▲ ▼ Democratisation of AI/ML/DL hardware
Organisations like OpenAI are doing a commendable job in democratising Artificial Intelligence software stack i.e. opensource AI tools accessible to anyone and not just the big firms. But democratisation of hardware required for Machine Learning or Deep Learning is nowhere in sight.
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. AI accelerator hardware like TPU, Graphcore AI available to only big cloud companies would further worsen this.
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.
Not only 'AI powered end product' will increase the inequality; but the ML education/research already has.
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.