Few people have the time and patience to answer surveys, even when they do they might not provide genuine opinions (consciously or not), and the sampling is inherently biased.
A well designed system collects data and analyzes users on the go; just look at Google, Facebook, and Amazon.
Algorithms can already devise algorithms and write programs.
It is just a matter of time before they can do that in a scale massive enough to displace many, if not most, programming jobs, just like what robots have already done to the manufacturing jobs.
I found inspirations from this Q/A session between Sam Altman and Michael Moritz not only for startups but anyone who needs to deal with an ever changing environment: do not sit on the past, keep a fresh team, and change with the future.
I have decided to feed the art works by (or related to) my father, grandfather, great*** grandfathers, etc., to this neural network inceptionism code.
Below are some results. Each group consists of the original image, and the results amplified for low and high level neurons.
戲院街的後巷 ~ The back side of theater street
Calligraphy, uniform font size
Calligraphy, varying font sizes
Kublai Khan
Never, ever, send your code to others and ask them to debug whatever functional or performance issues you have.
That is the most effective way to signal you being a liability rather than an asset. It is like asking others to wipe your ass for you.
Spend time figuring out what is going on inside your own code, and ask specific questions if you need help. Take a look at stackoverflow.com, a good forum for coding questions.
I was watching Super Bowl XLIX on my tablet using NBC’s live sports app, but had to attend a meeting during the middle of the third quarter.
So I paused the game, hoping to resume it later without spoiler.
Fortunately, nobody in Hong Kong mentioned anything about the game to me, and the app is robust enough to replay everything, including commercials.
After the meeting I went straight to solitary confinement and finished the rest of the game around midnight Arizona time.
Tom Brady threw his second interception when I paused the game; the world knew the rest before I resumed it.
🙂
I wonder if this can be an interesting HCI research topic.
The finance and tech industries have been the whipping boys for American inequality. Lie in their junction are the algorithm traders.
A specific form of algorithm trading, with high frequency as depicted in this book, is to arbitrage the time differentials between signals traveling through different electronic routes.
For example, say you want to buy or sell a block of stocks too large for any single exchange to fulfill. Your order is then broken down into smaller blocks, each routed to a different exchange. A high frequency trader, by placing small orders for all stocks in all exchanges all the time, like a fisherman placing baits, can detect your order arriving in the first exchange, and quickly insert itself as the counter party of all your other orders arriving later in other exchanges. This allows the trader to make a small amount profit multiplied by a very large number of trades.
In order to pull this off, a high frequency trader has to be on the frontier of high performance computing.
This is a highly entertaining read like many of Michael Lewis’ previous books. But the distinction is not all that clear between the narrated protagonists and antagonists, who are all wealthy financiers.
Instead, the most intriguing character I found in the book is Sergey Aleynikov, a former Goldman coder whose prosecution triggered the start of the book, in which he was quoted:
If the incarceration experience doesn’t break your spirit, it changes you in a way that you lose many fears.
You begin to realize that your life is not ruled by your ego and ambition and that it can end any day at any time. So why worry?
Instead of banning parking apps, the SF city should think about how to collect money leaving on the table.
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