Mensing.AI


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

Thought I can’t stop thinking 🤔

The current goal/trend seems to be to mimic the human brain in software/algorithmic form, but that standard and comparison might be part of the problem. It will certainly be a massive task to accomplish. Maybe we need to think about neural networks and the like as more of a colony of bees or farm/hill/whatever of ants. A bunch of individual … Read More


What We Get Wrong About Technology 🚫

This article ties in nicely some of the deadly sins shared previously, especially #6 (that article linked to this one). Here’s what I noted: When asked to think how new inventions might shape the future, our imaginations tend to leap to technologies that are sophisticated beyond comprehension. (remember Clark’s 3rd Law?) The most influential technologies are often humble and cheap … Read More


The Quartz guide to artificial intelligence 🗺️

Solid overview of AI: what it is, what it means, etc. So what is AI? Apparently it’s software with a mechanism to learn (careful with this word). It then uses that knowledge to make a decision in a new situation. (more suitcase words) Harking back to #3 above, a computer doesn’t have a flexible concept of “similar”. Humans know an … Read More


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