Tech-inzichten door Niek de Greef. Reflecties op technologie, software development en de impact van digitale innovaties op cultuur en maatschappij.
About two weeks ago, I wrote that I haven’t used Google for quite some time now, and I am missing nothing. Incidentally, Seth Godin writes this week how he uses Perplexity as an alternative to Google. Perplexity was not on my list; I haven’t researched Perplexity thoroughly yet, but I trust Seth’s words.
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.
There is no good reason why you should still rely on Google search for your search engine. Read this excellent article on Google’s practices, and Big Tech chills run down your spine. There are ample good alternative search engines these days that do have integrity:
Bing (though also Big Tech and historically suspect)
And there are more.
Lately, I’ve been using Ecosia and DuckDuckGo side by side. I don’t feel like I’m missing anything about Google.
When Artificial Intelligence-generated images win photo contests, should we oppose that?
I just think the developments of AI are telling us to do things differently, to stand out. AI has become the competition (and maybe just a tool), just like all other photographers are. So, we have to treat AI as competition, too. You can try to deny this reality, but you can also look at how you, as a photographer or artist, can differentiate yourself from this new collegue/competition.
Ideas:
Personal and analog distinguish you from the aggregated, statistically generated products of AI.
Amazon makes us pay too much, which is friendly phrasing for Amazon rips off all off.
Don’t buy through Amazon anymore. I quit.