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What to look for in AI Partner and Accelerator Programs for Startups and Collaborating with Intel on AI

Overview and link to Ramtin Davanlou, from Accenture* presentation at the oneAPI DevSummit for AI.

In this blog, we will introduce the concept of multimodal learning along with some of its main use cases, and discuss the progress made at Intel Labs towards creating of robust multimodal reasoning systems.

Intel and Google* have been collaborating to deliver optimized implementations of some of the most compute-intensive TensorFlow operations.

Intel Labs’ hardware-aware AI-based automation tools (AutoX) enable rapid adoption of AI models for different deployments to address productivity challenges. 

This post will discuss the five capabilities that make up an advanced KM and how these areas cannot be easily addressed by LMs in their present form. These capabilities are Scalability, Fidelity, Adaptability, Richness and Explainability.

In this work, we demonstrate Sentence Transformer Fine-tuning (SetFit), a simple and efficient alternative for few-shot text classification. The method is based on fine-tuning a Sentence Transformer with task-specific data and can easily be implemented with the sentence-transformers library.

Research finds that reversible Graph Neural Networks (RevGNNs) significantly outperform existing methods on multiple datasets and improve large models’ memory efficiency for AI applications.

Published October 26, 2021  Image adapted from Elentaris Photo / Shutterstock.com Gadi Singer is Vice President and Director of Emergent AI Research at Intel Labs leading the development of the third wave of AI capabilities. Cognitive AI, hierarchy of knowledge, … Continuer la lecture →

In this post, I present Tango architecture, a simple cascade student-teacher model, and exploit the simplicity of task instances to gain maximum throughput for text classification.