I recently attended #alt-ai, a mini conference on Art and MI organized by Gene Kogan and folks at the School for Poetic Computation (sfpc) in New York City. The event took place in a building that was previously occupied by Bell Labs and was the location of 9 evenings almost 50 years ago. The building later became the Westbeth Artist community (home to many influential and successful artists over the years) and is now home to sfpc. The first day started with a gallery opening (about 14 pieces, many shown on Openframe.io) Gene Kogan gave an intro and a bird's eye view of the sudden explosion of interest in this field over the last year.

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.

Many of the tech industry's biggest players are currently working to address the chatbot trend, but few are investing more into the effort than Microsoft. The company joined the fray early on with Cortana and is now doubling down by acquiring a low-called startup called Wand Labs Inc. that has developed its own virtual assistant for mobile devices. The software attempts to spare users the hassle of switching between apps by making it possible to interact with every service on their phones through a centralized chat window. The built-in bot can be instructed to find a restaurant on Yelp, add a song to an iTunes playlist and even change the settings on connected devices like the Nest Thermostat. It's an appealing value proposition, but Redmond appears to be more interested in the Wand team.

I visited my local health-insurance office a few months ago. After entering the building, I was welcomed into a long and dark corridor, full of nervous people carrying bloated folders. The atmosphere was gloomy, and it was obvious that no one wanted to be there. After about 30 minutes I realized why: During that time, the line had barely moved, and it took me the better part of the day to reach a clerk. As a result, I was late for two other errands I had planned.

Following on from my recent article, Artificial Intelligence and Real Estate: Can We Automate the Industry?, I've been talking to Angela Meadows, Senior VP of Client Development at Humley. They are one of the fastest-growing tech companies in the UK, concerned with harnessing cognitive computing to empower companies to access and implement an artificial intelligence platform within their business, whether this is to transform their customer service tools or business processes, or simply to enable the end-user to find and action solutions for themselves without the need or cost of human intervention. Historically, Humley began by creating experiences for network operators and handset manufacturers that allowed them to have on-device, ongoing, long term relationships with their customers via real-time communication personalised to the customer's context. The experience would help the customer easily set up their device (reducing customer service calls, thus reducing costs for the brand), as well as discover new exciting features about their device and/or network. They have now combined their contextual targeting with elements of artificial intelligence (powered by IBM Watson), that enables the end-user to ask questions in natural language and receive relevant answers, based on their individual context.

Intelligent computers that can make decisions like humans may someday be on Hewlett Packard Enterprise's product roadmap. The company has been showing off a prototype computer designed to emulate the way the brain makes calculations. It's based on a new architecture that could define how future computers work. The brain can be seen as an extremely power-efficient biological computer. Brains take in a lot of data related to sights, sounds and smell, which they have to process in parallel without lagging, in terms of computation speed.

Facebook is already disconcertingly good at recognizing faces in photos. But the company's director of artificial intelligence research, Yann LeCun, wants to push AI even further. Today at the 2016 WIRED Business Conference, he said he wants to teach chatbots common sense. That's an important part of Facebook's goal of enabling its Facebook M virtual assistant to actually understand the things you ask it to do. Today, Facebook M is powered in part by humans. But eventually Facebook wants to power the entire thing with AI.

Over the last year, stories pointing to a bright future for deep neural networks and deep learning in general have proliferated. However, most of what we have seen has been centered on the use of deep learning to power consumer services. Speech and image recognition, video analysis, and other features have spun from deep learning developments, but from the mainstream view, it would seem that scientific computing use cases are still limited. Deep neural networks present an entirely different way of thinking about a problem set and the data that feeds it. While there are established approaches for images and speech patterns both in terms of training and inference, research areas that could benefit are still lagging somewhat behind.

Everyone seemingly is complaining about the spread of ransomware, and now somebody is trying to do something about it using machine learning-based behavioral analytics techniques to track suspicious behavior on company networks. As the scale of the ransomware threat grows, including ransom payments by hospitals and universities and growing fears that it will soon spread to other sectors, a Silicon Valley security intelligence firm has rolled out an approach for detecting ransomware via machine learning. Exabeam, a specialist in user and "entity" behavior analytics based in San Mateo, Calif., unveiled its analytics approach to detecting ransomware attacks during a security conference this week. See the full story at sister publication Datanami. George Leopold has written about science and technology for more than 25 years, focusing on electronics and aerospace technology.

"Knowledge workers spend a significant amount of time searching for information, either through standard search engines or through specialized tools", said Pablo Brenner, co-founder of Collokia. "In many situations, co-workers have already searched for similar information, but the work is recreated because there is no knowledge or experience sharing, leaving them unaware. Collokia s platform automatically identifies such collaboration opportunities and eliminates the time wasted on redundant research efforts. For example, when searching for a specific subject, Collokia alerts users to similar activities that have already been completed, offering recommendations and connecting them with others within the organization that have expertise on the information they are seeking." In contrast with most collaboration platforms where sharing information requires extra effort by the employee, Collokia s knowledge mapping, collection and distribution is completely transparent and effortless for all involved parties.