panacea you expect it to; or rather, if AI gets to the point of making Human-run research in any field irrelevant - it may well do so in all fields shortly thereafter, so you're right back where you started.

I rather doubt it will happen that way at all; it seems to me in the forseeable future, the most likely scenario of computers and biology are as a force multiplier, allowing processes that are traditionally slow or tedious to be done rapidly and reliably, freeing humans to do that weird pattern-recognition and forecasting thing we do so well.

Did you have any thoughts on this thread?