Hacker Cinema
Flight
2012 · Film · 138 min
Directed by Robert Zemeckis
Commercial airline pilot Whip Whitaker has a problem with drugs and alcohol, though so far he's managed to complete his flights safely. His luck runs out when a disastrous mechanical malfunction sends his plane hurtling toward the ground. Whip pulls off a miraculous crash-landing that results in only six lives lost. Shaken to the core, Whip vows to get sober -- but when the crash investigation exposes his addiction, he finds himself in an even worse situation.
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12 comments on Hacker News
> Alcohol and caffeine are not a wise combination.
Yet, what does every single person that's trying to sober up ask for or have suggested to them? Coffee.
One of my favorite movie scenes is from "Flight - 2012" with Denzel and John Goodman trying to make Denzel's character "right". I love it for that fact that they don't try to call it "sober him up", but just to get him balanced. That's all the caffeine in the coffee is trying to do, yet people insist on saying "sober up"
In the film Flight it is implied the plane possibly does not crash because the pilot is on drugs.
A few weeks ago, I found the film Flight (2012) by Robert Zemeckis, starring Denzel, with John Goodman as a coke dealer. It was free to watch on YouTube.
The film is mostly human drama, but the first fifteen are a really gripping, suspenseful depiction of the air disaster at the center of the plot, from the cockpit view. And it's based on a real-life incident. I enjoyed the whole thing, and the aviation part really pulled me in.
In the movie https://www.themoviedb.org/movie/87502-flight the lawyer (Don Cheadle) successfully gets a blood alcohol test removed as evidence because the machine was not calibrated within the official time frame of proper calibration. A total technicality. I wonder how a lawyer would get a client out of a voice recording?
I'm not sure it really falls under the banner of "documentary" but Red Bull Media House make a film called "Flight" a few years ago that profiles a group of snowboarders flying around and doing snowboarding. You won't learn anything watching it but it's some of the best film making I've ever watched. It's beautiful.
Wasn't this a story element in the movie Flight (2012) taken from Alaskan 261?
> It's frustrating to see that car companies see themselves as "partners" to the NTSB or other investigators.
That is how the NTSB "party" system is ran[0]. Contrary to what is shown in various movies eg. Flight(2012), Sully (2016), an NTSB investigation is not an adversarial process, and is not a criminal investigation. Additionally, NTSB determined cause (an opinion by the board) cannot be used as evidence in civil litigation.
Asiana 214 [2] is a good example of the NTSB being somewhat divided in the opinion of the 777 autopilot having a design flaw vs "pilot human performance" as the cause of the crash.
IMHO both the autopilot design and the pilot performance were causal factors in the crash. It is probable the NTSB will determine both the Tesla driver and the autopilot were causal factors in this crash.
[0] https://www.ntsb.gov/investigations/process/Pages/default.aspx
[1] https://www.ntsb.gov/legal/Documents/NTSB_Investigation_Party_Form.pdf
[2] https://en.wikipedia.org/wiki/Asiana_Airlines_Flight_214
But nowadays drinking and drugs are a bigger problem than hours. Remember the movie "Flight"? Thanksfully those planes fly fully automatic, but still some weather decisions have to be made from time to time.
That's interesting, but we (as far as I know) weren't talking about game development, but about TV/movie production.
Most people don't realize it, but many recent high-budget movies were filmed in Georgia, e.g. Fast Five, Vacation, Ant-Man, all three Hunger Games movies, one of the recent X-Men movies, Flight, etc. After 2008, there was an abundance of empty warehouse space here available at deep discounts, not far from the highest volume airport in the world.
As the economy rebounded, momentum in that industry seems to have remained even though there aren't many empty/cheap warehouses here anymore. A few developers are even working on purpose-built movie studios around town now.
It has been interesting to watch, and certainly could parallel what could happen in the bay area if an exodus to somewhere like Austin began in earnest.
No, they don't really go far enough to be useful. Look at the netflix movies on their site, then look at the amazon prime movies... there's overlap. I've already seen Flight, but now I have to see it's box image every time I look at a list of movies for each streaming service.
