AI Articles and Analysis about Automation and robotics

The rise of AI industrial robotics experienced record double-digit expansion in various countries in 2014 and 2015, but such large scale segments i.e. 'industrial' versus 'medical' or 'military', were more or less one amalgam of parts a couple of decades ago. Examples of medical and military applications can be found in our updated machine learning in robotics guide. There was a time before the early 1980s when it was possible for AI researchers to keep up with all that was going on in the AI and the robotics industry as a whole, but it seems the tides had changed by 1982.

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 - Carnegie Mellon Receives $10 Million From K&L Gates To Study Ethical Issues Posed By Artificial Intelligence

Episode Summary: Big data is often a buzz word, but if you're trying to quantify data around homes in the U.S. and pair that with hard to quantify information  - like images - you're likely running into the frontiers of machine learning technology. This is something Zillow deals with daily. In this episode, Stan Humphries, chief analytics officer and economist for Zillow, speaks about where they're leveraging machine learning and artificial intelligence (hint: almost everywhere), and what he believes are the keys for deriving real ROI opportunities using this technology. Humphries also offers insights for how other companies can model the successful decision-making processes and implementation strategies used by Zillow.

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?

In the broad sweep of AI's current worldly ambitions, machine learning healthcare applications seem to top the list for funding and press in the last three years.

Episode Summary: “Artificial intelligence (AI) can be seen as a progression in our scalability of labor.” This quote comes from this week’s guest, Naveen Rao, who received his PhD in Neuroscience from Brown before becoming CEO at Nervana Systems, which works on full stack solutions to help companies solve machine learning (ML) problems at scale. In this week’s episode, Rao speaks about certain domains in industry where he feels optimistic about machine learning (ML) making a difference in the next five to 10 years, providing interesting perspectives that include advances in the areas of agriculture and oil & gas.

In the first week of 2016, Facebook’s Mark Zuckerberg announced in a post that his goal for the year was to “build a simple AI to run my home and help me with my work.” He clarified, "You can think of it kind of like Jarvis in Iron Man.” Zuckerberg went on to describe his plan to explore presently available smart home technologies, implement them into his home, and train the system to coordinate with his family life and workaday. (Interestingly, Zuckerberg’s AI may utilize a number of devices, but he refers to the technology as a singular system, implying that he intends to develop a unified AI to oversee the many individual devices.)