The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

Torch is a scientific computing framework with wide support for machine learning algorithms that puts GPUs first. It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation. The goal of Torch is to have maximum flexibility and speed in building your scientific algorithms while making the process extremely simple. Torch comes with a large ecosystem of community-driven packages in machine learning, computer vision, signal processing, parallel processing, image, video, audio and networking among others, and builds on top of the Lua community. At the heart of Torch are the popular neural network and optimization libraries which are simple to use, while having maximum flexibility in implementing complex neural network topologies.

The term deus ex machina means "a god from a machine." "Machine," in this example, pertains to a crane that held a god over a theater stage in ancient Greek drama. Typically, the playwright would introduce an actor portraying a god at the end of his play who, from his elevated perch on the crane, would magically provide a resolution to an impossible dilemma to advance the plot to its end. Over the centuries "deus ex machina" has evolved to mean the intervention of unlikely saviors, devices or surprising events that bring order out of chaos in fast and often remarkable ways. Today, machine learning is acting in much the same way.

Immediate and accurate delivery of information to customers is a major factor in running a successful online business, especially in the price sensitive and competitive hospitality and tourism industry. Since we are currently in a market that is driven by low customer loyalty, great customer experiences and effective engagements are crucial in ensuring that your visitors will book their next trip on your website. This is where chat bots come in handy by offering self-service experience with the benefit of actionable and personalized, but at the same time automatic responses. So, let's look at some of the benefits of using chat bots in the hospitality and tourism sector. Optimizing your customer service with automated, but personalized conversations in multiple languages provides an easy access to important information and a digital experience that stands out. If you add an effective re-channeling of customers to their individual value and needs, you can get a better understanding of your customer and their intent, and the result will be more orders and higher booking rates.

Google is worried about artificial intelligence. No, not that it will become sentient and take over the world, but that, say, a helpful house robot might accidentally skewer its owner with a knife. The company's latest AI research paper delves into this issue under the title "Concrete Problems In AI Safety." Really, though, that's just a fancy way of saying "How Are We Going To Stop These Terror-Bots Killing Us All In Our Sleep." To answer this brain-tickler, Google's computer scientists have landed on five "practical research problems" -- key issues that programmers will need to consider before they start creating the next Johnny Five.

"Nothing ever comes to one, that is worth having," Booker T. Washington once remarked, "except as a result of hard work." I'm a firm believer that worthwhile work is good for the body and the soul; however, I also believe that tedious and repetitive work can be soul crushing and boring. Rachel King (@sfwriter) reports, "Over the past year, some workers at AT&T have begun to automate the boring, repetitive parts of their jobs by using software bots."[1] Historically, technology has been developed to accomplish repetitive tasks because most people hate doing them and because bored workers are prone to making mistakes. In spite of concerns about automation eliminating jobs, technology's advance is not going to be halted.

Virtual agents and chatbots will be the top consumer applications of artificial intelligence over the next five years, according to a consensus poll released today by TechEmergence, a marketing research firm for AI and machine learning. The emphasis on virtual agents and chatbots is in many ways not surprising. After all, the tech industry's 800-pound gorillas have all made big bets: Apple with Siri, Amazon with Alexa, Facebook with M and Messenger, Google with Google Assistant, Microsoft with Cortana and Tay. However, the poll's data also suggests that chatbots may soon be viewed as a horizontal enabling technology for many industries. "The most unexpected result was that so many founders who were not directly involved in the chatbot space or smart home/device space were very excited about these areas," wrote Daniel Faggella, founder of TechEmergence, in an email interview.

In 1948 a young man by the name of Alan Turing penned a report entitled "Intelligent Machinery." The opening sentence "I propose to investigate the question as to whether it is possible for machinery to show intelligent behavior" (1) had instantly set the stage for what we today would call AI, or Artificial Intelligence. And ever since that time the world has looked towards the future with glossy stares and dreams of such a day. Turing, in 1935, was the pioneering mind behind the modern computer, though most people recognize the name based on the human computer test called the Turing Test. The test was introduced by Alan in a 1950 paper titled "Computing Machinery and Intelligence," and his goal was to "test if a machine's ability could exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human."

There's been much talk about how artificial intelligence will benefit society, but what about the potential impacts that AI has when the system is poorly designed and creates problems? This is a question several researchers and OpenAI, a non-profit artificial intelligence research company, tackled in a recent paper. The paper was written by researchers from Google Brain, Stanford University and the University of California, Berkeley, as well as John Schulman, research scientist at OpenAI. It's titled Concrete Problems in AI Safety, and it looks at research problems around ensuring that modern machine learning systems operate as intended. Researchers have started to focus on safety research in the machine learning community, including a recent paper from DeepMind and the Future of Humanity Institute that looked at how to make sure that human interventions during the learning process would not induce a bias toward undesirable behaviors in machine learning robots.

After several months of beefing up the Watson Health Unit, IBM on Wednesday announced it has recruited 16 other entities involved in the health care sector to from a new Watson Health medical imaging collaborative. The global collaborative aims to advance cognitive imaging in a range of medical specialties, from eye care to the treatment of heart and brain disease. The group plans to use Watson to analyze previously "invisible" unstructured imaging data, found in places such as radiology and pathology reports, as well as broad swaths of data collected from sources like population-based disease registries. "There is strong potential for systems like Watson to help to make radiologists more productive, diagnoses more accurate, decisions more sound, and costs more manageable," Nadim Michel Daher, a medical imaging and informatics analyst for Frost & Sullivan, said in a statement. "This is the type of collaborative initiative needed to produce the real-world evidence and examples to advance the field of medical imaging and address patient care needs across large and growing disease states."