In one of the most chilling technological demonstrations to date, the internet giant showed its digital assistant could mimic a human over the phone so convincingly the person on the other end of the line had no idea they were talking to a machine. (read more)
Suppose you are a hairdresser who is curious if any of your bookings are coming from Google Duplex… what are some questions you could ask in the phone call that might expose that it’s a robot?
Based on what Google have said about Duplex, is it likely to pass the Turing test?
“Just how much information is carried in our tone of voice and conversation patterns and how does that impact our relationships with others? Computational systems can already establish who people are from their voices, so could they also tell us anything about our love life? Amazingly, it seems like it.”
In 2013 Carl Benedikt Frey and Michael A. Osborne published a report titled “The Future of Employment: How susceptible are jobs to computerisation?”. The authors examine how susceptible jobs are to computerisation, by implementing a novel methodology to estimate the probability of computerisation for 702 detailed occupations, using a Gaussian process classifier.
According to their estimates, about 47 percent of total US employment is at risk. Although the report is specific to the US job market, it is easy to see how this might apply all over the world.
We extracted the jobs and the probability of automation from the report and have made it easy to search for your job. We’ve added some additional information from the Bureau of Labor Statistics to provide some additional information about the jobs.
This is a design outcome that uses embedded intelligent systems. Year 13s?
“Two years ago, we saw an opportunity that was more than just building drones or new cameras,” MQ said in an interview. “What we saw was a unique opportunity to embed AI in compact devices. I personally like to call it embedded AI. Through my grad studies at Stanford and Carnegie Mellon, it was all done on big data centers. What I’ve seen over the past few years is smartphones have not only significantly driven down the cost of components but we’re also seeing huge improvements. We can adopt top-of-the-line smartphones chips to run a lot of these computer vision algorithms.” – Meng Qiu “MQ” Wang (creator)