This week I have interviewed the founders or CEOs of 4 different AI and Machine Learning companies for upcoming articles on Forbes and INC.com.
In this interviews I learned a lot of interesting things that might not necessarily fit in the pieces I am writing but I figured I would share here as a sort of “Behind The Scenes” look .
And because I love list posts and I know you guys love to read them I put together my list of
7 Random Thoughts on AI and Machine Learning:
- What proprietary data do sports teams have that they aren’t telling us about? We already know a fair bit about Machine learning with sports data but everyone I talked to who worked in sports analytics confirmed that the teams that are using Machine Learning have WAY more data than we have access to on the outside. And I want to know what they have.
- How are we going to eliminate bias from legal data when the legal system itself is bias? The legal system in America is biased. One need only google the incarceration rate for African Americans vs White Americans to see that. But there’s also documentaries like 13th By Ava Duaverney which explain it way better than I ever could. To quote the great philospher Kanye West “Face it Jerome get more time than Brandon.” So another challenge arises in the legal field where machine learning data will need to be scrubbed of bias before it can give sentencing recommendations or offer rulings.
- What can’t they put a brain in? After talking to the folks over at BrainOS I am convinced that a big trend is going to be installing autonomous brains into everyday items essentially transforming any task that manual from floor cleaning to window washing into a task a robot can do.
- AI is going to revolutionize the drug discovery pipeline. The way drugs are discovered and brought to market now is horribly inefficient. It’s almost as inefficient as the Cleveland Browns search for a quarterback (AY-YO). It takes on average around 10 years and a billion dollars of investment to bring a drug to market for a rare disease. Companies like Recursion Pharma which are mapping both drug compounds and human biology at a rate of 7 terrabytes a week are going to completely change how fast drugs are brought to market and disrupt the entire drug discovery pipeline by providing a better service faster.
- Why couldn’t the 76ers fix Markelle Fultz’s shot with biometrics? This is a question that kept coming to my mind as I spoke with sports analytics people. The technology exists to bio-map Markelle Fultz’s broken shot but no one I talked to seemed to have an answer as to why the 76ers hadn’t used that technology or why it hadn’t worked.
6. Open Platforms are where the big breakthroughs are going to come from. Similarly to how smaller companies tend to innovate more effective content marketing strategies the companies that are creating open source platforms like Soundhound with Houndify and BrainOS with their brains are going to be poised to let “hackers” and DIYers make the major breakthroughs and then come in early and either copy or buy up those innovations to take to market.
7. Humans have ALWAYS feared Robots becoming intelligent and taking over. The first mention I could find of robots attacking humans goes back to the early 19th century and that doesn’t count the idea of “bronze” or “gold” men who weren’t quite human or friendly attacking all the way back to 600 BC. To me it seems like a guilty conscience. After all if we really believed we were living the right way wouldn’t we think smarter robots would agree? What does it say about us that we believe the first thing the robots will do once they attain sentience is get rid of us?