AI Articles and Analysis in Transportation

It would be great if instead of having our car break down - could have them fixed as soon as the underlying problem began. It would be great if instead of having to diagnose a malfunctioning piece of mechanical equipment - would could have the right "fix" presented to us immediately. As it turns out, artificial intelligence may be working its way to accomplish both of those goals in the not-so-distant future.

Thanks to the relative ease with which local governments can now gather real time data, combined with the capabilities of artificial Intelligence, cities are realizing interesting new ways to run more efficiently and effectively.

Episode summary: Machine learning currently faces a number of obstacles which prevent it from advancing as quickly as it might. How might these obstacles be overcome and what impact would this have on the machine learning across different industries in the coming decade? In this episode we talk to Dr. Hanie Sedghi, Research Scientist at the Allen Institute for Artificial Intelligence, about the developments in core machine learning technology that need to be made, and that researchers and scientists are working, on to further the application of machine learning in autonomous vehicles.

While self-driving trucks and self-driving cars make use of much of the same technology to power their AI systems, it would be a mistake to think the expected roll out date of both developments to would be identical. Their similarities can easily mask their significant differences.

Just as car companies are betting big that self-driving vehicles will change our roads, shipping companies are making a similar bet that automation will change how we move goods around the world. For autonomous ships, the open ocean may prove to be more fertile ground for the adoption of full automation than crowded city streets.

A company by company examination of the top car makers public investment and statements by their top executives makes it clear that most car companies are betting that artificial intelligence utilized in self-driving will be inevitable, and they're all jumping in with investment and initiatives.

Episode Summary: This week we speak with CEO and Founder of Nexar Inc., Eran Shir, whose company has created a dashboard app that allows drivers to mount a smartphone, which then collects visual information and other data, such as speed from your accelerometer, in order to help detect and prevent accidents.
The app also serves as a way to reconstruct what happens in a collision - a unique solution in a big and untapped market. In this episode, Shir gives his vision of a world where the roads are filled with cyborgs, rather than autonomous robots, i.e. people augmented with new sensory information that trigger notifications, warnings or prompts for safer driving behavior, amongst a network of cloud-connected cars.  He also touches on what the transition might look like in response to the question - when will autonomous cars be mainstream?

1 - Artificial-Intelligence System Surfs Web to Improve Its Performance

Information extraction involves classifying data items that are stored in plain text, and is a major area of research for machine learning scientists. Last week, a research team from MIT introduced a new approach to information extraction for machine learning systems at the Association for Computational Linguistics’ Conference on Empirical Methods on Natural Language Processing, and won a best-paper award. Instead of feeding their system as much data as possible, the team's winning approach takes a different route and focuses on a much smaller data set, a similar process used by human beings - if you're reading a paper that you don't understand, you're likely to do a search on the web and find articles that you are able to understand. This new system approach does something similar; if the system's confidence score is low in assessing a particular text, it will query for more information, pulling up a handful of new articles from the web that correlate with a specific set of terms. In future, this model could be applied to sparse data and save much time in reviewing databases.