Taking = p / gives the desired claim. Claim 2.7, we know that the multicalibration violation for The inequalities follow by Holder's inequality and the assumed bound on the weight of Recall that Cov[ y, z ]= E [ yz ] E [ y ] E [ z ] . Here, we give a high-level overview of the MCBoost algorithm of [ 20 ] and weak agnostic learning. Algorithm 2 MCBoost Parameters: hypothesis class C and > 0 Given: Dataset S sampled from D Initialize: p ( x) 1 / 2 . By Lemma 3.8, we know that In this Appendix, we give a full account of the definitions and results stated in Section 4 .

AI coding platform's flaws allow BBC reporter to be hacked The BBC has been shown a significant - and unfixed - cyber-security risk in a popular AI coding platform. Orchids is a so-called vibe-coding tool, meaning people without technical skills can use it to build apps and games by typing a text prompt into a chatbot. Such platforms have exploded in popularity in recent months, and are often heralded as an early example of how various professional services could be done quickly and cheaply by AI. But experts say the ease with which Orchids can be hacked demonstrates the risks of allowing AI bots deep access to our computers in exchange for the convenience of allowing them to carry out tasks autonomously. The BBC has repeatedly asked the company for comment but it has not replied.

In machine learning, these have a know-it-when-you-see-it character; e.g., changing the gender of a sentence's subject changes a sentiment predictor's output. To check for spurious correlations, we can'stress test' models by perturbing irrelevant parts of input data and seeing if model predictions change. In this paper, we study stress testing using the tools of causal inference. We introduce counterfactual invariance as a formalization of the requirement that changing irrelevant parts of the input shouldn'tchangemodelpredictions.

Modifications to a neural network's input and output layers are often required to accommodate the specificities of most practical learning tasks.

Plus: Trump has climbed down from his plan for the US to take Greenland. Yann LeCun's new venture is a contrarian bet against large language models Yann LeCun is a Turing Award recipient and a top AI researcher, but he has long been a contrarian figure in the tech world. He believes that the industry's current obsession with large language models is wrong-headed and will ultimately fail to solve many pressing problems. Instead, he thinks we should be betting on world models--a different type of AI that accurately reflects the dynamics of the real world. Perhaps it's no surprise, then, that he recently left Meta, where he had served as chief scientist for FAIR (Fundamental AI Research), the company's influential research lab that he founded. LeCun sat down with MIT Technology Review in an exclusive online interview from his Paris apartment to discuss his new venture, life after Meta, the future of artificial intelligence, and why he thinks the industry is chasing the wrong ideas.