Numenta Newsletter June 2017
I am pleased to announce that we published two new papers this month. The first is an article that our co-founder Jeff Hawkins was invited to write for an IEEE Spectrum Special Report titled “Can we Copy the Brain?” The report, which focuses on worldwide efforts to understand the brain and use that knowledge to enable the next generation of computing, contains a number of articles by industry experts.
Jeff’s article, “What Intelligent Machines Need to Learn from the Neocortex,” covers the importance of building biologically based intelligent machines, three features of intelligence we can’t ignore, and the far reaching implications of machine intelligence. This piece was particularly exciting for us because it is the first time we’ve published details on our latest discovery in sensorimotor integration. Our research team is currently working on a peer-reviewed paper on this topic, so expect to hear more about that in the coming months. In the meantime, we hope even non-technical readers will enjoy Jeff’s IEEE piece. I also sat down with Jeff to talk with him about this article. You can listen to our conversation here.
The second paper published this month is a peer-reviewed paper titled “Unsupervised real-time anomaly detection for streaming data” that appears in a special issue of the journal Neurocomputing.
This paper, authored by Subutai Ahmad, Alexander Lavin, Scott Purdy and Zuha Agha, highlights the importance of anomaly detection for streaming applications and introduces two contributions from Numenta within that field. First, it demonstrates how our online sequence memory algorithm, HTM, excels at finding anomalies in streaming, time-series data because it displays the characteristics of an ideal detector: it can process data in real time, learn continuously and is fully automated. Second, it presents the results of running selected algorithms, including HTM, on the Numenta Anomaly Benchmark(NAB). First released in late 2015, NAB is an open source benchmark that contains real-world data streams with known, labeled anomalies. It was created to provide a controlled yet open source tool that allows anyone to test anomaly detection algorithms on streaming data.
Anomaly detection remains one of the most sought after applications for machine learning in IoT, but machine learning systems often have difficulty processing data streams. We hope that this paper will encourage others to use HTM to perform anomaly detection on a variety of data streams, as well as evaluate other anomaly detection methods using NAB.
Thank you for continuing to follow Numenta. We’d love to hear your thoughts on our latest papers. Tag us on social media or join the discussions on our HTM Forum.