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.

If anything, the molecular biologists I know are discouraged by the size of the problem being solved relative to the number of people working on it-

The main question is the value of a marginal molecular biologist chipping away at the problems with current methods.

All those new knowledge about genes we got through the human genome project produces few promising leads for new drugs. Big Pharma companies sit on large pile of cash at a time where the interest rates are near zero and they buy back shares while laying off scientists.

Currently we don't know what 1/4 to 1/3 of the human genes do. Those where we do know a function might have additional functions. With a lot of hard work we might find out more functions, but that doesn't bring us much further. Few get a few new drug targets but drug targets aren't the limiting factor for drug discovery. Predicting which drugs actually help is the more important issues as clinical trials are really expensive. Most drugs put into clinical trials fail.

Apart from the actual use of the science, progress is hold back by poor ability to replicate findings. Some of that is because scientists don't work properly but it can also be that the monoclonal antibody you order today is not the same as the one that you ordered a month ago even through you ordered it from the same lab and it has the same label.

Then even if your finding is correct and you publish it, that doesn't mean that your paper is going to be read. The language in which papers are written is very complicated and not easily interpretable by computers.

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

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.

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

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.

The problem with being interested in biosecurity risks is that the books about witches are written by wizards, who get strange ideas around five in the morning... And it would require a synthesis of giant amounts of current research which [for a layman] looks very patchy and inconsistent; it wouldn't probably be a fulfilling occupation. Although of course this is otherwise a great idea. Perhaps if the person concentrated on a specific area, like crops or biofuels?

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.)

Interested in what you guys think about this. Jayman(hbd blogger) say's parenting has no effect on how children turn out. Seems empirically incorrect to me and it's just probably difficult to encapsulate the results/hard to see non-linearities to make it easy to reference.

He insists on twin-adoption studies contrary to my views.

Thoughts? This sort of seems like the two cultures divide we agree on. I might make a thread just for this.

Argument: Does parenting have any effect on child outcomes?

• His view: Zero effect & Breast milk confers no advantage either

• My view: Parenting has some variable effect that is difficult to encapsulate in the studies he references while maintaining the correctness and good taste of genetic arguments.

I cite decision theory, statistical inference, study design, and the related area as being primal over empirical references which have failed to encapsulate the effects he is pointing to in his observables. Statistical inference just doesn't work like that to give such strong conclusions. Any one who reads the literature on study design/inference knows that it's just not possible to give recommendations that are that strong. Sort of in the realm is Isaac Levi's "Gambling with the Truth" if not only the first few chapters although not quite, probably just statistical study design/inference in general.

Thoughts any one? I think scientists or empirical researchers are not used to being told that there is a higher plane of reference. Saying that there is zero influence is equivalent to saying all the relevant variables have been enumerated and assigned exact values for probability & effect and that there is nothing else to be assigned.

I believe my orientation is correct.

edit: I might add that not ONLY would that be saying that the relevant variables have been completely enumerated && assigned cost functions but that we are sure there is nothing else(no uncertainty) and that we are sure they all equal zero/canceled out.