The human ability to detect and segment moving objects works great in every case. We can observe walking stick bugs, since the insect is immediately visible when it starts moving. However, computers usually have problems with multiple objects, complex background geometry, motion of the observer, and even camouflage. People also detect motion instantaneously. There has been some recent progress in motion segmentation, but computers are still far from human capabilities.

Larry Doherty was in good hands, steady hands, like the metal ones you can find on an automaker's assembly line. The 64-year-old bean salesman from Bow Island, Alta., had come to the University of Calgary's Department of Clinical Neurosciences and Hotchkiss Brain Institute to undergo arteriovenous malformation surgery – to untie the tangled blood vessels in his brain. When everyone in the operating room was ready, the operating surgeon began his work sitting in a whole other room surrounded by computer monitors, including one with a 3-D image of Mr. Doherty's brain. Using specially designed hand controls, Dr. Garnette Sutherland manoeuvred the robot to its ready position. For Mr. Doherty, it was the first time in his life he had undergone surgery.

Today we are rolling out automatic alternative (alt) text on Facebook for iOS. Automatic alt text provides visually impaired and blind people with a text description of a photo using object recognition technology. Starting today, people using a screen reader to access Facebook on an iOS device will hear a list of items that may be shown in a photo. This feature is now available in English for people in the U.S., U.K., Canada, Australia, and New Zealand. We plan to roll it out to more platforms, languages, and markets soon.

At the NULLCON International Security Conference in Goa, India, a startup presented a prototype vulnerability scanner... This email address is already registered. By submitting my Email address I confirm that I have read and accepted the Terms of Use and Declaration of Consent. By submitting your email address, you agree to receive emails regarding relevant topic offers from TechTarget and its partners. You can withdraw your consent at any time.

Salesforce is set to acquire deep learning start up MetaMind in an effort to bolster it artificial intelligence capabilities. While terms of the deal have not been released, it would appear to be an "acqhire" based agreement, as Salesforce will integrate MetaMind's technology into its current services. Long-term intentions have not been announced, though MetaMind's capabilities will be used to automate and personalize customer support in the first instance. "With MetaMind and Salesforce coming together, we'll be able to offer customers real AI solutions with breakthrough capabilities that further automate and personalize customer support, marketing automation, and many other business processes," said MetaMind Founder Richard Socher. "We'll extend Salesforce's data science capabilities by embedding deep learning within the Salesforce platform."

With robotic research on the rise, the implementation of artificial intelligence is paving the way for robotic achievements. An AI passed the first round of a literary competition, begging the question, how safe are the creative arts? After an artificial intelligence software proved creative enough to -- with the help of humans -- pass the first round of a national Japanese literary competition, people have now become more afraid of AI. Of course, now AI has seeped into the arts, so there's no stopping them. We're going to be taken over by computers, right?

Employing artificial intelligent robots in Chinese restaurants – an idea that attracted national headlines – has not proved such a smart idea after all, mainland media reports. A number of restaurant owners have chosen to fire about 10 robots because they were just not clever or sophisticated enough to do their jobs properly, the Xiamen Daily reported. The plug has been pulled on a number of the robots – employed as chefs and waiters – only a few years after a catering business in the seaport city of Xiamen, in southern Fujian province, scrambled to employ them instead of people, the newspaper said on Tuesday. Workers are ready for the robot revolution, but are their managers? Another restaurant, which opened last October, made local headlines for using four automated waiters that were able to take orders and deliver food to customers' tables.

The folks over at x.ai – creators of Amy, the artificial intelligence answer to scheduling meetings – have had a shot at showing exactly what it looks like inside their bot's brain, using AI, of course. The team used a powerful deep-learning model, a Recurrent Neural Network (RNN), to trawl 500,000 words in its database, looking at their sequence in a sentence to understand what they mean, then predicting how to categorize them. Don't miss our biggest TNW Conference yet! Without a human ever telling the RNN the definitions of different word groups, it has managed to understand that Stanford is different from Instagram, and that Jesse, Luke and Jason are names. This data was cut to down to the 3,500 most frequently used words and has then been projected into a 2D shape in order to show the relationships the AI has made between different words.

Roy Harold Scherer Jr worked as a truck driver on the long haul to the top of his chosen profession. He later found film stardom under the name of Rock Hudson. Michael Dell, founder of US company Dell Computers, washed plates and was a waiter in Chinese and Mexican restaurants before he landed on a career in technology. Such humdrum tasks once allowed ambitious people to earn cash en route to the top. For others, they were full-time jobs.

In May 2015, Stanford PhD student Andrej Karpathy wrote a blog post entitled The Unreasonable Effectiveness of Recurrent Neural Networks and released a code repository called Char-RNN. Both received quite a lot of attention from the machine learning community in the months that followed, spurring commentary and a number of response posts from other researchers. I remember reading these posts early last summer. Initially, I was somewhat underwhelmed--as at least one commentator pointed out, much of the generated text that Karpathy chose to highlight did not seem much better than results one might expect from high order character-level Markov chains. Here is a snippet of Karpathy's Char-RNN generated Shakespeare: And without access to affordable GPUs for training recurrent neural networks, I continued to experiment with Markov chains, generative grammars, template systems, and other ML-free solutions for generating text.