I followed Jeff Hawkins’ work closely for a long time. In my view, several things happened, although due to the somewhat secretive nature of the startup, I may be grossly wrong and invite corrections to my viewpoint.
Firstly, their algorithm idea (HTM) was from the start based on a set of limiting simplifying assumptions that made it hard to generalize their work to problem domains outside of computer vision. Approximately the same time work on HTMs was starting, people in the deep learning community were starting to work seriously on complicated vision problems, making a large part of HTM moot/obsolete by the time a public release was made (by the way, deep neural nets and HTM share a lot in common, which is interesting since they were arrived at from very different directions).
Later, Hawkins and Dileep George (the ‘technical’ lead) had something of a rift where Hawkins emphasized temporal learning and George wanted to focus more on getting vision right. This led to George leaving Numenta and Numenta essentially becoming one of many companies offering ‘data mining’ and ‘big data analytics’ services. George, meanwhile, started his own company (Vicarious), focused on human-like computer vision software. Vicarious has not yet released a product, but have admitted their approach uses probabilistic graphical models, which would put it in line with most of the ‘mainstream’ work on the subject.
tl;dr: Numenta’s work was significant but the machine learning field as a whole is moving so rapidly that yesterday’s breakthroughs are today’s mundane trivialities.
Numenta’s stuff made a lot of sense. They kept things simple by removing the recursion of HTMs...and I think that is probably the key to the whole thing working.
All that being said, their latest product Grok seems to have some success in the network monitoring space. http://numenta.com/grok/
On Intelligence was my first intro to the idea of Bayesian thinking.
I followed Jeff Hawkins’ work closely for a long time. In my view, several things happened, although due to the somewhat secretive nature of the startup, I may be grossly wrong and invite corrections to my viewpoint.
Firstly, their algorithm idea (HTM) was from the start based on a set of limiting simplifying assumptions that made it hard to generalize their work to problem domains outside of computer vision. Approximately the same time work on HTMs was starting, people in the deep learning community were starting to work seriously on complicated vision problems, making a large part of HTM moot/obsolete by the time a public release was made (by the way, deep neural nets and HTM share a lot in common, which is interesting since they were arrived at from very different directions).
Later, Hawkins and Dileep George (the ‘technical’ lead) had something of a rift where Hawkins emphasized temporal learning and George wanted to focus more on getting vision right. This led to George leaving Numenta and Numenta essentially becoming one of many companies offering ‘data mining’ and ‘big data analytics’ services. George, meanwhile, started his own company (Vicarious), focused on human-like computer vision software. Vicarious has not yet released a product, but have admitted their approach uses probabilistic graphical models, which would put it in line with most of the ‘mainstream’ work on the subject.
tl;dr: Numenta’s work was significant but the machine learning field as a whole is moving so rapidly that yesterday’s breakthroughs are today’s mundane trivialities.
Numenta’s stuff made a lot of sense. They kept things simple by removing the recursion of HTMs...and I think that is probably the key to the whole thing working.
All that being said, their latest product Grok seems to have some success in the network monitoring space. http://numenta.com/grok/
On Intelligence was my first intro to the idea of Bayesian thinking.