Meta announced a slate of upcoming games today for its standalone VR headsets (including the upcoming Meta Quest 3). Apple is expected to enter the virtual headset space next week, so Meta is hoping to make a lasting impression with its lineup of upcoming VR titles from beloved franchises, including Assassin's Creed, Stranger Things, Ghostbusters and Attack on Titan -- along with some VR remakes of old-school classics. In addition to Asgard's Wrath 2, the most enticing game may be the one we know the least about. Although it was little more than a tease, Meta confirmed that Assassin's Creed Nexus VR isn't vaporware after all: The next VR installment in the long-running series will launch in the Meta Quest Store later this year. Unfortunately, further details must wait for its official reveal at Ubisoft Forward on June 12th.

The moment that Love Island fans have been waiting for is almost finally here, with Season 10 finally kicking off on Monday. To celebrate the imminent launch, a man has used AI to create the'perfect' Love Island contestants. Duncan Thomsen, 53, a freelance film editor from Brighton, trawled back through all previous nine series to see which couples have won the show so far. Then, using AI, he mashed photos of the winners together to make generic islanders, solely based on their looks. So, are his creations your type on paper?

People in Texas sounded off on AI job displacement, with half of people who spoke to Fox News convinced that the tech will rob them of work. America is at a once-in-a-generation turning point around work: 70% of us are unhappy with what we do; three-quarters of us say we plan to look for new work this year. Altogether, 100 million Americans will sit down with someone they love this year and say, "I'm not happy with what I'm doing and want to do work that makes me happy." The problem: Most of the advice we receive around work is outdated, misguided or flat wrong. I've spent the last six years crisscrossing the country collecting hundreds of stories of Americans who made enormous changes in their work lives.

People in Texas sounded off on AI job displacement, with half of the people who spoke to Fox News convinced that the tech will rob them of work. Humans have historically been excellent at identifying faces and photos compared to computers, but the advent of artificial intelligence-generated photos is throwing a curveball at humans, according to a new study that examines how people perceive fake images versus real ones. Tech experts have been warning that hyperrealistic images generated by AI could lead to the proliferation of misinformation online and cybersecurity issues. Last month, for example, panic spread after an AI-generated photo that apparently showed an explosion at the Pentagon went viral, leading to the stock market taking a short dip. Researchers in Australia examined how human brains perceive and differentiate realistic AI-generated photos using both behavioral testing and neuroimaging experiments.

Fox News' Eben Brown reports on how more companies are using A.I. technology to set retail prices based on data-driven supply-and-demand. Data science is an essential field within computer science and machine learning that uses statistics, algorithms, and technology to make meaningful analysis and predictions from large amounts of data. For instance, computer scientists often describe data science as an interdisciplinary academic field combining various tools within the computer science world to extrapolate information and meaning from large amounts of structured and unstructured data. Some of the biggest corporations and technology firms in the world have a wide variety of means for collecting and storing information related to their customers and products. Read below to learn everything you need about data science and how it relates to artificial intelligence.

Ben Goertzel said the sky's "not even the limit" when it comes to the potential impact of artificial general intelligence. The co-creator of the social humanoid robot Sophia says artifical general intelligence (AGI) and super AGI are mere decades away, and he warns that the subsequent disruption from these artificial intelligence (AI) models will cause a significant amount of political and economic "mayhem" before massive benefits to humanity are seen. Speaking with Fox News Digital on the global aspects of the transition from the present day to AGI, Dr. Ben Goertzel highlighted the need to develop a beneficial, compassionate super general intelligence model to ensure humanity flourishes. Often referred to as the "singularity" – the point AGI exceeds human intelligence and reasoning – humankind will be at the whim of the AI model's motivations and behaviors. AI researchers and futurologists have repeatedly said that this inflection point is still decades away. Given the current timeline of AI acceleration, Goertzel concurred with friend and computer scientist Ray Kurzweil, calling it a "fair approximation" that human-level AGI will be created around 2029.

