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Basically there is nothing preventing the lion from reaching the tamers' coordinates exactly, i.e. it's not a case of the distance only closing "in the limit".
The paper linked elsewhere here gives an illustrative example where this happens in what is clearly finite time.
(This is if the tamer is on the border as parent claimed - the paper also shows that the lion can't win if he doesn't do this)
I did multiple internships at Amazon and now I work here full-time. I love it here. But, like other people have pointed out, your experience can be very dependent on which team you join. Personally, I wouldn't want to work full-time on the team that I did my first internship on. The work there just wasn't fun. But my internships put me in a good position to know which teams suck and which teams are great, and so I chose a great team to join. Hopefully you are in a similar place because of your internship. Did you enjoy your team? If not, did you see any teams that you would like to work on?
My AWS team was good. I enjoyed my work as an intern. However I was warned that work in the group is operations intensive. I could see that myself in those 12 weeks. As an intern I was not given any operations work. Obviously it will change when I join as a full timer. As noted below I am looking at other AWS teams, Kindle silk browser team. I need to find out about these teams.
Yeah, I've heard that some AWS teams can have heavy operational load. If it's important to you to avoid that, you could consider somewhere in WAP/BuilderTools. That's where I work (so yes I am biased), and in my experience the operational load tends to be pretty light throughout the org. On my particular team we almost never get paged. Plus we get to build neat things in this org (remember how Stevey mentioned that Amazon's "versioned-library" system is good?).
Silk is probably a neat team to look into as well. They're building a cool product, they're still a small team, and they have good leadership. The director in charge of it used to be the head of Builder Tools and he's great.
But yeah, talk to a lot of teams and ask them about the things that are important to you (operational load, current/future projects, code quality standards, whatever other things you can think of to ask) and see if any of those teams sound cool to you. I won't lie and say that every place in Amazon is perfect, but if you choose well I think it's possible to find a great team to work on.
Just now found out that "no college hires" in Silk team. That sucks! College hires are high on energy and enthusiasm. I don't know why teams would not want college hires :(
At the risk of being slightly offensive, college hires are high on energy and enthusiasm but low on ability to produce reliable code (on average). They can produce a lot of code, but they tend to have blind spots when spotting failure scenarios, resulting in "gotcha!" outages or bugs. Its not a big deal if they have a more experienced developer reviewing their commits, but if you're iterating fast on something that's going to be a flagship product, it becomes less tolerable.
No offence taken :)
I understand your point. Silk was something I was really interested in. Tough luck. I wonder whether there is still a chance if I can talk to the Silk team manager.
I started watching the lectures at ml-class.org this weekend, and I found myself noticing the same thing. It's wonderful how quickly the professor goes through the introductory material so you can start learning right away. After only half an hour of watching, I had learned things I didn't know before (supervised learning vs. unsupervised learning, regression vs. classification, etc.). And after another hour he had covered all the way through a whole algorithm for solving regression problems. I'm finding this class to be much more efficient at teaching than a lot of my lectures in college were.
The biggest differences in my mind between the online ml-class and the lectures I attended in college are:
1) ability to watch the videos at 1.5x speed. this is great because I usually find speech to be such a low-bandwidth transport.
2) ability to have captions on the videos. I don't know why, but I understand what people are saying better if I have captioning. I do this with regular broadcast tv as well.
3) ability to pause or go back when I don't quite understand something. I can look at the formula for a little bit longer until it clicks, or I can jump back a minute or two to figure out where I went off the track.
4) broken up into 10 minute chunks with memory of what you've already watched. this way I can watch the lecture when I have time, but without having to go searching for where I left off.
5) the interstitial and end quizzes. the interstitials make it so that I can be sure that I understand the material before I move on. the end quizzes are awesome. I forgot how much I liked tests in school & how life in industry is pretty much devoid of such things.
