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.

Kik's is a bit different from its peers, and the differences reveal some fascinating nuances about how messaging is evolving into a new user-interface paradigm to rival apps. For people who want to create a bot, Kik offers a simple API that allows them to specify how the bot will behave: What it can do, what it will say, and what responses you can type in reply. For users, the bots can be summoned into any conversation with an "@" mention. So you might be making plans with a friend depending on the weather, and bring forth @weatherchannel to give you an update on the forecast. Then, the bot simply leaves your conversation--a telling, human-centered detail meant to teach users that bots are here to help rather than to listen passively to their conversations.

Sometimes engineers are prone to overkill- or making machine learning too complex for their needs. This guy from the complexity initiative at Quora gives explanations and valuable tips on how to avoid that. "Today, more and more products and engineering teams rely on machine learning (referred to as ML through out this blog post). The abundance of open source tools and libraries also makes it much easier to learn, develop, and build ML models even for people with little prior knowledge or experience. ML is a powerful tool for many problems, but it comes with costs -- it can introduce complexity to systems which builds up over time and evolves into large technical debt. A recent publication by Google argues that it is remarkably easy to incur massive ongoing maintenance costs at the system level when applying ML. At Quora, we've been using ML to tackle many interesting problems such as ranking, search, recommendation, and spam detection. We are constantly evaluating new approaches and building new product features with ML. At the same time, we also strive to be careful about the complexity that these models introduce and have developed principles and best practices to avoid or reduce such complexity. In this blog post, we will share our thinking about complexity in ML systems and describe some of our approaches to mitigate them."

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.

Here's What We Can Expect from Deep Learning in 2016 and Beyond

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.

Machine learning is one of the fastest-growing and most exciting fields out there, and deep learning represents its true bleeding edge. In this course, you'll develop a clear understanding of the motivation for deep learning, and design intelligent systems that learn from complex and/or large-scale datasets. We'll show you how to train and optimize basic neural networks, convolutional neural networks, and long short term memory networks. Complete learning systems in TensorFlow will be introduced via projects and assignments. You will learn to solve new classes of problems that were once thought prohibitively challenging, and come to better appreciate the complex nature of human intelligence as you solve these same problems effortlessly using deep learning methods.

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."

Automation has infiltrated nearly every facet of our lives -- but in the realm of managing money, the robot invasion is a welcome technological development. Where traditional money managers rely on their individual expertise to pick and manage investments for client portfolios, a robo-advisor uses computer algorithms to scour the world of investment options, calculate all the possibilities, and curate the ideal mix of investments for investors. These automated investment services are built with the kind of technology that would make the geekiest mathlete salivate. But it's the seamless, easy-to-use interface that makes robo-investing an appealing option for investors looking for low-cost, easy ways to invest. In five years, assets under management by robo-advisors will increase 68 percent annually to about 2.2 trillion, according to consulting firm A.T. Kearney.