This technology is geared toward improving the efficiency of Intelligent Personal Assistants (IPAs). IPAs such as [Apple’s Siri] (https://www.apple.com/ios/siri/) are becoming more common, and receiving more use each year. As use of these technologies increases, the servers that support them need to become more efficient at processing requests. Improvements in software architecture can allow hardware to remain scalable to increased user demand.
Lessons from Deep Learning
By exploring a large variety of platforms, it was shown that this technology’s architecture greatly increased the speed and efficiency of IPA function. Because of the huge number of applications for IPAs—information searching, scheduling, automotive assistance, and more—this technology has a wide potential for adoption. While there have been multiple technologies that are designed for particular kinds of problems in large, warehouse-scale computing problems, such as [RightScale services] (http://www.rightscale.com/learn/cloud-architecture), this technology tackles the specific and unique problems posed by IPAs.
- Computer Vision
- Natural Language Processing
- Signal Processing
- Machine Learning
- Scalability of the IPA market
- Order of magnitude increase in efficiency for standard IPA problems
- Utilizes modern existing processors and chips, not requiring new or advanced hardware