Sequences of events, such as crashes in the stock market or outages in a network, contain strong temporal dependencies, whose understanding is crucial to react to and influence future events. In this paper, we study the problem of discovering the underlying causal structure from event sequences. To this end, we introduce a new causal model, where individual events of the cause trigger events of the effect with dynamic delays. We show that in contrast to existing methods based on Granger causality, our model is identifiable for both instant and delayed effects. We base our approach on the Algorithmic Markov Condition, by which we identify the true causal network as the one that minimizes the Kolmogorov complexity. As the Kolmogorov complexity is not computable, we instantiate our model using Minimum Description Length and show that the resulting score identifies the causal direction.

B.4 Complete set of experimental results associated to section 4 In this section we display the complete set of results associated to figures shown in section 4. We display in figure 2 the rewards of all agents during training (calibrator, merchant on supertype 1 and n 1 merchants on supertype 2) for experiments 1-5 previously described.

At a company-wide hackathon this month, developers at finance firm Block built a dizzying number of prototype tools including a database debugger, a program for identifying duplicated code, and an app that automates Bitcoin support. The sudden productivity boost was driven by Goose, an artificial intelligence agent developed by Block several months ago that can help with coding and other work like knocking together data visualizations or mocking up new product features. "We've always had really strong hack weeks, but this one was at another level," says Jackie Brosamer, who leads the AI and data platform at Block. "We have tens of ideas that we're looking to bring to production." Goose helped developers at Block to develop a new agent-to-agent communication server at the hackathon.

SoftBank Group's investments in AI will be financed through a loan in which Mizuho Bank, Sumitomo Mitsui Banking and JPMorgan Chase serve as lead underwriters -- a sign of the Japanese tech investor's ability to secure financing for its outsized ambitions. The one-year 15 billion bridge loan, one of the biggest borrowings SoftBank has pulled off to date, will be financed by 21 banks and includes 1.35 billion from Mizuho, 1.25 billion from SMBC and 1 billion from JPMorgan, according to people familiar with the matter. It also includes a combined 950 million from HSBC Holdings and Barclays and 850 million jointly from seven banks including Goldman Sachs Group Inc., MUFG Bank and Credit Agricole, said the people, who asked not to be named as the details of the financing remain private. Representatives of the banks also declined to comment.

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Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. In 2021, Zia graduated from the University of Michigan–Dearborn with a degree in software engineering. With an internship under his belt, he had no shortage of job opportunities, and he landed a contract coding gig in January of 2022. It was good work, for a year and a half, until he got laid off in mid-2023. After taking a month to figure out what he wanted to specialize in, Zia decided that he'd go for the types of app- and site-building jobs that had been so plentiful when he was in school.

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Sakana AI has signed a 5 billion ( 34 million) deal with Mitsubishi UFJ Financial Group (MUFG) to automate the creation of banking documents, including credit approval memos, the companies announced Monday. "We are very happy to take on the meaningful challenge of revolutionizing banking -- a core domain of society -- with MUFG," said Ren Ito, the Japanese artificial intelligence startup's cofounder and chief operating officer. A six-month pilot phase begins in July, when MUFG's banking subsidiary will start generating documents using Sakana AI's The AI Scientist, "a fully AI-driven system" that was originally designed for automating scientific discovery, including manuscript writing and peer review.

Tom Leggett, vehicle technology manager at Thatcham Research - an independent car safety centre - said robotaxis would have to be "safety-led" in the UK. "Secondly, they will have to make sure the data is available to those who need it – insurers and those investigating incidents when they occur." The government says self-driving vehicles have the potential "to build an industry worth 42bn and provide 38,000 jobs by 2035." But of course they are source of concern for people who earn a living driving. Andy Prendergast, GMB national secretary, said the "significant social implications" driverless cars and taxis could have - such as potential less work or unemployment - for workers and the public must be fully considered.

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