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Blue Bloods is coming to an end after 14 seasons, but star Abigail Hawk still remembers meeting costar Tom Selleck for the very first time. Hawk, 42, exclusively revealed to Us Weekly that while Selleck, 79, is an icon — known for playing the lead on Magnum P.I. among other roles — she never called him “Magnum” or “Boss” even though he plays her supervisor on Blue Bloods . “He’s just always been Tom,” she said, noting that when they met at the start of season 1, which aired in 2010, she was only 28 years old , and she didn’t call him “much of anything” because she was “too terrified.” Hawk plays Abigail Baker on the police drama, who is the assistant and longtime confidant of Selleck’s Police Commissioner Frank Reagan. While their interactions are part of the charm of the series, the actress was very nervous to act alongside Selleck. “I would go up to the PA and be like, ‘If I wanna go talk to him, am I allowed to, like, approach him or should I just not?’ They were like, ‘I don’t know, it’s my first day too lady, like, why are you looking at me?’” she recalled. “And of course, he ended up coming to me and saying, ‘Hey, you know, we’re doing this thing. Let’s figure out some, some context.’ He just put me at ease right away.” Hawk has since become close friends with Selleck and learned a few of his secrets. “He’s very shy, which may not surprise people, but I think people assume that he commands the space just like Frank Reagan does ,” she shared with Us . She continued, “When in all actuality he just wants to be doing what he does with what Frank does with [his dad] , Henry ( Len Cariou ), sitting in an armchair working on a crossword. For Tom, that would be working on his farm and working with his dogs and working on a script.” While Selleck’s Frank Reagan is a strong, stern commissioner of New York City it’s his family-man side that is most similar to the actor himself. Hawk remembered getting to know her costar even better while he was writing his memoir, You Never Know , which is when she learned about his deep love for his mother. “I love that he would tell us his struggles through that [process] and show us the pictures that were going in the book. We got to see those before anybody else did,” Hawk said, getting choked up over their bond. “He would tell us the stories behind all of them and how he worshiped his mother.” She explained that Selleck’s mom “shaped his entire life,” which many might not guess based on his public persona. “I just think for somebody who is the literal embodiment of, like, the Marlboro Man [a.k.a. the smoking cowboy] and the Brawny Man [which was a lumberjack figure], and [a] mustached icon that so many of us are like, ‘That’s a man.’ He loved his mom, you know?” Hawk told Us . “I just feel like getting to see those little snapshots of the man behind the legend is what I treasure the most about him.” Hawk’s memories with Selleck stretch far and wide, but finishing out Blue Bloods , which ends on Friday, December 13 , is something she’ll never forget. “We did have an amazing, like, final 1PP team dinner at Elio’s, which is Tom’s Go-to place,” she said, looking back at the last few days as a cast. “He would go there literally every single day after work. So they knew him. He has his own little table in the corner.” While at the dinner with the show’s 1 Police Plaza team — which is also made up of Gregory Jbara ’s Garrett Moore and Robert Clohessy ’s Sid Gormley — Hawk said the “coolest thing” was seeing Selleck’s signed memoir up on the wall. “I’m sitting across from this like, living history and going, ‘Wow, that’s so cool,’” she continued, adding that over the years they created a father-daughter relationship, which extended to discussions about politics and popular topics. Hawk noted that she and Selleck “will always have differing viewpoints politically [and] personally on things,” but she treasured his banter over current events. You have successfully subscribed. By signing up, I agree to the Terms and Privacy Policy and to receive emails from Us Weekly Check our latest news in Google News Check our latest news in Apple News “He always had a seat at the table for me, even though I was like way down here in terms of me having knowledge about anything. And I’m a big feeling person. Tom is a fact person,” she shared. “So I would be like, ‘Oh, what about this?’ And he was like, ‘All right, but what are the facts?’ And I was like, ‘OK, you’re right.’ But it was always with a gentle hand.” She gushed, “I can’t say enough good things about him. He is a credible person. Truly.” Blue Bloods series finale airs on CBS Friday, December 13, at 10 p.m. ET. Stream every episode on Paramount+.Judge rejects request to sideline SJSU volleyball playerVideo game ends ‘collaboration’ with Conor McGregorPatriots coach Jerod Mayo believes narrow loss to Bills shows potential of his young team
