maxtollenaar

Wharton MBA has this Team-based Discussion (TBD) as a part of their application process. From what I hear, it is to weed out Asshole type of personality. In regard to how effective the method is, I'm not 100% convinced.

friends don't let friends run on us-east-1

Can't find the exact policy reference, but I remember that if you don't understand Jeff's problems(in HW or exams), you could write "I don't know" and get 25% of the grades for the problems

not exact reference towards the policy: "In the table below, green scores are above 95% and red scores are below 25% (equivalent to "I don't know" on every page); those outliers were excluded when computing statistics and cutoffs." - https://courses.engr.illinois.edu/cs473/fa2012/

Can confirm

Congrats on the work!

Google maps and other navigational apps could potentially flood residential routes that weren't supposed to handle traffic. People have done different measures to combat this. One anecdote is: "And in 2016, in Takoma Park, Maryland, residents went to great lengths to prevent Waze drivers from flooding their roads during a bridge reconstruction project. A Takoma Park man reportedly started reporting phantom wrecks and traffic jams on his street before Waze banned him" ref: https://www.usnews.com/news/national-news/articles/2018-05-0...

Used to intern at Epic in 2015. I asked my manager why they weren't on cloud. He said our customer data is too critical to be stored in cloud. Interesting how things have change in a very short time

You can also use the websocket proxy: https://pulsar.apache.org/docs/en/next/client-libraries-webs...

Marc brooker is a legend

Not related, but this is traffic micro simulation Reinforcement learning framwork that my previous research group started: https://m.youtube.com/watch?v=P7xx9uH2i7w

I’m not sure if you’re trolling or not, cus there is definitely an upper limit to how information can be encoded in data.

I’m not sure I could agree with that statistician can “learning new things” with smaller data points. Statisticans might come and see the pattern with “better” prior than ML models, that allow them to come up with better conclusion. However, given the same dataset, there is a maximum to thee information that can be extracted from the dataset. Ideally, Any human(think of human brain as a pattern recognizer) or appropirate statistical method would come up with the same information from the datast, given no prior

Could you elaborate more on the first point? I don't really get it

My favorite Knuth quote: "Email is a wonderful thing for people whose role in life is to be on top of things. But not for me; my role is to be on the bottom of things. What I do takes long hours of studying and uninterruptible concentration. I try to learn certain areas of computer science exhaustively; then I try to digest that knowledge into a form that is accessible to people who don't have time for such study."

source: https://www-cs-faculty.stanford.edu/~knuth/email.html

Here's a presentation on one of the examples in the blog post(https://www.youtube.com/watch?v=P7xx9uH2i7w). Used to work with the group, awesome innovation.

Took this class with Prof. Sahai last semester when they redesigned the syllabus. I would recommend to anyone who wants to understand ML with some math rigor. The class notes: http://snasiriany.me/files/ml-book.pdf is excellent. The hw problems are pretty fun too