Announcing the Intel Collaborative Research Institute for Computational Intelligence

Intel has founded a new Intel Collaborative Research Institute for Computational Intelligence (ICRI-CI).  The Institute will focus on long-term research in two directions: machine learning and heterogeneous computing architecture. Machine learning is critical to transform huge volumes of raw data (e.g., spatial and temporal sensory data, online dynamic data …) into “computational intelligence”. Heterogeneous architecture enables performing the required computation fast enough and within acceptable power and area constraints. The synergistic combination of new machine learning algorithms and computer architecture that makes these algorithms practical and efficient lay the foundation for many promising and attractive usages together with the needed  hardware and software.

The institute will be based in Israel at the Technion in Haifa and the Hebrew University in Jerusalem (HUJI). The Institute is co-led by Ronny Ronen (Intel, Senior PE), by Prof. Uri Weiser (Technion), and by Prof. Naftaly Tishby (HUJI). It will also draw researchers from other Israeli universities.

Bringing together a multi-university team that includes the top minds in the field of machine learning and computer architecture, Intel wants to accelerate the fundamental research in 3 major themes:

  • Advanced Machine Learning. Future devices will use a lot of data arriving from various sources (sensors, web etc) at high rate. Making fast, real time, intelligent decisions requires new type of machine learning algorithms;
  • Brain-inspired computing. Humans easily outperform computers in many domains, especially in learning and recognition tasks. We will apply the deep understanding of brain fundamental structures, principles and mechanisms to explore new, computing architecture that can do these tasks better than traditional computers.
  • Novel heterogeneous computing platforms, accelerators. Future usages demand a lot of computing power at tight energy budget to perform tasks like speech and gesture recognition. A promising way of bringing such performance demanding tasks to low power mobile devices is through heterogeneous systems, where several building blocks, differing in their capabilities and performance/power characteristics, are combined. The Institute will also investigate the applications of novel machine learning methods in traditional processor architecture to achieve higher performance and efficiency at reasonable complexity.

The institute will apply findings of the above fundamental themes into two applications areas and examples of usage scenarios:

  • Intelligent Agents. We envision future devices which use machine learning to proactively assist the user’s daily activities, based on data coming from “real” sensors as well personal and global data accumulated over a long time via many sources.

Imagine that you just landed in a city you have never visited before. It is quite late and you do not speak the local language. The intelligent device – or agent – will direct you to the taxi station nearby. When you are at the hotel it will suggest a good Italian restaurant in the neighborhood, knowing you like Italian cuisine. It will also remind you to take a coat since it is cold out there…

  • Learning Audio/Visual Systems. Most of the world’s data today consists of video and audio streams. The amount of data exceeds the human ability to view and infer from. We envision systems that use machine learning to automatically analyze this data and extract useful relevant information.

Recent series of criminal incidents impacts people’s sense of security. Future security cameras will not only record the activity nearby, but will be able to identify exceptional events w/o human intervention and initiate timely actions to prevent them and identify the criminals.

As in other Intel Science and Technology Centers funded by Intel, the Collaborative Research Institute for Computational Intelligence will be based on truly open IP model to foster an innovative ecosystem that will lay a foundation for future computing paradigms and usages.

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