AI Articles and Analysis in Transportation

Episode summary: In the heavy industry sector, the cost of unpredicted repairs or machine failures can be very expensive. For example: A cargo train with an engine failure in will incur costs from its own repairs, from the transit required to reach the broken down engine, and with holding up other trains and cargo in the process.

The transportation domain is beginning to apply Artificial Intelligence (AI) in mission-critical tasks (for example, self-driving vehicles carrying passengers) where the reliability and safety of an AI system will be under question from the general public. Major challenges in the transportation industry like capacity problems, safety, reliability, environmental pollution, and wasted energy are providing ample opportunity (and potential for high ROI) for AI innovation.

A 2017 Tractica report on the natural language processing (NLP) market estimates the total NLP software, hardware, and services market opportunity to be around $22.3 billion by 2025. The report also forecasts that NLP software solutions leveraging AI will see a market growth from $136 million in 2016 to $5.4 billion by 2025.

The U.S. commercial airline system is an economic engine which generated an estimated $168.2 billion in operating revenue in 2016. Ticket fares represented 74.5 percent of operating revenue or $125.2 billion. In 2016, the overall category of transportation represented approximately 2.7 percent of the national GDP.

As a human, we can often understand the mood, intention, and future action of another person just by looking at them. We see their posture, their facial expression, where their eyes are focused, and we can get a decent understanding of what they might do next. The problem of computer vision for body language is a much harder problem to solve, but we are indeed making progress.

The vision of an army of small unmanned aerial vehicles streaming through the sky to rapidly deliver food packages has captured the imagination of the public, the media, and most importantly some of the largest companies in the world. Achieving this vision won’t just require overcoming the technological and safety challenges, but also significant buy-in from the public and policy makers.

Several car makers predict they will able to make true self-driving cars in the next few years - as we've covered in our recent self-driving car timeline article. This technology, though, is only valuable if there are plenty of roads these self-driving cars are legally allowed to travel on. Even if the technologies allow for true autonomy, without legal permission the self-driving cars are mostly worthless to individuals and companies.

Major companies including GE, Siemens, Intel, Funac, Kuka, Bosch, NVIDIA and Microsoft are all making significant investments in machine learning-powered approaches to improve all aspects of manufacturing. The technology is being used to bring down labor costs, reduce product defects, shorten unplanned downtimes, improve transition times, and increase production speed.