Deep Learning is possibly one of the best modern examples of what marketing would call a ‘disruptive technology’. Investopedia states that disruptive technology is any technology that can alter the way a business of an entire industry works significantly. In the case of Deep Learning applications, these can benefit almost any industry immensely, but until recently the ability to integrate deep learning into applications has been limited because of an inability to incorporate it with existing hardware. That is, until now.
The Giants have Spoken
Recently, NVidia released the libraries that can utilize the processing power of their GPU for end-to-end analytics processing. According to NVidia’s website, RAPIDs seeks to optimize analytics data preparation and processing and enable users to directly tap into the latent power of their GPU’s. Seeing NVidia adopt a forward-thinking approach of embracing Deep Learning technologies bodes well for the technology going into the future. Networking and infrastructure giant Cisco also got in on the act, announcing a partnership with NVidia in the production of the C480ML server series. By partnering together, these companies have handed business and industry unique tools for experimentation and development of deep learning applications.
Stronger Through Integration
The power of large companies working together, committed to solving huge problems, cannot be denied. From the perspective of Deep Learning, however, these companies provide a stable and secure platform for working on Deep Learning apps along with a large database of information that can be drawn upon to make those apps better. Machine learning depends on having large amounts of data to work with. The Guardian compares data to coal in the context of Deep Learning because quite a lot of it is needed to ‘prime the pump’ before it can go on to process data itself. The availability of these Deep learning libraries collected and adapted by NVidia and an anonymous company, and now available to the average programmer now means that the pump has been ready-primed for us. All we have to do is to integrate it into our applications to take advantage of it.
How Deep Learning Apps Benefit
Now that we have access to artificial intelligence trained from hundreds of thousands of sample data sets, we can integrate those into Deep Learning apps and use that as a basis to teach those apps the things we’re looking for. It’s a bit like a kid learning the basics of math and science and then going on to become an engineer. The framework of RAPID’s means that we don’t have to reinvent the wheel every time we need to create an image detection and categorizing AI because the heavy lifting has already been done for us. If anything, this release will lead to faster adaptation of Deep Learning and an explosion in its popularity. The downside is, of course, the fact that RAPIDs focuses heavily on the NVidia GPU instruction set. If it does manage to grow in popularity, more manufacturers might be inclined to do the same for their own processors.
What This Means for Users
Users tend to be very impressed with applications that can successfully predict things given the correct input to work with. With a large pool of data like that provided by RAPIDs and other Deep Learning libraries, the potential exists for applications to aid users even more in their daily lives. More and ever we are likely to see data be considered valuable as it not only allows for insights but also powers the very brains of our machines. The only way we can know for sure is if more Deep Learning apps start being developed and marketed as time goes along. Innovation is the name of the game and those in a position to innovate will be the leaders on this new frontier of application development.
