Technology evangelists dream about a future where we're all liberated from the more mundane aspects of our jobs by artificial intelligence. Other futurists go further, imagining AI will enable us to become superhuman, enhancing our intelligence, abandoning our mortal bodies, and uploading ourselves to the cloud. Paradise is all very well, although your mileage may vary on whether these scenarios are realistic or desirable. The real question is, how do we get there? Economist John Maynard Keynes notably argued in favor of active intervention when an economic crisis hits, rather than waiting for the markets to settle down to a more healthy equilibrium in the long run.

This originally appeared on Quora. USA has been the leader in machine learning, and in tech hubs like Silicon Valley it seems like every company has data scientists employed. The trend has spread to the rest of the country, and there are no indications that any of this is slowing down. SoftBank Corp's human-like robot named "Pepper" gives a coffee cup to a TV personality Kyoko Uchida as they introduce Nestle's coffee machines during a promotion event at an electronics shop in Tokyo December 1, 2014. Nestle SA started to use robots to help sell its coffee makers at electronics stores across Japan, becoming the first corporate customer for the chatty, bug-eyed androids unveiled in June by tech conglomerate SoftBank Corp.

Wondering why chatbots are gaining popularity? Interested in how savvy businesses are using bots to improve communications with their customers? In this article, you'll discover insights from research that show how bots are evolving and affecting customer service experiences across many industries. Many companies in a variety of industries are learning firsthand how bot technology can change the way they provide customer service. This technology is being used to assist with personal online banking, booking travel accommodations, managing insurance claims, offering internal support, answering customer service requests, and even providing better mental health access.

Porsche Consulting has entered a strategic partnership with UnternehmerTUM, the Center for Innovation and Business Creation at the Technical University in Munich, to create applications for artificial intelligence. In collaboration with established companies, start-ups, and scientists, the management consultancy wants to advance the use of artificial intelligence in actual practice. To this end, the appliedAI initiative has now been launched in Munich. With this appliedAI partnership, the management consultancy is expanding its own range of services offered in the fields of analytics and artificial intelligence. Teams made up of consultants and AI experts will support the projects from conception to the test run.

EXPERTS warn that "the substitution of machinery for human labour" may "render the population redundant". They worry that "the discovery of this mighty power" has come "before we knew how to employ it rightly". Such fears are expressed today by those who worry that advances in artificial intelligence (AI) could destroy millions of jobs and pose a "Terminator"-style threat to humanity. But these are in fact the words of commentators discussing mechanisation and steam power two centuries ago. Back then the controversy over the dangers posed by machines was known as the "machinery question".

Users of Facebook from Europe have been reporting that Facebook is sending continuous push notifications to turn on the facial recognition feature. A journalist from Metro named Jimmy Nsubuga has reported that he is getting continuous notifications from Facebook to turn on the facial recognition technology. The company advised that an opt-in option would be pushed to users as part of changes to its terms and conditions within the EU. The company is hoping to convince users to voluntarily allow the controversial tech. Facebook started to use facial recognition back in 2012 as the app started automatically tagging users in photo uploads.

Science-fiction thrillers, like the 1980s classic film The Terminator, illuminate our imaginations, but they also stoke fears about autonomous, intelligent killer robots eradicating the human race. And while this scenario might seem far-fetched, last year, over 100 robotics and artificial intelligence technology leaders, including Elon Musk and Google's DeepMind co-founder Mustafa Suleyman, issued a warning about the risks posed by super-intelligent machines. In an open letter to the UN Convention on Certain Conventional Weapons, the signatories said that once developed, killer robots -- weapons designed to operate autonomously on the battlefield -- "will permit armed conflict to be fought at a scale greater than ever, and at timescales faster than humans can comprehend." The letter states: "These can be weapons of terror, weapons that despots and terrorists use against innocent populations, and weapons hacked to behave in undesirable ways. We do not have long to act. Once this Pandora's box is opened, it will be hard to close."

Classification of human emotions remains an important and challenging task for many computer vision algorithms, especially in the era of humanoid robots which coexist with humans in their everyday life. Currently proposed methods for emotion recognition solve this task using multi-layered convolutional networks that do not explicitly infer any facial features in the classification phase. In this work, we postulate a fundamentally different approach to solve emotion recognition task that relies on incorporating facial landmarks as a part of the classification loss function. To that end, we extend a recently proposed Deep Alignment Network (DAN), that achieves state-of-the-art results in the recent facial landmark recognition challenge, with a term related to facial features. Thanks to this simple modification, our model called EmotionalDAN is able to outperform state-of-the-art emotion classification methods on two challenging benchmark dataset by up to 5%.

General purpose planners enable AI systems to solve many different types of planning problems. However, many different planners exist, each with different strengths and weaknesses, and there are no general rules for which planner would be best to apply to a given problem. In this paper, we empirically compare the performance of state-of-the-art planners that use either the Planning Domain Description Language (PDDL), or Answer Set Programming (ASP) as the underlying action language. PDDL is designed for automated planning, and PDDL-based planners are widely used for a variety of planning problems. ASP is designed for knowledge-intensive reasoning, but can also be used for solving planning problems. Given domain encodings that are as similar as possible, we find that PDDL-based planners perform better on problems with longer solutions, and ASP-based planners are better on tasks with a large number of objects or in which complex reasoning is required to reason about action preconditions and effects. The resulting analysis can inform selection among general purpose planning systems for a particular domain.

To find solutions to problems commonly used in science and engineering, algorithms are required. An algorithm is a step-by-step computational procedure or a set of rules to be followed by a computer. One of the oldest algorithms is the Euclidean algorithm for finding the greatest common divisor (gcd) of two integers such as 12345 and 125, and this algorithm was first given in detail in Euclid's Elements about 2300 years ago (Chabert 1999). Modern computing involves a large set of different algorithms from fast Fourier transform (FFT) to image processing techniques and from conjugate gradient methods to finite element methods. Optimization problems in particular require specialized optimization techniques, ranging from the simple Newton-Raphson's method to more sophisticated simplex methods for linear programming. Modern trends tend to use a combination of traditional techniques in combination with contemporary stochastic metaheuristic algorithms such as genetic algorithms, firefly algorithm and particle swarm optimization.