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Limitations One limitation of our model is its potential for data bias. KOSMOS-1 is trained on a2 web-scale multimodal corpus, which means that it is likely to be biased towards the data that it was3 trained on. This could lead to the model generating text that is biased towards certain demographics4 or viewpoints.5 Another limitation of KOSMOS-1 is its relatively small size compared to other large language models.6 This means that the model may not be able to learn as complex relationships between different7 modalities. This could lead to the model making mistakes when it is asked to perform tasks that8 require a deep understanding of multiple modalities.9 Finally, KOSMOS-1 only supports vision modality.

A.1 Creation of the Multimodal Web Document Dataset A.1.1 Collecting of a Large Number of HTMLFiles Our data collection process begins by considering the 25 most recent Common Crawl6 dumps available at the time of dataset creation. It contains webpages spanning from February 2020 to January/February 2023. We use a modified version of readability-lxml7 to extract the main text from the pages, discarding any pages that contain text of excessively high perplexity. This process yields a total of 41.2 billion documents. Selection of English content To identify non-English content, we apply the FastText classifier (Joulin et al., 2017) to the extracted text, e ectively filtering out 63.6% of the documents. Early text deduplication Often, a set of URLs is crawled repeatedly across di erent Common Crawl snapshots. However, the content of these websites may vary as web administrators make changes over time. Hence, at this stage, we refrain from deduplicating documents based on their URLs. Instead, we perform MinHash (Broder, 1997) deduplication with 16 hashes calculated over 5-grams. To further refine the data, we eliminate documents containing substantial proportions of repeated paragraphs and n-grams, employing the methodology described in MassiveText (Rae et al., 2022).

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.