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”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.
Via reddit
Continue reading “Video– First year of life for six Swainson’s Hawks”Video– 3D animation of my route on Mount Everest
3D model and animation by Jeremy White via NYT.
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”
Mars mission will take 6 years roundtrip including prep
Photo credit National Geographic Magazine, November 2016.
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”
Video– 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.
Video– Talk at Battery SF about Fix Maps
Here’s the recorded video of a 5-minute talk I gave at Battery San Francisco about Fix Maps. The talk format is called “Ignite” which calls for 20 slides that auto-advance every 15 seconds.
Here’s the slides from the talk.
How To Build A Snow Cave
Snow caves are fun to build and provide warm places to sleep and take shelter from a storm. This is a brief how-to guide to building a snow cave.
The snow cave in this example was built by 4 people near Skinner Hut at the edge of the timberline in late-December 2015 at 11,620 feet. Builders were Brett Poulin, Chris, Nick, and me, Neal Mueller. The cave we built was large enough to sleep and provide eating quarters and shelter for 4 people. It included a vapor escape for cooking.