The California State Bar's botched roll out of a new exam -- a move that the cash-strapped agency made in the hopes of saving money -- could ultimately end up costing it an additional 5.6 million. Leah T. Wilson, executive director of the State Bar, told state lawmakers at a Senate Judiciary hearing Tuesday that the agency expects to pay around 3 million to offer free exams to test takers, an additional 2 million to book in-person testing sites in July, and 620,000 to return the test to its traditional system of multiple-choice questions in July. Wilson, who announced last week she will step down when her term ends this summer, revealed the costs during a 90-minute hearing called by Sen. Thomas J. Umberg (D-Orange), chair of the Senate Judiciary Committee, to find out what went so "spectacularly wrong." Chaos ensued in February when thousands of test takers seeking to practice law in California sat for the new exam. Some reported they couldn't log into the exam because online testing platforms repeatedly crashed.

Technologists tend to predict that the economic impacts of their creations will be unprecedented--and this is especially true when it comes to artificial intelligence. Last year, Elon Musk predicted that continued advances in AI would render human labor obsolete. OpenAI CEO Sam Altman has written that AI will inevitably continue the shift in economic power from labor to capital and create "phenomenal wealth." Jensen Huang, CEO of semiconductor design firm Nvidia, has compared AI's development and deployment to a "new industrial revolution." But while the technologists are bullish on the economic impacts of AI, members of that other technocratic priesthood with profound influence over public life--the economists--are not.

In modeling multivariate time series, it is important to allow time-varying smoothness in the mean and covariance process. In particular, there may be certain time intervals exhibiting rapid changes and others in which changes are slow. If such locally adaptive smoothness is not accounted for, one can obtain misleading inferences and predictions, with over-smoothing across erratic time intervals and under-smoothing across times exhibiting slow variation. This can lead to miscalibration of predictive intervals, which can be substantially too narrow or wide depending on the time. We propose a continuous multivariate stochastic process for time series having locally varying smoothness in both the mean and covariance matrix. This process is constructed utilizing latent dictionary functions in time, which are given nested Gaussian process priors and linearly related to the observed data through a sparse mapping. Using a differential equation representation, we bypass usual computational bottlenecks in obtaining MCMC and online algorithms for approximate Bayesian inference. The performance is assessed in simulations and illustrated in a financial application.

Artificial intelligence could cause a financial crisis with "catastrophic" consequences, according to the historian and author Yuval Noah Harari, who says the technology's sophistication makes forecasting its dangers difficult. Harari told the Guardian a concern about safety testing AI models was foreseeing all the problems that a powerful system could cause. Unlike with nuclear weapons, there was not one "big, dangerous scenario" that everyone understood, he said. "With AI, what you're talking about is a very large number of dangerous scenarios, each of them having a relatively small probability that taken together … constitutes an existential threat to the survival of human civilisation." The Sapiens author, who has been a prominent voice of concern over AI development, said last week's multilateral declaration at the global AI safety summit in Bletchley Park was a "very important step forward" because leading governments had come together to express concern about the technology and to do something about it.

Financial distress of municipalities, although comparable to bankruptcy of private companies, has a far more serious impact on the well-being of communities. For this reason, it is essential to detect deficits as soon as possible. Predicting financial distress in municipalities can be a complex task, as it involves understanding a wide range of factors that can affect a municipality's financial health. In this paper, we evaluate machine learning models to predict financial distress in Italian municipalities. Accounting judiciary experts have specialized knowledge and experience in evaluating the financial performance, and they use a range of indicators to make their assessments. By incorporating these indicators in the feature extraction process, we can ensure that the model is taking into account a wide range of information that is relevant to the financial health of municipalities. The results of this study indicate that using machine learning models in combination with the knowledge of accounting judiciary experts can aid in the early detection of financial distress, leading to better outcomes for the communities.

An artificial intelligence tool could help governments decide whether or not to bail out a bank in crisis by predicting if the intervention will save money for taxpayers in the long term. The AI tool, developed by researchers at University College London (UCL) and Queen Mary University of London, assesses not only if a bailout is the best strategy for taxpayers, but also suggests how much should be invested in the bank, and which bank or banks should be bailed out at any given time. It is detailed in a new paper to be published today (November 17) in the journal Nature Communications. Using data from the European Banking Authority, the algorithm was tested by the authors on a network of 35 European financial institutions judged to be the most important to the global financial system. However, it can also be used and calibrated by national banks using detailed proprietary data unavailable to the public.

Artificial Intelligence Speeds Up The Planet's Financial System Both the financial crisis of 2008 and the COVID-19 pandemic stressed the financial markets. They resulted in uncertainties, market declines, and negative economic growth. Yet the financial market recovered much faster after the COVID-19 outbreak than the 2008 crisis. The main differences in recovery speeds between the two crises are the timing of the Fed's support, fintech innovations, and technology developments on trading and on general productivity. These factors change the data flow, market dynamics, and recovery speed.

Surely you have heard ever about this emerging term "Bitcoin". In nowadays scenario, according to researchers, it is said that future of transactions and exchange rely on this system. What is it, how it is created, why we invest in such types of digital currency etc? Let's have a look at this technology. It is the first decentralized digital currency also known as a digital coin a computer file that is stored in a digital wallet on your pc, laptop, or mobile. A node-to-node version of digital cash will allow online payments to be sent directly from one party to another without having any financial institution.

The current COVID-19 situation has alerted the world about the impending global financial crisis that we may face at the starting of this decade. It also reminded us of the Great Depression, triggered by the Wall Street crash of 1929 and last till 1939, and economic meltdown of 2007-2008 which was caused due to the collapse of Lehman Brothers (one of the biggest investment banks in the world). While such seismic scale emergencies on the economic front are rare, the financial crisis is always unfortunate. This is why experts and leaders are looking for solutions using modern technologies like artificial intelligence (AI) to mitigate any future occurrences. Since most of these financial crises happened because of stock market crashes and loosened credit lending standards, AI can play an instrumental role in the early forecast of potential market crashes and detecting faulty lending standards.

It is one of the most important sites in the history of computing and of Britain's victory against Nazism. But now, for the second time in little more than a decade, the future of Bletchley Park hangs in the balance. Over the weekend, it emerged that the museum at the wartime code-breaking centre was facing a financial crisis because of the coronavirus - and that meant it was preparing to lay off 35 people, a third of its workforce. After being forced to close for over three months, it's now opened with reduced capacity due to social distancing regulations and the museum is on course to lose £2m ($2.6m). Iain Standen, the chief executive of Bletchley Park, said the principal strength of the successful museum and visitor attraction that had been built over recent years was its people: "However, the economic impact of the current crisis is having a profound effect on the Trust's ability to survive.