Intelligent Trends for the Transportation and Industrial Enterprise
As data acquisition and cloud infrastructure investments mature, AI becomes an integral component of industrial enterprise DNA to enable transformational business outcomes.
We saw artificial intelligence (AI) re-emerge to become the main hero of 2016. The application of artificial intelligence in the enterprise is still in it’s infancy as several primary cloud and technology vendors like IBM, Microsoft, Amazon Web Services (AWS), and nVidia came to the market with deep learning and machine learning stacks, readily available for enterprises. The challenge has always been “applying” AI to solve anything meaningful, achieving significant business outcomes.
There are so many factors impacting the business climate that predictions are challenging for even the best of labor economists. Many areas are shifting and evolving quickly. From the brisk pace of technological innovations to new agendas that come with the change in political leadership, one thing is certain, the future will not be boring!
The transportation and industrial markets are continuing to see big changes. Along with a shift in labor demographics to an increase of younger technicians, we are seeing an increase in service-oriented revenues. Because of these changes there is an increased need for more advanced tools that aid technicians to diagnose and repair smart machines in the ever changing landscape of the connected, data-driven world of the internet of things (IoT). Artificial intelligence already shows great promise by bringing that specialized expertise right into the hands of technicians. As far as AI applications go, the future is wide open and the possibilities are endless.
Even with all of this uncertainty, at Predii we have come up with our top trend predictions for the innovation and application of AI for the transportation and industrial sectors.
The trends we will see are:
More Availability of High Quality Industrial Big Data for Training Machine Learning Models - The service industry will produce more interesting and readily accessible training data for machine learning. This is extremely valuable because the greater volume of data, the more effective learning can be derived. The lessons learned from data-driven successes and failures, coupled with input from real-world experts (technicians) are both required for greater innovation and will lead to future breakthroughs.
Increasing Adoption of IoT-Driven Intelligence - The focus on IoT derived data will be significant and it will continue to impact business development and applications in the near future. Manufacturers will look for ways to monetize the data their products produce and infrastructure providers will look to build their businesses around the growing storage and network needs for the large volumes of data being created.
Smarter Machines that Auto-Correct or Use Built-in Prognostics and Over-the-Air Updates - There will be a significant decrease in downtime associated with maintenance related mechanical breakdowns due to the increased use of artificial intelligence and data-driven applications. This technology is already being seen in the 2017 automotive market in several models of GM and Tesla vehicles. There are models available that come ready equipped with built-in prognostics and/or over-the-air updates which inform consumers of impending vehicle issues and required maintenance.
Applied AI Use Cases Become More Domain Specific and Outcome-Driven Instead of Purely Data-Driven - After an experimental approach to AI over the last 2-3 years, more and more industries are now rolling out true data-driven applications at enterprise scale. This equates to the automation of many mission-critical tasks in order to drive towards specific enterprise goals, such as maximizing utilization, and increasing customer uptime. AI also becomes more specialized in domains through an increasing focus on niches, which is made possible by the escalation in the volume of available training data.
Use of Virtual Intelligence to Revolutionize Repair Strategies - We will see the continued shift from the old-school: ratchet-n-wrench to the new school: ratchet-n-wrench with virtual intelligence! In 2017, automotive technicians will continue to rely more on virtual tools like handheld diagnostic equipment, tablets and/or laptops to repair or maintain a vehicle or machinery and less on traditional tools like a wrench or a rachet.
Regulations Will Drive Availability and Sharing of Repair Data Standards for Safety and Maintenance of Connected Software-driven Vehicles - Connected mobility solutions (trains, cars, fleets) require the need for increased standardization of regulations which allow for the open sharing of data via a gateway. This will promote innovation in value-added applications eco-system to be built to support safety, entertainment, and a repair network application to benefit consumers. The US Department of Transportation and The National Highway Traffic Safety Administration (www.nhtsa.gov) recently released Federal Automated Vehicle Policy to accelerate innovations in highly automated vehicles (HAV) keeping safety as the primary goal. We believe these policies will not only drive how OEMs and suppliers innovate towards HAVs, but it also creates opportunities to define standards around the sharing of data coming from these vehicles through gateways to support repair and maintenance networks.
There is no doubt these top intelligent trends will have a big impact on the future of the transportation and industrial enterprise. We are looking forward to seeing what that future holds.
This article originally appeared HERE on inside BIGDATA.com.