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Krinal Joshi

krinal

kjdeveloper8

AI & ML interests

NLP, Speech

Recent Activity

upvoted an article about 20 hours ago

Powerful ASR + diarization + speculative decoding with Hugging Face Inference Endpoints

reacted to frimelle's post with 👍 about 20 hours ago

Seeing AI develop has been a wild ride, from trying to explain why we'd bother to generate a single sentence with a *neural network* to explaining that AI is not a magic, all-knowing box. The recent weeks and months have been a lot of talking about how AI works; to policy makers, to other developers, but also and mainly friends and family without a technical background. Yesterday, the first provisions of the EU AI Act came into force, and one of the the key highlights are the AI literacy requirements for organisations deploying AI systems. This isn't just a box-ticking exercise. Ensuring that employees and stakeholders understand AI systems is crucial for fostering responsible and transparent AI development. From recognising biases to understanding model limitations, AI literacy empowers individuals to engage critically with these technologies and make informed decisions. In the context of Hugging Face, AI literacy has many facets: allowing more people to contribute to AI development, providing courses and documentation to ensuring access is possible, and accessible AI tools that empower users to better understand how AI systems function. This isn't just a regulatory milestone; it’s an opportunity to foster a culture where AI literacy becomes foundational, enabling stakeholders to recognise biases, assess model limitations, and engage critically with technology. Embedding these principles into daily practice, and eventually extending our learnings in AI literacy to the general public, is essential for building trustworthy AI that aligns with societal values.

reacted to singhsidhukuldeep's post with 👍 about 20 hours ago

Exciting breakthrough in Streaming Recommendation Systems! @BytedanceTalk researchers have developed "Long-Term Interest Clock" (LIC), a revolutionary approach to understand user preferences throughout the day. >> Technical Innovation The system introduces two groundbreaking modules: - Clock-based General Search Unit (Clock-GSU): Intelligently retrieves relevant user behaviors by analyzing time patterns and content similarity - Clock-based Exact Search Unit (Clock-ESU): Employs time-gap-aware attention mechanism to precisely model user interests >> Key Advantages LIC addresses critical limitations of existing systems by: - Providing fine-grained time perception instead of discrete hour-based recommendations - Analyzing long-term user behavior patterns rather than just short-term interactions - Operating at item-level granularity versus broad category-level interests >> Real-World Impact Already deployed in Douyin Music App, the system has demonstrated remarkable results: - 0.122% improvement in user active days - Significant boost in engagement metrics including likes and play rates - Enhanced user satisfaction with reduced dislike rates >> Under the Hood The system processes user behavior sequences spanning an entire year, utilizing multi-head attention mechanisms and sophisticated time-gap calculations to understand user preferences. It pre-computes embeddings stored in parameter servers for real-time performance, making it highly scalable for production environments. This innovation marks a significant step forward in personalized content delivery, especially for streaming platforms where user preferences vary throughout the day. The research has been accepted for presentation at WWW '25, Sydney.

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New activity in krinal/span-marker-robert-base about 1 year ago

Librarian Bot: Add base_model information to model

#1 opened about 1 year ago by

librarian-bot