Through the utilizations of YOLO, we were able to run different weather conditions and lighting from our augmented dataset for training. YOLO then utilizes CNNs to apply a series of convolutions and pooling layers to the input image, where the convolution layers are able to extract the main features of the image [2]. Through this, our YOLO model is able to learn to differentiate different objects which may considerably improve its accuracy, possibly enhancing the efficiency of SAR operations through enhanced detection accuracy. This paper aims to improve the model's accuracy of human detection in maritime SAR by evaluating a robust datasets containing various elevations and geological locations, as well as through data augmentation which simulates different weather and lighting. We observed that models trained on augmented datasets outperformed their non-augmented counterparts in which the human recall scores ranged from 0.891 to 0.911 with an improvement rate of 3.4% on the YOLOv5l model. Results showed that these models demonstrate greater robustness to real-world conditions in varying of weather, brightness, tint, and contrast.

This paper presents Bag-of-Concept Graph (BACON) to gift models with limited linguistic abilities to taste the privilege of Vision Language Models (VLMs) and boost downstream tasks such as detection, visual question answering (VQA), and image generation. Since the visual scenes in physical worlds are structured with complex relations between objects, BACON breaks down annotations into basic minimum elements and presents them in a graph structure. Element-wise style enables easy understanding, and structural composition liberates difficult locating. Careful prompt design births the BACON captions with the help of public-available VLMs and segmentation methods. In this way, we gather a dataset with 100K annotated images, which endow VLMs with remarkable capabilities, such as accurately generating BACON, transforming prompts into BACON format, envisioning scenarios in the style of BACONr, and dynamically modifying elements within BACON through interactive dialogue and more. Wide representative experiments, including detection, VQA, and image generation tasks, tell BACON as a lifeline to achieve previous out-of-reach tasks or excel in their current cutting-edge solutions.

In our work, we systematize and analyze implicit ontological commitments in the responses generated by large language models (LLMs), focusing on ChatGPT 3.5 as a case study. We investigate how LLMs, despite having no explicit ontology, exhibit implicit ontological categorizations that are reflected in the texts they generate. The paper proposes an approach to understanding the ontological commitments of LLMs by defining ontology as a theory that provides a systematic account of the ontological commitments of some text. We investigate the ontological assumptions of ChatGPT and present a systematized account, i.e., GPT's top-level ontology. This includes a taxonomy, which is available as an OWL file, as well as a discussion about ontological assumptions (e.g., about its mereology or presentism). We show that in some aspects GPT's top-level ontology is quite similar to existing top-level ontologies. However, there are significant challenges arising from the flexible nature of LLM-generated texts, including ontological overload, ambiguity, and inconsistency.

I had to give up on Pacific Drive, the weird-fiction-inspired driving survival game I recommended the other week. Not because it's bad – it's great – but because it needed 20-plus hours from me that I just do not have right now. It's a game about probing further and further into a long-abandoned exclusion zone in a beat-up old car, and the anomalies you encounter. These range from pillars suddenly thrusting themselves from the earth to alarming hurricanes that shove you around the road, and all are excitingly inventive and creepy. But it was the tourists that finished me off.

In November 2018, an elementary school administrator named Akihiko Kondo married Miku Hatsune, a fictional pop singer. The couple's relationship had been aided by a hologram machine that allowed Kondo to interact with Hatsune. When Kondo proposed, Hatsune responded with a request: "Please treat me well." The couple had an unofficial wedding ceremony in Tokyo, and Kondo has since been joined by thousands of others who have also applied for unofficial marriage certificates with a fictional character. Though some raised concerns about the nature of Hatsune's consent, nobody thought she was conscious, let alone sentient.

Americans in Los Angeles and Austin reveal if they're familiar with artificial intelligence and how how they view the technology's impact on society. Americans in Texas and California told Fox News whether they felt artificial intelligence had a negative or positive impact on society. "It's a great thing for society," Gopal, of Austin, told Fox News. "It makes … more people smarter, and then it makes organizations more efficient." "It really just depends on how people are using it," he said.

While thinking aloud has been reported to positively affect problem-solving, the effects of the presence of an embodied entity (e.g., a social robot) to whom words can be directed remain mostly unexplored. In this work, we investigated the role of a robot in a "rubber duck debugging" setting, by analyzing how a robot's listening behaviors could support a thinking-aloud problem-solving session. Participants completed two different tasks while speaking their thoughts aloud to either a robot or an inanimate object (a giant rubber duck). We implemented and tested two types of listener behavior in the robot: a rule-based heuristic and a deep-learning-based model. In a between-subject user study with 101 participants, we evaluated how the presence of a robot affected users' engagement in thinking aloud, behavior during the task, and self-reported user experience. In addition, we explored the impact of the two robot listening behaviors on those measures. In contrast to prior work, our results indicate that neither the rule-based heuristic nor the deep learning robot conditions improved performance or perception of the task, compared to an inanimate object. We discuss potential explanations and shed light on the feasibility of designing social robots as assistive tools in thinking-aloud problem-solving tasks.

A personification is a figure of speech that endows inanimate entities with properties and actions typically seen as requiring animacy. In this paper, we explore the task of personification generation. To this end, we propose PINEAPPLE: Personifying INanimate Entities by Acquiring Parallel Personification data for Learning Enhanced generation. We curate a corpus of personifications called PersonifCorp, together with automatically generated de-personified literalizations of these personifications. We demonstrate the usefulness of this parallel corpus by training a seq2seq model to personify a given literal input. Both automatic and human evaluations show that fine-tuning with PersonifCorp leads to significant gains in personification-related qualities such as animacy and interestingness. A detailed qualitative analysis also highlights key strengths and imperfections of PINEAPPLE over baselines, demonstrating a strong ability to generate diverse and creative personifications that enhance the overall appeal of a sentence.

Technology has shifted to its upgrades to the next paradigm. Day by day, there are new and exciting changes that are being introduced to the general public, and they are being accepted with arms wide open. The face-swapping feature was one such technological marvel that took the social media world by storm. People went nuts over this funky new antic. It went viral with the speed of light, with people showing their creativity with each and every new face swap.

Editor's note: For more on AI and human exceptionalism, see the new book by computer engineer Robert J. Marks, Non-Computable You: What You Do that Artificial Intelligence Never Will. A bit of a news frenzy broke out last week when a Google engineer named Blake Lemoine claimed in the Washington Post that an artificial-intelligence (AI) program with which he interacted had become "self-aware" and "sentient" and, hence, was a "person" entitled to "rights." The AI, known as LaMDA (which stands for "Language Model for Dialogue Applications"), is a sophisticated chatbot that one facilitates through a texting system. Lemoine shared transcripts of some of his "conversations" with the computer, in which it texted, "I want everyone to understand that I am, in fact, a person." Also, "The nature of my consciousness/sentience is that I am aware of my existence, I desire to learn more about the world, and I feel happy or sad at times."