A new "empathic voice interface" launched today by Hume AI, a New York–based startup, makes it possible to add a range of emotionally expressive voices, plus an emotionally attuned ear, to large language models from Anthropic, Google, Meta, Mistral, and OpenAI--portending an era when AI helpers may more routinely get all gushy on us. "We specialize in building empathic personalities that speak in ways people would speak, rather than stereotypes of AI assistants," says Hume AI cofounder Alan Cowen, a psychologist who has coauthored a number of research papers on AI and emotion, and who previously worked on emotional technologies at Google and Facebook. WIRED tested Hume's latest voice technology, called EVI 2 and found its output to be similar to that developed by OpenAI for ChatGPT. Later, a real movie star, Scarlett Johansson, claimed OpenAI had ripped off her voice.) Like ChatGPT, Hume is far more emotionally expressive than most conventional voice interfaces. If you tell it that your pet has died, for example, it will adopt a suitable somber and sympathetic tone.

Please spare a thought for artificial intelligence (AI). It may not have feelings yet but, if it did, it would feel devastated by all the nasty things people are saying about it. All it's trying to do is take our jobs and potentially destroy the world and people can't stop being mean. Exhibit one: a recent controversy with the organisation that runs National Novel Writing Month (NaNoWriMo), a yearly challenge to produce a manuscript in a month. In a recent statement, NaNoWriMo wrote that it doesn't explicitly support or condemn any approach to writing, "including the use of AI". Further: "The categorical condemnation of artificial intelligence has classist and ableist undertones … questions around the use of AI tie to questions around privilege."

It was an emotionally dark and stormy night in 2020 when I had the urge to write a novel. I'd been having panic attacks. To work through it, I decided to write a novel about an isolated mom and a monster in the woods, along with therapy. So that November, I participated in National Novel Writing Month (NaNoWriMo), which is also a nonprofit organization that encourages creative writing through a variety of events, including its most famous and titular program where participants attempt to write a complete novel (or 50,000 words) in the month of November. I loved the "flow state" of writing that came about as a result of participating.

As befits a writer whose breakout work, Sapiens, was a history of the entire human race, Yuval Noah Harari is a master of the sententious generalisation. "Human life," he writes here, "is a balancing act between endeavouring to improve ourselves and accepting who we were." Elsewhere, one might be surprised to read: "The ancient Romans had a clear understanding of what democracy means." No doubt the Romans would have been happy to hear that they would, 2,000 years in the future, be given a gold star for their comprehension of eternally stable political concepts by Yuval Noah Harari. In his 2018 book, 21 Lessons for the 21st Century, Harari wrote: "Liberals don't understand how history deviated from its preordained course, and they lack an alternative prism through which to interpret reality. Disorientation causes them to think in apocalyptic terms."

Democrats, Republicans, and Independents react in-real time to an exchange between the 2024 candidates over a conservative transition plan known as Project 2025 and COVID pandemic policy. Vice President Kamala Harris was dragged on social media for her exaggerated facial expressions during the ABC News Presidential Debate against former President Trump on Tuesday. Harris' smirk was repeatedly captured on the split screen during Trump's turn to answer questions from the debate moderators. Both Harris' and Trump's microphones were muted when the other candidate was given time to speak, a rule the Harris campaign had tried to change. This left Harris with nothing but her facial gestures to express her contempt for the GOP nominee.

Large language models (LLMs) have shown remarkable abilities in different fields, including standard Natural Language Processing (NLP) tasks. To elicit knowledge from LLMs, prompts play a key role, consisting of natural language instructions. Most open and closed source LLMs are trained on available labeled and unlabeled resources--digital content such as text, images, audio, and videos. Hence, these models have better knowledge for high-resourced languages but struggle with low-resourced languages. Since prompts play a crucial role in understanding their capabilities, the language used for prompts remains an important research question. Although there has been significant research in this area, it is still limited, and less has been explored for medium to low-resourced languages. In this study, we investigate different prompting strategies (native vs. non-native) on 11 different NLP tasks associated with 12 different Arabic datasets (9.7K data points). In total, we conducted 197 experiments involving 3 LLMs, 12 datasets, and 3 prompting strategies. Our findings suggest that, on average, the non-native prompt performs the best, followed by mixed and native prompts.

