Le projet THINK

Intel Labs creates new research effort to investigate and develop advanced AI algorithms and technologies to accelerate the discovery of new material systems more economically.

Thrill-K will be introduced as the AI systems architecture blueprint that implements 3LK principles for next-generation AI.

Research in anomalous sound detection to improve semiconductor manufacturing production for both Intel and its partners by using artificial intelligence (AI) and audio detection to monitor machine condition and health.

Developers can now leverage model caching through the OpenVINO™ Execution Provider for ONNX Runtime

Neuroevolution-Enhanced Multi-Objective Optimization (NEMO) for Mixed-Precision Quantization delivers state-of-the-art compute speedups and memory improvements for artificial intelligence (AI) applications.

Intel Labs has created a novel framework for producing a class of parameter- and compute-efficient models called AttentionLite, which leverages recent advances in self-attention as a substitute for convolutions.

Our latest work, presented recently at the 2021 International Conference on Learning Representations (ICLR), forces a deep network to memorize some of the training examples by randomly changing their labels.

This blog will outline the third class in this classification and its promising role in supporting machine understanding, context-based decision making, and other aspects of higher machine intelligence.

In this post, I introduce a metric for estimating the complexity level of your dataset and task, and I describe how to utilize it to optimize distillation performance.

Model distillation is a powerful pruning technique, and in many use cases, it yields significant speedup and memory size reduction.