The hybrid neural differentiable models mark a significant advancement in the field of scientific machine learning. These models, integrating numerical representations of known physics into deep neural networks, offer enhanced predictive capabilities and show great potential for data-driven modeling of complex physical systems. However, a critical and yet unaddressed challenge lies in the quantification of inherent uncertainties stemming from multiple sources. Addressing this gap, we introduce a novel method, DiffHybrid-UQ, for effective and efficient uncertainty propagation and estimation in hybrid neural differentiable models, leveraging the strengths of deep ensemble Bayesian learning and nonlinear transformations. Specifically, our approach effectively discerns and quantifies both aleatoric uncertainties, arising from data noise, and epistemic uncertainties, resulting from model-form discrepancies and data sparsity. This is achieved within a Bayesian model averaging framework, where aleatoric uncertainties are modeled through hybrid neural models. The unscented transformation plays a pivotal role in enabling the flow of these uncertainties through the nonlinear functions within the hybrid model. In contrast, epistemic uncertainties are estimated using an ensemble of stochastic gradient descent (SGD) trajectories. This approach offers a practical approximation to the posterior distribution of both the network parameters and the physical parameters. Notably, the DiffHybrid-UQ framework is designed for simplicity in implementation and high scalability, making it suitable for parallel computing environments. The merits of the proposed method have been demonstrated through problems governed by both ordinary and partial differentiable equations.

We present symbol tuning - finetuning language models on in-context input-label pairs where natural language labels (e.g., "positive/negative sentiment") are replaced with arbitrary symbols (e.g., "foo/bar"). Symbol tuning leverages the intuition that when a model cannot use instructions or natural language labels to figure out a task, it must instead do so by learning the input-label mappings. We experiment with symbol tuning across Flan-PaLM models up to 540B parameters and observe benefits across various settings. First, symbol tuning boosts performance on unseen in-context learning tasks and is much more robust to underspecified prompts, such as those without instructions or without natural language labels. Second, symbol-tuned models are much stronger at algorithmic reasoning tasks, with up to 18.2% better performance on the List Functions benchmark and up to 15.3% better performance on the Simple Turing Concepts benchmark. Finally, symbol-tuned models show large improvements in following flipped-labels presented in-context, meaning that they are more capable of using in-context information to override prior semantic knowledge.

Computing kinodynamically feasible motion plans and repairing them on-the-fly as the environment changes is a challenging, yet relevant problem in robot-navigation. We propose a novel online single-query sampling-based motion re-planning algorithm - PiP-X, using finite-time invariant sets - funnels. We combine concepts from sampling-based methods, nonlinear systems analysis and control theory to create a single framework that enables feedback motion re-planning for any general nonlinear dynamical system in dynamic workspaces. A volumetric funnel-graph is constructed using sampling-based methods, and an optimal funnel-path from robot configuration to a desired goal region is then determined by computing the shortest-path subtree in it. Analysing and formally quantifying the stability of trajectories using Lyapunov level-set theory ensures kinodynamic feasibility and guaranteed set-invariance of the solution-paths. The use of incremental search techniques and a pre-computed library of motion-primitives ensure that our method can be used for quick online rewiring of controllable motion plans in densely cluttered and dynamic environments. We represent traversability and sequencibility of trajectories together in the form of an augmented directed-graph, helping us leverage discrete graph-based replanning algorithms to efficiently recompute feasible and controllable motion plans that are volumetric in nature. We validate our approach on a simulated 6DOF quadrotor platform in a variety of scenarios within a maze and random forest environment. From repeated experiments, we analyse the performance in terms of algorithm-success and length of traversed-trajectory.

Jack Krawczyk discusses how Google Bard helps users connect and communicate -- and what the future holds for the platform. Michael Cohen, former President Trump's onetime fixer and lawyer, admitted in a filing unsealed Friday that he inadvertently gave his lawyer fake legal case citations generated by artificial intelligence in connection with a motion to end his supervised release early. U.S. District Judge Jesse M. Furman previously called the citations into question, writing earlier this month, "In the letter brief, Mr. Cohen asserts that, "[a]s recently as 2022, there have been District Court decisions, affirmed by the Second Circuit Court, granting early termination of supervised release." Furman added, "As far as the Court can tell, none of these cases exist." Cohen said in his sworn declaration released Friday that he had found the phony citations through Google Bard, an AI service that he said he thought was a "supercharged" search engine. Michael Cohen admitted to inadvertently citing fake legal cases in a motion to end his early release in a sworn declaration released Friday. "As a non-lawyer, I have not kept up with emerging trends (and related risks) in legal technology and did not realize that Google Bard was a generative text service that, like Chat-GPT, could show citations and descriptions that looked real but actually were not," Cohen said. "Instead, I understood it to be a super-charged search engine and had repeatedly used it in other contexts to (successfully) find accurate information online." In 2018, Cohen pleaded guilty to tax evasion, campaign finance charges and lying to Congress, spending more than a year in prison before he was put on supervised release. He was also disbarred as a lawyer. "It did not occur to me then and remains surprising to me now--that Mr. Schwartz would drop the cases into his submission wholesale without even confirming that they existed," he added, citing his lawyer David Schwartz. "I deeply regret any problems Mr. Schwartz's filing may have caused." He said Schwartz's alleged mistake was "a product of inadvertence, not any intent to deceive." E. Danya Perry, who represents Cohen and discovered the citations were fake, told the judge, "Mr.

