AI Articles and Analysis in Transportation
Explore articles and reports related to artificial intelligence in transportation, including coverage of self-driving cars, public transportation systems, and more.
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.
As the term "machine learning" has heated up, interest in "robotics" (as expressed in Google Trends) has not altered much over the last three years. So how much of a place is there for machine learning in robotics?
"Machine learning" is a term that's heard more often in startup and big data circles than "artificial intelligence", and interestingly enough, Google Trends confirms what's already heard through the technological grapevine:
One thing that all emerging companies need? A great tagline. See “brains for botsTM”, and automatically think Neurala, a Boston-based company at the forefront of developing brain-mimicking software for cost-effective, efficient, and more intelligent robots. A recent interview with Neurala’s CEO Massmilano Versace sheds light on the company’s roots, progress, and vision for the future.
Neurala got its start in 2006, after Versace and fellow PhD students, who were pursuing computational neuroscience at Boston University, enrolled in a business course “for fun” and later realized that neuronal-based technology had profound commercial implications. These experiences fed the seed of an idea that sprouted into Neurala. The organization’s first project was in collaboration with another BU colleague, who was working on developing a sniper-detecting robot for the U.S. Army. After the first few years of taking a more consultory approach, Neurala decided to build a software business in 2011.