Yet AI still cannot even find a sustainable alternative to their inferencing, training and fine-tuning processes to combat their mass consumption of electricity and water for years.
At least, cryptocurrencies have managed to combat this criticism with alternatives to proof-of-work and even Ethereum made it possible for a wasteful PoW blockchain to migrate to proof-of-stake which is an energy efficient alternative consensus mechanism [0][1] and have reduced their consumption by 99%.
The field of Deep Learning has made little efficient alternatives with any measurable impact and have always needed tons of GPUs in data-centers and the demand is made even worse with generative AI whilst continuing to green-wash the public with faux green proposals for years.
Not much progress in these so-called practical alternatives to this waste that AI has produced or any reduction of energy usage. As it data and model scales, the energy consumption and costs will only just get worse even by 2027.
At least, cryptocurrencies have managed to combat this criticism with alternatives to proof-of-work and even Ethereum made it possible for a wasteful PoW blockchain to migrate to proof-of-stake which is an energy efficient alternative consensus mechanism [0][1] and have reduced their consumption by 99%.
The field of Deep Learning has made little efficient alternatives with any measurable impact and have always needed tons of GPUs in data-centers and the demand is made even worse with generative AI whilst continuing to green-wash the public with faux green proposals for years.
Not much progress in these so-called practical alternatives to this waste that AI has produced or any reduction of energy usage. As it data and model scales, the energy consumption and costs will only just get worse even by 2027.
[0] https://digiconomist.net/ethereum-energy-consumption
[1] https://www.cell.com/patterns/fulltext/S2666-3899(22)00265-3