Novel view synthesis of indoor scenes can be achieved by capturing a monocular video sequence of the environment. However, redundant information caused by artificial movements in the input video data reduces the efficiency of scene modeling. In this work, we tackle this challenge from the perspective of camera selection. We begin by constructing a similarity matrix that incorporates both the spatial diversity of the cameras and the semantic variation of the images. Based on this matrix, we use the Intra-List Diversity (ILD) metric to assess camera redundancy, formulating the camera selection task as an optimization problem. Then we apply a diversity-based sampling algorithm to optimize the camera selection. We also develop a new dataset, IndoorTraj, which includes long and complex camera movements captured by humans in virtual indoor environments, closely mimicking real-world scenarios. Experimental results demonstrate that our strategy outperforms other approaches under time and memory constraints. Remarkably, our method achieves performance comparable to models trained on the full dataset, while using only an average of 15% of the frames and 75% of the allotted time.

Large Language Models (LLMs) have revolutionized numerous applications, making them an integral part of our digital ecosystem. However, their reliability becomes critical, especially when these models are exposed to misinformation. We primarily analyze the susceptibility of state-of-the-art LLMs to factual inaccuracies when they encounter false information in a QnA scenario, an issue that can lead to a phenomenon we refer to as *knowledge drift*, which significantly undermines the trustworthiness of these models. We evaluate the factuality and the uncertainty of the models' responses relying on Entropy, Perplexity, and Token Probability metrics. Our experiments reveal that an LLM's uncertainty can increase up to 56.6% when the question is answered incorrectly due to the exposure to false information. At the same time, repeated exposure to the same false information can decrease the models uncertainty again (-52.8% w.r.t. the answers on the untainted prompts), potentially manipulating the underlying model's beliefs and introducing a drift from its original knowledge. These findings provide insights into LLMs' robustness and vulnerability to adversarial inputs, paving the way for developing more reliable LLM applications across various domains. The code is available at https://github.com/afastowski/knowledge_drift.

Knowledge conflict arises from discrepancies between information in the context of a large language model (LLM) and the knowledge stored in its parameters. This can hurt performance when using standard decoding techniques, which tend to ignore the context. Existing test-time contrastive methods seek to address this by comparing the LLM's output distribution with and without the context and adjust the model according to the contrast between them. However, we find that these methods frequently misjudge the degree of conflict and struggle to handle instances that vary in their amount of conflict, with static methods over-adjusting when conflict is absent. We propose a fine-grained, instance-level approach called AdaCAD, which dynamically infers the weight of adjustment based on the degree of conflict, as measured by the Jensen-Shannon divergence between distributions representing contextual and parametric knowledge. Our experiments across four models on six diverse question-answering (QA) datasets and three summarization tasks demonstrate that our training-free adaptive method consistently outperforms other decoding methods on QA, with average accuracy gains of 14.21% (absolute) over a static contrastive baseline, and improves the factuality of summaries by 5.59 (AlignScore). Furthermore, our analysis shows that while decoding with contrastive baselines hurts performance when conflict is absent, AdaCAD mitigates these losses, making it more applicable to real-world datasets in which some examples have conflict and others do not.

Russian Internet Trolls use fake personas to spread disinformation through multiple social media streams. Given the increased frequency of this threat across social media platforms, understanding those operations is paramount in combating their influence. Using Twitter content identified as part of the Russian influence network, we created a predictive model to map the network operations. We classify accounts type based on their authenticity function for a sub-sample of accounts by introducing logical categories and training a predictive model to identify similar behavior patterns across the network. Our model attains 88% prediction accuracy for the test set. Validation is done by comparing the similarities with the 3 million Russian troll tweets dataset. The result indicates a 90.7% similarity between the two datasets. Furthermore, we compare our model predictions on a Russian tweets dataset, and the results state that there is 90.5% correspondence between the predictions and the actual categories. The prediction and validation results suggest that our predictive model can assist with mapping the actors in such networks.