I dug into the predictions that we generate of daystozero.org. Comparing Finland, Norway, and Sweden, it looks like something that Sweden did could result in about 2,600 people dying that don’t have to:
- Create distance-based sort lists for countries.
- Get Lat/Lon centroids from country data (gis.stackexchange.com/questions/71921/list-of-central-coordinates-centroid-for-all-countries)
- For each country
- Find longitude offset
- Subtract from all countries and wrap if past -180/+180
- Calculate distance
- Sort list
- Add to dict and create json file
- IRS proposal – done!
- A better snippet: the best way to cheat on taxes is to deliberately lie to the IRS about what you earned over a year, what you spent over a year, and the ways you would fill out those forms. This is where “time of year” really comes into play. The IRS assumes you worked on April 15 through the 15th of the following year in order to report and pay taxes on your actual income from April 15 through the following year. I’ve put some pictures and thoughts below. There are some really great readers who have put some excellent guides and resources out there on this topic. If you have any additional questions, please feel free to leave a comment below and I will do my best to answer them.
- Another good snippet: The best way to cheat on taxes is to set up an LLC or other tax-sheltered company that makes up for your sloth in paying business taxes. By doing this, you can deduct the business expenses and pay your taxes at a much lower tax rate, while also getting a tax refund. So, for example, if your net operating income for 2014 was $5,000 and you think you should owe about $2,000 in taxes for 2015, I suggest you set up a S-Corporation for 2015 that only owes $500 in taxes. Then, you can send the IRS a check for the difference between the $2,000 difference you owe them and the $5,000 net operating income for 2015.
- Finish first pass? Done! And sent to Antonio!
- Deep learning has triggered the current rise of artificial intelligence and is the workhorse of today’s machine intelligence. Numerous success stories have rapidly spread all over science, industry and society, but its limitations have only recently come into focus. In this perspective we seek to distil how many of deep learning’s problem can be seen as different symptoms of the same underlying problem: shortcut learning. Shortcuts are decision rules that perform well on standard benchmarks but fail to transfer to more challenging testing conditions, such as real-world scenarios. Related issues are known in Comparative Psychology, Education and Linguistics, suggesting that shortcut learning may be a common characteristic of learning systems, biological and artificial alike. Based on these observations, we develop a set of recommendations for model interpretation and benchmarking, highlighting recent advances in machine learning to improve robustness and transferability from the lab to real-world applications.