If you're keeping up to date on technology news, you're probably seeing references to machine learning everywhere, and for good reason: machine learning is an integral component of the way that computers process information. Machine learning is all around us, informing our day to day lives from the way we navigate Google maps right down to the way we check our inboxes. But what is it exactly, and when did it start being such a big deal? Here's a quick explainer to get you up to date: In 1959, MIT engineer Arthur Samuel described machine learning as a "Field of study that gives computers the ability to learn without being explicitly programmed." Samuel was busy creating his own computing machine: an autonomous checker program that he envisioned would someday beat the top world checker player champion.

The federal government is considering allowing private companies to use its national facial recognition database for a fee, documents released under Freedom of Information laws reveal. The partially redacted documents show that the Attorney General's Department is in discussions with major telecommunications companies about pilot programs for private sector use of the Facial Verification Service in 2018. The documents also indicate strong interest from financial institutions in using the database. The government has argued that the use of facial recognition is necessary for national security and to cut down on crimes such as identity fraud. The Attorney General's Department says private companies could only use the service with the person's consent.

If you asked people about Artificial Intelligence, most, if they had an idea of what it was, would likely respond with concerns about the rise of the robots, job losses, sentient machine armies, privacy and transparency. You only need to look at the comments about Boston Dynamics back-flipping robot this week to see how quickly we get dystopian about the prospect of advances in this area. Dave Heiner, Vice President and Deputy General Counsel of the Regulatory Affairs team at Microsoft, sees things a bit differently. His main concern is we're not deploying it fast enough. It's a surprising statement from a man whose job contains the words'regulatory affairs' but he's a self-proclaimed AI convert and has spent the last few years contemplating all manner of issues in relation to AI including data privacy, ethics, transparency and trust, and has been advising Microsoft, as well as governments and organisations on behalf of Microsoft, on AI policy frameworks and regulation.

Have you often felt that no matter what you asked politicians, they'd automatically reply with a stock response? Now you can address a real robot that plans on running for office -- or at least, that's what its creators intend. SAM is an AI chatbot'representing' New Zealand's constituents that you can talk with on Facebook Messenger right now. Of course, SAM doesn't currently hold any office -- nor could it likely legally run for one, under current laws -- and her (yes, yer) conversations are still very limited. But she's an experiment to create a representative that listens to people and responds to their questions with facts and policy positions.

Soul Machines, a New Zealand startup, thinks so. It builds a customer service bot with an amazingly human face and a simulated nervous system that interprets how customers feel and reacts accordingly--in part by watching them over a webcam. As I reported today, design software maker Autodesk will be the first big client to try out the technology next year--what Soul Machines calls a "digital human"--with a remake of its AVA customer service bot. While many companies boast about personalized service but can't afford to hire enough people (and while most existing customer service bots haven't exactly managed to fill in the gap), Soul Machines sees an opportunity for new CGI and AI-driven techniques. AVA's photorealistic appearance--based on scans and recordings of actress Shushila Takao--is an outgrowth of the work the company's founder, Mark Sagar, has done as a CGI engineer for Hollywood films like Avatar; her "emotional intelligence," guiding how she responds to human cues, comes from his AI research simulating a human nervous system in software.

Dr. Leila Etaati gained her PhD in University of Auckland. She is world well-known speaker in Machine Learning and Analytics topics, and spoke in world's best international conferences in Data Platform topics, such as; PASS Summits, Data Insight Summit, PASS Rally, SQL Nexus, Microsoft Ignite, and so on. She has more than 10 years experience in Data Mining and Analytics. She is also Microsoft Most Valuable Professional (MVP) because of her dedication on Microsoft Analytics and Machine Learning technologies. She writes blog posts in RADACAD and also publishes YouTube videos in our channel.

Among the attributes credited for Apple's famous customer loyalty is a network of stores where curious or frustrated consumers can meet the company face-to-face. The 3D design software maker Autodesk is trying to achieve something similar online with a help service that allows people to interact with what sure looks like an actual human. The company says that next year it will introduce a new version of its Autodesk Virtual Agent (AVA) avatar, with an exceedingly lifelike face, voice, and set of "emotions" provided by a New Zealand AI and effects startup called Soul Machines. Born in February as a roughly sketched avatar on a chat interface, AVA's CGI makeover will turn her into a hyper-detailed, 3D-rendered character–what Soul Machines calls a digital human. The Autodesk deal is Soul Machines' first major gig, following a pilot project with the Australia National Disability Insurance Agency from February to September, 2016, and some proof-of-concept demos, like a recent one with Air New Zealand.

There is an extensive literature on the complexity of planning, but explicit bounds on time and space complexity are very rare. On the other hand, problems like the constraint satisfaction problem (CSP) have been thoroughly analysed in this respect. We provide a number of upper- and lower-bound results (the latter based on various complexity-theoretic assumptions such as the Exponential Time Hypothesis) for both satisficing and optimal planning. We show that many classes of planning instances exhibit a dichotomy: either they can be solved in polynomial time or they cannot be solved in subexponential time. In many cases, we can even prove closely matching upper and lower bounds. Our results also indicate, analogously to CSPs, the existence of sharp phase transitions. We finally study and discuss the trade-off between time and space. In particular, we show that depth-first search may sometimes be a viable option for planning under severe space constraints.

In 2013 Bagnall wrote a Gstreamer plug-in that used a recurrent neural network (RNN) to generate video in imitation of a program it was watching. Pretty soon the same RNN library was being used in another Gstreamer plug-in to classify speech on the radio according to language, and to detect birds by listening for their calls (the language classification is quite accurate and runs at 1500 faster than real time on an old laptop, which is at least a data-point for those wondering about spying capabilities). The RNN has also been used to generate text and code, and to classify text by language and author at a fine-grained level. He shows how the RNN is trained, and how it might be adapted for other forms of time-series data. He demonstrates the various plug-ins and text utilities and, for excitement, execute RNN-generated code on the fly.

They're usually a waste of your time, so why not have them waste someone else's instead? Better yet: why not have them waste an email scammer's time. That's the premise behind Re:scam, an email chatbot operated by New Zealand cybersecurity firm Netsafe. Next time you get a dodgy email in your inbox, says Netsafe, forward it on to me@rescam.org, You can see a few sample dialogues in the video above, or check out a longer back-and-forth below.