Lithuania's Foreign Minister, Gabrielius Landsbergis, talked with Fox News Digital about Russia, China and the'global rules-based order' on the 20th anniversary of his country joining NATO. Lithuania commemorated its entry into NATO this last week and its long-standing partnership with the U.S. as leaders look ahead to the increasingly complex security landscape developing around the world. President George W. Bush visited the Lithuanian capital of Vilnius 20 years ago to welcome the country into the still-growing NATO alliance, applauding the character of member states to "stand in the face of evil, to have the courage to always face danger." "President [George W.] Bush made the most famous speech any American has ever made in Lithuania exactly 20 years ago," Lithuanian Foreign Minister Gabrielius Landsbergis told Fox News Digital in an exclusive interview. "That was even before we were a member of NATO, and it was probably the most important security guarantee that we got before Article Five started covering us with its umbrella."

I talked to her about the significance of the role in The Peripheral, where she plays a trans person in the future. The show is based on a novel by William Gibson, who coined the term cyberspace, and it was produced by Westworld creators Lisa Joy and Jonathan Nolan. It's a complicated story that moves around in time and explores whether the digital world is real or not. And the show is different from the book, as it uses Gibson's story as a jumping off point for ideas about our future. And that gives Billings some interesting leeway to play Lowbeer as a trans person in the show.

A personal, guided tour to the best scoops and stories every day in The Wall Street Journal. Dr. Frederick P. Brooks Jr. liked building things, first laying foundations for modern computer systems at International Business Machines Corp. and later at the University of North Carolina, where he started the computer-science department. Dr. Brooks managed the development of IBM's System/360 family of compatible mainframe computers and then the software system that went with them during the 1960s. The computers became some of IBM's most popular models of the era, offering customers a choice of big or small computers with different processing speeds that could be used for both business and scientific tasks. The system was easy to expand since all the hardware ran off the same software, a departure from other systems that required software reprogramming when computers were added.

Deep neural networks (DNNs), are widely used in many industries such as image recognition, supply chain, medical diagnosis, and autonomous driving. However, prior work has shown the high accuracy of a DNN model does not imply high robustness (i.e., consistent performances on new and future datasets) because the input data and external environment (e.g., software and model configurations) for a deployed model are constantly changing. Hence, ensuring the robustness of deep learning is not an option but a priority to enhance business and consumer confidence. Previous studies mostly focus on the data aspect of model variance. In this article, we systematically summarize DNN robustness issues and formulate them in a holistic view through two important aspects, i.e., data and software configuration variances in DNNs. We also provide a predictive framework to generate representative variances (counterexamples) by considering both data and configurations for robust learning through the lens of search-based optimization.

I want to share an experiment with you. The latest posts have been a streak of not-so-good news, non-optimistic takes, and anti-hype arguments. I think it's paramount to talk about all that, but it's as important to let a positive vibe out every so often. Otherwise, we risk burning out--and I don't want that! That's why today I bring you a different perspective on AI.

Center for American Security's Fred Fleitz unpacks the national security risks posed by China's access to TikTok data and Chinese-made drones flying over Washington D.C. World Economic Forum founder and Chair Klaus Schwab recently sat down for an interview with a Chinese state media outlet and proclaimed that China was a "role model" for other nations. Schwab, 84, made these comments during an interview with CGTN's Tian Wei on the sidelines of last week's APEC CEO Summit in Bangkok, Thailand. Schwab said he respected China's "tremendous" achievements at modernizing its economy over the last 40 years. FILE: World Economic Forum (WEF) founder and Executive Chairman Klaus Schwab sits, as German Chancellor Olaf Scholz (not pictured) addresses the delegates, during the last day of the WEF in Davos, Switzerland May 26, 2022. "I think it's a role model for many countries," Schwab said, before qualifying that he thinks each country should make its own decisions about what system it wants to adapt.

Open-domain chatbots are supposed to converse freely with humans without being restricted to a topic, task or domain. However, the boundaries and/or contents of open-domain conversations are not clear. To clarify the boundaries of "openness", we conduct two studies: First, we classify the types of "speech events" encountered in a chatbot evaluation data set (i.e., Meena by Google) and find that these conversations mainly cover the "small talk" category and exclude the other speech event categories encountered in real life human-human communication. Second, we conduct a small-scale pilot study to generate online conversations covering a wider range of speech event categories between two humans vs. a human and a state-of-the-art chatbot (i.e., Blender by Facebook). A human evaluation of these generated conversations indicates a preference for human-human conversations, since the human-chatbot conversations lack coherence in most speech event categories. Based on these results, we suggest (a) using the term "small talk" instead of "open-domain" for the current chatbots which are not that "open" in terms of conversational abilities yet, and (b) revising the evaluation methods to test the chatbot conversations against other speech events.

Federated learning (FL) is a distributed model training paradigm that preserves clients' data privacy. It has gained tremendous attention from both academia and industry. FL hyper-parameters (e.g., the number of selected clients and the number of training passes) significantly affect the training overhead in terms of computation time, transmission time, computation load, and transmission load. However, the current practice of manually selecting FL hyper-parameters imposes a heavy burden on FL practitioners because applications have different training preferences. In this paper, we propose FedTune, an automatic FL hyper-parameter tuning algorithm tailored to applications' diverse system requirements in FL training. FedTune iteratively adjusts FL hyper-parameters during FL training and can be easily integrated into existing FL systems. Through extensive evaluations of FedTune for diverse applications and FL aggregation algorithms, we show that FedTune is lightweight and effective, achieving 8.48%-26.75% system overhead reduction compared to using fixed FL hyper-parameters. This paper assists FL practitioners in designing high-performance FL training solutions. The source code of FedTune is available at https://github.com/DataSysTech/FedTune.

CES has announced the innovation award winners for the upcoming CES 2023 event happening in Las Vegas, on January 5-8, 2023. We went through the list of honorees and highlighted the robotics-related solutions for this story. French robotics company, ACWA Robotics, built a water pipe infrastructure mapping robot. The robot moves through the pipes while the water is running. The robot is able to precisely track its location while imaging the pipes, creating a map of exactly where repairs are needed.

As AI algorithms increasingly participate in daily activities that used to be the sole province of humans, we are inevitably called upon to consider how much machines are really like us. To address this question, we turn to the Turing test and systematically benchmark current AIs in their abilities to imitate humans. We establish a methodology to evaluate humans versus machines in Turing-like tests and systematically evaluate a representative set of selected domains, parameters, and variables. The experiments involved testing 769 human agents, 24 state-of-the-art AI agents, 896 human judges, and 8 AI judges, in 21,570 Turing tests across 6 tasks encompassing vision and language modalities. Surprisingly, the results reveal that current AIs are not far from being able to impersonate human judges across different ages, genders, and educational levels in complex visual and language challenges. In contrast, simple AI judges outperform human judges in distinguishing human answers versus machine answers. The curated large-scale Turing test datasets introduced here and their evaluation metrics provide valuable insights to assess whether an agent is human or not. The proposed formulation to benchmark human imitation ability in current AIs paves a way for the research community to expand Turing tests to other research areas and conditions. All of source code and data are publicly available at https://tinyurl.com/8x8nha7p