All the AI and Machine Learning Writing I’ve been doing lately.

Lately I have been focused on carving out a space as a writer in the Artificial intelligence and Machine Learning space.

So I’ve been doing a ton of research and writing a bunch of articles on Medium and other places.

Here’s some links to all the AI and ML writing I have been doing lately so that’s its easy to find in one handy dandy blog post.

I’ll also give some Te-nahisi Coates style post breakdowns on what I could have done better or liked about each post.

  1. What is Deep Learning?

This is the most recent thing I wrote on AI and goes into the process of deep learning using Artificial Neural Networks (ANN). This piece is pretty good but I could have included other examples of deep learning besides the 4 I put in there.

2. 9 Ways Intelligent Automation Can Grow Your Business!

This post was good for beginners in AI and business development. I think looking back on the piece I could have positioned it more to be specific to small and medium sized businesses as larger businesses have been doing this stuff.

3. Separating the steak from the sizzle when it comes to AI and ML

This post is good at outlining what is available right now, but I probably could have done a better job at explaining how close some these breakthroughs are to being reality.

4. How Machine Learning and AI are Influencing Data Driven Marketing

This post would be perfectly at home here on the Content Marketing King blog. If anything this posts shows how much data is being collected and how data focused the next generation of content creators will have to be to survive.

5. Millennials, This how AI and ML will affect your job search

I used to write a lot about the job search process because I find the process to be inherently unfair. Employers have all the power and often lie in job applications about the job or compensation. This post accurately lays out how the age of intelligent machines is going to affect finding a job over the next 20 years or so.

6. 7 Ways That Machine Learning is Affecting the World

This is a pretty basic list of applications of machine learning. It essentially lays out the areas that I would be writing about in later pieces. It is interesting to see how some of these areas like autonomous driving have been in the news lately.

7. How Predictive Analysis is predicting Earthquakes, Court Decisions and Everything in between

In the first piece I wrote around AI and ML I looked at the idea of predictive analysis and how AI and ML are being used to make predictions about everything from court cases to earthquakes with various levels of success. At this point I was still figuring out the differences between ML and AI.

Ok that’s most of what I have written about AI and ML that is on Medium I’ll have to track down some of the links in INC and Fast Company as they tend to change fast.

I’ll be sure to post more about AI and ML in the future as I continue to find a place for my writing on the subject.



7 Random Thoughts On AI and Machine Learning

This week I have interviewed the founders or CEOs of 4 different AI and Machine Learning companies for upcoming articles on Forbes and

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:

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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?