One icy morning in February, nearly 200 people gathered in a church in downtown Richmond, Va. Most had awakened before dawn and driven in from across the state. There were Republicans and Democrats from rural farms and D.C. exurbs. They shared one goal: to fight back against AI development in a region with the largest concentration of data centers in the world. "Aren't you tired of being ignored by both parties, and having your quality of life and your environment absolutely destroyed by corporate greed?" state senator Danica Roem said, to a standing ovation. The activists--wearing homemade shirts with slogans like Boondoggle: Data Center in Botetourt County--marched to the state capitol and spent the day testifying to lawmakers about their fears over data centers' impacts on electricity, water, noise pollution, and more. Some lawmakers pledged to help: "You're getting a sh-t deal," state delegate John McAuliff told activists. The phrase captured many people's feelings toward the AI industry as a whole. Not much unites Americans these days.

In 2012 or so, if you'd asked most people in tech about'neural networks', if they had any answer at all they might well have said that it was an obscure idea from the 1980s that had never really worked - rather like VR. Then, in 2013, Imagenet gave us an explosive realisation that this could work now - again, rather like VR in 2013. Since then, the tech industry has been remaking itself around machine learning. There's a naive view that'Google will have all the data' or China will have all the AI' or'Data is the new oil', but it's more interesting to look at how many different kinds of deployment are now happening. The first phase was the creation of companies building platforms (or'primitives' or'substrates') for specific low-level ML applications - image recognition, voice recognition, sentiment analysis etc.

We worried that these databases would contain bad data or bad assumptions, and in particular that they might inadvertently and unconsciously encode the existing prejudices and biases of our societies and fix them into machinery. We worried people would screw up. That is, we worried what would happen if these systems didn't work and we worried what would happen if they did work. We're now having much the same conversation about AI in general (or more properly machine learning) and especially about face recognition, which has only become practical because of machine learning. And, we're worrying about the same things - we worry what happens if it doesn't work and we worry what happens if it does work.

Recently members of our development team here at Twipe attended the AWS Summit in Berlin and came back with new insights on machine learning. Coupled with the call from Benedict Evans at GEN Summit to change the way we talk about artificial intelligence, it's time to make sure we're all on the same page when it comes to AI, ML, and all the other important acronyms you hear in every conversation about the future of news these days.