5 ms·

I am very grateful for this release from Mozilla, and more generally for the broad vision of their effort. As time passes, the quest for openness and freedom i

I am very grateful for this release from Mozilla, and more generally for the broad vision of their effort. As time passes, the quest for openness and freedom in software moves higher up in the stack. Thanks to the latest ~30 years of effort, we basically came to a point in which we have free OSes, basic infrastructure, building tools, end-user applications. In the last ~10 years we changed paradigm: autonomous desktop computing progressively transitioned to mobile, with a lot of functionality offloaded to "the cloud". What I feel is needed, going forward, is working towards building a viable free replacement for these distributed services. DeepSpeech is a step in the right direction. Edit: just speaking about SW, here. HW is worth a different topic, and probably poses even more challenges.

tonmoy 9y ago
It’s exciting to see open source RISC-V gaining momentum on the HW world
- TaylorAlexander 9y ago
  Agreed!
TaylorAlexander 9y ago
Thanks for the note about HW. In my field in robotics openness is not common (ROS being a major exception). I hold a rather uncommon view that a path to a more free society requires abundance of open source tools. In the software world we are getting closer, but the whole hardware world is still one big unmoving binary blob. I make open source robots and I’m hoping to help in that area. But we need open source machine tools, open source factories, and open manufacturing processes. There’s lots of work do be done in hardware land.
- 24gttghh 9y ago
  I would think 3D printing, portable CNC machines[0], and custom PCB fabrications[1] are making headway into the openness of the HW space. I think that even before the tools, or maybe alongside of, there needs to be an abundance of cheap, clean energy to power the open source tools for the real revolution in manufacturing to take off. [0]http://www.goliathcnc.com/ http://www.goliathcnc.com/ [1]https://oshpark.com/ https://oshpark.com/
  - TaylorAlexander 9y ago
    Yes! Home manufacturing tools are definitely headed in the right direction! My interest is in going much farther though - to the point where a community or society can survive off of a totally open source chain. To me that means the machines that make the bagels at the bagel shop are open source and the vehicles that deliver the wheat that make the bagels are open source and so is the farm equipment and the solar panel manufacturing equipment etc. I’m happy that we’re moving to more sustainable energy but for me the revolution I’m most interested in is the one where the people control the manufacturing technology. And I hope we’re caring for the earth while we do it. Oh and see my latest 3D printed robot in that vein: http://reboot.love/t/rover-a-robot-you-can-make-at-home/94 http://reboot.love/t/rover-a-robot-you-can-make-at-home/94
sitkack 9y ago
This period is only a phase while CNNs offer the best bang. As algorithms improve, the amount of data needed to train a model will drop 1000x making the big5's data hoards not worth nearly as much. In five years, the power will shift away from petabyte sized datasets.
- charlysl 9y ago
  My understanding is that data needed (aka data complexity) is independent of the algorithm. It depends only on model complexity (roughly # of free parameters in model). AFAIK this is a fundamental principle of ML that has been proven mathematically and which is inescapable. https://en.m.wikipedia.org/wiki/VC_dimension https://en.m.wikipedia.org/wiki/VC_dimension
  - ekidd 9y ago
    > AFAIK this is a fundamental principle of ML that has been proven mathematically and which is inescapable. Well, the human brain manages to master several complex tasks using much smaller data sets than current machine learning algorithms. Natural language acquisition, for example, seems to require fewer than 10 million spoken words per year, and even academically successful 12-year-olds might be reading 1 to 4 million words per year. These are not exactly tiny data sets, but they don't require Google's scale to recreate, either. Sure, the human genome probably "knows" what set of models to try when building the brain, which gives it an advantage. But I can't think of any reason why machine learning couldn't ultimately try similar techniques with similarly-sized data sets, and get competitive results.
    - charlysl 9y ago
      You are right. I didn't phrase it correctly. What is algorithm independent is the theoretical performance bound for a given data complexity. Sure, not all algorithms are the same, but the best performance that can theoretically be achieved with a given iid sample of a given size is algorithm independent. So yes, a better algo can get same performance with less data, but limit stays the same.
- 9y ago

tonmoy 9y ago

It’s exciting to see open source RISC-V gaining momentum on the HW world

TaylorAlexander 9y ago
Agreed!

TaylorAlexander 9y ago

Thanks for the note about HW. In my field in robotics openness is not common (ROS being a major exception). I hold a rather uncommon view that a path to a more free society requires abundance of open source tools. In the software world we are getting closer, but the whole hardware world is still one big unmoving binary blob. I make open source robots and I’m hoping to help in that area. But we need open source machine tools, open source factories, and open manufacturing processes. There’s lots of work do be done in hardware land.

24gttghh 9y ago
I would think 3D printing, portable CNC machines[0], and custom PCB fabrications[1] are making headway into the openness of the HW space. I think that even before the tools, or maybe alongside of, there needs to be an abundance of cheap, clean energy to power the open source tools for the real revolution in manufacturing to take off. [0]http://www.goliathcnc.com/ http://www.goliathcnc.com/ [1]https://oshpark.com/ https://oshpark.com/
- TaylorAlexander 9y ago
  Yes! Home manufacturing tools are definitely headed in the right direction! My interest is in going much farther though - to the point where a community or society can survive off of a totally open source chain. To me that means the machines that make the bagels at the bagel shop are open source and the vehicles that deliver the wheat that make the bagels are open source and so is the farm equipment and the solar panel manufacturing equipment etc. I’m happy that we’re moving to more sustainable energy but for me the revolution I’m most interested in is the one where the people control the manufacturing technology. And I hope we’re caring for the earth while we do it. Oh and see my latest 3D printed robot in that vein: http://reboot.love/t/rover-a-robot-you-can-make-at-home/94 http://reboot.love/t/rover-a-robot-you-can-make-at-home/94

sitkack 9y ago

This period is only a phase while CNNs offer the best bang. As algorithms improve, the amount of data needed to train a model will drop 1000x making the big5's data hoards not worth nearly as much. In five years, the power will shift away from petabyte sized datasets.

charlysl 9y ago
My understanding is that data needed (aka data complexity) is independent of the algorithm. It depends only on model complexity (roughly # of free parameters in model). AFAIK this is a fundamental principle of ML that has been proven mathematically and which is inescapable. https://en.m.wikipedia.org/wiki/VC_dimension https://en.m.wikipedia.org/wiki/VC_dimension
- ekidd 9y ago
  > AFAIK this is a fundamental principle of ML that has been proven mathematically and which is inescapable. Well, the human brain manages to master several complex tasks using much smaller data sets than current machine learning algorithms. Natural language acquisition, for example, seems to require fewer than 10 million spoken words per year, and even academically successful 12-year-olds might be reading 1 to 4 million words per year. These are not exactly tiny data sets, but they don't require Google's scale to recreate, either. Sure, the human genome probably "knows" what set of models to try when building the brain, which gives it an advantage. But I can't think of any reason why machine learning couldn't ultimately try similar techniques with similarly-sized data sets, and get competitive results.
  - charlysl 9y ago
    You are right. I didn't phrase it correctly. What is algorithm independent is the theoretical performance bound for a given data complexity. Sure, not all algorithms are the same, but the best performance that can theoretically be achieved with a given iid sample of a given size is algorithm independent. So yes, a better algo can get same performance with less data, but limit stays the same.
9y ago