NCB Foundation celebrates 12 years of granting wishesA massacre of more than 200 people in Haiti this month followed a gang-ordered manhunt that saw victims, many of them elderly, pulled from their homes and shot or killed with machetes, the UN said Monday. The victims were suspected of involvement in voodoo and accused by a gang leader of poisoning his child, with the suspects taken to a "training center" where many were dismembered or burned after being killed. A civil society organization had said at the time that the gang leader was convinced his son's illness was caused by followers of the religion. "On the evening of December 6, (Micanor Altes) ordered the members of his gang -- around 300 -- to carry out a brutal 'manhunt.' They stormed into about ten alleys of the (Port-au-Prince) neighborhood and forcibly dragged the victims out of their homes," said the report, authored jointly by the UN office in Haiti, BINUH, and the UN Human Rights Commissioner (OCHR). In the days that followed, the gang returned to the neighborhood, abducting adherents from a voodoo temple, targeting individuals suspected of tipping off local media and slaughtering people seeking to escape. Some of the bodies "were then burned with gasoline, or dismembered and dumped into the sea," the report concluded. A total of 134 men and 73 women were killed in total over six days, the report said. A mosaic of violent gangs control most of the Haitian capital Port-au-Prince. The impoverished Caribbean country has been mired for decades by political instability, made worse in recent years by gangs that have grown in strength and organizational sophistication. Despite a Kenyan-led police support mission, backed by the United States and UN, violence has continued to soar. "According to BINUH and OHCHR, since January 2024, more than 5,358 people have been killed and 2,155 injured," the report said. "This brings the total number of people killed or injured in Haiti to at least 17,248 since the beginning of 2022." The UN Security Council "strongly condemned the continued destabilizing criminal activities of armed gangs and stressed the need for the international community to redouble its efforts to provide humanitarian assistance to the population." A spokeswoman for UN Secretary-General Antonio Guterres said "these crimes touched the very foundation of Haitian society, targeting the most vulnerable populations." Voodoo was brought to Haiti by African slaves and is a mainstay of the country's culture. It was banned during French colonial rule and only recognized as an official religion by the Haitian government in 2003. While it incorporates elements of other religious beliefs, including Catholicism, voodoo has been historically attacked by other religions. gw
Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More MRI images are understandably complex and data-heavy. Because of this, developers training large language models (LLMs) for MRI analysis have had to slice captured images into 2D. But this results in just an approximation of the original image, thus limiting the model’s ability to analyze intricate anatomical structures. This creates challenges in complex cases involving brain tumors , skeletal disorders or cardiovascular diseases. But GE Healthcare appears to have overcome this massive hurdle, introducing the industry’s first full-body 3D MRI research foundation model (FM) at this year’s AWS re:Invent . For the first time, models can use full 3D images of the entire body. GE Healthcare’s FM was built on AWS from the ground up — there are very few models specifically designed for medical imaging like MRIs — and is based on more than 173,000 images from over 19,000 studies. Developers say they have been able to train the model with five times less compute than previously required. GE Healthcare has not yet commercialized the foundation model; it is still in an evolutionary research phase. An early evaluator, Mass General Brigham , is set to begin experimenting with it soon. “Our vision is to put these models into the hands of technical teams working in healthcare systems, giving them powerful tools for developing research and clinical applications faster, and also more cost-effectively,” GE HealthCare chief AI officer Parry Bhatia told VentureBeat. Enabling real-time analysis of complex 3D MRI data While this is a groundbreaking development, generative AI and LLMs are not new territory for the company. The team has been working with advanced technologies for more than 10 years, Bhatia explained. One of its flagship products is AIR Recon DL , a deep learning-based reconstruction algorithm that allows radiologists to more quickly achieve crisp images. The algorithm removes noise from raw images and improves signal-to-noise ratio, cutting scan times by up to 50%. Since 2020, 34 million patients have been scanned with AIR Recon DL. GE Healthcare began working on its MRI FM at the beginning of 2024. Because the model is multimodal, it can support image-to-text searching, link images and words, and segment and classify diseases. The goal is to give healthcare professionals more details in one scan than ever before, said Bhatia, leading to faster, more accurate diagnosis and treatment. “The model has significant potential to enable real-time analysis of 3D MRI data, which can improve medical procedures like biopsies, radiation therapy and robotic surgery,” Dan Sheeran, GM for health care and life sciences at AWS, told VentureBeat. Already, it has outperformed other publicly-available research models in tasks including classification of prostate cancer and Alzheimer’s disease. It has exhibited accuracy up to 30% in matching MRI scans with text descriptions in image retrieval — which might not sound all that impressive, but it’s a big improvement over the 3% capability exhibited by similar models. “It has come to a stage where it’s giving some really robust results,” said Bhatia. “The implications are huge.” Doing more with (much less) data The MRI process requires a few different types of datasets to support various techniques that map the human body, Bhatia explained. What’s known as a T1-weighted imaging technique, for instance, highlights fatty tissue and decreases the signal of water, while T2-weighted imaging enhances water signals. The