Seems to be getting some focus lately. Nvidia’s Volta, ARM’s Trillium, Intel with Nervana, Microsoft with Catapult and Google with TPU are the starting points for the silicon AI.
With comparisons to 80387, GPUs the AI in silicon seems to be suited for getting high speed analytics into edge devices if dedicated chips can help in reducing device footprint rather than using general purpose CPUs.
In the keynote address at HIMSS17 last week, IBM CEO mentioned three goals that should drive efforts on Deep learning and AI solution development:
1 Augment people and not replace
2 How and who trained the data, what was Business value that drove that.
3 New Collar jobs, data driven workforce.
Sounds common for other areas too!
Check this video series from Microsoft Azure Learning Series presented by Brandon Rohrer. It is presented in a very simple and easy to understand examples for complete novice!.
Amazon Kinesis Analytics gives you a easy way to run the SQL query on the stream that comes in and take appropriate actions, this is another feature towards enabling serverless architecture capabilities on this platform.
A good tutorial here
Very useful for usecases around IoT, Live audience tracking in shows etc.
Cloud is slowly moving from a data warehousing and storage place into the computing infrastructure for analytics, this survey points to that.
There is an alternate way to collect weather related data and helping people with warnings at more granular level(local weather tracking) : IoT technologies, BigData and Predictive Analytics can help in this area..