The US military's secretive X-37B robot spaceplane blasted off from Florida on Thursday night on its seventh mission, the first launched atop a SpaceX Falcon Heavy rocket capable of delivering it to a higher orbit than ever before. The Falcon Heavy, composed of three liquid-fueled rocket cores strapped together, roared off its launch pad from NASA's Kennedy Space Center at Cape Canaveral in a spectacular liftoff carried live on a SpaceX webcast. The launch followed more than two weeks of false starts and delays attributed to poor weather and unspecified technical issues, leading ground crews to roll the spacecraft back to its hangar before proceeding with Thursday's flight. It came two weeks after China's own robot spaceplane, the Shenlong, or'Divine Dragon,' was launched on its third mission to orbit since 2020, adding a new twist to the growing US-Sino rivalry in space. The Pentagon has disclosed scant details about the X-37B mission, conducted by the US Space Force under the military's National Security Space Launch program.

One day in late November, I cradled a red Samsung flip phone in my hands as if it was a ruby gemstone. To me, it was just as precious. Deep inside an overstuffed dresser in my childhood bedroom, I had spotted the glint of my first-ever cellphone, a Samsung SGH-A707 purchased in the waning days of the George W. Bush presidency. The device, no bigger than a credit card, had long ago succumbed to the spider web of cracks on its screen. For a moment, I was brought back to life before the smartphone, clicking the phone's plastic keys for the first time in more than a decade.

You can manage your newsletter subscriptions at any time. As is now tradition, the final quiz of the year is a look back at the past 12 months. It was a year fraught with discord, so grab your favorite beverage, maybe a cookie or two, and take a deep, relaxing breath before you plunge into 2023 for one last time in this week's Slate News Quiz. If this is your first time playing, read the rules here. The quiz may require you to turn on cookies in your browser for it to function properly.

America's Newsroom anchor Bill Hemmer looks back at the top headlines of the past 12 months. Supreme Court rulings, wars waged over parental rights, crackdowns on conservative school boards and scandals that imbued some districts with controversy: Education's rocky landscape showcased this year's equally tumultuous cultural climate, and the issue has taken center stage for candidates going into 2024. Republicans continued to capitalize on parents' concerns that children are being exposed to age-inappropriate content in the classroom while calling for school choice and cautioning against giving transgender students access to single-sex spaces. Democrats, meanwhile, called out the opposition for alleged "book bans" and a majority defended transgender students' access to spaces corresponding with their preferred gender. The gridlock is expected to augment the intensity of an already explosive election season next year, and the issues aren't expected to fade anytime soon.

Weeks after a deadly drone attack on November 30 killed five civilians in the town of Wegel Tena in Ethiopia's Amhara region about 570km (350 miles) north of the capital, Addis Ababa, a witness is still reeling from the trauma. "It's extremely difficult to even describe the scene of the aftermath," said Gebeyehu, who requested use of his first name only for safety reasons. "Bodies were burned so badly they had turned to dust. I saw the finger bones of one of the victims still shaped as though it was still clutching a mobile phone." Several witnesses told Al Jazeera that a drone fired on an ambulance as it approached the Delanta Primary Hospital in Wegel Tena and obliterated it.

The artificial intelligence (AI) industry began 2023 with a bang as schools and universities struggled with students using OpenAI's ChatGPT to help them with homework and essay writing. Less than a week into the year, New York City Public Schools banned ChatGPT – released weeks earlier to enormous fanfare – a move that would set the stage for much of the discussion around generative AI in 2023. As the buzz grew around Microsoft-backed ChatGPT and rivals like Google's Bard AI, Baidu's Ernie Chatbot and Meta's LLaMA, so did questions about how to handle a powerful new technology that had become accessible to the public overnight. In March, a group of more than 1,000 signatories, including Apple co-founder Steve Wozniak and billionaire tech entrepreneur Elon Musk, called for a pause in the development of more advanced AI in light of its "profound risks to society and humanity". While a pause did not happen, governments and regulatory authorities began rolling out new laws and regulations to set guardrails on the development and use of AI.