The cost of AI and other challenges

I stumbled upon this fascinating article by Stuart Mills looking at the challenges that further development and operations of AI models face.

The costs of model development and operation are increasing. Efficiencies in development and operation are challenging but may be addressed in the future. However, model quality remains a significant challenge that is more difficult to solve.

Data is running out. Solutions such as synthetic data also have their limitations.

There is also a severe challenge around chips. There is a supply shortage in the context of geopolitical tensions between China, the US, and the EU. Also, the environmental costs of running large AI models are significant.

The costs of model development and operation are increasing. Efficiencies in development and operation are challenging but may be addressed in the future. However, model quality remains a significant challenge that is more difficult to solve.

Data is running out. Solutions such as synthetic data also have their limitations.

There is also a severe challenge around chips. There is a supply shortage in the context of geopolitical tensions between China, the US, and the EU. Also, the environmental costs of running large AI models are significant.

Two revenue models may emerge in the AI industry, each with its own take on the cost aspects highlighted above. The first is the ‘foundation model as a platform’ (OpenAI, Microsoft, Google), which demands increasing generality and functionality of foundation models.

The second is the ‘bespoke model’ (IBM), which focuses on developing specific models for corporate clients.

Government action can support and undermine the AI industry. Investment in semiconductor manufacturing in the US and China may increase the supply of chips, and strategic passivity from governments around regulations such as copyrights is suitable for the industry. Government interventions should regulate the AI industry in areas related to the socially and environmentally damaging effects of data centers, copyright infringement, exploitation of laborers, discriminatory practices, and market competition.

Links 2 September 2023

Large organizations using open source AI should wonder what they are really adopting. I think large institutions like (benevolent) governments, banks, energy companies, banks, … – I probably mean those companies that have a lot of money and the capacity to change the system – should develop a more sophisticated and progressive open source agenda to create a human-oriented framework. Read Cory Doctorow’s Open AI isn’t.

This is so good: 100 things I know – Mari Andrew

Kevin Kelly: the best magazine articles ever

Into Linda Barry. Great video of her drawing workshop. I am reading What Is It.

Tried fixing the battery my iPhone 8 through iFixit. Broke a display cable doing that. My fault I did not read the instuction well enough. Great tools though. Fix everything.

Apple announced Apple self repair. Probably anticipating Right to Repair directions by the EU. I wonder why these kind of laws never comes from the US.