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Large language models (LLMs) are increasingly automating tasks like translation, text classification and customer service. But tapping into an LLM’s power typically requires users to send their requests to a centralized server—a process that’s expensive, energy-intensive and often slow.

Now, researchers have introduced a technique for compressing an LLM’s reams of data, which could increase privacy, save energy and lower costs. Their findings are published on the arXiv preprint server.

The new algorithm, developed by engineers at Princeton and Stanford Engineering, works by trimming redundancies and reducing the precision of an LLM’s layers of information. This type of leaner LLM could be stored and accessed locally on a device like a phone or laptop and could provide performance nearly as accurate and nuanced as an uncompressed version.

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The dream of building a practical, fault-tolerant quantum computer has taken a significant step forward.

In a breakthrough study recently published in Nature, researchers from Google DeepMind and Google Quantum AI said they have developed an AI-based decoder, AlphaQubit, which drastically improves the accuracy of quantum error correction—a critical challenge in quantum computing.

“Our work illustrates the ability of machine learning to go beyond human-designed algorithms by learning from data directly, highlighting machine learning as a strong contender for decoding in quantum computers,” researchers wrote.

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Dopamine is a powerful signal in the brain, influencing our moods, motivations, movements, and more. The neurotransmitter is crucial for reward-based learning, a function that may be disrupted in a number of psychiatric conditions, from mood disorders to addiction.

Now, researchers led by MIT Institute Professor Ann Graybiel have found surprising patterns of dopamine signaling that suggest neuroscientists may need to refine their model of how occurs in the brain. The team’s findings were published recently in the journal Nature Communications.

Dopamine plays a critical role in teaching people and other animals about the cues and behaviors that portend both positive and negative outcomes; the classic example of this type of learning is the dog that Ivan Pavlov trained to anticipate food at the sound of bell.

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AI has gone so far now, and various state-of-the AI models are evolving that are used in Chatbots, Humanoid Robots, Self-driving cars, etc. It has become the fastest-growing technology, and Object Detection and Object Classification are trendy these days.

In this blog post, we will cover the complete steps of building and training an Image Classification model from scratch using Convolutional Neural Network. We will use the publicly available Cifar-10dataset to train the model. This dataset is unique because it contains images of everyday seen objects like cars, aeroplanes, dogs, cats, etc. By training the neural network to these objects, we will develop intelligent systems to classify such things in the real world. It contains more than 60,000 images of size 32×32 of 10 different types of objects. By the end of this tutorial, you will have a model which can determine the object based on its visual features.

Learn how to build and train your first Image Classification model with Keras and TensorFlow using Convolutional Neural Network.

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Through MIT OpenCourseWare, MITx, and MIT xPRO learn about machine learning, computational thinking, deepfakes, and more, all for free.

With the rise of artificial intelligence, the job landscape is changing — rapidly. MIT Open Learning offers online courses and resources straight from the MIT classroom that are designed to empower learners and professionals across industries with the competencies essential for succeeding in an increasingly AI-powered world.

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Controlling matter at the atomic level has taken a major step forward, thanks to groundbreaking nanotechnology research by an international team of scientists led by physicists at the University of Bath.

This advancement has profound implications for fundamental scientific understanding. It is also likely to have important practical applications, such as transforming the way researchers develop new medications.

S smallest movie. In the film, single molecules, consisting of two atoms bonded together, were magnified 100-million times and positioned frame-by-frame to tell a stop-motion story on an atomic scale. +.

Physicists are getting closer to controlling single-molecule chemical reactions – could this shape the future of pharmaceutical research?

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Nonlinear optics explores how powerful light (e.g. lasers) interacts with materials, resulting in the output light changing colour (i.e. frequency) or behaving differently based on the intensity of the incoming light. This field is crucial for developing advanced technologies such as high-speed communication systems and laser-based applications. Nonlinear optical phenomena enable the manipulation of light in novel ways, leading to breakthroughs in fields like telecommunications, medical imaging, and quantum computing. Two-dimensional (2D) materials, such as graphene—a single layer of carbon atoms in a hexagonal lattice—exhibit unique properties due to their thinness and high surface area. Graphene’s exceptional electronic properties, related to relativistic-like Dirac electrons and strong light-matter interactions, make it promising for nonlinear optical applications, including ultrafast photonics, optical modulators, saturable absorbers in ultrafast lasers, and quantum optics.

Dr. Habib Rostami, from the Department of Physics at the University of Bath, has co-authored pioneering research published in Advanced Science. This study involved an international collaboration between an experimental team at Friedrich Schiller University Jena in Germany and theoretical teams at the University of Pisa in Italy and the University of Bath in the UK. The research aimed to investigate the ultrafast opto-electronic and thermal tuning of nonlinear optics in graphene.

This study discovers a new way to control high-harmonic generation in a graphene-based field-effect transistor. The team investigated the impact of lattice temperature, electron doping, and all-optical ultrafast tuning of third-harmonic generation in a hexagonal boron nitride-encapsulated graphene opto-electronic device. They demonstrated up to 85% modulation depth along with gate-tuneable ultrafast dynamics, a significant improvement over previous static tuning. Furthermore, by changing the lattice temperature of graphene, the team could enhance the modulation of its optical response, achieving a modulation factor of up to 300%. The experimental fabrication and measurement took place at Friedrich Schiller University Jena. Dr. Rostami played a crucial role in the study by crafting theoretical models. These models were developed in collaboration with another theory team at the University of Pisa to elucidate new effects observed in graphene.

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Introduction Artificial intelligence has rapidly evolved over the last decade, leading to breakthroughs in natural language processing (NLP), machine learning, and multimodal applications. OpenAI’s O1 model exemplifies this innovation, offering capabilities that extend beyond traditional AI models. O1 is not just a tool; it is a revolutionary framework that brings advanced language understanding, multimodal integration, and real-time adaptability to the table. This comprehensive guide explores the intricacies of OpenAI’s O1 model, its applications, benefits, limitations, and how to optimize related content for search engine visibility.

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We can expect to see more recommendations for VR in catastrophic injury cases.

Immersive Virtual Reality (IVR or VR) as a tool in rehabilitation is changing at pace and has far reaching consequences that will increasingly be seen in the claims space.

Combined with AI powered treatment planning and smart home devices for daily rehabilitation, innovative technologies are now evident in all aspects of rehabilitation.

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