After selling 23 million Switches two years ago and 18 million in the last year, Nintendo expects demand for the aging console to continue to fall. "Sustaining the Switch's sales momentum will be difficult in its seventh year," said president Shuntaro Furukawa in a call. "Our goal of selling 15 million units this fiscal year is a bit of a stretch." To achieve that, he added: "We try to not only put one system in every home but several in every home." Get our daily audio briefings, Monday through Friday, by subscribing right here. Volvo's compact electric SUV will be the EX30 Spotify has reportedly pulled tens of thousands of tracks from generative AI company Boomy.

Fox News correspondent Grady Trimble has the latest on fears the technology will spiral out of control on'Special Report.' Century-old writings by American inventor Nikola Tesla seem to predict the development of artificial intelligence, foreshadowing the rise of the groundbreaking tech. The technology and electricity pioneer's scientific brilliance set him up to make eerily accurate predictions, including prescient insight into the emergence of machines with their "own mind." "I purpose to show that, however impossible it may now seem, an automaton may be contrived which will have its'own mind,'" Tesla wrote in June 1900," and by this I mean that it will be able, independent of any operator, left entirely to itself, to perform, in response to external influences affecting its sensitive organs, a great variety of acts and operations as if it had intelligence." The comments were published in "The Century Magazine" in an essay titled "The Problem of Increasing Human Energy."

Fox News contributor Joe Concha joins "Fox & Friends First" to discuss Elon Musk's warning that AI could threaten elections and his concerns on the declining birth rate. The top lawyer for OpenAI, the company that developed ChatGPT, argued that the best way to regulate artificial intelligence is not to start with government mandated rules and regulations but to allow the companies themselves to set standards that ensure AI is used safely and responsibly. OpenAI General Counsel Jason Kwon made that argument during a Tuesday panel discussion in Washington, D.C., which was hosted by BSA/The Software Alliance, even as he acknowledged that AI is developing so quickly that it can often lead to unexpected results that companies quickly need to rein in. Still, when asked what his message to policymakers was, Kwon recommended voluntary, industry-led standards for AI, calling for a tactic that many companies in most industries tend to favor over government mandates. The top lawyer at OpenAI, run by CEO Sam Altman, above, said this week that the company recommends voluntary industry standards to regulate AI, not government mandates.

Angie Wisdom and Dr. Chirag Shah discuss how artificial intelligence could play a role in online and professional relationships. As national conversations around artifical intelligence (AI) intensify, faith leaders and scholars are examining the potential ramifications these emerging technologies will have on worship – both its practice and its role in modern life. Some experts and faith leaders are also concerned about whether religion will have any place in AI programming – or if the intellectual will eventually take precedence over the spiritual in society. It's possible and even probable, say experts. Dan Schneider, Media Research Center and Free Speech America vice president, is both blunt and emphatic in his assessment of AI. "The [political] left controls AI, and the left is going to what the left wants to do," Schneider, whose headquarters are in Reston, Virginia, told Fox News Digital in a recent phone interview.

In this study, we focus on the graph representation learning (a.k.a. network embedding) in attributed graphs. Different from existing embedding methods that treat the incorporation of graph structure and semantic as the simple combination of two optimization objectives, we propose a novel semantic graph representation (SGR) method to formulate the joint optimization of the two heterogeneous sources into a common high-order proximity based framework. Concretely, we first construct an auxiliary weighted graph, where the complex homogeneous and heterogeneous relations among nodes and attributes in the original graph are comprehensively encoded. Conventional embedding methods that consider high-order topology proximities can then be easily applied to the newly constructed graph to learn the representations of both node and attribute while capturing the nonlinear high-order intrinsic correlation inside or among graph structure and semantic. The learned attribute embeddings can also effectively support some semantic-oriented inference tasks (e.g., semantic community detection), helping to reveal the graph's deep semantic. The effectiveness of SGR is further verified on a series of real graphs, where it achieves impressive performance over other baselines.

