A 2019 study found that an algorithm widely used in the U.S. for making decisions about enrollment in health care programs assigned white patients higher scores than Black patients with the same ...

Adam Aleksic talks about his new book 'Algospeak,' which details how algorithms are changing our vocabulary; plus, we check in with Hennessy + Ingalls bookstore.

There are three key reasons why predictive algorithms can make big mistakes. 1. The Wrong Data An algorithm can only make accurate predictions if you train it using the right type of data.

For example, the algorithm may learn that people with a history of hypertension are more likely to experience a cardiac event than those with normal blood pressure. However, this training process gets ...

For example, users can feed their locally stored data into a large language model (LLM), such as Llama. The so-called SIFT algorithm (Selecting Informative data for Fine-Tuning), developed by ETH ...

Making algorithms completely transparent could create other problems, however. In 2006, for example, Netflix offered $1 million to the developers who submitted the best possible recommendation ...

Lopez-Lira said an algorithm can place an order for one share right now, then seven shares in 13 seconds, then another 14 and another in 25 seconds. That’s why it’s tough to try to beat the ...

For example, the kidney allocation system is an algorithm-based protocol used to prioritize patients for kidney transplants on the basis of the amount of time they have been on the national ...

Helping robots make good decisions in real time Caltech's algorithm called Spectral Expansion Tree Search helps autonomous robotic systems make optimal choices on the move Date: December 5, 2024 ...

Researchers from Caltech developed an algorithm for autonomous robots to assist with planning and decision-making. This system helps robots determine the best course of action while navigating the ...

How social media algorithms warp our perceptions A key question is what can be done to make algorithms foster accurate human social learning rather than exploit social learning biases.