In online forums like Reddit, users share their experiences with medical conditions and treatments, including making claims, asking questions, and discussing the effects of treatments on their health. Building systems to understand this information can effectively monitor the spread of misinformation and verify user claims. The Task-8 of the 2023 International Workshop on Semantic Evaluation focused on medical applications, specifically extracting patient experience- and medical condition-related entities from user posts on social media. The Reddit Health Online Talk (RedHot) corpus contains posts from medical condition-related subreddits with annotations characterizing the patient experience and medical conditions. In Subtask-1, patient experience is characterized by personal experience, questions, and claims. In Subtask-2, medical conditions are characterized by population, intervention, and outcome. For the automatic extraction of patient experiences and medical condition information, as a part of the challenge, we proposed language-model-based extraction systems that ranked $3^{rd}$ on both subtasks' leaderboards. In this work, we describe our approach and, in addition, explore the automatic extraction of this information using domain-specific language models and the inclusion of external knowledge.

Fox News correspondent Madeleine Rivera has more on the rise of artificial intelligence as the federal government looks to address concerns and overcome the learning curve. Artificial intelligence tech has the ability to crack any kind of seven-character password in just six minutes, a new study has found. The research, shared by identity theft prevention company Home Security Heroes, said the same was true even if the password contains symbols. The company used a generative AI service called PassGAN to run through 15,680,000 common passwords from the Rockyou dataset to determine how long it would take to crack them. Rockyou is a data group used to train intelligent systems on password analysis.

DJI has unveiled its new flagship consumer drone, the Mavic 3 Pro, with a triple-camera setup that includes a new 70mm lens designed for "powerful subject framing." It also includes a new 10-bit D-Log M color mode, improvements in the tele cameras, and ProRes capture on the Mavic 3 Pro Cine option. Like the Mavic 3, it's available in regular and Cine models, with the latter having advanced features for filmmakers like Apple ProRes capture (ProRes 422 HQ, ProRes 422, and ProRes 422 LT), a 1TB SSD drive and a 10Gbps lightspeed data cable. However, you'll pay a premium of nearly $1,000 to get those. The new 70mm camera has a 1/1.3-inch sensor that's the same size as on the Mini 3 Pro. Though considerably smaller than the 4/3 chip on the main Hasselblad camera, DJI says the camera is designed for a "range of different scenarios from framing intriguing buildings to cars in commercial shoots."

"The View" co-host Whoopi Goldberg criticized re-editing books in an effort to avoid offending modern audiences and argued "that's how kids learn." Among the most tragic events in human cultural history was the destruction of works from the great library of Alexandria. Blamed on Julius Caesar as well as later Christian and Muslim zealots, the net loss of knowledge from this font of ancient wisdom roughly coincided with what we call the Dark Ages, and we may be repeating history. From its beginnings one of the great promises of computer technology was the possibility of maintaining a library of all human writing that could not burn, that would neither fade nor wither. The irony, that has not been considered closely enough, is how easily this same technology can revise or fabricate literary and historical classics, which is tantamount to destroying them.

Fox News correspondent Grady Trimble has the latest on fears that AI technology will spiral out of control on "Special Report." As Silicon Valley races to build powerful and popular artificial intelligence systems, troubling "incidents" ranging from convincing AI deepfakes, banking fraud, bias and even deaths will increase this year, a tech expert says. Following the release of ChatGPT last November, tech companies have been rushing to develop powerful AI systems to keep the pace with competitors. The AI Incident Database, which is run by nonprofit Responsible AI Collaborative, tracks various incidents caused by AI and is projected to record double the number of incidents this year compared to last. The database defines incidents through examples such as an autonomous car killing a pedestrian, a "trading algorithm" causing a "market'flash crash' where billions of dollars transfer between parties," or a "facial recognition system" causing "an innocent person to be arrested."

