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These kinds of questions have an essential unknowability to them, in that every phenomenon you try to model through computing is an inexact approximation - whet

These kinds of questions have an essential unknowability to them, in that every phenomenon you try to model through computing is an inexact approximation - whether it's something like the precision of a mathematical computation or "what is this human being's real name". What we have done to date is the low-hanging fruit: take known categories of application and juice them up by applying CPU power. And for that, the amount of power you need is "enough to have an effect". A lion's share of the benefits of putting computers in the loop were realized in the 70's or 80's, even though the measured amount of power that could be applied then was relatively miniscule. There were plenty of alternatives at various times that were "better" or "worse" in some technical sense, but succeeded or failed based on other market factors. The real story going forward from then has been changes in I/O interfaces, connectivity and portability - computing has merely "kept up with" the demands imposed by having higher definition display, networking, etc. So then we have to ask, what are the remaining apps? If we can engineer a new piece of hardware that addresses those, it'll see some adoption. And that's the thrust of going parallel: It'll potentially hit more categories at once than trying to do custom fast silicon for single tasks. But the long-term trend is likely to be one of consolidating the bottom end of the market with better domain solutions. These solutions can be software-first, hardware-later, because the software now has the breathing room to define the solution space and work with the problem domain abstractly, instead of being totally beholden to "worse-is-better" market forces.

K0SM0S 7y ago
What an intelligent bird's-eye view of the problem. Thank you. I love the perspective, you kind of blend the evolution of simple bits at the hardware level (increasingly lots of them, but nonetheless "known category of applications") with complex high-level software space. The clarity, looking forward, is greatly improved through this lens. That last paragraph confirms my own vision for the next cycle, the next decade or so. About "remaining apps": the current cycle of ML, post-parallism (GPUs, DL, etc. since early 2010s) is imho one such candidate for transformative computing, where like vapor or hydrocarbons or electricity, computing lends itself to enabling a whole new category of 'machines', of tools. That's really interesting (just not the end-all be-all some seem to think, but a whole new solution space to build upon). I guess robotics is a fitting candidate as well (insofar as interacting with real physical objects changes everything) but we are many years away from commercially-viable solutions, afaik. I also like to think there are (potentially transformative) social or behavioral use of compute-enabled machines that we haven't scratched much yet. Areas of life/civilization generally too complex to be brute-forced or even fully modeled, like biology and health, or the more advanced social behaviors (topics best described by Shakespeare, Tocqueville, Stephen Covey, Robert Greene...); or things we 'just' need to brute-force e.g. life-like graphics/VR or seamless/ambient/'augmented' computing/reality. Some of these may be in for the taking during the next cycle or two.

K0SM0S 7y ago

What an intelligent bird's-eye view of the problem. Thank you. I love the perspective, you kind of blend the evolution of simple bits at the hardware level (increasingly lots of them, but nonetheless "known category of applications") with complex high-level software space. The clarity, looking forward, is greatly improved through this lens. That last paragraph confirms my own vision for the next cycle, the next decade or so. About "remaining apps": the current cycle of ML, post-parallism (GPUs, DL, etc. since early 2010s) is imho one such candidate for transformative computing, where like vapor or hydrocarbons or electricity, computing lends itself to enabling a whole new category of 'machines', of tools. That's really interesting (just not the end-all be-all some seem to think, but a whole new solution space to build upon). I guess robotics is a fitting candidate as well (insofar as interacting with real physical objects changes everything) but we are many years away from commercially-viable solutions, afaik. I also like to think there are (potentially transformative) social or behavioral use of compute-enabled machines that we haven't scratched much yet. Areas of life/civilization generally too complex to be brute-forced or even fully modeled, like biology and health, or the more advanced social behaviors (topics best described by Shakespeare, Tocqueville, Stephen Covey, Robert Greene...); or things we 'just' need to brute-force e.g. life-like graphics/VR or seamless/ambient/'augmented' computing/reality. Some of these may be in for the taking during the next cycle or two.