Algorithms Quotes | Quote Catalog

AI is akin to building a rocket ship. You need a huge engine and a lot of fuel. The rocket engine is the learning algorithms but the fuel is the huge amounts of data we can feed to these algorithms.

— Andrew Ng, The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future

AI Artificial intelligence Algorithms Building Smart Computers

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Remember: If your Facebook feed is filled with garbage, it means you were reading garbage in the first place. The algorithm simply gives you more of what you crave.

— Jim VandeHei, 4 ways we all can work to fix "fake news"

Media Cliff Notes Facebook Facebook Feed Feeding You What You WAnt Algorithms

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In ML, where algorithms get published quickly and state-of-the-art frameworks are open-source, there isn't any first-mover advantage.

— François Chollet, via Twitter.com

Machine Learning Algorithms Business Analysis Market Competition

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Naive Bayes is a family of probabilistic algorithms that take advantage of probability theory and Bayes’ Theorem to predict the category of a sample (like a piece of news or a customer review). They are probabilistic, which means that they calculate the probability of each category for a given sample, and then output the category with the highest one. The way they get these probabilities is by using Bayes’ Theorem, which describes the probability of a feature, based on prior knowledge of conditions that might be related to that feature.

— Bruno Stecanella, A practical explanation of a Naive Bayes classifier

Naïve Bayes Algorithms

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Score = (P-1) / (T+2)^G

where,
P = points of an item (and -1 is to negate submitters vote)
T = time since submission (in hours)
G = Gravity, defaults to 1.8 in news.arc

— Amir Salihefendic, How Hacker News ranking algorithm works – Hacking and Gonzo – Medium

Algorithms Ranking

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A Bloom filter is a data structure designed to tell you, rapidly and memory-efficiently, whether an element is present in a set.

The price paid for this efficiency is that a Bloom filter is a probabilistic data structure: it tells us that the element either definitely is not in the set or may be in the set.

— Bill Mill, Bloom Filters by Example

Algorithms

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For an understanding of just how much space HLLs really save, consider the r/pics post included at the top of this blog post. It received over 1 million unique users. If we had to store 1 million unique user IDs, and each user ID is an 8-byte long, then we would require 8 megabytes of memory just to count the unique users for a single post!

— Krishnan Chandra, View Counting at Reddit

System Architecture Algorithms Scale

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The model tries to build a set of hyperplanes in a high dimensional space that tries to separate instances of different classes by getting the largest separation between the nearest instances from different classes. The concept intuitively is simple, but the model can be very complex and powerful. In fact, for some domains it is one of the best Machine Learning algorithms you can use nowadays.

— Raúl Garreta, A Gentle Guide to Machine Learning

Machine Learning Support Vector Machines Algorithms

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The way a NFA works is to consume one character at a time and compare that to a state graph which it has built up.

— Nathan Hammond, route-recognizer

Javascript Algorithms Frontend Nondeterministic Finite Automata

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Second, the surveillance economy is way too dangerous. Even if you trust everyone spying on you right now, the data they're collecting will eventually be stolen or bought by people who scare you. We have no ability to secure large data collections over time.

— Maciej Cegłowski, The Moral Economy of Tech

Tech Morality Machine Learning Algorithms Software Is Eating The World

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When we talk about the moral economy of tech, we must confront the fact that we have created a powerful tool of social control. Those who run the surveillance apparatus understand its capabilities in a way the average citizen does not. My greatest fear is seeing the full might of the surveillance apparatus unleashed against a despised minority, in a democratic country.

What we've done as technologists is leave a loaded gun lying around, in the hopes that no one will ever pick it up and use it.

— Maciej Cegłowski, The Moral Economy of Tech

Tech Morality Machine Learning Algorithms Software Is Eating The World

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Those who benefit from the death of privacy attempt to frame our subjugation in terms of freedom, just like early factory owners talked about the sanctity of contract law. They insisted that a worker should have the right to agree to anything, from sixteen-hour days to unsafe working conditions, as if factory owners and workers were on an equal footing.

Companies that perform surveillance are attempting the same mental trick. They assert that we freely share our data in return for valuable services. But opting out of surveillance capitalism is like opting out of electricity, or cooked foods—you are free to do it in theory. In practice, it will upend your life.

— Maciej Cegłowski, The Moral Economy of Tech

Tech Morality Machine Learning Algorithms Software Is Eating The World

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Fortunately we are smart people and have found a way out of this predicament. Instead of relying on algorithms, which we can be accused of manipulating for our benefit, we have turned to machine learning, an ingenious way of disclaiming responsibility for anything. Machine learning is like money laundering for bias. It's a clean, mathematical apparatus that gives the status quo the aura of logical inevitability. The numbers don't lie.

— Maciej Cegłowski, The Moral Economy of Tech

Tech Morality Machine Learning Algorithms Software Is Eating The World

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So the only thing we can do is to continue to demand that these services provide what we think we need, the 'we' being both individual and collective, and keep paying attention to the way they have structural problems in doing so. Whether that's algorithm or editorial acumen or yellow journalism, these are just the kinds of problems that emerge when institutions try to produce information on a public and commercial basis across a technical platform. We're just facing the newest version of that.

