One of Anthropic's lawyers had asked the company's AI model Claude to create a citation for a legal article, but Claude included the wrong title and author. Anthropic's attorney admitted that the mistake was not caught by anyone reviewing the document. Lastly, and perhaps most concerning, is a case unfolding in Israel. After police arrested an individual on charges of money laundering, Israeli prosecutors submitted a request asking a judge for permission to keep the individual's phone as evidence. But they cited laws that don't exist, prompting the defendant's attorney to accuse them of including AI hallucinations in their request.

Jack Krawczyk discusses how Google Bard helps users connect and communicate -- and what the future holds for the platform. ChatGPT has been found to have shared inaccurate information regarding drug usage, according to new research. In a study led by Long Island University (LIU) in Brooklyn, New York, nearly 75% of drug-related, pharmacist-reviewed responses from the generative AI chatbot were found to be incomplete or wrong. In some cases, ChatGPT, which was developed by OpenAI in San Francisco and released in late 2022, provided "inaccurate responses that could endanger patients," the American Society of Health System Pharmacists (ASHP), headquartered in Bethesda, Maryland, stated in a press release. ChatGPT also generated "fake citations" when asked to cite references to support some responses, the same study also found.

Technology Assisted Review (TAR) stopping rules aim to reduce the cost of manually assessing documents for relevance by minimising the number of documents that need to be examined to ensure a desired level of recall. This paper extends an effective stopping rule using information derived from a text classifier that can be trained without the need for any additional annotation. Experiments on multiple data sets (CLEF e-Health, TREC Total Recall, TREC Legal and RCV1) showed that the proposed approach consistently improves performance and outperforms several alternative methods.

So is David Grossman's formulation correct? Is Hootie the soundtrack of the uncomplicated phase of Francis Fukuyama's end of history, the peak of liberal confidence and American power and post-ideological relaxation? Shouldn't a pure "it's the end of ideological conflict, and I feel fine" work of art be a little bit less angsty, a little sunnier than Darius Rucker singing, "Let her cry, if the tears fall down like rain/Let her sing, if it eases all her pain"? Or Adam Duritz crooning mournfully, "It's raining in Baltimore, baby/But everything else is the same"? Still, when I look back on this music, there's something about Grossman's analysis that rings true.

Active learning has demonstrated data efficiency in many fields. Existing active learning algorithms, especially in the context of deep Bayesian active models, rely heavily on the quality of uncertainty estimations of the model. However, such uncertainty estimates could be heavily biased, especially with limited and imbalanced training data. In this paper, we propose BALanCe, a Bayesian deep active learning framework that mitigates the effect of such biases. Concretely, BALanCe employs a novel acquisition function which leverages the structure captured by equivalence hypothesis classes and facilitates differentiation among different equivalence classes. Intuitively, each equivalence class consists of instantiations of deep models with similar predictions, and BALanCe adaptively adjusts the size of the equivalence classes as learning progresses. Besides the fully sequential setting, we further propose Batch-BALanCe -- a generalization of the sequential algorithm to the batched setting -- to efficiently select batches of training examples that are jointly effective for model improvement. We show that Batch-BALanCe achieves state-of-the-art performance on several benchmark datasets for active learning, and that both algorithms can effectively handle realistic challenges that often involve multi-class and imbalanced data.

Artificial intelligence systems that gather visual cues from the environment, and learn from them, can recognize human faces more accurately than we can. But how do such systems make the leap from pixels to people? Weizmann Institute neuroscientists have now revealed part of the secret: the most advanced AI vision systems evolve as they learn, spontaneously creating connections that bear a surprising resemblance to how neural networks function in the human brain. The research, published in Nature Communications, was performed by Prof. Rafi Malach of the Department of Neurobiology, together with Shany Grossman, a graduate student in the Malach lab. Today's most advanced systems for artificial vision are based on an AI approach called deep convolutional neural networks (DCNNs).

This past "Star Wars Day," May 4, I watched some of the original trilogy a bit mournfully: Peter Mayhew, who played Chewbacca, passed away the day before. When The Empire Strikes Back took us to the Yoda-dwelling Dagobah, I recalled what the exiled Jedi Master had told premonition-plagued Anakin Skywalker decades earlier, about how to deal with the fear of losing loved ones. "Death is a natural part of life," he tells Anakin. "Rejoice for those around you who transform into the Force. Attachment leads to jealousy--the shadow of greed, that is." Yoda is often held up as an avatar or icon of sagacity.

International Business Machines Corp. rode the success of old technology to big gains at the end of last year, but investors will be looking for success from newer businesses in order to believe in Big Blue's future. IBM IBM, -0.86% is scheduled to report earnings on Tuesday after the close of markets. Key points will be the potential impact of China tariffs and the company's gross margins. Most important might be the company's services revenue, which is far behind systems revenue growth despite comprising newer efforts. Don't miss: IBM broke a long losing streak thanks to some of its oldest technology Compared with the year-ago quarter, analysts expect technology services and cloud-platform revenue to rise 0.8% to $8.28 billion and cognitive-solutions revenue to rise 3.8% to $4.22 billion.

President Trump will meet with "the video game industry" on Thursday to discuss violence in video games and … stopping it? The meeting's agenda hasn't been revealed, probably because the gathering has been a slow-motion face-plant since the White House announced it. To recap: White House press secretary Sarah Huckabee Sanders announced on March 1 that Trump would meet with video game executives to discuss violent video games in the wake of the Parkland, Florida, school shooting that killed 17. According to Kotaku's Jason Schreier, this was news to the video game industry, and at least one of the attendees did not actually receive an invitation until Monday, four days after the summit had been announced. The list of expected attendees, first reported by CNN's Jake Tapper on Twitter, pretty much clinches how farcical the meeting will be.

Now a company called Open Data Group is aiming to close that gap with a Docker-based model deployment framework. Open Data Group's CTO Stu Baily describes the company's FastScore framework as an abstraction layer that makes it easier for enterprise IT professionals to deploy data science models into production environments. "We're exclusively focused on bridging analytic professionals, data scientists, quants, model builders, analytic engineers – whatever you want to call them -- and IT for deploying analytic models," he says. "Our solution focuses on getting models deployed as durable, cloud portable assets that will have a very long life time, but can be easily changed, easily migrated." FastScore doesn't care what language or environments the analytic model is developed in.