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Cohere AI Researchers Investigate Overcoming Quantization Cliffs in Large-Scale Machine Learning Models Through Optimization Techniques
The rise of large language models driven by artificial intelligence has reshaped natural language processing. Post-training quantization (PTQ) presents a challenge in deploying these models, with optimization choices during pre-training significantly impacting quantization performance. Cohere AI’s research delves into these intricacies, challenging the belief that quantization sensitivity is solely determined by model scale. The study’s…
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Researchers from the National University of Singapore Developed a Groundbreaking RMIA (Robust Membership Inference Attack) Technique for Enhanced Privacy Risk Analysis in Machine Learning
Privacy in machine learning models has become a critical concern due to Membership Inference Attacks (MIA). The new Relative Membership Inference Attack (RMIA) method, developed by researchers at the National University of Singapore, demonstrates its superiority in identifying membership within machine learning models, offering practical and scalable privacy risk analysis. For more information, visit the…
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Excitement grows over upcoming 2024 NVIDIA GTC AI experience
The NVIDIA 2024 GTC AI conference unites industry influencers in AI and accelerated computing. The in-person event, taking place from March 18-21, 2024, at the San Jose Convention Center, will feature workshops, networking opportunities, and presentations from tech leaders. The event promises to showcase the latest NVIDIA technologies, while offering insightful discussions and hands-on workshops.…
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Congress concerned about RAND’s influence on AI safety body
President Biden issued an executive order tasking NIST with researching AI model safety. RAND Corporation’s influence on NIST is under scrutiny due to its advisory role in shaping the order. Concerns have been raised about NIST’s outsourcing of AI safety research, particularly related to organizations like RAND, and its potential impact on AI regulation.
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This AI Paper Explores How Vision-Language Models Enhance Autonomous Driving Systems for Better Decision-Making and Interactivity
Autonomous driving technology combines AI, machine learning, and sensors to create vehicles capable of human-like decision making. DriveLM, a new model, employs Vision-Language Models for autonomous driving, demonstrating superior adaptability in handling complex driving scenarios. This approach represents a significant advancement in enhancing vehicle perception and decision-making, potentially revolutionizing autonomous driving technology.
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MyShell Open-Sources OpenVoice: An Instant Voice Cloning AI Library that Takes a Short Audio Clip from the Reference Speaker and Generate Speech in Multiple Language
MIT, MyShell.ai, and Tsinghua University researchers have developed OpenVoice, an open-source instant voice cloning method. It overcomes voice cloning challenges by enabling flexible voice style control and zero-shot cross-lingual cloning. OpenVoice can replicate a voice, generate speech in multiple languages, control voice styles, and accurately clone the reference speaker’s tone color.
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Midjourney V6 released with big improvements and image text
Midjourney has released V6 of its AI image-generating model, introducing the ability to add text to images, along with significant detail and realism upgrades. Founder David Holz highlighted the model’s capability to produce more lifelike imagery. V6 requires more explicit prompts, offers longer detailed prompts, and has enhanced image remixing and upscaling. The release has…
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Silicon Valley Companies Set to Outspend Venture Capital Firms on AI
Silicon Valley’s big tech companies, including Microsoft, Google, and Amazon, are leading AI startup investments, surpassing traditional venture capital groups this year. The surge in funding, driven by advancements like OpenAI’s ChatGPT, poses challenges for venture capitalists. Despite high valuations for AI startups, some VCs focus on applications beyond foundational models.
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Convolution Explained — Introduction to Convolutional Neural Networks
This article provides an introduction to Convolutional Neural Networks (CNNs), explaining their pivotal role in computer vision tasks. It discusses the limitations of traditional neural networks for image recognition and the concept of convolution as a fundamental building block of CNNs. The article also addresses important concepts such as dimensionality, stride, padding, and their effects…
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Meet OpenMetricLearning (OML): A PyTorch-based Python Framework to Train and Validate the Deep Learning Models Producing High-Quality Embeddings
The Open Metric Learning (OML) library, built with PyTorch, addresses the challenge in large-scale classification problems by offering an end-to-end solution that prioritizes practical use cases. It stands out with modular architecture, adaptability, efficient performance, and integration with self-supervised learning. OML democratizes advanced metric learning techniques, making them accessible to a wider audience.