— Tarleton Gillespie, Q&A: Tarleton Gillespie says algorithms may be new, but editorial calculations aren’t

Algorithms Insights Publishing The internet

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The funny thing about clickbait as an idea is it's basically shorthand for: People really wanted to read this. Writing a really juicy headline to get people to read it, whether you got the substance or not, is not new to BuzzFeed and Upworthy. Is that gaming the algorithm? Was the algorithm of the penny newspaper — 'You can see the front page on the shelf, and you can't see the content in it, so those words better be big and gripping and delicious'? Is that gaming the algorithm for how newspapers were sold? Or is that just trying to get people to read your paper?

— Tarleton Gillespie, Q&A: Tarleton Gillespie says algorithms may be new, but editorial calculations aren’t

Algorithms Buzzfeed Clickbait Insights The internet

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One of the things I found really interesting about Twitter Trends is that they'll weight tweets or hashtags that appear across different clusters of people that aren't connected to each other on Twitter higher than a lot of activity that happens in a densely connected cluster of people. You can imagine the opposite choice, where something that happens in a cluster of people really intensely, but isn't escaping — maybe that's exactly what should be revealed. Something like: You may not know anything about this, but somewhere, there's a lot of discussion about this, and you may want to know what that is. That's fulfills a very different public or journalistic thing. Yes, there are things that seem to be talked about on a wide basis, and we want to reflect those back, but we also want to say, over here in the world, in a place you don't have access to, there's something going on.

— Tarleton Gillespie, Q&A: Tarleton Gillespie says algorithms may be new, but editorial calculations aren’t

Algorithms Facebook The internet Twitter

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It's in some ways a very old problem. NBC has to decide what's acceptable at 8 p.m. And they do that within some guidance of what the FCC says, but mostly they're working within those barriers, and deciding what they think their audience will accept, what they think their moral compass is, what their advertisers will blanch at.
Now you've got Facebook and Apple and YouTube being in a similar situation, but I think the game is different. You can do classic stuff, like setting rules and deciding where the line is, but the other thing you can do now is you can design the platform so that you manage where those things go. It's not like there's no precedent for that either — you can build a video rental store and put all the porn in the back room, and have rules about how gets in there and who doesn't. That's using the material architecture and the rules to keep the right stuff in front of the right people and vice versa. But you can do that with such sophistication when you think about how to design a social media platform. You know much more about people, you know much more about what their preferences are, about where they're coming from, about the content. You can use the algorithm — the very same algorithm that's designed to deliver what you searched for — to also keep away what they think you don't want to see.

— Tarleton Gillespie, Q&A: Tarleton Gillespie says algorithms may be new, but editorial calculations aren’t

Algorithms Facebook The internet

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Facebook, and every other large social-networking and information-aggregation company, both convenes and intervenes. Indeed, it convenes in order to intervene. The platform is the conversation. To fully analyze online social dynamics, one has to attend not only to the tension between the group and the individual but between the platform and both the group and the individual. The problem is that whereas the group-individual tension is visible, the manipulations of the platform are invisible. With the publication of the Facebook study, the veil trembled. We all knew the veil was there — we all knew we were inside a Skinner Box — but suddenly we had to admit the fact.

— Nicholas Carr, The platform is the conversation

Algorithms Facebook The internet

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AI is akin to building a rocket ship. You need a huge engine and a lot of fuel. The rocket engine is the learning algorithms but the fuel is the huge amounts of data we can feed to these algorithms.

— Andrew Ng, The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future

Remember: If your Facebook feed is filled with garbage, it means you were reading garbage in the first place. The algorithm simply gives you more of what you crave.

— Jim VandeHei, 4 ways we all can work to fix "fake news"

In ML, where algorithms get published quickly and state-of-the-art frameworks are open-source, there isn't any first-mover advantage.

— François Chollet, via Twitter.com

— Bruno Stecanella, A practical explanation of a Naive Bayes classifier

Score = (P-1) / (T+2)^G

where,
P = points of an item (and -1 is to negate submitters vote)
T = time since submission (in hours)
G = Gravity, defaults to 1.8 in news.arc

— Amir Salihefendic, How Hacker News ranking algorithm works – Hacking and Gonzo – Medium

— Bill Mill, Bloom Filters by Example

— Krishnan Chandra, View Counting at Reddit

— Raúl Garreta, A Gentle Guide to Machine Learning

The way a NFA works is to consume one character at a time and compare that to a state graph which it has built up.

— Nathan Hammond, route-recognizer

Second, the surveillance economy is way too dangerous. Even if you trust everyone spying on you right now, the data they're collecting will eventually be stolen or bought by people who scare you. We have no ability to secure large data collections over time.

— Maciej Cegłowski, The Moral Economy of Tech

— Maciej Cegłowski, The Moral Economy of Tech

— Maciej Cegłowski, The Moral Economy of Tech

— Maciej Cegłowski, The Moral Economy of Tech

— Tarleton Gillespie, Q&A: Tarleton Gillespie says algorithms may be new, but editorial calculations aren’t

— Tarleton Gillespie, Q&A: Tarleton Gillespie says algorithms may be new, but editorial calculations aren’t

— Tarleton Gillespie, Q&A: Tarleton Gillespie says algorithms may be new, but editorial calculations aren’t

— Tarleton Gillespie, Q&A: Tarleton Gillespie says algorithms may be new, but editorial calculations aren’t

— Nicholas Carr, The platform is the conversation