Learnings & Musings on AI, ML, Data Science & Python

Domo Arigato Mr. Roboto 🌊🗾

Looking for a break from all the fire and brimstone about AI? (Electrical smoke and rare earth metals?) Well then, take a few deep breaths, brew up some herbal tea, prep your favorite scented candle or bath bomb, and settle in; this one’s for you. (But seriously, this guy has the credentials to put you at ease) Also, props for … Read More

The Seven Deadly Sins of Predicting the Future of AI ☠

What are these 7 deadly sins? Over & Underestimating – see: Amara’s Law (below) Imagining Magic – see: Clark’s 3rd Law (also below), be wary of predictions Performance vs. Competence – computers don’t do general (more later) Suitcase Words – it’s not “learning” in the human sense, this word has baggage for us Exponentials – use caution when extrapolating from … Read More

What Is Explainable AI? How Does It Affect Your Job? ⬛

From Hacker Noon Don’t believe the SkyNet hype. Good overview of narrow vs. super intelligence in AI. Narrow intelligence is what we see most of now (AlphaGo, Siri, autopilot). Super Intelligence is the good at everything one, but is (probably) a long ways off. What we’re really scared of with AI (or what probably drives a lot of the fear … Read More

From Infinity to 8: Translating AI into real numbers 🐔🥚🥓

From O’Reilly AI isn’t magic, it just seems like it. It depends on data in so make sure you have good data (good meaning useful, not necessarily quality). In the AI chicken-or-egg scenario, algorithms are the chickens, data is (are?) the eggs, and the results are bacon (because mmm….bacon). Also, data should follow the 4 V’s: volume, variety, velocity, veracity … Read More

Challenges in Deep Learning 🔮

Challenges in Deep Learning [on Hacker Noon] It ain’t all sunshine and rainbows, we’ve got some shiznit to figure out. A lot of the challenges raised seem to fall on the planning/people end, basically these systems are only as good as the people that program them. The biases, aversions, and misunderstanding of humans can be transferred to the machines through … Read More

Machine Learning Explained 🕯️

[FoR&AI] Machine Learning Explained by Rodney Brooks A great look at the history of machine learning (it started with matchboxes in ’40s). The first machine learning setup (it wasn’t a computer) was designed to play tic-tac-toe. It shows that machines don’t learn in the way humans do; we go for 3 in a row, the machine picks moves based on … Read More