What's New: This week at the annual Neural Information Processing Systems (NeurIPS) conference in Vancouver, British Columbia, Intel is contributing almost three dozen conference, workshop and spotlight papers covering deep equilibrium models, imitation learning, machine programming and more. "Intel continues to push the frontiers in fundamental and applied research as we work to infuse AI everywhere, from low-power devices to data center accelerators. This year at NeurIPS, Intel will present almost three dozen conference and workshop papers. We are fortunate to collaborate with excellent academic communities from around the world on this research, reflecting Intel's commitment to collaboratively advance machine learning." Research topics span the breadth of artificial intelligence (AI) topics, from fundamental understanding of neural networks to applying machine learning to software programming to particle physics.

The Intel AI Lab has open-sourced a library for natural language processing to help researchers and developers give conversational agents like chatbots and virtual assistants the smarts necessary to function, such as name entity recognition, intent extraction, and semantic parsing to identify the action a person wants to take from their words. Just a few months old, the Intel AI Lab plans to open-source more libraries to help developers train and deploy artificial intelligence, publish research, and reproduce the latest innovative techniques from members of the AI research community in order to "push AI and deep learning into domains it's not a part of yet." "We would like to contribute this back to the open source community so that either as a beginner or as an engineer or researcher you can look at what with reproduce and investigated and verified and then use it for your own purpose," Intel AI Lab head of data science Yinyin Liu told VentureBeat in an interview at Intel AI DevCon. The first-ever conference by Intel for AI developers is being held Wednesday and Thursday, May 23 and 24, at the Palace of Fine Arts in San Francisco. The Intel AI Lab now employs about 40 data scientists and researchers and works with divisions of the company developing products like the nGraph framework and hardware like Nervana Neural Network chips, Liu said.

By developing state-of-the-art algorithms, our team is building models and applications in areas including natural language processing, computer vision, autonomous driving, speech recognition, personalization, anomaly detection, and robotic learning. Many of the standard and benchmark models are developed and provided using our open source neon or Intel nGraph (formerly known as Intel Nervana Graph) AI software platforms and are available on the Model Zoo or as examples in neon. These frameworks take deep learning models through compilation and optimization to run efficiently on a variety of hardware platforms. The models we provide demonstrate the usage and flexibility of Intel frameworks, and enable deep learning researchers and developers to leverage the most relevant topologies and Intel technologies for their use cases. As we move forward with more original research, we will continue to publish papers and open source models.