A Quote by Hannah Fry

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.
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.
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.
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.
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.
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.
We have this imbalance where the people who are making algorithms aren't talking to the people who are using them. And the people who are using them aren't talking to the people who are having decisions made about their lives by them.
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.
The most important question in 21st-century economics may well be, 'What should we do with all the superfluous people, once we have highly intelligent non-conscious algorithms that can do almost everything better than humans?'
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.
We don't let a car company just throw out a car and start driving it around without checking that the wheels are fastened on. We know that would result in death; but for some reason we have no hesitation at throwing out some algorithms untested and unmonitored even when they're making very important life-and-death decisions.
I would argue that one of the major problems with our blind trust in algorithms is that we can propagate discriminatory patterns without acknowledging any kind of intent.
Because, ultimately, we can't just think of algorithms in isolation. We have to think of the failings of the people who design them - and the danger to those they are supposedly designed to serve.
This is what I think. You can’t have everything at once. Like the pockets in your clothes, there’s a limit to how much we can have at once. There are times when to put something in your pocket, you have to throw something else away. You have to prioritize those decisions by yourself. There are things that you can’t get back once you’ve thrown them away.
Nature creates natural swarms. UNU uses networking technology and algorithms to take advantage of the knowledge, wisdom, and insights of a large group of people by allowing them to think as one.
We dont have better algorithms, we just have more data
Algorithms and data should support the human decision, not replace it.
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