The two Silicon Valley tycoons are headed to court. The two Silicon Valley tycoons are headed to court. Musk's lawsuit accuses Altman of fraud, while OpenAI says that Musk is'motivated by jealousy' A lawsuit between two of Silicon Valley's biggest tycoons goes to trial Monday in California, the culmination of a years-long bitter feud. Elon Musk has accused Sam Altman of betraying the founding agreement of the non-profit they started together, OpenAI, by changing it to a for-profit enterprise. Musk accuses Altman, OpenAI, its president Greg Brockman, and its major partner Microsoft of breach of contract and unjust enrichment in the lawsuit.

The tech titans are slated to duke it out in court. The tech titans are slated to duke it out in court. Musk and Altman's bitter feud over OpenAI to be laid bare in court Tesla chief believes Altman broke company's founding agreement - and legal battle promises to be explosive T he bitter rivalry between two of the tech world's most powerful men arrives in court this week, as Elon Musk's lawsuit against Sam Altman and OpenAI heads to trial in Oakland, California. The case is set to feature some of the biggest names in Silicon Valley, and its outcome could affect the course of the AI boom. Musk's suit, filed in 2024, focuses on the formative years of OpenAI when he, Altman and others co-founded the artificial intelligence company as a nonprofit with a grand purpose.

OpenAI's Sam Altman apologizes for not reporting ChatGPT account of Tumbler Ridge suspect to police Altman penned a letter addressed to the community of Tumbler Ridge, two months following the mass shooting incident. Two months following the deadly shooting in Tumbler Ridge, British Columbia, OpenAI's Sam Altman has formally apologized for not informing police of the alarming ChatGPT conversations seen with the suspect's account. Before the incident, OpenAI banned the account belonging to the alleged shooter, Jesse Van Rootselaar, for violating its usage policy due to potential for real-world violence. I am deeply sorry that we did not alert law enforcement to the account that was banned in June, Altman wrote in the letter. While I know words can never be enough, I believe an apology is necessary to recognize the harm and irreversible loss your community has suffered.

Expressing universal semantics common to all languages is helpful in understanding the meanings of complex and culture-specific sentences. The research theme underlying this scenario focuses on learning universal representations across languages with the usage of massive parallel corpora. However, due to the sparsity and scarcity of parallel data, there is still a big challenge in learning authentic "universals" for any two languages. In this paper, we propose EMMA-X: an EM-like Multilingual pre-training Algorithm, to learn (X)Cross-lingual universals with the aid of excessive multilingual non-parallel data.

We explore the minimax optimal error associated with a demographic parityconstrained regression problem within the context of a linear model. Our proposed model encompasses a broader range of discriminatory bias sources compared to the model presented by Chzhen and Schreuder [6]. Our analysis reveals that the minimax optimal error for the demographic parity-constrained regression problem under our model is characterized by Θ(dM/n), where ndenotes the sample size, d represents the dimensionality, and M signifies the number of demographic groups arising from sensitive attributes. Moreover, we demonstrate that the minimax error increases in conjunction with a larger bias present in the model.

We explore the minimax optimal error associated with a demographic parityconstrained regression problem within the context of a linear model. Our proposed model encompasses a broader range of discriminatory bias sources compared to the model presented by Chzhen and Schreuder [6]. Our analysis reveals that the minimax optimal error for the demographic parity-constrained regression problem under our model is characterized by Θ(dM/n), where ndenotes the sample size, d represents the dimensionality, and M signifies the number of demographic groups arising from sensitive attributes. Moreover, we demonstrate that the minimax error increases in conjunction with a larger bias present in the model.