An examination of antitrust and competition considerations relating to the use of pricing algorithms and other AI systems, ...

The bookshelf problem (which computer scientists call the “list labeling” problem) is one of the most basic topics in the field of data structures. “It’s the kind of problem you’d teach to freshman or ...

LeetCode isn’t a sprint; it’s a marathon. You won’t become a coding master overnight. It takes time, effort, and, most ...

Lawsuit is one of several cases brought by copyright holders against tech companies over alleged misuse of material in ...

RE-HUMANIZE: How to Build Human-Centric Organizations in the Age of Algorithms offers a compelling guide to designing organizations that harmonize digital efficiency with human-centric values ...

This algorithm had two properties: It was “deterministic,” meaning that its decisions did not depend on any randomness, and it was also “smooth,” meaning that the books must be spread ...

Three books explore the promise and peril of artificial intelligence. A metronome ticks. A record spins. And as a feel-good pop track plays, a giant compactor slowly crushes a Jenga tower of ...

Also known as CLRS, “Introduction to Algorithms” is hailed as the bible of algorithms. It not only provides a systemized overview of this new computer science discipline but also delves deep into a ...

Algorithms may entrench our biases, homogenize and flatten culture, and exploit and suppress the vulnerable and marginalized. But these aren’t completely inscrutable systems or inevitable outcomes.

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Machine learning uses algorithms to turn a data set into a model that can identify patterns or make predictions from new data. Which algorithm works best depends on the problem.

Amid all the hype and hysteria about ChatGPT, Bard, and other generative large language models (LLMs), it’s worth taking a step back to look at the gamut of AI algorithms and their uses. After ...