Complex Named Entity Recognition (NER) is the task of detecting linguistically complex named entities in low-context text. In this paper, we present ACLM Attention-map aware keyword selection for Conditional Language Model fine-tuning), a novel data augmentation approach based on conditional generation to address the data scarcity problem in low-resource complex NER. ACLM alleviates the context-entity mismatch issue, a problem existing NER data augmentation techniques suffer from and often generates incoherent augmentations by placing complex named entities in the wrong context. ACLM builds on BART and is optimized on a novel text reconstruction or denoising task - we use selective masking (aided by attention maps) to retain the named entities and certain keywords in the input sentence that provide contextually relevant additional knowledge or hints about the named entities. Compared with other data augmentation strategies, ACLM can generate more diverse and coherent augmentations preserving the true word sense of complex entities in the sentence. We demonstrate the effectiveness of ACLM both qualitatively and quantitatively on monolingual, cross-lingual, and multilingual complex NER across various low-resource settings. ACLM outperforms all our neural baselines by a significant margin (1%-36%). In addition, we demonstrate the application of ACLM to other domains that suffer from data scarcity (e.g., biomedical). In practice, ACLM generates more effective and factual augmentations for these domains than prior methods. Code: https://github.com/Sreyan88/ACLM

Chain-of-Thought (CoT) prompting can dramatically improve the multi-step reasoning abilities of large language models (LLMs). CoT explicitly encourages the LLM to generate intermediate rationales for solving a problem, by providing a series of reasoning steps in the demonstrations. Despite its success, there is still little understanding of what makes CoT prompting effective and which aspects of the demonstrated reasoning steps contribute to its performance. In this paper, we show that CoT reasoning is possible even with invalid demonstrations - prompting with invalid reasoning steps can achieve over 80-90% of the performance obtained using CoT under various metrics, while still generating coherent lines of reasoning during inference. Further experiments show that other aspects of the rationales, such as being relevant to the query and correctly ordering the reasoning steps, are much more important for effective CoT reasoning. Overall, these findings both deepen our understanding of CoT prompting, and open up new questions regarding LLMs' capability to learn to reason in context.

The acquisition of explicit user feedback (e.g., ratings) in real-world recommender systems is often hindered by the need for active user involvement. To mitigate this issue, implicit feedback (e.g., clicks) generated during user browsing is exploited as a viable substitute. However, implicit feedback possesses a high degree of noise, which significantly undermines recommendation quality. While many methods have been proposed to address this issue by assigning varying weights to implicit feedback, two shortcomings persist: (1) the weight calculation in these methods is iteration-independent, without considering the influence of weights in previous iterations, and (2) the weight calculation often relies on prior knowledge, which may not always be readily available or universally applicable. To overcome these two limitations, we model recommendation denoising as a bi-level optimization problem. The inner optimization aims to derive an effective model for the recommendation, as well as guiding the weight determination, thereby eliminating the need for prior knowledge. The outer optimization leverages gradients of the inner optimization and adjusts the weights in a manner considering the impact of previous weights. To efficiently solve this bi-level optimization problem, we employ a weight generator to avoid the storage of weights and a one-step gradient-matching-based loss to significantly reduce computational time. The experimental results on three benchmark datasets demonstrate that our proposed approach outperforms both state-of-the-art general and denoising recommendation models. The code is available at https://github.com/CoderWZW/BOD.

Stance Detection is concerned with identifying the attitudes expressed by an author towards a target of interest. This task spans a variety of domains ranging from social media opinion identification to detecting the stance for a legal claim. However, the framing of the task varies within these domains, in terms of the data collection protocol, the label dictionary and the number of available annotations. Furthermore, these stance annotations are significantly imbalanced on a per-topic and inter-topic basis. These make multi-domain stance detection a challenging task, requiring standardization and domain adaptation. To overcome this challenge, we propose $\textbf{T}$opic $\textbf{E}$fficient $\textbf{St}$anc$\textbf{E}$ $\textbf{D}$etection (TESTED), consisting of a topic-guided diversity sampling technique and a contrastive objective that is used for fine-tuning a stance classifier. We evaluate the method on an existing benchmark of $16$ datasets with in-domain, i.e. all topics seen and out-of-domain, i.e. unseen topics, experiments. The results show that our method outperforms the state-of-the-art with an average of $3.5$ F1 points increase in-domain, and is more generalizable with an averaged increase of $10.2$ F1 on out-of-domain evaluation while using $\leq10\%$ of the training data. We show that our sampling technique mitigates both inter- and per-topic class imbalances. Finally, our analysis demonstrates that the contrastive learning objective allows the model a more pronounced segmentation of samples with varying labels.