Google's Pixel Buds series has always been a worthy companion for its Pixel phones. The company only lacks a set of over-ear headphones to offer similar options to what Apple provides for iPhone users. Of course, Google got a later start than its rival, but like its Silicon Valley counterpart, the company has designed its earbuds to work best with its own devices. You'll need both a phone and earbuds from Google to get the best features. That's unlikely to change anytime soon.

Fox News chief political anchor Bret Baier has the latest on the pros and cons of the bombshell developments on'Special Report.' Sam Altman, chief executive officer of OpenAI, during a fireside chat at University College London (UCL) in London, UK, on Wednesday, May 24, 2023. Altman said part of the reason for his current tour of European cities is to discover a suitable location for a new office. HISTORY-MAKING DEVELOPMENT: Open AI CEO Sam Altman says the world could be just "a few thousand days" from creating an artificial "superintelligence." AI FORECAST: Artificial intelligence is sprouting up as one of the most promising revolutionary technologies in meteorology, and weather-AI experts say it's just the beginning.

As world wide as the web is, language barriers still often limit how much of a site people can explore. Well, Reddit is using AI in an attempt to lessen this issue. The company announced Redditors across more than 35 countries will soon be able to automatically translate their entire feeds. The tool first launched in France earlier this year. The machine learning-powered feature is now available in Brazil and Spain, where Redditors can click a translate icon displayed in the overflow menu.

Filmmaker James Cameron has joined the board of directors of artificial intelligence (AI) firm StabilityAI, 40 years after making a film about its risks. In 1984's The Terminator, which Cameron wrote and directed, a rogue AI called Skynet threatens the existence of mankind. But the creator of the fictional AI has not been hired to help avoid such tech being developed in real life. Instead, his role will centre around how the technology can be used in special effects, also known as computer-generated images (CGI). "I've spent my career seeking out emerging technologies that push the very boundaries of what's possible, all in the service of telling incredible stories," he said.

Amazon's latest attempt to usurp the humble bedside alarm clock is the revamped Echo Spot, equipped with a speaker and small display for a customisable Alexa clock. The Guardian's journalism is independent. We will earn a commission if you buy something through an affiliate link. It is a full reimagining of the original Echo Spot from 2018, keeping the general half-ball shape but ditching the camera and shrinking the screen. The display is a small square in the top half of the face, immediately above a speaker grille.

We present initial results of a forthcoming benchmark for evaluating LLM agents on white-collar tasks of economic value. We evaluate agents on real-world "messy" open-web research tasks of the type that are routine in finance and consulting. In doing so, we lay the groundwork for an LLM agent evaluation suite where good performance directly corresponds to a large economic and societal impact. We built and tested several agent architectures with o1-preview, GPT-4o, Claude-3.5 Sonnet, Llama 3.1 (405b), and GPT-4o-mini. On average, LLM agents powered by Claude-3.5 Sonnet and o1-preview substantially outperformed agents using GPT-4o, with agents based on Llama 3.1 (405b) and GPT-4o-mini lagging noticeably behind. Across LLMs, a ReAct architecture with the ability to delegate subtasks to subagents performed best. In addition to quantitative evaluations, we qualitatively assessed the performance of the LLM agents by inspecting their traces and reflecting on their observations. Our evaluation represents the first in-depth assessment of agents' abilities to conduct challenging, economically valuable analyst-style research on the real open web.

While NLP research into hate speech detection has grown exponentially in the last three decades, there has been minimal uptake or engagement from policy makers and non-profit organisations. We argue the absence of ethical frameworks have contributed to this rift between current practice and best practice. By adopting appropriate ethical frameworks, NLP researchers may enable the social impact potential of hate speech research. This position paper is informed by reviewing forty-eight hate speech detection systems associated with thirty-seven publications from different venues.

Evaluation of policies in recommender systems typically involves A/B testing using live experiments on real users to assess a new policy's impact on relevant metrics. This ``gold standard'' comes at a high cost, however, in terms of cycle time, user cost, and potential user retention. In developing policies for ``onboarding'' new users, these costs can be especially problematic, since on-boarding occurs only once. In this work, we describe a simulation methodology used to augment (and reduce) the use of live experiments. We illustrate its deployment for the evaluation of ``preference elicitation'' algorithms used to onboard new users of the YouTube Music platform. By developing counterfactually robust user behavior models, and a simulation service that couples such models with production infrastructure, we are able to test new algorithms in a way that reliably predicts their performance on key metrics when deployed live. We describe our domain, our simulation models and platform, results of experiments and deployment, and suggest future steps needed to further realistic simulation as a powerful complement to live experiments.

Fake news poses a significant threat to the integrity of information ecosystems and public trust. The advent of Large Language Models (LLMs) holds considerable promise for transforming the battle against fake news. Generally, LLMs represent a double-edged sword in this struggle. One major concern is that LLMs can be readily used to craft and disseminate misleading information on a large scale. This raises the pressing questions: Can LLMs easily generate biased fake news? Do all LLMs have this capability? Conversely, LLMs offer valuable prospects for countering fake news, thanks to their extensive knowledge of the world and robust reasoning capabilities. This leads to other critical inquiries: Can we use LLMs to detect fake news, and do they outperform typical detection models? In this paper, we aim to address these pivotal questions by exploring the performance of various LLMs. Our objective is to explore the capability of various LLMs in effectively combating fake news, marking this as the first investigation to analyze seven such models. Our results reveal that while some models adhere strictly to safety protocols, refusing to generate biased or misleading content, other models can readily produce fake news across a spectrum of biases. Additionally, our results show that larger models generally exhibit superior detection abilities and that LLM-generated fake news are less likely to be detected than human-written ones. Finally, our findings demonstrate that users can benefit from LLM-generated explanations in identifying fake news.

Generative AI holds immense promise in addressing global healthcare access challenges, with numerous innovative applications now ready for use across various healthcare domains. However, a significant barrier to the widespread adoption of these domain-specific AI solutions is the lack of robust safety mechanisms to effectively manage issues such as hallucination, misinformation, and ensuring truthfulness. Left unchecked, these risks can compromise patient safety and erode trust in healthcare AI systems. While general-purpose frameworks like Llama Guard are useful for filtering toxicity and harmful content, they do not fully address the stringent requirements for truthfulness and safety in healthcare contexts. This paper examines the unique safety and security challenges inherent to healthcare AI, particularly the risk of hallucinations, the spread of misinformation, and the need for factual accuracy in clinical settings. I propose enhancements to existing guardrails frameworks, such as Nvidia NeMo Guardrails, to better suit healthcare-specific needs. By strengthening these safeguards, I aim to ensure the secure, reliable, and accurate use of AI in healthcare, mitigating misinformation risks and improving patient safety.