Top 132 Algorithms Quotes & Sayings

Increasingly, our decisions will be made by the algorithms that surround us. Whenever there is a big dilemma, you just ask Google what to do. And what kind of life is that?

Facebook, Twitter and YouTube create algorithms that promote and highlight information. That is an active engineering decision.

Mathematics is as much an aspect of culture as it is a collection of algorithms.

Computing should be taught as a rigorous - but fun - discipline covering topics like programming, database structures, and algorithms. That doesn't have to be boring.

If someone stole your keys to encrypt the data, it didn't matter how secure the algorithms were.

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.

In some ways, chocolate chip cookie recipes are my favorite algorithms. You put a bunch of bad-for-you stuff in a bowl and get a delicious result.

Algorithms don't do a good job of detecting their own flaws.

Lead generation excels when a campaign is looking to capture a piece of factual intelligence that could never be modelled or predicted through profiling and sophisticated propensity algorithms.

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.

I want to talk about privacy, the quality of the information you receive, whether it's neutral or commercial or pointed, bringing consciousness to the lack of neutrality in the algorithms.

We dont have better algorithms, we just have more data

All around us, algorithms provide a kind of convenient source of authority: an easy way to delegate responsibility, a short cut we take without thinking.

Fancy algorithms are slow when N is small, and N is usually small.

If I'm reading my Facebook feed, it's using algorithms, procedures, and methods to give me what I want, or what it thinks that I want, or what suits its business plan.

I design genetic algorithms, neural network and artificial intelligence systems.

When we miss with all the metadata collection we've had, the San Bernardino couple and the Tsarnaev brothers, what that suggests to me is that we are using the wrong algorithms to search through all this data.

Fashion brands are really useful in producing algorithms to find out how people think and how they feel.

There's a belief that whatever it is I'm looking for is out there, but I have a really difficult time finding it. Search algorithms alone are falling short in being able to provide real context around information.

I love sophisticated algorithms that help consumers in a tangible way.

We are splintering what was the 'camera' and its functionality - lens, sensors, and processing - into distinct parts, but, instead of lenses and shutters, software and algorithms are becoming the driving force.

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?'

I don't like the algorithms that don't do what they claim they do.

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.

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.

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.

I would say the larger the pool you have to select from, the more likely you are to find the most compatible person for you if you have the right algorithms working on your behalf.

I grew up as a computer scientist, and I've always been fascinated by algorithms.

Data science is the combination of analytics and the development of new algorithms.

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.

Facebook and Google are essentially an advertising duopoly, and we have almost no idea how their algorithms work.

The beauty of compounding iterative algorithms - evolution, fractals, organic growth, art - derives from their irreducibility.

In fact, there was general agreement that minds can exist on nonbiological substrates and that algorithms are of central importance to the existence of minds.

You have to teach your algorithm what it can do and what it cannot do because, otherwise, there is a risk that the algorithms will learn the tricks of the old cartels.

The idea that humans will always have a unique ability beyond the reach of non-conscious algorithms is just wishful thinking.

Learn when and how to use different data structures and their algorithms in your own code. This is harder as a student, as the problem assignments you'll work through just won't impart this knowledge. That's fine.

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.

Algorithms are great, but they're very limited in what they can do as far as playing songs and playing a mood.

I decry the current tendency to seek patents on algorithms. There are better ways to earn a living than to prevent other people from making use of one's contributions to computer science.

Mathematics is not about numbers, equations, computations, or algorithms: it is about understanding.

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.

There's a whole company called Palantir that does nothing but derive and create algorithms riches to search through big data. We're not using their capabilities. For heaven's sake, some of this is just ineptitude.

Sexism, racism, and other forms of discrimination are being built into the machine-learning algorithms that underlie the technology behind many 'intelligent' systems that shape how we are categorized and advertised to.

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.

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.

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.

My particular focus at the moment is on the development of genetic algorithms and neural networks that work together to create computer architectural systems.

Epic will manually curate the Epic Games storefront rather than relying on algorithms or paid ads.

I remember that mathematicians were telling me in the 1960s that they would recognize computer science as a mature discipline when it had 1,000 deep algorithms. I think we've probably reached 500.

Algorithms and data should support the human decision, not replace it.

By 2025, 80 percent of the functions doctors do will be done much better and much more cheaply by machines and machine learned algorithms.

[The Euclidean algorithm is] the granddaddy of all algorithms, because it is the oldest nontrivial algorithm that has survived to the present day.

The most important goal I had in mind was to convince people to stop blindly trusting algorithms and assuming that they are inherently fair and objective.

It took me 1057 pages to describe the hundreds of mathematical equations, algorithms and programming techniques that I invented and used.

To understand the limits and opportunities of algorithms in the context of artistic creation, we need to understand that the latter usually consists of three elements: discovery, production, and recommendation.

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.

Humans are very good at making algorithms work eventually.

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.

My style is basically trend following, with some special pattern recognition and money management algorithms.

It is cheaper to pay mathematicians and computer scientists to design algorithms that will eliminate webspamming, rather than to pay lawyers to do lawsuits.