Many companies are working to build AGI (artificial general intelligence), defined as "AI that can learn and perform most intellectual tasks that human beings can, including AI development." Below we'll discuss why this may rapidly lead to superintelligence, defined as "general intelligence far beyond human level". I'm often told that AGI and superintelligence won't happen because it's impossible: human-level Intelligence is something mysterious that can only exist in brains. Such carbon chauvinism ignores a core insight from the AI revolution: that intelligence is all about information processing, and it doesn't matter whether the information is processed by carbon atoms in brains or by silicon atoms in computers. AI has been relentlessly overtaking humans on task after task, and I invite carbon chauvinists to stop moving the goal posts and publicly predict which tasks AI will never be able to do.

In this paper, we present a novel approach for the design of leaky-wave holographic antennas that generates OAM-carrying electromagnetic waves by combining Flat Optics (FO) and machine learning (ML) techniques. To improve the performance of our system, we use a machine learning technique to discover a mathematical function that can effectively control the entire radiation pattern, i.e., decrease the side lobe level (SLL) while simultaneously increasing the central null depth of the radiation pattern. Precise tuning of the parameters of the impedance equation based on holographic theory is necessary to achieve optimal results in a variety of scenarios. In this research, we applied machine learning to determine the approximate values of the parameters. We can determine the optimal values for each parameter, resulting in the desired radiation pattern, using a total of 77,000 generated datasets. Furthermore, the use of ML not only saves time, but also yields more precise and accurate results than manual parameter tuning and conventional optimization methods.

Evidence-aware fake news detection aims to conduct reasoning between news and evidence, which is retrieved based on news content, to find uniformity or inconsistency. However, we find evidence-aware detection models suffer from biases, i.e., spurious correlations between news/evidence contents and true/fake news labels, and are hard to be generalized to Out-Of-Distribution (OOD) situations. To deal with this, we propose a novel Dual Adversarial Learning (DAL) approach. We incorporate news-aspect and evidence-aspect debiasing discriminators, whose targets are both true/fake news labels, in DAL. Then, DAL reversely optimizes news-aspect and evidence-aspect debiasing discriminators to mitigate the impact of news and evidence content biases. At the same time, DAL also optimizes the main fake news predictor, so that the news-evidence interaction module can be learned. This process allows us to teach evidence-aware fake news detection models to better conduct news-evidence reasoning, and minimize the impact of content biases. To be noted, our proposed DAL approach is a plug-and-play module that works well with existing backbones. We conduct comprehensive experiments under two OOD settings, and plug DAL in four evidence-aware fake news detection backbones. Results demonstrate that, DAL significantly and stably outperforms the original backbones and some competitive debiasing methods.

Passive space-borne radiometers operating in the 1400-1427 MHz protected frequency band face radio frequency interference (RFI) from terrestrial sources. With the growth of wireless devices and the appearance of new technologies, the possibility of sharing this spectrum with other technologies would introduce more RFI to these radiometers. This band could be an ideal mid-band frequency for 5G and Beyond, as it offers high capacity and good coverage. Current RFI detection and mitigation techniques at SMAP (Soil Moisture Active Passive) depend on correctly detecting and discarding or filtering the contaminated data leading to the loss of valuable information, especially in severe RFI cases. In this paper, we propose an autoencoder-based RFI mitigation method to remove the dominant RFI caused by potential coexistent terrestrial users (i.e., 5G base station) from the received contaminated signal at the passive receiver side, potentially preserving valuable information and preventing the contaminated data from being discarded.

This paper explains the participation of team Hitachi to SemEval-2023 Task 3 "Detecting the genre, the framing, and the persuasion techniques in online news in a multi-lingual setup.'' Based on the multilingual, multi-task nature of the task and the low-resource setting, we investigated different cross-lingual and multi-task strategies for training the pretrained language models. Through extensive experiments, we found that (a) cross-lingual/multi-task training, and (b) collecting an external balanced dataset, can benefit the genre and framing detection. We constructed ensemble models from the results and achieved the highest macro-averaged F1 scores in Italian and Russian genre categorization subtasks.