The main driver for the shift to a robotic workforce is the rise of Artificial Intelligence.

I have a fairly dumb question about Tensorflow: Every NN example I've come across has described network layer creation and training and computing accuracy but they all seem to leave off the step where you utilize the trained model to predict your outputs -- How is this accomplished? After training it, I try to pass in a single X value to produce a single Y value, expecting Y sin(X) but what I actually receive as output is garbage. Can someone show me method definition to have this NN operate as a replacement for sin(X)?

It was an early form of machine learning based on rules. The difference was that we were "bold enough" to claim that the program incorporated …

BBC America's science fiction show Dirk Gently's Holistic Detective Agency is based on a pair of novels by Douglas Adams, author of The Hitchhiker's Guide to the Galaxy. Science fiction author Tom Gerencer loves the idea of Dirk Gently--a detective who trusts in fate and leaves everything up to chance. "He's not a brilliant detective," Gerencer says in Episode 281 of the Geek's Guide to the Galaxy podcast, "but in a way he's making these other brilliant realizations that step completely off logic and go into the realm of'let go of all that stuff and get into the flow of things, and you're going to find that things work out a lot better for you that way.'" The show has a lot going for it, including an original voice, brilliant writing, and complex characters, but it's failed to connect with many viewers. Writer Leah Schnelbach loves how the show's many mysteries slowly come together, but acknowledges that Dirk Gently can be a challenge for newcomers.

This reminds me a bit of what /u/sixwings used to say. I think the idea was that (most) neural networks were still basically just rule-based systems and that they all used supervised learning (even the reinforcement/unsupervised learning ones). I will also note that often the network's inputs and outputs are symbolic in the sense that we associate them with local and (somewhat) interpretable meanings (although this is a bit more debatable for things like pixels). Under all of this lies a question of what "a GOFAI approach" is. Neural networks have certainly been around for a very long time, so someone could say they're old(-fashioned), good and AI...

Almost 12,000 people in 139 countries have taken our AI survey -- Do You Speak Human? We are living in the age of artificial intelligence and we scarcely even realise it. AI explains what we see in our Facebook news feed, how Netflix determines what we should watch next, and why Google Maps can predict where we're heading when we jump in the car. And this is just the beginning. Increasingly we will see computer-based life forms in our homes, our cars, our household goods -- technology woven into the very fabric of our lives, living alongside us and making decisions on our behalf.

Researchers have ventured into uncharted territory to find ways to improve computer algorithms -- the brains of fruit flies. While search algorithms work by analyzing users' previous searches, a fruit fly searches for fruits by remembering the odor of the fruit they have fed on. "This is a problem that pretty much every technology company with any kind of information retrieval system has to solve, so it's been something that computer scientists have studied for years. Now, we have this new approach to similarity searches thanks to the fly," said Saket Navlakha, assistant professor at Salk's Integrative Biology Laboratory and lead author of the research paper titled "A neural algorithm for a fundamental computing problem." The paper was published in the Science Journal on Thursday.

Originally posted on The Horizons Tracker. The past year has seen a big jump in artificial approaches to learning. For instance, the Cornell-led Tell Me Dave project uses human volunteers to'train' the machine using a game interface. A team from Georgia Tech, by contrast, used a traditional expert systems style approach to train IBM's Watson to get better at answering specific queries. Researchers at Maryland University meanwhile trained their robots by showing them YouTube videos of humans performing certain tasks that the machines were subsequently able to pick up.

Spotify, the largest on-demand music service in the world, has a history of pushing technological boundaries and using big data, artificial intelligence and machine learning to drive success. The digital music company with more than 100 million users has been busy this year enhancing its service and tech capabilities through several acquisitions. Industry watch dogs predict the company will launch an IPO in 2018. When you have tens of millions of people listening to music every minute of the day, you have access to an extraordinary amount of intel that includes what songs get the most play time, to where listeners are tuning in from and even what device they are using to access the service. There's no doubt Spotify is a data-driven company and it uses the data in every part of the organization to drive decisions.