two methods are complementary and create a full picture of the brain to help clinicians detect abnormalities like tumors, trauma or cancer. “MRI images come in all different shapes and sizes, similar to how you would have books in different formats and sizes, right?” said Bhatia. To overcome challenges presented by diverse datasets, developers introduced a “resize and adapt” strategy so that the model could process and react to different variations. Also, data may be missing in some areas — an image may be incomplete, for instance — so they taught the model simply to ignore those instances. “Instead of getting stuck, we taught the model to skip over the gaps and focus on what was available,” said Bhatia. “Think of this as solving a puzzle with some missing pieces.” The developers also employed semi-supervised student-teacher learning, which is particularly helpful when there is limited data. With this method, two different neural networks are trained on both labeled and unlabeled data, with the teacher creating labels that help the student learn and predict future labels. “We’re now using a lot of these self-supervised technologies, which don’t require huge amounts of data or labels to train large models,” said Bhatia. “It reduces the dependencies, where you can learn more from these raw images than in the past.” This helps to ensure that the model performs well in hospitals with fewer resources, older machines and different kinds of datasets, Bhatia explained. He also underscored the importance of the models’ multimodality. “A lot of technology in the past was unimodal,” said Bhatia. “It would look only into the image, into the text. But now they’re becoming multi-modal, they can go from image to text, text to image, so that you can bring in a lot of things that were done with separate models in the past and really unify the workflow.” He emphasized that researchers only use datasets that they have rights to; GE Healthcare has partners who license de-identified data sets, and they’re careful to adhere to compliance standards and policies. Using AWS SageMaker to tackle computation, data challenges Undoubtedly, there are many challenges when building such sophisticated models — such as limited computational power for 3D images that are gigabytes in size. “It’s a massive 3D volume of data,” said Bhatia. “You need to bring it into the memory of the model, which is a really complex problem.” To help overcome this, GE Healthcare built on Amazon SageMaker , which provides high-speed networking and distributed training capabilities across multiple GPUs, and leveraged Nvidia A100 and tensor core GPUs for large-scale training. “Because of the size of the data and the size of the models, they cannot send it into a single GPU,” Bhatia explained. SageMaker allowed them to customize and scale operations across multiple GPUs that could interact with one another. Developers also used Amazon FSx in Amazon S3 object storage, which allowed for faster reading and writing for datasets. Bhatia pointed out that another challenge is cost optimization; with Amazon’s elastic compute cloud (EC2), developers were able to move unused or infrequently used data to lower-cost storage tiers. “Leveraging Sagemaker for training these large models — mainly for efficient, distributed training across multiple high-performance GPU clusters — was one of the critical components that really helped us to move faster,” said Bhatia. He emphasized that all components were built from a data integrity and compliance perspective that took into account HIPAA and other regulatory regulations and frameworks. Ultimately, “these technologies can really streamline, help us innovate faster, as well as improve overall operational efficiencies by reducing the administrative load, and eventually drive better patient care — because now you’re providing more personalized care.” Serving as a basis for other specialized fine-tuned models While the model for now is specific to the MRI domain, researchers see great opportunities to expand into other areas of medicine. Sheeran pointed out that, historically, AI in medical imaging has been constrained by the need to develop custom models for specific conditions in specific organs, requiring expert annotation for each image used in training. But that approach is “inherently limited” due to the different ways diseases manifest across individuals, and introduces generalizability challenges. “What we truly need is thousands of such models and the ability to rapidly create new ones as we encounter novel information,” he said. High-quality labeled datasets for each model are also essential. Now with generative AI, instead of training discrete models for each disease/organ combination, developers can pre-train a single foundation model that can serve as a basis for other specialized fine-tuned models downstream. For instance, GE Healthcare’s model could be expanded into areas such as radiation therapy, where radiologists spend significant time manually marking organs that might be at risk. It could also help reduce scan time during x-rays and other procedures that currently require patients to sit still in a machine for extended periods, said Bhatia. Sheeran marveled that “we’re not just expanding access to medical imaging data through cloud-based tools; we’re changing how that data can be utilized to drive AI advancements in healthcare.” If you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI. Read our Privacy Policy Thanks for subscribing. Check out more VB newsletters here . An error occured.
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