Algorithmic road trip to visit a street named after each day of the year

Ben Ashforth set out to visit a street named after a day of the year for each date. He used OpenStreetMap to find the streets and then algorithmically routed a trip. Then he followed through and went on the trip. In a five-minute lightning talk, he describes the journey. See a photo for every day here. [via Waxy]

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Building fair algorithms

Emma Pierson and Kowe Kadoma, for Fred Hutchinson Cancer Center, have a short and free course on Coursera on practical steps for building fair algorithms:

Algorithms increasingly help make high-stakes decisions in healthcare, criminal justice, hiring, and other important areas. This makes it essential that these algorithms be fair, but recent years have shown the many ways algorithms can have biases by age, gender, nationality, race, and other attributes. This course will teach you ten practical principles for designing fair algorithms. It will emphasize real-world relevance via concrete takeaways from case studies of modern algorithms, including those in criminal justice, healthcare, and large language models like ChatGPT. You will come away with an understanding of the basic rules to follow when trying to design fair algorithms, and assess algorithms for fairness.

It’s geared for beginners and no coding is required.

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Animated map shows the search for the best path

When you look up directions with a mapping application, there are algorithms that run to find the shortest route. Jan Pánek made an interactive map that animates the search with various algorithms. Click the map for an origin and destination, and then watch it play out.

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Algorithmic rent increase

It’s growing more common for landlords to use software to set the rental prices of their properties. This of course leads to software companies promising optimized pricing for maximum profit, which leads to higher rent for residents. Heather Vogell, Haru Coryne, and Ryan Little, for ProPublica, look closer at the practice, with a focus on pricing company RealPage:

To arrive at a recommended rent, the software deploys an algorithm — a set of mathematical rules — to analyze a trove of data RealPage gathers from clients, including private information on what nearby competitors charge.

For tenants, the system upends the practice of negotiating with apartment building staff. RealPage discourages bargaining with renters and has even recommended that landlords in some cases accept a lower occupancy rate in order to raise rents and make more money.

One of the algorithm’s developers told ProPublica that leasing agents had “too much empathy” compared to computer generated pricing.

Oh.

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TikTok algorithms work because of a lot of data

Ben Smith for The New York Times got an internal document that outlines TikTok’s recommendation system. This quote caught my eye:

Julian McAuley, a professor of computer science at the University of California San Diego, who also reviewed the document, said in an email that the paper was short on detail about how exactly TikTok does its predictions, but that the description of its recommendation engine is “totally reasonable, but traditional stuff.” The company’s edge, he said, comes from combining machine learning with “fantastic volumes of data, highly engaged users, and a setting where users are amenable to consuming algorithmically recommended content (think how few other settings have all of these characteristics!). Not some algorithmic magic.”

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Lightning algorithm

Matt Henderson on Numberphile shows off a “lightning algorithm” which is actually a maze-solving algorithm that shows the solution at the end. Come for the demo at the beginning but stay for the explanation.

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Testing the TikTok algorithm

The Wall Street Journal tested out the TikTok algorithm with bots to see how quickly the app converged towards a user’s pre-specified interests. As viewing time of videos as the main signal, and to nobody’s surprise (I think), it only took a couple of hours for TikTok to narrow down interests.

This is how most social services work these days? The concerning part is that almost all TikTok videos are served based on the algorithm, which makes it easy to fall into terrible rabbit holes.

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Definition of an algorithm

Oftentimes we see “algorithms” referenced in various contexts, but the definition of an algorithm is often unclear. For MIT Technology Review, Kristian Lum describes what an “algorithm” means these days:

In statistics and machine learning, we usually think of the algorithm as the set of instructions a computer executes to learn from data. In these fields, the resulting structured information is typically called a model. The information the computer learns from the data via the algorithm may look like “weights” by which to multiply each input factor, or it may be much more complicated. The complexity of the algorithm itself may also vary. And the impacts of these algorithms ultimately depend on the data to which they are applied and the context in which the resulting model is deployed. The same algorithm could have a net positive impact when applied in one context and a very different effect when applied in another.

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Through the eyes of the algorithm

Eugene Wei looks closer at the algorithms that drive TikTok and how its design provided an effective feedback loop:

But for TikTok (or Douyin, its Chinese clone), who needed an algorithm that would excel at recommending short videos to viewers, no such massive publicly available training dataset existed. Where could you find short videos of memes, kids dancing and lip synching, pets looking adorable, influencers pushing brands, soldiers running through obstacle courses, kids impersonating brands, and on and on? Even if you had such videos, where could you find comparable data on how the general population felt about such videos? Outside of Musical.ly’s dataset, which consisted mostly of teen girls in the U.S. lip synching to each other, such data didn’t exist.

In a unique sort of chicken and egg problem, the very types of video that TikTok’s algorithm needed to train on weren’t easy to create without the app’s camera tools and filters, licensed music clips, etc.

At first I was confused by TikTok. I’m still confused by TikTok. But one thing that is for sure is that the system knows how to serve up videos that one might find interesting. Whether that’s good in the long run is anyone’s guess.

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Millions of data points with deep scatterplots

Ben Schmidt uses deep scatterplots to visualize millions of data points. It’s a combination of algorithm-based display and hiding of points as you zoom in and out like you might an interactive map. Schmidt describes the process and made the code available on GitHub.

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