The wildfire in Alberta, Canada, seems to be diminishing and residents should be able to return to the city of Fort McMurray over the next two weeks. The fire had appeared to be out of control just a few days ago but thanks to favourable weather conditions appears under control. The weather has played a huge part, but what about technology? AI, drones and satellites have all been used. Dr Guillermo Rein, from Imperial College, London and Editor-in-Chief of the journal Fire Technology explains how tech is now incorporated in fire management.

Debugging code is a perennial headache for software developers, but scientists have announced a new technique that could make the process significantly easier. Developed at MIT's Computer Science and Artificial Intelligence Laboratory and the University of Maryland, the method essentially bridges the gap between the traditional technique of symbolic execution and today's modern software, making it possible to debug code far more efficiently. Symbolic execution is a software-analysis technique that can be used to locate and repair bugs automatically by tracing out every path a program might take during execution. The problem is, that technique doesn't tend to work well with applications written using today's programming frameworks. That's because modern applications generally import functions from those frameworks, which include huge libraries of frequently reused code.

In this short post I want to discuss an important deployment feature from Chatterbox Labs which has generated great partner feedback, and that really differentiates Chatterbox Labs' Cognitive Framework: we deploy our source code. This codebase represents over 6 years of academic research and development within an enterprise grade software framework. Unlike a Software as a Service (SaaS) delivery model, where you pay to access software that is running in the cloud, accessed either in the browser or via a set of APIs, Chatterbox Labs deploy our technology on premise at a partner site, with complete source code access. This is not a huge deployment process; it takes a maximum of 2 days to complete. Once this is done, our partners have complete access and modification rights to our codebase.

The title for this monthly talk from WWCode-SV Data Science Group is "Scalable Machine Learning in R and Python with H2O" . The focus of this talk is scalable machine learning using the H2O R and Python packages. H2O is an open source, distributed machine learning platform designed for big data, with the added benefit that it's easy to use on a laptop (in addition to a multi-node Hadoop or Spark cluster). The core machine learning algorithms of H2O are implemented in high-performance Java, however, fully-featured APIs are available in R, Python, Scala, REST/JSON, and also through a web interface. Since H2O's algorithm implementations are distributed, this allows the software to scale to very large datasets that may not fit into RAM on a single machine.

A bioinformatics company called Insilico Medicine has just announced Aging.AI, an online platform that guesses a person's age using data from blood tests. In the study to be published in Aging, the researchers designed a modular ensemble of 21 deep neural networks (DNNs) of varying depth and structure to predict age using a basic blood test. This ensemble was able to determine an individual's age around 80% of the time. The study also determined the five most important markers for predicting human chronological age: albumin, glucose, alkaline phosphatase, urea, and erythrocytes. Determining these biomarkers is important, since one of the major impediments in aging research is the absence of a set of biomarkers that may be measured to track the effectiveness of therapies.

We now live in a world where Ozlo and Viv bots can find your ideal coffee and sushi. Where your car wants to drive on its own. Where your thermostat is cooling the house, as it knows you will be home in 15 minutes. And where the world champion at Go, a game with more permutations than the number of atoms in the observable universe, was roundly beaten by a computer program named AlphaGo. In the history of artificial intelligence, we have seen a lot of hype, immense promise, countless movie plots and many false starts.

Microsoft on Wednesday announced that it was further "streamlining" its mobile phone business. The firm will lay off more than 1,800 workers, and will focus its phone efforts where they have differentiation, according to Microsoft chief executive Satya Nadella. So while this certainly doesn't mean that Microsoft will ignore smartphones in the future, it certainly sounds like they're not interested in selling Lumia phones -- or any others -- to the average consumer anymore. It may sound a little bonkers to get out of the smartphone market now, when everyone and their mothers are glued to their mobile devices. And this latest move does mean that Microsoft has written down more than the 9.6 billion it spent to acquire Nokia's handset business in 2014.

Well, if I have a look at my personal life, there are some work categories where AI will be of great value to me--a maid, a driver, an on call doctor, even an assistant to help me filter through the information I have to churn through. When it comes down to it, I don't think AI can replace human communication, a combination of emotional and analytical perspective. We know when we're interacting with a machine, and as complex as we can make AI, will we ever truly feel it is more than talking to a toaster? Camilla Urdahl works for unified communications company Unified Inbox, and will speak to strangers at events about communications strategies, new business models, and how best to accelerate innovation.

In articles, movies, books and TV shows, speculation has been rife that artificial intelligence is going to make us all redundant. Well, most of us, a useless underclass where we are outclassed by AI on every level, our jobs being done better by AI replacements. Facebook has just opened up Messenger to a number of chatbots, which haven't strictly been one hundred percent reliable or user-friendly, not to mention Microsoft's AI twitter bot, Tay, taking a nasty turn into racism and sexism. These, while amusing, initial teething problems will be sorted. As with anything it will be a steep learning curve, but our collective knowledge will get AI to the point where it is useful to society.

Terrapattern is a visual search engine that, from the first moment you use it, you wonder: why didn't Google come up with this ten years ago? Click on a feature on the map -- a baseball diamond, a marina, a roundabout -- and it immediately highlights everything its algorithm thinks looks like it. It's remarkably fast, simple to use, and potentially very powerful. Go ahead and give it a try first to see how natural it is to search for something. And how did a handful of digital artists and developers create it -- and for under 35,000?