In-depth– Spend analysis of 6 years 686 rides and $12,041 spent on Uber

What I learned from my Uber data

I save $703/month using Uber versus owning 2 cars (🚙🏎). I’ve spent $12,041 on 686 Ubers over 6 years. By comparison, car ownership cost $11,783 per year, most of it depreciation. What could $703/month buy you? Well, four years of $703/month and you’ll save enough to climb Mount Everest 🏔 unguided. Or you could buy a shiny new laptop every 3 months. More details below. Continue reading “In-depth– Spend analysis of 6 years 686 rides and $12,041 spent on Uber”

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eBay Adds to Machine Learning Hype

Earlier this week I blogged about 5 simple questions you can ask to determine AI hype.

Today I saw something with more hype than I would expect from a respected tech company. “eBay determines this price through a machine learned model of the product’s sales prices within the last 90 days.”

In my opinion this price prediction is not machine learning. It’s just math. It’s not a machine learned anything. It fails 1-5 of Stephen’s tests.

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Simple questions you should ask to help reduce AI hype

As Machine Learning captures our imagination, it’s important to separate the material from the hype. Here are 5 simple questions you should ask to help reduce AI hype:

  • “How much training data is required?”
  • “Can this work unsupervised (= without labelling the examples)?”
  • “Can the system predict out of vocabulary names?” (i.e. Imagine if I said “My friend Rudinyard was mean to me” – many AI systems would never be able to answer “Who was mean to me?” as Rudinyard is out of its vocabulary)
  • “How much does the accuracy fall as the input story gets longer?”
  • “How stable is the model’s performance over time?”

source: Stephen Merity of Salesforce

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