Christmas Day is the least popular birthday in several countries.
These questions can help us think more critically about new developments in artificial intelligence.
- The media often exaggerate and overhype the latest discoveries in artificial intelligence.
- It's important to add context to new findings by asking questions: Is there a demo available? How narrow was the task the computer performed?
- A more robust approach to artificial intelligence involves solving problems in generalized situations rather than just laboratory demonstrations.
Correlation doesn't equal causation — we all know this. Well, except robots.
- There are a lot of people in the tech world who think that if we collect as much data possible, and run a lot of statistics, that we will be able to develop robots where artificial "intelligence" organically emerges.
- However, many A.I.'s that currently exist aren't close to being "intelligent," it's difficult to even program common sense into them. The reason for this is because correlation doesn't always equal causation — robots that operate on correlation alone may have skewed algorithms in which to operate in the real world.
- When it comes to performing simple tasks, such as opening a door, we currently don't know how to encode that information — the varied process that is sometimes required in differing situations, i.e. jiggling the key, turning the key just right — into a language that a computer can understand.
Gary Marcus is the author of Rebooting AI: Building Artificial Intelligence We Can Trust.
A new study challenges international life expectancies.
It's far earlier than most teams currently do.
Bruce Bennett / Staff
- A 2018 study used data from the 2015–2016 NHL season to conduct an analysis on the advantages of pulling the goalie.
- The results suggest the optimal time to be about three times earlier than convention calls for.
- The authors believe the results have implications in areas outside of hockey, such as investing.