The Saturn V rocket got to the moon burning 20 tones of fuel per second. An African elephant weighs roughly 5 tons. So roughly 4 elephants worth of fuel per second. Amazing video illustration:
Read MoreSpaceX Falcon 9: How Elon Musk’s Rocket Is Winning the Reusability Race
This wonderful illustration from July 2019 National Geographic explains one of Elon Musk’s greatest space innovations – rocket reuse.
Continue reading “SpaceX Falcon 9: How Elon Musk’s Rocket Is Winning the Reusability Race” Read More
Video– First year of life for six Swainson’s Hawks
This mapped graphical visualization of six Swainson’s Hawks from Data is Beautiful caught my attention.
Continue reading “Video– First year of life for six Swainson’s Hawks”
Read More
Video– 3D animation of my route on Mount Everest
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”
Read More
Mars mission will take 6 years roundtrip including prep
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.
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
Continue reading “Simple questions you should ask to help reduce AI hype”
Read MoreVideo– Excellent product description by Chevy from 1937
Skip to 3:01 to learn how the automobile differential allows a vehicle to turn a corner while keeping the wheels from skidding. It’s a brilliant product break-down using language and concepts to conceptualize the product features into something that everyone understands and wants to buy.
Read More