What would be better is a list of movies across all services, I see Flight, then I click, "I've already seen it", then I never have to see it again.
Or, I see Flight (and want to watch it), click it, then I'm presented with links to watch on Netflix, free for Amazon Prime, $3.99 on Amazon Instant, 4.99 on Vudu, etc...
A recommendation engine would be gravy. What I want is a global view of media available that lets me say, "Queue this to watch", "I've seen this and stop showing it to me", "I have no interest in see this". Then the movies it's showing me are new to me.
Basically, the problem is finding a needle in a haystack. You could rummage through the haystack, or you could remove the hay, straw by straw and say "Is this a needle"?
I suppose it says something that Argo and Moonrise Kingdom are the only movies that I can remember really liking in 2012. Flight was also memorable, if you're into character dramas. I've also heard really good things about Searching for Sugar Man, but I didn't get a chance to see it in theaters.
Avengers was solid entertainment, but I wouldn't make a special trip to see it. Most of the other big releases with nerd draw were disappointments: Lincoln was overhyped Oscar-bait, Brave was forgettable, and the last Dark Knight movie was just bad (below average for summer movies, which is a low bar).
It's a fun way to find sequential tokens (words) that have a high probability of being the names of people.
- Take a lexicon (list) of single words in a given language (English for example).
- Take a block of text and think of it as an ordered sequence of tokens, news articles work really well for this approach, books not as much.
Example (from http://www.cnn.com/2012/11/02/showbiz/movies/flight-review-charity/index.html?hpt=hp_bn9): With its spectacular plane crash -- I would rate it fractionally behind the air disasters director Robert Zemeckis staged in "Cast Away" and Joe Carnahan in "The Grey," but still more than gut-wrenching enough to make you think about taking the train, next time -- "Flight" immediately raises the stakes on your typical addiction drama. But that's essentially what it is -- with a courtroom finish for extra lift.
- Stream through the text, any word that is in your lexicon, throw away.
---- ---- ----------- ----- ----- -- - ----- ---- -- ------------ ------ --- --- --------- ------- Robert Zemeckis ------ -- "---- ----" --- Joe Carnahan -- "--- ----," --- ----- ---- ---- ------------- ------ -- ---- --- ----- ---- ----- --- -----, ---- ---- -- "------" ----------- ------ --- ------ -- ---- ------- --------- -----. --- ----'- ----------- ---- -- -- -- ---- - --------- ------ --- ----- ----.
- treat each group of remaining tokens as separate objects, in this case we have 2
- write these non-lexical (not in your original lexicon) sequences out:
Robert Zemeckis
Joe Carnahan
Boom, you just made an entity extractor that plucks names out of text without having to model the English Language too rigorously. And it generally works in most languages that have a low intersection between name-part tokens and lexicon tokens. And it can be brutally fast.
Where this gets interesting is in suppressing junk and tweaking the algorithm around things like parenthesis, apostrophese, and sentence boundaries. There's lots of little edge cases like this that you have to be mindful of - numbers in the text for example are never parts of names but aren't in your lexicon so you have to figure out what to do with those. And then you can use other heuristics to improve the results, suppose another sentence just had "Zemeckis" in it, that's a name, but then suppose another sentence had a token not in your lexicon like "Samoflange"...do you count that as a name? What about lexical tokens that are names like "Bush"? So you can try things like only counting sequences of tokens that have more than 2 tokens (like "Robert Zemeckis") and ignoring ones that have only 1.
And it goes on and on -- endless tweaks to improve the quality of the names you get and suppress non-name sequences.
To make the project more interesting, try storing your lexicon in a database or some kind of index so you can search it quickly, I like to use SQLite files with indexes on the lexicon table myself, but it's a fun assignment to try different things like in-memory TRIEs.
If you want to try threading, you can try playing around with searching the text at different start and end points (thread 1 searches the first 25% of the text, thread 2 the second 25%, etc.) or have different threads search different articles.
You can try all kinds of different things to keep it interesting and as you start abstracting the problem you can play with all kinds of different control and data structures to accomplish the task. Trying to make this as fast as possible (with all the heuristics turned on) can also be a fun challenge.