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Machine learning

Researchers from Google’s DeepMind unveil Diffusion Augmented Agents: A proficient framework for exploration and transfer learning in machine learning.

Reinforcement learning (RL), a field that focuses on shaping agent decision-making through hypothesizing environment interactions, has the challenge of large data requirements and the complexities of incorporating sparse or non-existant rewards in real-world scenarios. Major challenges include data scarcity in embodied AI where agents are called to interact with physical environments, and the significant amount…

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The release of NeuralForecast 1.7.4: Usability and Resilience Transform Neural Prediction Through Nixtla’s Cutting-Edge Library.

Nixtla has announced the launch of NeuralForecast, an advanced library of neural forecasting models set to revolutionise the forecasting community. The library addresses long-standing issues such as usability, accuracy, and computational efficiency, providing a bridge between neural networks' complexity and their practical use. NeuralForecast comprises multiple neural network architectures, from Multi-Layer Perceptrons (MLP) and Recurrent Neural…

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SPRITE (Spatial Spread and Amplification of Estimated Transcript Expression): Improving Predictions of Spatial Gene Expression and Subsequent Analysis via Meta-Algorithmic Combination.

Researchers from Harvard and Stanford universities have developed a new meta-algorithm known as SPRITE (Spatial Propagation and Reinforcement of Imputed Transcript Expression) to improve predictions of spatial gene expression. This technology serves to overcome current limitations in single-cell transcriptomics, which can currently only measure a limited number of genes. SPRITE works by refining predictions from existing…

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Evaluating the Impact of Ambient Noise on Voice Disorder Assessment Using Machine Learning Models

Deep learning has transformed the field of pathological voice classification, particularly in the evaluation of the GRBAS (Grade, Roughness, Breathiness, Asthenia, Strain) scale. Unlike traditional methods that involve manual feature extraction and subjective analysis, deep learning leverages 1D convolutional neural networks (1D-CNNs) to autonomously extract relevant features from raw audio data. However, background noise can…

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Wolf: A Composite Expert Video Captioning System Surpassing GPT-4V and Gemini-Pro-1.5 in General Scenarios, Self-Driving Vehicles, and Robotic Videos.

Video captioning is crucial for content understanding, retrieval, and training foundational models for video-related tasks. However, it's a challenging field due to issues like a lack of high-quality data, the complexity of captioning videos compared to images, and the absence of established benchmarks. Despite these challenges, recent advancements in visual language models have improved video…

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Begin developing a superior AI assistant by imitating the unpredictable actions of individuals.

MIT and the University of Washington researchers have devised a method to model human or machine agent behaviour incorporating unknown computational constraints limiting problem-solving abilities. The technique generates an "inference budget" by observing a few previous actions, effectively predicting future behaviour. Lead author Athul Paul Jacob believes the work could help AI systems better understand…

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This compact microchip can ensure the protection of user information, while facilitating effective processing on a mobile phone.

Researchers at MIT and the IBM Watson AI lab have developed a machine-learning accelerator chip which is more resilient to common types of cyber attacks. The chip is designed to protect sensitive user data, such as health records or financial information, whilst also enabling large-scale AI models to run efficiently on devices. The design of…

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A dataset for Artificial Intelligence paves fresh ways for identifying tornadoes.

Researchers at MIT Lincoln Laboratory have developed a new open-source dataset, named TorNet, to detect and predict tornadoes. By using artificial intelligence (AI) models trained on TorNet, researchers hope to improve tornado forecasts and warning accuracy, potentially saving lives and minimizing damage. Tornadoes are challenging to predict, and this represents a high false alarm rate…

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To develop an improved AI assistant, begin by simulating the unpredictable actions of human beings.

Researchers at MIT and the University of Washington have devised a model for detecting the computational limitations of an agent, whether human or machine, that obstruct their ability to solve problems. Agents' performance is monitored to calculate their "inference budget", estimates of the time and effort likely to be re-invested in similar tasks, which then…

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This miniature device can protect user information while facilitating effective computation on a mobile device.

MIT and the MIT-IBM Watson AI Lab researchers have developed a machine-learning accelerator ingrained with defenses against the most common cyber-attacks. The device, which could find use in advanced AI applications like VR/AR and autonomous vehicles, offers robust security at the cost of increased power consumption and a slightly higher price tag. But maintaining optimum…

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A dataset for artificial intelligence paves fresh ways for identifying tornadoes.

Around 1,200 tornadoes occur every year in the U.S., causing billions of dollars in damage and claiming an average of 71 lives. Predicting tornadoes is notoriously difficult due to gaps in understanding the precise conditions that cause them to form. The team from MIT's Lincoln Laboratory hopes to address this challenge, using a new open-source…

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For crafting an improved AI assistant, begin by emulating the unpredictable tendencies of humans.

Researchers from MIT and University of Washington have developed a novel method that utilizes a good model of human behaviour, specifically involving the computational constraints in decision-making, in order to improve the collaboration between AI and humans. The unique technique of their new model permits an automatic inference regarding an agent's computational constraints solely based…

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