'Media Buzz' host Howard Kurtz join'Sunday Night in America with Trey Gowdy' to discuss a poll claiming Americans are blaming the media for divisiveness in America. Be afraid, be very afraid. That's the message that is starting to dominate the media's many channels when it comes to artificial intelligence. And it's not just prognosticators but such voices as Elon Musk and the grandfather of AI that are saying an apocalyptic future may loom in the distance. I'm not hitting the panic button yet, but the sheer velocity of what AI is either able to achieve or is moving toward achieving seems to increase exponentially each week.

OpenAI CEO Sam Altman, the artificial intelligence lab behind ChatGPT, took questions from reporters after his congressional hearing, including his definition of "scary AI." OpenAI CEO Sam Altman testified before Congress in Washington, D.C., this week about regulating artificial intelligence as well as his personal fears over the tech and what "scary" AI systems means to him. Fox News Digital asked OpenAI's wildly popular chatbot, ChatGPT, to also weigh in on examples of "scary" artificial intelligence systems, and it reported six hypothetical instances of how AI could become weaponized or have potentially harmful impacts on society. When asked by Fox News Digital on Tuesday after his testimony before a Senate Judiciary subcommittee, Altman gave examples of "scary AI" that included systems that could design "novel biological pathogens." "An AI that could hack into computer systems," he continued. "I think these are all scary. These systems can become quite powerful, which is why I was happy to be here today and why I think this is so important."

Sen. Marsha Blackburn, R-Tenn., shares her takeaways from Tuesday's AI hearing with OpenAI CEO Sam Altman. She also reveals what next steps she and her colleagues are prepared to take to protect consumer data amid the AI boom. EXCLUSIVE: Nashville musicians are increasingly worried about complications with artificial intelligence's growing sophistication that could threaten their livelihood, Sen. Marsha Blackburn, R-Tenn., warned this week. "We met with the Nashville Technology Council a couple of weeks ago, and we have talked with so many of the musicians. They're concerned that using AI, they will do a copycat of their voice and take the lyrics of their song, which you can get on ChatGPT," Blackburn told Fox News Digital during an interview in her Senate office.

A former Google CEO said during a Congressional hearing on Wednesday that artificial intelligence (AI) is at the "center" of the technology competition between the United States and China. Eric Schmidt, who was CEO of Google from 2001 to 2011, made the comment during Wednesday's House hearing focusing on strategic competition between the United States and the Chinese Communist Party (CCP). "I think the technology competition between China and the U.S. is the defining moment of all of the competitions," Schmidt said. "And of that, artificial intelligence, AI, which is now a lot of people are talking about, is very much at the center of this competition." Elaborating on his point, Schmidt said that "China is now dedicating enormous resources to outpace the US and technologies, in particular AI." Former CEO & Chairman of Google and Chainlink Advisor Eric Schmidt speaks at Chainlink's SmartCon 2022 Web3 Conference on September 28, 2022 in New York City.

A parametric adaptive physics-informed greedy Latent Space Dynamics Identification (gLaSDI) method is proposed for accurate, efficient, and robust data-driven reduced-order modeling of high-dimensional nonlinear dynamical systems. In the proposed gLaSDI framework, an autoencoder discovers intrinsic nonlinear latent representations of high-dimensional data, while dynamics identification (DI) models capture local latent-space dynamics. An interactive training algorithm is adopted for the autoencoder and local DI models, which enables identification of simple latent-space dynamics and enhances accuracy and efficiency of data-driven reduced-order modeling. To maximize and accelerate the exploration of the parameter space for the optimal model performance, an adaptive greedy sampling algorithm integrated with a physics-informed residual-based error indicator and random-subset evaluation is introduced to search for the optimal training samples on the fly. Further, to exploit local latent-space dynamics captured by the local DI models for an improved modeling accuracy with a minimum number of local DI models in the parameter space, a k-nearest neighbor convex interpolation scheme is employed. The effectiveness of the proposed framework is demonstrated by modeling various nonlinear dynamical problems, including Burgers equations, nonlinear heat conduction, and radial advection. The proposed adaptive greedy sampling outperforms the conventional predefined uniform sampling in terms of accuracy. Compared with the high-fidelity models, gLaSDI achieves 17 to 2,658x speed-up with 1 to 5% relative errors.

