In 2021 pieces of Mars will land safely on earth as part of Mars 2020. The Mars 2020 rover collects samples and leaves them in canisters on the surface. The lander deploys a fetch rover to collect the samples and deposit them in an ascent vehicle, which blasts into Mars orbit. There, a return orbiter collects the samples for transport back to Earth.
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:
This wonderful illustration from July 2019 National Geographic explains one of Elon Musk’s greatest space innovations – rocket reuse.
Adding reusable technology reduces the payload and cost. In order to make the Falcon 9 reusable and return to the launch site, extra propellant and landing gear must be carried on the first stage, requiring around a 30-percent reduction of the maximum payload to orbit in comparison with the expendable Falcon 9.
With full reusability on all three booster cores, the Falcon Heavy will lift approximately 18,000 lb to geosynchronous transfer orbit at a cost of $4200/pound. The ultimate goal with the development of SpaceX is to bring the cost down to $500/pound, which is believed to be possible only with rocket reuse.
This mapped graphical visualization of six Swainson’s Hawks from Data is Beautiful caught my attention.
3D model and animation by Jeremy White via NYT.
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”
Photo credit National Geographic Magazine, November 2016.
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.
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