Outside-knowledge visual question answering is a challenging task that requires both the acquisition and the use of open-ended real-world knowledge. Some existing solutions draw external knowledge into the cross-modality space which overlooks the much vaster textual knowledge in natural-language space, while others transform the image into a text that further fuses with the textual knowledge into the natural-language space and completely abandons the use of visual features. In this paper, we are inspired to constrain the cross-modality space into the same space of natural-language space which makes the visual features preserved directly, and the model still benefits from the vast knowledge in natural-language space. To this end, we propose a novel framework consisting of a multimodal encoder, a textual encoder and an answer decoder. Such structure allows us to introduce more types of knowledge including explicit and implicit multimodal and textual knowledge. Extensive experiments validate the superiority of the proposed method which outperforms the state-of-the-art by 6.17% accuracy. We also conduct comprehensive ablations of each component, and systematically study the roles of varying types of knowledge. Codes and knowledge data can be found at https://github.com/PhoebusSi/Thinking-while-Observing.

Analogical reasoning is a fundamental cognitive ability of humans. However, current language models (LMs) still struggle to achieve human-like performance in analogical reasoning tasks due to a lack of resources for model training. In this work, we address this gap by proposing ANALOGYKB, a million-scale analogy knowledge base (KB) derived from existing knowledge graphs (KGs). ANALOGYKB identifies two types of analogies from the KGs: 1) analogies of the same relations, which can be directly extracted from the KGs, and 2) analogies of analogous relations, which are identified with a selection and filtering pipeline enabled by large LMs (InstructGPT), followed by minor human efforts for data quality control. Evaluations on a series of datasets of two analogical reasoning tasks (analogy recognition and generation) demonstrate that ANALOGYKB successfully enables LMs to achieve much better results than previous state-of-the-art methods.

Multimodal misinformation on online social platforms is becoming a critical concern due to increasing credibility and easier dissemination brought by multimedia content, compared to traditional text-only information. While existing multimodal detection approaches have achieved high performance, the lack of interpretability hinders these systems' reliability and practical deployment. Inspired by NeuralSymbolic AI which combines the learning ability of neural networks with the explainability of symbolic learning, we propose a novel logic-based neural model for multimodal misinformation detection which integrates interpretable logic clauses to express the reasoning process of the target task. To make learning effective, we parameterize symbolic logical elements using neural representations, which facilitate the automatic generation and evaluation of meaningful logic clauses. Additionally, to make our framework generalizable across diverse misinformation sources, we introduce five meta-predicates that can be instantiated with different correlations. Results on three public datasets (Twitter, Weibo, and Sarcasm) demonstrate the feasibility and versatility of our model.

Despite huge improvements in automatic speech recognition (ASR) employing neural networks, ASR systems still suffer from a lack of robustness and generalizability issues due to domain shifting. This is mainly because principal corpus design criteria are often not identified and examined adequately while compiling ASR datasets. In this study, we investigate the robustness of the state-of-the-art transfer learning approaches such as self-supervised wav2vec 2.0 and weakly supervised Whisper as well as fully supervised convolutional neural networks (CNNs) for multi-domain ASR. We also demonstrate the significance of domain selection while building a corpus by assessing these models on a novel multi-domain Bangladeshi Bangla ASR evaluation benchmark - BanSpeech, which contains approximately 6.52 hours of human-annotated speech and 8085 utterances from 13 distinct domains. SUBAK.KO, a mostly read speech corpus for the morphologically rich language Bangla, has been used to train the ASR systems. Experimental evaluation reveals that self-supervised cross-lingual pre-training is the best strategy compared to weak supervision and full supervision to tackle the multi-domain ASR task. Moreover, the ASR models trained on SUBAK.KO face difficulty recognizing speech from domains with mostly spontaneous speech. The BanSpeech will be publicly available to meet the need for a challenging evaluation benchmark for Bangla ASR.

With an instantly recognizable hook and effervescent melody, Vanessa Carlton's debut single A Thousand Miles hit the 2002 Billboard Charts like a neutron bomb, earning nominations for the Grammy Award for Song of the Year and the Billboard Music Award for Top 40 Track of the Year. Featured prominently in 2004's White Chicks, Terry Crews credits the undeniable smash with helping launch his acting career. The accompanying music video saw Carlton and her piano rolling through Newbury Park, California, and portions of downtown Los Angeles. Twenty-one years later, a team of hobbyist roboticists have brought Carlton's music back to the public ear -- this time, to the streets of San Francisco with an animatronic performer and remotely deployable disco ball. The robot, which currently doesn't have much of a moniker from the team beyond "The Robot," is the brainchild of San Francisco-based aerospace engineer Ben Howard, electrical engineer Noah Klugman, lawyer Lane Powell (with additional assistance from local puppeteer, Adam Kreutinger).