I was dreading buying Christmas gifts this year. My family tends to buy things they need as they go, and my sister would kill me if I bought her another sweater. So when my editor suggested I use ChatGPT to plan my Christmas shopping for me and write about it, I jumped at the opportunity. And I figured it was a win-win. If its suggested gifts were good, I wouldn't need to worry about coming up with present ideas for another 12 months! If they were a disaster, it would be a good opportunity to showcase how rudimentary artificial intelligence is (I'm extremely skeptical about the predictions of AI enslaving us in the future).

While Barbenheimer was undoubtedly the biggest movie story of 2023, the year in film was one jam-packed with dozens of truly great movies--not all of which managed to generate the nonstop headlines or mainstream traction that an iconic doll and the "father of the atomic bomb" did. It was a stellar year for first-time directors as well, as evidenced by films like Emily, The Unknown Country, and A Thousand and One. If you've seen Barbie, Oppenheimer, and many of the year's higher-profile movies, here are 15 that you maybe haven't seen that are definitely worth your time. If you buy something using links in our stories, we may earn a commission. This helps support our journalism.

Kurt Knutsson shows how this companion bot can act like a home security guard and life alert if you have fallen and can't get help on your own. If you are looking for some weird and, in some cases, bizarre tech that will blow your mind, you have come to the right place. We've compiled some of the most fascinating and futuristic gadgets that have wowed us over the past year. From a hamster ball robot that can fly and crawl, to a pair of jeans that can protect you from motorcycle accidents to an AI-powered wearable gadget, these are some of the 10 coolest and craziest things you will ever see. 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 The latest sensation in robotics is the Hybrid Mobility Robot (HMR) from Revolute Robotics.

As the disruptive changes in the media economy and the proliferation of alternative news media outlets, news intent has progressively deviated from ethical standards that serve the public interest. News intent refers to the purpose or intention behind the creation of a news article. While the significance of research on news intent has been widely acknowledged, the absence of a systematic news intent understanding framework hinders further exploration of news intent and its downstream applications. To bridge this gap, we propose News INTent (NINT) frame, the first component-aware formalism for understanding the news creation intent based on research in philosophy, psychology, and cognitive science. Within this frame, we define the news intent identification task and provide a benchmark dataset with fine-grained labels along with an efficient benchmark method. Experiments demonstrate that NINT is beneficial in both the intent identification task and downstream tasks that demand a profound understanding of news. This work marks a foundational step towards a more systematic exploration of news creation intents.

Conversational recommender systems (CRS) utilize natural language interactions and dialogue history to infer user preferences and provide accurate recommendations. Due to the limited conversation context and background knowledge, existing CRSs rely on external sources such as knowledge graphs to enrich the context and model entities based on their inter-relations. However, these methods ignore the rich intrinsic information within entities. To address this, we introduce the Knowledge-Enhanced Entity Representation Learning (KERL) framework, which leverages both the knowledge graph and a pre-trained language model to improve the semantic understanding of entities for CRS. In our KERL framework, entity textual descriptions are encoded via a pre-trained language model, while a knowledge graph helps reinforce the representation of these entities. We also employ positional encoding to effectively capture the temporal information of entities in a conversation. The enhanced entity representation is then used to develop a recommender component that fuses both entity and contextual representations for more informed recommendations, as well as a dialogue component that generates informative entity-related information in the response text. A high-quality knowledge graph with aligned entity descriptions is constructed to facilitate our study, namely the Wiki Movie Knowledge Graph (WikiMKG). The experimental results show that KERL achieves state-of-the-art results in both recommendation and response generation tasks.

