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.
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’?
“Solvable in principle by humans” and “solvable by humans with our current methods” are not the same thing.
Most of the fruits that you can gather with the current tools of molecular biology seem to be picked. There are also a lot of man-hours thrown on them.
Progress in biology will come more from developing new methods than using the existing methods.
Most of the fruits that you can gather with the current tools of molecular biology seem to be picked.
I am not quite sure what the scope of the statement is, but that’s strongly counter to the things I’m hearing from the molecular biologists that I know (two family members and a few close friends- I’m plugged in to the field, but not a member of it). Could you elaborate on your reasons for this belief?
My impression is that the discipline has spent the last couple decades amassing a huge (huge) database of observed genes and proteins and whatnot, and isn’t even close to slowing down. The problem is in navigating that wealth of observation and translating it in to actionable technologies. New methods will make discovery radically more efficient, but the technologically available space that these scientists have yet to explore is so large as to be intimidating. 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- they feel like their best efforts can only chip away at an incredibly large edifice.
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.
This all hits the nail on the head I think. The marginal value of my PhD is, I’m convinced, at most zero, and perhaps negative, because it adds to the noise. The replicability of papers is significantly hindered by lack of automation, to my mind.
Also, saying that we don’t know what 1⁄4 to 1⁄3 of human genes do is wildly optimistic. Better to say we have some idea what 2⁄3 of them do.
I don’t mean to come across as super optimistic with respect to strong A.I., or even A.I. in general. I should have written ’50 years give or take 50′. It’s just that i think my field’s progress rate is determined by the inflow of methods from other fields, and that the current problems it faces are insoluble using current ones. I think people who aren’t immersed in the field get a mistaken impression about this because papers and press releases must communicate an artificial sense of progress and certainty to succeed. Word in the trenches is that we’re mired in an intractable mess of unknowns.
As an example—take Aubrey de Grey’s SENS program. He lays out all these alterations he thinks he can make to fix the problem of aging. But he seems to think of biology as modular and easily mutable. A biologist expects each individual step he proposes to face dozens of unforeseen problems, and to have many, many unpredictable knock-on effects, over a wide range of detectability and severity. Dealing with them all isn’t doable right now, while single grad students take 5 year to determine a few of each genes many interactions and functions.
As for burnout—I’d agree with you if this was a recent development. But I’ve felt this way for years. It’s just that now action is required. It’s possible I’ve been burnt out for years. This has been suggested to me—my working environment is exceptionally poor—which is something I can say semi-objectively due to the number of people who have quit and/or echoed my feelings on the matter. I’m trying not to let those feelings influence me too much however.
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.
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.
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.
“Solvable in principle by humans” and “solvable by humans with our current methods” are not the same thing. Most of the fruits that you can gather with the current tools of molecular biology seem to be picked. There are also a lot of man-hours thrown on them.
Progress in biology will come more from developing new methods than using the existing methods.
I am not quite sure what the scope of the statement is, but that’s strongly counter to the things I’m hearing from the molecular biologists that I know (two family members and a few close friends- I’m plugged in to the field, but not a member of it). Could you elaborate on your reasons for this belief?
My impression is that the discipline has spent the last couple decades amassing a huge (huge) database of observed genes and proteins and whatnot, and isn’t even close to slowing down. The problem is in navigating that wealth of observation and translating it in to actionable technologies. New methods will make discovery radically more efficient, but the technologically available space that these scientists have yet to explore is so large as to be intimidating. 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- they feel like their best efforts can only chip away at an incredibly large edifice.
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.
This all hits the nail on the head I think. The marginal value of my PhD is, I’m convinced, at most zero, and perhaps negative, because it adds to the noise. The replicability of papers is significantly hindered by lack of automation, to my mind.
Also, saying that we don’t know what 1⁄4 to 1⁄3 of human genes do is wildly optimistic. Better to say we have some idea what 2⁄3 of them do.
I don’t mean to come across as super optimistic with respect to strong A.I., or even A.I. in general. I should have written ’50 years give or take 50′. It’s just that i think my field’s progress rate is determined by the inflow of methods from other fields, and that the current problems it faces are insoluble using current ones. I think people who aren’t immersed in the field get a mistaken impression about this because papers and press releases must communicate an artificial sense of progress and certainty to succeed. Word in the trenches is that we’re mired in an intractable mess of unknowns.
As an example—take Aubrey de Grey’s SENS program. He lays out all these alterations he thinks he can make to fix the problem of aging. But he seems to think of biology as modular and easily mutable. A biologist expects each individual step he proposes to face dozens of unforeseen problems, and to have many, many unpredictable knock-on effects, over a wide range of detectability and severity. Dealing with them all isn’t doable right now, while single grad students take 5 year to determine a few of each genes many interactions and functions.
As for burnout—I’d agree with you if this was a recent development. But I’ve felt this way for years. It’s just that now action is required. It’s possible I’ve been burnt out for years. This has been suggested to me—my working environment is exceptionally poor—which is something I can say semi-objectively due to the number of people who have quit and/or echoed my feelings on the matter. I’m trying not to let those feelings influence me too much however.