Adapting pre-trained deep learning models to new and unknown environments is a difficult challenge in underwater acoustic localization. We show that although pre-trained models have performance that suffers from mismatch between the training and test data, they generally exhibit a higher ``implied uncertainty'' in environments where there is more mismatch. Leveraging this notion of implied uncertainty, we partition the test samples into more certain and less certain sets, and implement an estimation method using the certain samples to improve the labeling for uncertain samples, which helps to adapt the model. We use an efficient method to quantify model prediction uncertainty, and an innovative approach to adapt a pre-trained model to unseen underwater environments at test time. This eliminates the need for labeled data from the target environment or the original training data. This adaptation is enhanced by integrating an independent estimate based on the received signal energy. We validate the approach extensively using real experimental data, as well as synthetic data consisting of model-generated signals with real ocean noise. The results demonstrate significant improvements in model prediction accuracy, underscoring the potential of the method to enhance underwater acoustic localization in diverse, noisy, and unknown environments.

The exploration of whether agents can align with their environment without relying on human-labeled data presents an intriguing research topic. Drawing inspiration from the alignment process observed in intelligent organisms, where declarative memory plays a pivotal role in summarizing past experiences, we propose a novel learning framework. The agents adeptly distill insights from past experiences, refining and updating existing notes to enhance their performance in the environment. This entire process transpires within the memory components and is implemented through natural language, so we character this framework as In-memory Learning. We also delve into the key features of benchmarks designed to evaluate the self-improvement process. Through systematic experiments, we demonstrate the effectiveness of our framework and provide insights into this problem.

Former presidential candidate Andrew Yang spoke with Fox News Digital about the dangers of AI. Former Democratic presidential candidate Andrew Yang spoke to Fox News Digital about the dangers of artificial intelligence, known as AI, and said the government is not doing nearly enough to prepare for the potentially harmful effects. "AI is a very, very powerful technology and set of tools and there's nothing intrinsically positive or negative about tools, but there is something positive and negative about how tools can be used," Yang told Fox News Digital this week. "And you can very clearly see deepfake videos already being employed for political purposes. Fake pictures of terrorist attacks being used to manipulate the stock market. A robocall in President Biden's voice trying to discourage turnout and we're just at the beginning of this." "We're not going to be able to tell up from down and left from right and if people show you a video of me doing something heinous, I'll just shrug and be like, didn't happen and that could be the best defense before too long."

Dean Phillips may not go quietly into the night after the expected fizzle of his longshot primary challenge to President Joe Biden The Minnesota Democratic Rep. told the New York Times Saturday that he would mull running on the ticket of No Labels, the third party organization loathed by liberals that is considering mounting a presidential bid. Phillips said that he might join No Labels if Biden and Trump were set for rematch--as it appears they currently are--and if polls showed that "Biden is almost certain to lose." Currently general elections polls suggest a dead heat. No Labels has courted self-styled centrist figures like Sen. Joe Manchin (D-W.V.) and former Gov. Larry Hogan of Maryland to lead its potential ticket. The dark money group denies that its possible entrance into the presidential race would drag down Biden more than Trump, but it has received funding from longtime GOP donors, Mother Jones has revealed, and reportedly also received support from Harlan Crow, the eccentric consevative donor whose lavish gifts to Supreme Court Justice Clarence Thomas have ignited scrutiny of high court corruption.

Democratic Rep. Dean Phillips, who's primary challenging the president, said he's'disappointed' Biden ally Rep. James Clyburn accused him of disrespecting Black voters. President Biden is kicking off his rural America tour Wednesday in Minnesota, the home state of Rep. Dean Phillips, who launched his 2024 Democratic presidential primary challenge just days ago. Biden is expected to announce $5 billion in new investments, including $1.7 billion in "climate-smart agriculture programs," $1 billion in broadband deployment, and some $2 billion in rural development programs. "I think there are obviously a lot of folks in Minnesota who understand and appreciate climate-smart agriculture and the enormous new income opportunities and environmental benefits that that accrues," U.S. Department of Agriculture Secretary Tom Vilsack told the Minneapolis Star Tribune. DEAN PHILLIPS SAYS VOTE BY'SQUAD' MEMBERS AGAINST RESOLUTION CONDEMNING HAMAS ATTACK ON ISRAEL IS'APPALLING' President Biden delivers remarks about government regulations on artificial intelligence systems during an event at the White House, Monday, Oct. 30, 2023.

"Father of the Video Game" Ralph Baer escaped Jewish persecution in Nazi Germany as a teen and served in the U.S. Army in WWII. After coming of age in tough times, he felt driven to bring "more fun and whimsy" into the world. Ralph Baer's childhood was stolen by the Nazis. The German-born Jew gained a semblance of revenge overseas, imagining a new way for children of all ages to play. Ralph Baer invented video games.

Samuel Holmes Doten of Plymouth, Mass., was born June 5, 1812, so after the Civil War ended in 1865, he would joke that he "served in the infantry in the war of that date." William Kendall Crossfield, a Peterborough, N.H. native, was having a rest during the battle of Fredericksburg when he was shot in the neck while turning over. The blanket he had pulled up to his chin miraculously cushioned the bullet, but he passed out from the shock of the blow. Vermonter Almeron C. Inman was recommended for the Medal of Honor of Feb. 9, 1887, "for intelligent coolness and bravery" in two 1864 engagements. After going missing for three months in 1895, he was found dead, thought to have killed himself.

Woodie Flowers SM '68, MEng '71, PhD '73, the Pappalardo Professor Emeritus of Mechanical Engineering, passed away on Oct. 11 at the age of 75. Flowers' passion for design and his infectious kindness have impacted countless engineering students across the world. Flowers was instrumental in shaping MIT's hands-on approach to engineering design education, first developing teaching methods and learning opportunities that culminated in a design competition for class 2.70, now called 2.007 (Design and Manufacturing I). This annual MIT event, which has now been held for nearly five decades, has impacted generations of students and has been emulated at universities around the world. Flowers expanded this concept to high school and elementary school students, working to help found the world-wide FIRST Robotics Competition, which has introduced millions of children to science and engineering.

FedEx's robot has a top speed of 10 mph and can carry about 100 pounds. A company spokesman said its typical speed would vary depending on the route. The robot relies on sensors typically used on self-driving cars to identify and avoid pedestrians. The SameDay Bot is capable of climbing steps, but customers will need to be home to accept packages -- it won't leave a package on a doorstep or open a front gate. The robot is being developed by DEKA, the Manchester, New Hampshire engineering company owned by Segway inventor Dean Kamen.

Risk adjustment has become an increasingly important tool in healthcare. It has been extensively applied to payment adjustment for health plans to reflect the expected cost of providing coverage for members. Risk adjustment models are typically estimated using linear regression, which does not fully exploit the information in claims data. Moreover, the development of such linear regression models requires substantial domain expert knowledge and computational effort for data preprocessing. In this paper, we propose a novel approach for risk adjustment that uses semantic embeddings to represent patient medical histories. Embeddings efficiently represent medical concepts learned from diagnostic, procedure, and prescription codes in patients' medical histories. This approach substantially reduces the need for feature engineering. Our results show that models using embeddings had better performance than a commercial risk adjustment model on the task of prospective risk score prediction.