Dr. Ole J. Mengshoel is a Principal Systems Scientist in the Department of Electrical and Computer Engineering at CMU Silicon Valley. His current research focuses on: scalable computing in artificial intelligence and machine learning; machine learning and inference in Bayesian networks; stochastic optimization; and applications of artificial intelligence and machine learning. Dr. Mengshoel holds a Ph.D. in Computer Science from the University of Illinois, Urbana-Champaign. His undergraduate degree is in Computer Science from the Norwegian Institute of Technology, Norway. Prior to joining CMU, he held research and leadership positions at SINTEF, Rockwell, and USRA/RIACS at the NASA Ames Research Center.
Scalability of artificial Intelligence (AI) and machine learning (ML) algorithms, methods, and software has been an important research topic for a while. In ongoing and future work at CMU Silicon Valley, we take advantage of opportunities that have emerged due to recent dramatic improvements in parallel and distributed hardware and software. With the availability of Big Data, powerful computing platforms ranging from small (smart phones, wearable computers, IoT devices) to large (elastic clouds, data centers, supercomputers), as well as large and growing business on the Web, the importance and impact of scalability in AI and ML is only increasing. We will now discuss a few specific results and projects.
In the area of parallel and distributed algorithms, we have developed parallel algorithms and software for junction tree propagation, an algorithm that is a work-horse in commercial and open-source software for probabilistic graphical models. On the distributed front, we are have developed and are developing MapReduce-based algorithms for speeding up learning of Bayesian networks from complete and incomplete data, and experimentally demonstrated their benefits using Apache Hadoop* and Apache Spark*. Finally, we have an interest in matrix factorization (MF) for recommender systems on the Web, and have developed an incremental MF algorithm that can take advantage of Spark. Large-scale recommender systems, which are currently essential components of many Web sites, can benefit from this incremental method since it adapts more quickly to customer choices compared to traditional batch methods, while retaining high accuracy.
Caffe* is a deep learning framework - originally developed at the Berkeley Vision and Learning Center. Recently, Caffe2*, a successor to Caffe, has been officially released. Facebook has been the driving force in developing developing the open source Caffe2 framework. Caffe2 is a lightweight, modular, and scalable deep learning framework supported by several companies, including Intel. In our hands-on machine learning experience with Caffe2, we have found it to support rapid prototyping and experimentation, simple compilation, and better portability than earlier versions of Caffe.
We are experimenting with Intel’s PyLatte machine earning library, which is written in Python and is optimized for Intel CPUs. Goals of PyLatte includes ease of programming, high productivity, high performance, and leveraging the power of CPUs. A CMU SV project has focused on implementation of speech recognition and image classification models using PyLatte, using deep learning with neural networks. In speech recognition experiments, we have found PyLatte to be ease to use, with a flexible training step and short training time.
We look forward to continuing to develop parallel, distributed, and incremental algorithms for scalable intelligent models and systems as an Intel® Parallel Computing Center at CMU Silicon Valley. We create novel algorithms, models, and applications that utilize novel hardware and software computing platforms including multi- and many-core computers, cloud computing, MapReduce, Hadoop, and Spark.
- Ole Mengshoel, Aniruddha Basak, and Tong Yu, November 2017, Machine Learning with HPC: Optimizing for Big Data, Accuracy, and Response Time, HPCDevCon17