Recently, an increasing interest in the management of water and health resources has been recorded. This interest is fed by the global sustainability challenges posed to the humanity that have water scarcity and quality at their core. Thus, the availability of effective, meaningful and open data is crucial to address those issues in the broader context of the Sustainable Development Goals of clean water and sanitation as targeted by the United Nations. In this paper, we present the Water Health Open Knowledge Graph (WHOW-KG) along with its design methodology and analysis on impact. WHOW-KG is a semantic knowledge graph that models data on water consumption, pollution, infectious disease rates and drug distribution. The WHOW-KG is developed in the context of the EU-funded WHOW (Water Health Open Knowledge) project and aims at supporting a wide range of applications: from knowledge discovery to decision-making, making it a valuable resource for researchers, policymakers, and practitioners in the water and health domains. The WHOW-KG consists of a network of five ontologies and related linked open data, modelled according to those ontologies.

In this work, we explore a useful but often neglected methodology for robustness analysis of text generation evaluation metrics: stress tests with synthetic data. Basically, we design and synthesize a wide range of potential errors and check whether they result in a commensurate drop in the metric scores. We examine a range of recently proposed evaluation metrics based on pretrained language models, for the tasks of open-ended generation, translation, and summarization. Our experiments reveal interesting insensitivities, biases, or even loopholes in existing metrics. For example, we find that BERTScore is confused by truncation errors in summarization, and MAUVE (built on top of GPT-2) is insensitive to errors at the beginning or middle of generations. Further, we investigate the reasons behind these blind spots and suggest practical workarounds for a more reliable evaluation of text generation. We have released our code and data at https://github.com/cloudygoose/blindspot_nlg.

With the development of large language models (LLMs), zero-shot learning has attracted much attention for various NLP tasks. Different from prior works that generate training data with billion-scale natural language generation (NLG) models, we propose a retrieval-enhanced framework to create training data from a general-domain unlabeled corpus. To realize this, we first conduct contrastive pretraining to learn an unsupervised dense retriever for extracting the most relevant documents using class-descriptive verbalizers. We then further propose two simple strategies, namely Verbalizer Augmentation with Demonstrations and Self-consistency Guided Filtering to improve the topic coverage of the dataset while removing noisy examples. Experiments on nine datasets demonstrate that REGEN achieves 4.3% gain over the strongest baselines and saves around 70% of the time compared to baselines using large NLG models. Besides, REGEN can be naturally integrated with recently proposed large language models to boost performance.

Robotic systems have subsystems with a combinatorially large configuration space and hundreds or thousands of possible software and hardware configuration options interacting non-trivially. The configurable parameters are set to target specific objectives, but they can cause functional faults when incorrectly configured. Finding the root cause of such faults is challenging due to the exponentially large configuration space and the dependencies between the robot's configuration settings and performance. This paper proposes CaRE -- a method for diagnosing the root cause of functional faults through the lens of causality. CaRE abstracts the causal relationships between various configuration options and the robot's performance objectives by learning a causal structure and estimating the causal effects of options on robot performance indicators. We demonstrate CaRE's efficacy by finding the root cause of the observed functional faults and validating the diagnosed root cause by conducting experiments in both physical robots (Husky and Turtlebot 3) and in simulation (Gazebo). Furthermore, we demonstrate that the causal models learned from robots in simulation (e.g., Husky in Gazebo) are transferable to physical robots across different platforms (e.g., Husky and Turtlebot 3).

In an indivisible participatory budgeting (PB) framework, we have a limited budget that is to be distributed among a set of projects, by aggregating the preferences of voters for the projects. All the prior work on indivisible PB assumes that each project has only one possible cost. In this work, we let each project have a set of permissible costs, each reflecting a possible degree of sophistication of the project. Each voter approves a range of costs for each project, by giving an upper and lower bound on the cost that she thinks the project deserves. The outcome of a PB rule selects a subset of projects and also specifies their corresponding costs. We study different utility notions and prove that the existing positive results when every project has exactly one permissible cost can also be extended to our framework where a project has several permissible costs. We also analyze the fixed parameter tractability of the problem. Finally, we propose some important and intuitive axioms and analyze their satisfiability by different PB rules. We conclude by making some crucial remarks.