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A recent article in Forbes discusses Google's current strategy concerning it's deep learning technology. After a quick overview of how Google has utilized its Artificial Intelligence (AI) technology, the article states, "Some companies have built their own AI research units and need to build highly customized models for specific applications. Yet, in doing so they quickly run up against the immense hardware requirements of building large deep learning models, often requiring entire accelerator farms for rapid iteration. In Google’s case it offers a hosted deep learning platform called Cloud Machine Learning Engine that takes care of the hardware needs of deep learning development, allowing companies to focus on building their models and offload the computing requirements to Google. After all, few companies have invested so much in AI that they have built their own custom accelerator hardware like Google did with its Tensor Processing Units (TPUs)." Further into the article, the author, Kalev Leetaru, states analytics companies "are interested in building services for their customers, not conducting AI research. In following its externalization trend, Google has risen to this challenge by releasing many of its internal AI systems as public cloud APIs."
ARC has recommended this approach for some time now. Traditionally, manufacturing companies have been able to purchase technology and hire or train people to best utilize that technology. This technology would reside on premise. The people would be on the payroll. However, this model cannot work moving forward. The expertise is difficult to hire and these resources don't really want to work full time in a manufacturing environment. Additionally, manufacturing companies need to make product not host large data centers. Instead, manufacturers should focus on keeping the product expertise and contracting out the supporting services. To reiterate, the ability to "own" and maintain this technology is beyond the capability of most companies because of the infrastructure expense and the scarcity of people in the marketplace. Manufacturing companies must learn to embrace off-premise solutions for more than just business operations in order to stay competitive.
The Forbes article also provides an example of quick implementation of the Google technology. "Teowaki’s Javier Ramirez offers a glimpse at just how easy it is to rapidly build an entire workflow (and an immensely powerful one at that) from these APIs with just a few minutes of time and a few lines of code." It then ends with, "In the end, Google has effectively democratized access to some of the world’s most advanced AI algorithms by making them so easy to use (literally just an API call away) that even the smallest businesses can now leverage the full power of deep learning to revolutionize how they do business."
Access to this level of technology, unlike the past, will be available to your smallest competitor. Technology that helped the big players outdo the smaller ones is becoming available to all. The technology isn't going to offer competitive value any longer, expertise is. The game is changing. Is your organization able to react quickly enough?
ARC has recommended this approach for some time now. Traditionally, manufacturing companies have been able to purchase technology and hire or train people to best utilize that technology. This technology would reside on premise. The people would be on the payroll. However, this model cannot work moving forward. The expertise is difficult to hire and these resources don't really want to work full time in a manufacturing environment. Additionally, manufacturing companies need to make product not host large data centers. Instead, manufacturers should focus on keeping the product expertise and contracting out the supporting services. To reiterate, the ability to "own" and maintain this technology is beyond the capability of most companies because of the infrastructure expense and the scarcity of people in the marketplace. Manufacturing companies must learn to embrace off-premise solutions for more than just business operations in order to stay competitive.
The Forbes article also provides an example of quick implementation of the Google technology. "Teowaki’s Javier Ramirez offers a glimpse at just how easy it is to rapidly build an entire workflow (and an immensely powerful one at that) from these APIs with just a few minutes of time and a few lines of code." It then ends with, "In the end, Google has effectively democratized access to some of the world’s most advanced AI algorithms by making them so easy to use (literally just an API call away) that even the smallest businesses can now leverage the full power of deep learning to revolutionize how they do business."
Access to this level of technology, unlike the past, will be available to your smallest competitor. Technology that helped the big players outdo the smaller ones is becoming available to all. The technology isn't going to offer competitive value any longer, expertise is. The game is changing. Is your organization able to react quickly enough?