Mensing.AI

My journey into AI/ML/DL and other areas of interest.

Explaining AI to Mom 👵

Fear mongering and buzzword-y overuse/loosey-goosey usage have been hurting AI’s appeal with the broader population. Are there terrifying possibilities? Yes (I’m terrified of the implications of Deep Fakes). But guaranteeing dystopia is a disservice. It plays to the reptilian brain while we’re trying to recreate the higher level brain in silicon. 🦎 I agree with …

Nvidia Takes it to the Cloud

Cloud and software providers are moving in on the chip game, so why shouldn’t a chip maker move in on the cloud game? Nvidia has announced that they’re combining 16 of their super AI friendly GPUs into high-performance computing clusters for cloud use. The new tech race is well underway and it is to become …

The Whole Will Be Greater Than the Sum of the Parts

I’m incredibly optimistic about the potential of human-machine teams, or centaurs. And not just because the name for them is cool and mythical. I think they make AI more immediately useful as the human can handle the intangible “soft skills” we’ve yet to figure out how to recreate in code and the machine can handle …

Common Sense is Uncommon in AI

It seems like there is a two-party system developing in the AI-sphere: one argues for purposefully imbuing the systems with common sense, the other suggests that the cognitive abilities of human infants will eventually appear spontaneously in these models. Not quite sure how that last part works yet. I think the big difference between the …

Facebook Chips in on Live Video

I’m starting to get obsessed with watching who is making a chip for what AI purpose these days. Facebook has announced they’re designing a chip to help with analysis and filtering of live-streaming video. Why? Two reasons: To minimize reliance on outside suppliers (here’s looking at you Intel) To avoid the embarrassing mistake of letting …

Book Notes 📚: Weapons of Math Destruction

Light Hearted Moment 😂 The above is to offset a little of the doom-and-gloom that might follow. Also, it fits pretty well.I recently read Weapons of Math Destruction by Cathy O’Neil. It covers some of the concerns I’ve mentioned previously. What is a weapon of math destruction? 💣 It’s basically an algorithm that utilizes Big …

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 …

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 …

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 …

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 …