In our First IT Origins Survey, we’re asking the community on of our standard interview questions: What are the best and worst trends in IT right now? We’re pulled together some early responses, but we’d love your feedback as well.
The phrases “Machine Learning” and “Artificial Intelligence” get thrown around a lot in enterprise IT. Every solution seemingly has one of the two baked in. But what do those terms actually mean? How can be tell the difference between actual implementations and marketing bluster? We talked to mathematician Dr. Rachel Traylor to find out.
It’s become common now for IT companies to list deep learning algorithms as a major platform feature, from analytics to automation. But home does deep learning compare to actual human intelligence? Ray Lucchesi looked at some of its issues in the context of the MIT Intelligence Quest.
In this edition of Gestalt News:
– we generate new IT slogans using machine learning
– Matt Leib sits down for the IT Origins interview
– the Gestalt IT Rundown discuss the chip market crunch, Samsung surpassing Intel, and what a potential Dell EMC – VMware merger does
We used a predictive text keyboard from Botnik to make up some fake IT companies and slogans. Plus we threw in some stock photos for fun.
James Green takes his turn in the IT Origins hot seat. He reveals how he got started in IT (a career slightly delayed thanks to girls, beer, and video games), what he’s reading, how caffeine changed his life, and what tools he uses to stay organized.
Technical debt is more than the cost of not adopting a new technology. Dr. Rachel Traylor points out that it can also be the cost of hastily adopting a new technology without considering how it will fit into your bigger strategy.
Wireless IT also seems to personally effect end-users. Perhaps it’s because it’s easier for them to seemingly isolate Wi-Fi as the source of their frustration, it seems less bundled into other IT infrastructure (even if it really isn’t).
This makes these end-users both insanely frustrating, with the blanket declaration that “Wi-Fi sucks”, but also useful as the ultimate arbiter of performance. There’s generally only binary reactions of approving apathy or vocal derision.
Checkers is the game I played to kill time waiting for tables at restaurants. But solving checkers turns out to be a fascinating exercise. Recently, Alphabet’s AlphaGo team has made a lot of headlines with their neural network-based ability to beat human Go masters. But Ray Lucchesi looks back at earlier days trying to solve checkers with much more limited hardware and fundamentally different approaches.
Today, the term artificial intelligence is a lot like a baseball at a tee-ball game, it gets thrown around a lot, albeit not very accurately. Often in the rush to brand something as trendy, all meaning gets tossed out the window. So when I saw Trove in the iOS App Store claiming to bring AI to email, I was skeptical.