Ghost in the Well

Thoughts on AI

The Ghost in the Well

Is it time to update the Turing Test?

Instead of communicating through a computer, what if we had someone stranded in a well, so dark and so deep we couldn’t see them. So far down we could only hear their cries, but not their words.

If we could only communicate by sending the water bucket up and down, how could we know if they were human, or artificial?

What level of human-seeming do we need to send the rescue works down into the depths? Are they hurt? can we tell with limited information, when would we call the diggers in to tear apart the well and rescue them?

And if we believed them fake? Would we leave them there? Return to discuss things with them? Strand them only with the one point of contact, staring up to the point of light that might be sky, sun or moon?

—Stacie - May 22nd 2017

Documentation for Mycroft Core
If you ask 100 people for the definition of "artificial intelligence," you'll get at least 100 answers, if not more. At AWS, we define it as a service or system which can perform tasks that usually require human-level intelligence such as visual perception, speech recognition, decision making, or translation.
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Like a lot of people, we've been pretty interested in TensorFlow, the Google neural network software. If you want to experiment with using it for speech recognition, you'll want to check out [Silicon Valley Data Science's] GitHub repository which promises you a fast setup for a speech recognition demo.
Four years ago, Google was faced with a conundrum: if all its users hit its voice recognition services for three minutes a day, the company would need to double the number of data centers just to handle all of the requests to the machine learning system powering those services.
Sonnet is a new open source library announced by Alphabet's DeepMind. It is built on top of their existing machine learning library TensorFlow along with extra features that fit DeepMind's research requirements. Sonnet is designed to make it easier to create complex neural networks using TensorFlow.
Aditya Tiwari / Fossbytes
SAN FRANCISCO (Reuters) - Inc's () machine-learning specialists uncovered a big problem: their new recruiting engine did not like women. The team had been building computer programs since 2014 to review job applicants' resumes with the aim of mechanizing the search for top talent, five people familiar with the effort told Reuters.
Jeffrey Dastin / U.S.
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For the past five years, the hottest thing in artificial intelligence has been a branch known as deep learning. The grandly named statistical technique, put simply, gives computers a way to learn by processing vast amounts of data. Thanks to deep learning, computers can easily identify faces and recognize spoken words, making other forms of humanlike intelligence suddenly seem within reach.