A Quote by John Seabrook
When Spotify launched in the U.S. in 2011, it relied on simple usage-based algorithms to connect users and music, a process known as 'collaborative filtering.' These algorithms were more often annoying than useful.
Related Quotes
These algorithms, which I'll call public relevance algorithms, are-by the very same mathematical procedures-producing and certifying knowledge. The algorithmic assessment of information, then, represents a particular knowledge logic, one built on specific presumptions about what knowledge is and how one should identify its most relevant components. That we are now turning to algorithms to identify what we need to know is as momentous as having relied on credentialed experts, the scientific method, common sense, or the word of God.
In deep learning, the algorithms we use now are versions of the algorithms we were developing in the 1980s, the 1990s. People were very optimistic about them, but it turns out they didn't work too well.
As algorithms push humans out of the job market, wealth and power might become concentrated in the hands of the tiny elite that owns the all-powerful algorithms, creating unprecedented social and political inequality. Alternatively, the algorithms might themselves become the owners.
Mathematics my foot! Algorithms are mathematics too, and often more interesting and definitely more useful.
The problem with Google is you have 360 degrees of omnidirectional information on a linear basis, but the algorithms for irony and ambiguity are not there. And those are the algorithms of wisdom.
Spotify, Tidal, and even YouTube, to a degree, are vast and rich troves of music, but they primarily function as search engines organized by algorithms. You typically have to know what you're looking for in order to find it.
Once you see the problems that algorithms can introduce, people can be quick to want to throw them away altogether and think the situation would be resolved by sticking to human decisions until the algorithms are better.
Data dominates. If you've chosen the right data structures and organized things well, the algorithms will almost always be self-evident. Data structures, not algorithms, are central to programming.
It's difficult to make your clients understand that there are certain days that the market will go up or down 2%, and it's basically driven by algorithms talking to algorithms. There's no real rhyme or reason for that. So it's difficult. We just try to preach long-term investing and staying the course.
When The Daily Muse initially wanted to launch a job board, our first ideas were insanely (and needlessly) complex. We wanted to integrate with social networks, gather rich personal data to build predictive algorithms, and put together numerous cool visualization tools before launching out to the world. We were just sure users would love it!
Fashion brands are really useful in producing algorithms to find out how people think and how they feel.
Algorithms learn by being fed certain images, often chosen by engineers, and the system builds a model of the world based on those images. If a system is trained on photos of people who are overwhelmingly white, it will have a harder time recognizing nonwhite faces.
As our understanding of fraud evolves, we might one day be able to develop predictive algorithms that could identify would-be con artists based on patterns of behavior.
We dont have better algorithms, we just have more data
Artificial intelligence uses a complex set of rules - algorithms - to get to a conclusion. A computer has to calculate its way through all those rules, and that takes a lot of processing. So AI works best when a small computer is using it on a small problem - your car's anti-lock brakes are based on AI. Or you need to use a giant computer on a big problem - like IBM using a room-size machine to compete against humans on Jeopardy in 2011.
The classes of problems which are respectively known and not
known to have good algorithms are of great theoretical interest. [...]
I conjecture that there is no good algorithm for the traveling
salesman problem. My reasons are the same as for any mathematical
conjecture: (1) It is a legitimate mathematical possibility, and
(2) I do not know.
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