Kurt "The CyberGuy" Knutsson shows how the Smart Compose feature can help you compose emails on Gmail. Do you ever get tired of typing out emails and wish someone could just read your mind and write it for you? If so, you might be interested in a feature from Google called Smart Compose that uses artificial intelligence to help you compose your messages faster and easier. CLICK TO GET KURT'S FREE CYBERGUY NEWSLETTER WITH SECURITY ALERTS, QUICK VIDEO TIPS, TECH REVIEWS, AND EASY HOW-TO'S TO MAKE YOU SMARTER Gmail uses artificial intelligence to help you write emails. Smart Compose is a feature that Google introduced for its Gmail service.

"Now and Then", a new song from The Beatles, was created with the assistance of AI The Beatles will release what is said to be their last ever song this week, pieced together from recordings spanning more than four decades – and it would not have been possible without AI. Now and Then has been edited together from a recording of the late John Lennon playing piano and singing at his home in New York in 1979. Now, artificial intelligence has been used to extract usable sections from that noisy tape. These have been combined with guitar tracks from the late George Harrison, recorded in 1995 when efforts were made to finish the song. These were reportedly called off due to poor sound quality, which AI has now been able to solve.

In the weeks since Hamas launched its October 7 surprise attack on Israel, the ensuing conflict has generated an unprecedented wave of disinformation, an "algorithmically driven fog of war" that has tripped up major new organizations and left social media companies floundering. Yet, amid all of the deceptive images and video moving around on social media, the content generated by artificial intelligence tools has remained relatively peripheral. Even as some wondered if the Israel-Hamas war would be the first conflict dominated by false generative AI images, the technology has had a more complex and subtle impact. "There are definitely AI images circulating but not to the degree where I think it's playing a central role in the spread of information," says Layla Mashkoor, an associate editor at the Atlantic Council's Digital Forensic Research Lab, which studies online disinformation. Primarily, Mashkoor says, AI-generated disinformation is being used by activists to solicit support--or give the impression of wider support--for a particular side.

Kara Frederick, tech director at the Heritage Foundation, discusses the need for regulations on artificial intelligence as lawmakers and tech titans discuss the potential risks. The U.S. and European Union have started exploring how to use artificial intelligence to enhance the oncoming 6G communications technology as Western nations look to stave off competition from China and its own 5G offering. "Up to now, governments have always had access to communications, certainly, but now it's more about treating telecommunications as a critical national security resource," Eric Plam, president of wireless data connection service SIMO Inc., told Fox News Digital. "I think that's why you're starting to see… an arms race in telecommunications. "The primary factions are China, and then EU, plus America, too," he added. "There will be other factions, of course, but they understand the importance of controlling information and controlling the flow of data." The U.S. and EU issued a joint ...

PragerU personality Aldo Buttazzoni joins'Fox News @ Night' to discuss the dating trends among Gen Z men and shares how Americans feel about a bug-based diet. Gen Z teens and young adults are having less sex than past generations and want less sexually explicit content shown in the media they watch. A new study from UCLA found that Gen Z teenagers and adults are asking for fewer sex scenes in the television and movies they consume. The "Teens and Screens" report out of the school's Center for Scholars and Storytellers found that 51.5% of adolescents would prefer to see more content that portrays platonic relationships and close friendships. The study also found that 44.4% of youth surveyed felt that romance in media was "overused."

Advances in artificial intelligence are driven by technologies inspired by the brain, but these technologies are orders of magnitude less powerful and energy efficient than biological systems. Inspired by the nonlinear dynamics of neural networks, new unconventional computing hardware has emerged with the potential to exploit natural phenomena and gain efficiency, in a similar manner to biological systems. Physical reservoir computing demonstrates this with a variety of unconventional systems, from optical-based to memristive systems. Reservoir computers provide a nonlinear projection of the task input into a high-dimensional feature space by exploiting the system's internal dynamics. A trained readout layer then combines features to perform tasks, such as pattern recognition and time-series analysis. Despite progress, achieving state-of-the-art performance without external signal processing to the reservoir remains challenging. Here we perform an initial exploration of three magnetic materials in thin-film geometries via microscale simulation. Our results reveal that basic spin properties of magnetic films generate the required nonlinear dynamics and memory to solve machine learning tasks (although there would be practical challenges in exploiting these particular materials in physical implementations). The method of exploration can be applied to other materials, so this work opens up the possibility of testing different materials, from relatively simple (alloys) to significantly complex (antiferromagnetic reservoirs).

Although large language models (LLMs) have advanced the state-of-the-art in NLP significantly, deploying them for downstream applications is still challenging due to cost, responsiveness, control, or concerns around privacy and security. As such, trainable models are still the preferred option in some cases. However, these models still require human-labeled data for optimal performance, which is expensive and time-consuming to obtain. In order to address this issue, several techniques to reduce human effort involve labeling or generating data using LLMs. Although these methods are effective for certain applications, in practice they encounter difficulties in real-world scenarios. Labeling data requires careful data selection, while generating data necessitates task-specific prompt engineering. In this paper, we propose a unified data creation pipeline that requires only a single formatting example, and which is applicable to a broad range of tasks, including traditionally problematic ones with semantically devoid label spaces. In our experiments we demonstrate that instruction-following LLMs are highly cost-effective data creators, and that models trained with these data exhibit performance better than those trained with human-labeled data (by up to 17.5%) on out-of-distribution evaluation, while maintaining comparable performance on in-distribution tasks. These results have important implications for the robustness of NLP systems deployed in the real-world.

While recent generative models can produce engaging music, their utility is limited. The variation in the music is often left to chance, resulting in compositions that lack structure. Pieces extending beyond a minute can become incoherent or repetitive. This paper introduces an approach for generating structured, arbitrarily long musical pieces. Central to this approach is the creation of musical segments using a conditional generative model, with transitions between these segments. The generation of prompts that determine the high-level composition is distinct from the creation of finer, lower-level details. A large language model is then used to suggest the musical form.

Humans are widely involved in the evaluation of open-ended natural language generation tasks (NLG) that demand creativity, as automatic metrics often exhibit weak correlations with human judgments. Large language models (LLMs) recently have emerged as a scalable and cost-effective alternative to human evaluations. However, both humans and LLMs have limitations, i.e., inherent subjectivity and unreliable judgments, particularly for open-ended tasks that require adaptable metrics tailored to diverse task requirements. To explore the synergy between humans and LLM-based evaluators and address the challenges of existing inconsistent evaluation criteria in open-ended NLG tasks, we propose a Collaborative Evaluation pipeline CoEval, involving the design of a checklist of task-specific criteria and the detailed evaluation of texts, in which LLM generates initial ideation, and then humans engage in scrutiny. We conducted a series of experiments to investigate the mutual effects between LLMs and humans in CoEval. Results show that, by utilizing LLMs, CoEval effectively evaluates lengthy texts, saving significant time and reducing human evaluation outliers. Human scrutiny still plays a role, revising around 20% of LLM evaluation scores for ultimate reliability.

Sentiment analysis (SA) is commonly applied to digital textual data, revealing insight into opinions and feelings. Many systematic reviews have summarized existing work, but often overlook discussions of validity and scientific practices. Here, we present an overview of reviews, synthesizing 38 systematic reviews, containing 2,275 primary studies. We devise a bespoke quality assessment framework designed to assess the rigor and quality of systematic review methodologies and reporting standards. Our findings show diverse applications and methods, limited reporting rigor, and challenges over time. We discuss how future research and practitioners can address these issues and highlight their importance across numerous applications.