3 ms·

I'm afraid these AI accelerators available only to the Big cloud companies would further 'prevent' democratisation of AI/ML. GPU is already a big bottleneck fo

I'm afraid these AI accelerators available only to the Big cloud companies would further 'prevent' democratisation of AI/ML. GPU is already a big bottleneck for an individual/startup from a poor economic region. Considering, even entry level Software Engineering jobs now expect the candidates to know ML; it is imposing a huge disadvantage over a large sector of students from poor countries. I understand this issue has largely to do with how semiconductor industry by itself is structured. With only handful of fabrication plants capable of mass producing semiconductors of this nature and they being integral part of 'soft power' in Geo-politics; it's hard for a startup to enter this space and when they do, they have no option to tie-up with these cloud companies. But, without the hardware bottleneck being addressed not only 'AI powered end product' will increase the in-equality; but the ML education/research ecosystem is already raising the inequality.

sanxiyn 7y ago
Note that in this case, exactly same accelerators will be available as PCIe cards attached to Dell machines. So none of your concerns apply.
- Abishek_Muthian 7y ago
  Availability of Graphicscore as PCIe only on Dell machines will be more inaccessible than expensive GPUs. Only mass market open hardware based on RISC-V or at-least affordable ARM based hardware like Jetson Nano but with actual training capabilities can address this issue. To put it blunt, we need openAI for hardware & not just software stack.
  - nl 7y ago
    Nvidia GTX 1060s are available for less than a Jetson and train small models fairly well, and fine tune large models very well.
    - Abishek_Muthian 7y ago
      Not in developing nations, card itself is twice that of Nano, total PC cost/power profile would be much more expensive.
solveit 7y ago
I hate how people perceive unequal growth as a bad thing even when everyone benefits on an absolute scale. In particular, it's a completely ridiculous standard to apply to technology. The fact that Google can train a 12 billion parameter model does not make my 2 million parameter model any less effective. And when you're concerned with actually doing useful things to create value, that's all that matters. I will not go into how completely detached from reality the rest of the comment is except to say there are approximately zero people who cannot learn ML because they don't have access to a GPU.
- Abishek_Muthian 7y ago
  > there are approximately zero people who cannot learn ML because they don't have access to a GPU That sounds like an entitled, myopic view. There are reputed, fully meritorious govt. run Engineering colleges in India, where tuition fee is ~100 USD/Year. Many of the students studying there are in poverty, govt. provides free laptop albeit obviously cheap one. Almost everyone studying there are placed in top companies around the world. In my previous startup I've conducted ~80 ML/DS interviews, fresh graduates from above colleges perform very well in DSA & other CS aspects; but perform poor with ML when compared to those from expensive private institutions. When I enquired them, it came down to lack of proper access to ML hardware. Their college labs are not equipped to provide ML training at scale, their access to Internet is limited to make use of Google Colaboratory or similar services. Yes, they could run a CPU bound training for days, but it isn't practical and many don't have consistent power supply. Economic disadvantage in education is real, more pronounced when it comes to ML in my experience.

sanxiyn 7y ago

Note that in this case, exactly same accelerators will be available as PCIe cards attached to Dell machines. So none of your concerns apply.

Abishek_Muthian 7y ago
Availability of Graphicscore as PCIe only on Dell machines will be more inaccessible than expensive GPUs. Only mass market open hardware based on RISC-V or at-least affordable ARM based hardware like Jetson Nano but with actual training capabilities can address this issue. To put it blunt, we need openAI for hardware & not just software stack.
- nl 7y ago
  Nvidia GTX 1060s are available for less than a Jetson and train small models fairly well, and fine tune large models very well.
  - Abishek_Muthian 7y ago
    Not in developing nations, card itself is twice that of Nano, total PC cost/power profile would be much more expensive.

solveit 7y ago

I hate how people perceive unequal growth as a bad thing even when everyone benefits on an absolute scale. In particular, it's a completely ridiculous standard to apply to technology. The fact that Google can train a 12 billion parameter model does not make my 2 million parameter model any less effective. And when you're concerned with actually doing useful things to create value, that's all that matters. I will not go into how completely detached from reality the rest of the comment is except to say there are approximately zero people who cannot learn ML because they don't have access to a GPU.

Abishek_Muthian 7y ago
> there are approximately zero people who cannot learn ML because they don't have access to a GPU That sounds like an entitled, myopic view. There are reputed, fully meritorious govt. run Engineering colleges in India, where tuition fee is ~100 USD/Year. Many of the students studying there are in poverty, govt. provides free laptop albeit obviously cheap one. Almost everyone studying there are placed in top companies around the world. In my previous startup I've conducted ~80 ML/DS interviews, fresh graduates from above colleges perform very well in DSA & other CS aspects; but perform poor with ML when compared to those from expensive private institutions. When I enquired them, it came down to lack of proper access to ML hardware. Their college labs are not equipped to provide ML training at scale, their access to Internet is limited to make use of Google Colaboratory or similar services. Yes, they could run a CPU bound training for days, but it isn't practical and many don't have consistent power supply. Economic disadvantage in education is real, more pronounced when it comes to ML in my experience.