Just about everything you do on the internet is filtered through a handful of tech companies. Google is synonymous with search, Amazon with shopping; much of that happens on phones made by Apple. You might not always know when you're interacting with the tech giants. Google and Meta alone capture something like half of online ad revenue in the United States. Movies, music, workplace software, and government benefits are all hosted on Big Tech's data servers.

Models can (and should) be shrunk whenever possible to use less computing power. And knowledge can be recycled and reused instead of starting the deep learning training process from scratch. Ultimately, finding ways to reduce model size and related computing power (without sacrificing performance or accuracy) will be the next great unlock for deep learning. That way, anyone will be able to run these applications in production at lower cost, without having to make a massive environmental tradeoff. Anything is possible when we think small about big AI – even the next application to help stop the devastating effects of climate change.

It seems like everyone is talking about artificial intelligence (AI) and machine learning these days. Large, multinational industrials are embracing AI in an effort to make machines smarter, so they can compete effectively in the digital Industrial Revolution that's well underway. Witness the 2016 article in the MIT Sloan Management Review focusing on how GE is making major investments in AI and industrial analytics to help drive its digital transformation. But even small and mid-size industrials and manufacturing enterprises should be thinking about AI…at least a little bit. After all, if you aren't thinking about machine learning and AI, why are you collecting all that data from production systems?