This is probably skewed by H1B applications, where employers have an easier time with the lottery with a more advanced degree. Bachelor's fall into the common 65k/year bucket that puts applicants at risk regardless of qualifications.
One government is optimistic in this case. This is the EU talking, they'll gladly deliberate for months before any action is taken.
Furthermore, wouldn't this worsen an authoritarian or Trump-like scenario? We are expecting the government to moderate itself? Wouldn't a yes man/crony just sit in that seat ala William Barr and let the tweets go unchecked?
Yeah, in the US, depending on which dystopia you think we're living in, it's very easy to envision either some Trump crony making sure nobody was disrespectful to him on Twitter OR some "deep state" agents starting to censor him way back in 2015 to try to prevent his getting elected in the first place.
I've yet to see a realistic proposal for what should replace Twitter's ability to choose its own TOS that isn't either a worse situation like that, or isn't some "only illegal stuff should be taken down" that probably results in far fewer open places on the internet accepting user-generated content for broadcast in the first place.
The crown has to go to machine learning and neural network advancements. This builds on the increase in data and processing capacity, but it's still an amazing advancement over a mere 10 years. One may argue that the actual CS has been around for longer but I don't think that's giving enough credit to recent advancements.
Hi everyone! This is Dimitri and Steve, the creators of Count: a machine learning company looking to quantify the organic data inside photos, video, and sound. Anybody that has lived in Manhattan understands the dynamics of the Shake Shack line in Madison Square Park. We thought it would be fun to practice our craft quantifying the Shake Shack line in real-time as our first experiment.
If you are interested in the technology or want to partner with us to quantify organic data sources email us at count@thoughtmerchants.com to start a conversation.
Counting the number of people in a line outside is a challenge and there are a few steps to ensure an accurate prediction. First, the raw camera feed from Shake Shack observes the line, the park behind the line, and outside seating. The perspective captures a dense crowd with most bodies and faces obscured and therefore difficult to analyze with traditional machine learning models. In addition, the line is outside in the elements, with snow, inconsistent lighting, shadows, and even umbrellas.
Count creates a density map predicting the likelihood of each pixel being a person, allowing us to then calculate the number of people in the entire frame. We then use another model to determine the line from the crowd: people waiting at the starting point, side by side. Being able to differentiate between a distant crowd and an actual line has its own set of subtle challenges.
