Numenta redesigned its website. Here are a few nuggets from the new site:
1. Some new videos were added, including Hawkins' 2008 keynote from the HTM workshop and a speech by Subutai Ahmad from the 2009 workshop. Ahmad's talk was particularly interesting because he discussed a number of corporate partnerships and some early results from them. For instance, Numenta is/was working with a major automaker on the creation of a pedestrian detection system, where the car looks for pedestrians in front of the vehicle. The early testing resulted in 96-97% accuracy, or closer to 99% accuracy if one counts a false positive as a good result (situations where the system detects a pedestrian where there wasn't one). The talk also mentioned some interesting work that Numenta did with Tyzx, which provides computer vision systems. They used an HTM network to look for objects/persons in security camera footage. Subutai specifically mentioned robotics as a potential application. Interestingly, only three days ago Tyzx announced a deal with Irobot to provide vision systems for its military robots, including person detection capabilities. The press release did not mention whether HTM's are a part of that technology. It would be interesting to see what companies Numenta has been working within in the 14 months since Subutai's talk.
2. The website also contains a basic description of its new learning algorithms. It is difficult not to notice how huge of a leap forward that Numenta views these algorithms as. In one place, Numenta states that the new algorithms are a "radical" improvement. In another place, it states that the new learning algorithms are "far superior" to the old ones. One thing that I wish the website contained was some experimental results showing these huge improvements. One thing that I found confusing was its description of prediction in the new algorithms. It described prediction as something flowing up the hierarchy. That seems different from prediction as described in the original HTM theory, which envisioned incoming data flowing up the hierarchy and predictions flowing down the hierarchy. In any event, it was an interesting read.