Finally, he gave the summaries to his fine-tuned model, and he asked it to compose passages "in the style of Vauhini Vara." Going into all this, I was self-assured, even smug. I'd always felt that my style was original and, more important, that my books were totally distinct from one another. I figured that, even if the A.I. model could imitate my past books, it couldn't predict the style of the novel in progress. So, when Chakrabarty sent me the A.I.-generated imitations, I was genuinely confused.

System identification has greatly benefited from deep learning techniques, particularly for modeling complex, nonlinear dynamical systems with partially unknown physics where traditional approaches may not be feasible. However, deep learning models often require large datasets and significant computational resources at training and inference due to their high-dimensional parameterizations. To address this challenge, we propose a meta-learning framework that discovers a low-dimensional manifold within the parameter space of an over-parameterized neural network architecture. This manifold is learned from a meta-dataset of input-output sequences generated by a class of related dynamical systems, enabling efficient model training while preserving the network's expressive power for the considered system class. Unlike bilevel meta-learning approaches, our method employs an auxiliary neural network to map datasets directly onto the learned manifold, eliminating the need for costly second-order gradient computations during meta-training and reducing the number of first-order updates required in inference, which could be expensive for large models. We validate our approach on a family of Bouc-Wen oscillators, which is a well-studied nonlinear system identification benchmark. We demonstrate that we are able to learn accurate models even in small-data scenarios.

LLM-based applications are helping people write, and LLM-generated text is making its way into social media, journalism, and our classrooms. However, the differences between LLM-generated and human-written text remain unclear. To explore this, we hired professional writers to edit paragraphs in several creative domains. We first found these writers agree on undesirable idiosyncrasies in LLM-generated text, formalizing it into a seven-category taxonomy (e.g. cliches, unnecessary exposition). Second, we curated the LAMP corpus: 1,057 LLM-generated paragraphs edited by professional writers according to our taxonomy. Analysis of LAMP reveals that none of the LLMs used in our study (GPT4o, Claude-3.5-Sonnet, Llama-3.1-70b) outperform each other in terms of writing quality, revealing common limitations across model families. Third, we explored automatic editing methods to improve LLM-generated text. A large-scale preference annotation confirms that although experts largely prefer text edited by other experts, automatic editing methods show promise in improving alignment between LLM-generated and human-written text.

Today it launched a new tool called Callaia, which amateur writers and professional script readers alike can use to analyze scripts at 79 each. Using AI, it takes Callaia less than a minute to write its own coverage, which includes a synopsis, a list of comparable films, grades for areas like dialogue and originality, and actor recommendations. It also makes a recommendation on whether or not the film should be financed, giving it a rating of "pass," "consider," "recommend," or "strongly recommend." Though the foundation of the tool is built with ChatGPT's API, the team had to coach the model on script-specific tasks like evaluating genres and writing a movie's logline, which summarize the story in a sentence. "It helps people understand the script very quickly," says Tobias Queisser, Cinelytic's cofounder and CEO, who also had a career as a film producer.

What a cold and heartless way to end things. You might feel this way if you have ever been dumped via text message. But what if your ex took it a step further and used an AI chatbot to craft their goodbye? Would you even be able to tell? Large language models, like the ones touted by AI companies like OpenAI, have quickly transformed the way we communicate.

The ability to understand and generate similes is an imperative step to realize human-level AI. However, there is still a considerable gap between machine intelligence and human cognition in similes, since deep models based on statistical distribution tend to favour high-frequency similes. Hence, a large-scale symbolic knowledge base of similes is required, as it contributes to the modeling of diverse yet unpopular similes while facilitating additional evaluation and reasoning. To bridge the gap, we propose a novel framework for large-scale simile knowledge base construction, as well as two probabilistic metrics which enable an improved understanding of simile phenomena in natural language. Overall, we construct MAPS-KB, a million-scale probabilistic simile knowledge base, covering 4.3 million triplets over 0.4 million terms from 70 GB corpora. We conduct sufficient experiments to justify the effectiveness and necessity of the methods of our framework. We also apply MAPS-KB on three downstream tasks to achieve state-of-the-art performance, further demonstrating the value of MAPS-KB.

