Melania Trump is positioning herself as a leading voice on artificial intelligence and education, her senior advisor says, highlighting her Fostering the Future Together initiative.

Traditional cameras produce desirable vision results but struggle with motion blur in high-speed scenes due to long exposure windows. Existing frame-based deblurring algorithms face challenges in extracting useful motion cues from severely blurred images. Recently, an emerging bio-inspired vision sensor known as the spike camera has achieved an extremely high frame rate while preserving rich spatial details, owing to its novel sampling mechanism. However, typical binary spike streams are relatively low-resolution, degraded image signals devoid of color information, making them unfriendly to human vision. In this paper, we propose a novel approach that integrates the two modalities from two branches, leveraging spike streams as auxiliary visual cues for guiding deblurring in high-speed motion scenes. We propose the first spike-based motion deblurring model with bidirectional information complementarity. We introduce a content-aware motion magnitude attention module that utilizes learnable mask to extract relevant information from blurry images effectively, and we incorporate a transposed cross-attention fusion module to efficiently combine features from both spike data and blurry RGB images. Furthermore, we build two extensive synthesized datasets for training and validation purposes, encompassing high-temporal-resolution spikes, blurry images, and corresponding sharp images. The experimental results demonstrate that our method effectively recovers clear RGB images from highly blurry scenes and outperforms state-of-the-art deblurring algorithms in multiple settings.

Researchers show scammers are using AI-manipulated footage of celebrity interviews to trick users into sharing their personal data. Last week, Taylor Swift filed a trio of trademark applications to protect her image and voice. One is meant to cover a well-known photograph of the pop singer holding a pink guitar during a concert on her record-breaking Eras tour, while the two sound trademarks are for simple identifying phrases: "Hey, it's Taylor Swift" and "Hey, it's Taylor." The move comes as AI deepfakes continue to proliferate across social media. Any individual stands to have their likeness exploited in the creation of nonconsensual AI-generated material; earlier this month, an Ohio man was the first person convicted under a new federal law criminalizing "intimate" visual deceptions of this sort.

Technology AI Texas Instruments' newest calculator is intentionally dumb The $160 device is not powered by AI, won't send annoying notifications, and can't connect to Wi-Fi. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. The new TI-84 keeps the good old-fashioned physical buttons. Breakthroughs, discoveries, and DIY tips sent six days a week. In a world drowning in notifications and devices that want to be everything all at once, calculator giant Texas Instruments (TI) is going back to basics.

Speech language models (SpeechLMs) process and generate acoustic data only, without textual supervision. In this work, we propose TWIST, a method for training SpeechLMs using a warm-start from a pretrained textual language models. We show using both automatic and human evaluations that TWIST outperforms a cold-start SpeechLM across the board. We empirically analyze the effect of different model design choices such as the speech tokenizer, the pretrained textual model, and the dataset size. We find that model and dataset scale both play an important role in constructing better-performing SpeechLMs. Based on our observations, we present the largest (to the best of our knowledge) SpeechLM both in terms of number of parameters and training data. We additionally introduce two spoken versions of the StoryCloze textual benchmark to further improve model evaluation and advance future research in the field. We make speech samples, code and models publicly available.2

A diversity of tasks use language models trained on semantic similarity data. While there are a variety of datasets that capture semantic similarity, they are either constructed from modern web data or are relatively small datasets created in the past decade by human annotators.

HCCV datasets constructed through nonconsensual web scraping lack crucial metadata for comprehensive fairness and robustness evaluations. Current remedies are post hoc, lack persuasive justification for adoption, or fail to provide proper contextualization for appropriate application. Our research focuses on proactive, domain-specific recommendations, covering purpose, privacy and consent, and diversity, for curating HCCV evaluation datasets, addressing privacy and bias concerns. We adopt an ante hoc reflective perspective, drawing from current practices, guidelines, dataset withdrawals, and audits, to inform our considerations and recommendations.

Japan zoo staffer allegedly dumps wife's body inside incinerator A popular Japanese zoo has delayed its opening for the summer season after an employee told police he had disposed of his wife's body in the zoo's incinerator, local media reported. Asahiyama Zoo in the northern city of Asahikawa was supposed to welcome visitors on Wednesday in time for Japan's Golden Week holiday period, after completing a three-week maintenance break. But the city government says the zoo will now remain closed until Friday as investigations continue. Last week, police searched the zoo grounds after the employee told them he had disposed of his wife's body in the zoo's incinerator, local media reported. The incinerator was used to dispose of animal carcasses when they died.