Social platforms have emerged as crucial platforms for disseminating information and discussing real-life social events, offering researchers an excellent opportunity to design and implement novel event detection frameworks. However, most existing approaches only exploit keyword burstiness or network structures to detect unspecified events. Thus, they often need help identifying unknown events regarding the challenging nature of events and social data. Social data, e.g., tweets, is characterized by misspellings, incompleteness, word sense ambiguation, irregular language, and variation in aspects of opinions. Moreover, extracting discriminative features and patterns for evolving events by exploiting the limited structural knowledge is almost infeasible. To address these challenges, in this paper, we propose a novel framework, namely EnrichEvent, that leverages the linguistic and contextual representations of streaming social data. In particular, we leverage contextual and linguistic knowledge to detect semantically related tweets and enhance the effectiveness of the event detection approaches. Eventually, our proposed framework produces cluster chains for each event to show the evolving variation of the event through time. We conducted extensive experiments to evaluate our framework, validating its high performance and effectiveness in detecting and distinguishing unspecified social events.

Sequential Recommender Systems (SRSs) are widely employed to model user behavior over time. However, their robustness in the face of perturbations in training data remains a largely understudied yet critical issue. A fundamental challenge emerges in previous studies aimed at assessing the robustness of SRSs: the Rank-Biased Overlap (RBO) similarity is not particularly suited for this task as it is designed for infinite rankings of items and thus shows limitations in real-world scenarios. For instance, it fails to achieve a perfect score of 1 for two identical finite-length rankings. To address this challenge, we introduce a novel contribution: Finite Rank-Biased Overlap (FRBO), an enhanced similarity tailored explicitly for finite rankings. This innovation facilitates a more intuitive evaluation in practical settings. In pursuit of our goal, we empirically investigate the impact of removing items at different positions within a temporally ordered sequence. We evaluate two distinct SRS models across multiple datasets, measuring their performance using metrics such as Normalized Discounted Cumulative Gain (NDCG) and Rank List Sensitivity. Our results demonstrate that removing items at the end of the sequence has a statistically significant impact on performance, with NDCG decreasing up to 60%. Conversely, removing items from the beginning or middle has no significant effect. These findings underscore the criticality of the position of perturbed items in the training data. As we spotlight the vulnerabilities inherent in current SRSs, we fervently advocate for intensified research efforts to fortify their robustness against adversarial perturbations.

AI Generated Content (AIGC) has received tremendous attention within the past few years, with content generated in the format of image, text, audio, video, etc. Meanwhile, AIGC has become a double-edged sword and recently received much criticism regarding its responsible usage. In this article, we focus on 8 main concerns that may hinder the healthy development and deployment of AIGC in practice, including risks from (1) privacy; (2) bias, toxicity, misinformation; (3) intellectual property (IP); (4) robustness; (5) open source and explanation; (6) technology abuse; (7) consent, credit, and compensation; (8) environment. Additionally, we provide insights into the promising directions for tackling these risks while constructing generative models, enabling AIGC to be used more responsibly to truly benefit society.

More than two decades after J.K. Rowling introduced the world to a universe of magical creatures, forbidden forests and a teenage wizard, Harry Potter is finding renewed relevance in a very different body of literature: AI research. A growing number of researchers are using the best-selling Harry Potter books to experiment with generative artificial intelligence technology, citing the series' enduring influence in popular culture and the wide range of language data and complex wordplay within its pages. Reviewing a list of studies and academic papers referencing Harry Potter offers a snapshot into cutting-edge AI research -- and some of the thorniest questions facing the technology. In perhaps the most notable recent example, Harry, Hermione and Ron star in a paper titled "Who's Harry Potter?" that sheds light on a new technique helping large language models to selectively forget information. That has led to lawsuits and public scrutiny for some AI companies.

Elon Musk's favourite movie quote, according to his biographer, is Gladiator's "Are you not entertained?" Some members of Musk's considerable global audience were also horrified as controversial statements and management decisions grabbed the headlines yet again this year. Musk's brother, Kimbal, told Walter Isaacson, author of the Elon Musk biography published in September, that his sibling was a "drama magnet". He added: "That's his compulsion, the theme of his life." That impulse was on full display in 2023.