"This is the first study to address the most common intracranial tumors and to directly determine the tumor class or the absence of tumor from a 3D MRI volume," said Satrajit Chakrabarty, M.S., a doctoral student under the direction of Aristeidis Sotiras, Ph.D., and Daniel Marcus, Ph.D., in Mallinckrodt Institute of Radiology's Computational Imaging Lab at Washington University School of Medicine in St. Louis, Missouri. The six most common intracranial tumor types are high-grade glioma, low-grade glioma, brain metastases, meningioma, pituitary adenoma and acoustic neuroma. Each was documented through histopathology, which requires surgically removing tissue from the site of a suspected cancer and examining it under a microscope. "Non-invasive MRI may be used as a complement, or in some cases, as an alternative to histopathologic examination," he said. To build their machine learning model, called a convolutional neural network, Chakrabarty and researchers from Mallinckrodt Institute of Radiology developed a large, multi-institutional dataset of intracranial 3D MRI scans from four publicly available sources.

Figure shows coarse attention maps generated using GradCAM for correctly classified high-grade glioma (HGG), low-grade glioma (LGG), brain metastases (METS), meningioma (MEN), acoustic neuroma (AN), and pituitary adenoma (PA). For each pair, the postcontrast T1-weighted scan, and the GradCAM attention map (overlaid on scan) have been shown. In GradCAM maps, warmer and colder colors represent high and low contribution of pixels toward a correct prediction, respectively. A team of researchers at Washington University School of Medicine have developed a deep learning model that is capable of classifying a brain tumor as one of six common types using a single 3D MRI scan, according to a study published in Radiology: Artificial Intelligence. "This is the first study to address the most common intracranial tumors and to directly determine the tumor class or the absence of tumor from a 3D MRI volume," said Satrajit Chakrabarty, M.S., a doctoral student under the direction of Aristeidis Sotiras, Ph.D., and Daniel Marcus, Ph.D., in Mallinckrodt Institute of Radiology's Computational Imaging Lab at Washington University School of Medicine in St. Louis, Missouri.

Washington [US], August 14 (ANI): Researchers have developed a deep learning model that is capable of classifying a brain tumour as one of six common types, using a single 3D MRI scan, during a new study. The study by researchers from the Washington University School of Medicine has been published in Radiology: Artificial Intelligence. "This is the first study to address the most common intracranial tumours and to directly determine the tumour class or the absence of tumour from a 3D MRI volume," said Satrajit Chakrabarty, M.S., a doctoral student under the direction of Aristeidis Sotiras, PhD, and Daniel Marcus, PhD, in Mallinckrodt Institute of Radiology's Computational Imaging Lab at Washington University School of Medicine in St. Louis, Missouri. The six most common intracranial tumour types are high-grade glioma, low-grade glioma, brain metastases, meningioma, pituitary adenoma and acoustic neuroma. Each was documented through histopathology, which requires surgically removing tissue from the site of suspected cancer and examining it under a microscope.

If a poisonous gas were released in a bioterrorism attack, the ability to predict the path of its molecules -- through turbulent winds, temperature changes and unstable buoyancies -- could mean life or death. Understanding how a city will grow and change over a 20-year period could lead to more sustainable planning and affordable housing. Deriving equations to solve such problems -- adding up all of the relevant forces -- is, at best, difficult to the point of near-impossibility and, at worst, actually impossible. But machine learning can help. Using the motion of aerosol particles through a system in flux, researchers from the McKelvey School of Engineering at Washington University in St. Louis have devised a new model, based on a deep learning method, that can help researchers predict the behavior of chaotic systems, whether those systems are in the lab, in the pasture or anywhere else.