The default Singularity scenario is probably a bad one, and most scientific/technological progress just brings the Singularity closer without making a positive scenario more likely.
How much of modern science brings one closer to a potential intelligence explosion type Singularity event? If such an event is something that is likely to occur it would need to not be dependent on a lot of different technologies.
So what technologies could actively be a problem?
Well one obvious one is faster computers. The nightmare scenario is that we find some little clever trick we're missing to run smart AI and the first one we turn on thinks hundreds of times faster than us at the start.
The next possible really bad set of technologies are nanotech stuff. If the AI finds an easy way to get access to highly flexible nanotech based on methods we have then we're sort of screwed. (This one seems extremely unlikely to me. The vast majority of people in nanotech keep emphasizing how difficult any sort of constructor bot would be.) The next issue is possible advanced mathematical algorithms. The really bad case here is that an AI gets to look at the arXiv and quickly sees a set of papers which when put together give something like a general SAT solver that solves 3-SAT with n conditionals in Kn^2 steps for some really small constant K. This is bad.
Similar remarks apply to an AI that finds a really fast quantum algorithm to effectively solve some NP hard problem. Seriously, one of the worst possible ideas you can have in the world is to run an AI on a functioning quantum computer or give it access to one. Please don't do this. I'm someone who considers fooming-AI to be unlikely, and believe that BQP is a proper subset of NP and this possibility makes me want to run out and scream at people like Roger Penrose who specifically want to see if we need a quantum computer for intelligence to work. Let's not test this.
But outside these four possibilities the remaining issues are all more exotic and less likely. For example, I'm not worried that an AI will right out of the box figure out a way to make small wormholes and take advantage of closed-time like curves simply because if it has that sort of tech level then it has already won.
So the vast majority of scientific research seems to do very little for helping an AI go foom.
But it does seem that continued scientific research does help us understand which sort of AI threats are more likely. For example if we end up proving some very strong version of P!=NP, then this will make the clever algorithms attack much less likely. If BQP is strictly less than NP in a strong sense, and room temperature strong nanotech turns out to be not doable then most of the nasty foom scenarios go away. Similarly improving computer security directly reduces the chance that an AI will manage to get access to internet things it shouldn't (although again, basic sanity says anything like a strong AI should not be turned on with internet access, so if it gets internet access it has possibly already won. This is reduces the chance of a problem in one specific scenario that isn't terribly likely but it does reduce it.)
Furthermore, scientific progress helps us deal with other existential risks as well as get more of a handle on which existential risks are a problem. Astronomy, astrophysics and astrobiology all help us get a better handle on whether the great filter lies behind us or in front of us and what the main causes are. It wouldn't for example surprise me if in 30 or 40 years we will have good enough telescopes that we can not only see Earth like planets we can see if they had massive nuclear wars (indicating that that might be a possible major filtration event) or that the planet's surface is somehow covered with something like diamond (indicating some point possibly in the very far past, a serious nanotech disaster occurred). A better space program also helps deal with astronomical existential risks like asteroids.
So, overall it seems that most science is neutral to a Singularity situation. Of the remainder some might increase the chance of a near term Singularity and some might decrease it. A lot of science though helps deal with other existential risks and associated problems.
So the vast majority of scientific research seems to do very little for helping an AI go foom.
I guess it wasn't clear but I also consider a Hansonian/Malthusian upload-driven Singularity to be bad.
So, overall it seems that most science is neutral to a Singularity situation.
The mechanism I had in mind was that most scientific/technological progress (like p4wnc608's field of machine vision for example) has the effect of increasing the demand for computing hardware and growing the overall economy, which allows continued research and investment into more powerful computers, bringing both types of Singularity closer.
I was inspired by the recent post discussing self-hacking for the purpose of changing a relationship perspective to achieve a goal. Despite my feeling inspired, though, I also felt like life hacking was not something I could ever want to do even if I perceived benefits to doing it. It seems to me that the place where I would need to begin is hacking myself in order to cause myself to want to be hacked. But then I started contemplating whether this is a plausible thing to do.
In my own case, there are two concrete examples in mind. I am a graduate student working on applied math and probability theory in the field of machine vision. I was one of those bright-eyes, bushy-tailed dolts as an undergrad who just sort of floated to grad school believing that as long as I worked sufficiently hard, it was a logical conclusion that I would get a tenure-track faculty position at a desirable university. Even though I am a fellowship award winner and I am working with a well-known researcher at an Ivy League school, my experience in grad school (along with some noted articles) has forced me to re-examine a lot of my priorities. Tenure-track positions are just too difficult to achieve and achieving them is based on networking, politics, and whether the popularity of your research happens to have a peak at the same time that your productivity in that area also has a peak.
But the alternatives that I see are: join the consulting/business/startup world, become a programmer/analyst for a large software/IT/computer company, work for a government research lab. I worked for two years at MIT's Lincoln Laboratory as a radar analyst and signal processing algorithm developer prior to grad school. The main reason I left that job was because I (foolishly) thought that graduate school was where someone goes to specifically learn the higher-level knowledge and skills to do theoretical work that transcends the software development / data processing work that is so common. I'm more interested in creating tools that go into the toolbox of an engineer than with actually using those tools to create something that people want to pay for.
I have been deeply thinking about these issues for more than two years now, almost every day. I read everything that I can and I try to be as blunt and to-the-point about it as I can be. Future career prospects seem bleak to me. Everyone is getting crushed by data right now. I was just talking with my adviser recently about how so much of the mathematical framework for studying vision over the last 30 years is just being flushed down the tubes because of the massive amount of data processing and large scale machine learning we can now tractably perform. If you want to build a cup-detector for example, you can do lots of fancy modeling, stochastic texture mapping, active contour models, fancy differential geometry, occlusion modeling, etc. Or.. you can just train an SVM on 50,000,000 weakly labeled images of cups you find on the internet. And that SVM will utterly crush the performance of the expert system based on 30 years of research from amazing mathematicians. And this crushing effect only stands to get much much worse and at an increasing pace.
In light of this, it seems to me that I should be learning as much as I can about large-scale data processing, GPU computing, advanced parallel architectures, and the gross details of implementing bleeding edge machine learning. But, currently, this is exactly the sort of thing I hate and went to graduate school to avoid. I wanted to study Total Variation minimization, or PDE-driven diffusion models in image processing, etc. And these are things that are completely crushed by large data processing.
So anyway, long story short: suppose that I really like "math theory and teaching at a respected research university" but I see the coming data steamroller and believe that this preference will cause me to feel unhappy in the future when many other preferences I have (and some I don't yet know about) are effected negatively by pursuit of a phantom tenure-track position. But suppose also that another preference I have is that I really hate "writing computer code to build widgets for customers" which can include large scale data analyses, and thus I feel an aversion to even trying to *want* to hack myself and orient myself to a more practical career goal.
How does one hack one's self to change one's preferences when the preference in question is "I don't want to hack myself?"