Artificial Intelligence (AI) was a movie in 2001 telling a story about a childlike android programmed with the ability to love. Set in a post climate change world of the 22nd Century the movie was ahead of its time. Dial ahead just 16 years, not a whole century, and AI is being talked about a lot, mainly through fear of the unknown and its impact on jobs. In other recent AI news Elon Musk announced Neuralink a venture to merge the human brain with AI, adding this to his SpaceX retrievable rocket technology and the Tesla cars we are now seeing on New Zealand's roads. Leading media including The Wall Street Journal, McKinsey Quarterly, Harvard Business Review and Financial Times all have a plethora of articles about AI.

This paper proposes three different distributed event-triggered control algorithms to achieve leader-follower consensus for a network of Euler-Lagrange agents. We firstly propose two model-independent algorithms for a subclass of Euler-Lagrange agents without the vector of gravitational potential forces. By model-independent, we mean that each agent can execute its algorithm with no knowledge of the agent self-dynamics. A variable-gain algorithm is employed when the sensing graph is undirected; algorithm parameters are selected in a fully distributed manner with much greater flexibility compared to all previous work concerning event-triggered consensus problems. When the sensing graph is directed, a constant-gain algorithm is employed. The control gains must be centrally designed to exceed several lower bounding inequalities which require limited knowledge of bounds on the matrices describing the agent dynamics, bounds on network topology information and bounds on the initial conditions. When the Euler-Lagrange agents have dynamics which include the vector of gravitational potential forces, an adaptive algorithm is proposed which requires more information about the agent dynamics but can estimate uncertain agent parameters. For each algorithm, a trigger function is proposed to govern the event update times. At each event, the controller is updated, which ensures that the control input is piecewise constant and saves energy resources. We analyse each controllers and trigger function and exclude Zeno behaviour. Extensive simulations show 1) the advantages of our proposed trigger function as compared to those in existing literature, and 2) the effectiveness of our proposed controllers.

The objective of this paper is to present the process of building a Deep Learning Model for optimising the output for a Production Process from a Training sample using Weka Multilayer Perceptron. The scope is limited to implementation only and does not cover the theory behind Artificial Neural Networks. This work is the outcome of a comprehensive prototyping and proof-of-concept exercise conducted at Turing Point (http://www.turing-point.com/) a consulting company focused on providing genuine Enterprise Machine Learning solutions based on highly advanced techniques such as 3D discrete event simulation, deep learning and genetic algorithms. Predictive Analytics is the process of extracting information from the data for predicting future trends. There are a number of Machine Learning approaches available to model the behaviour.

Today on In Case You Missed It: While we're all focused on cars becoming autonomous and electric, automakers have also been making important advancements in safety. No where is that more apparent than in a collision between a 1998 Toyota Corolla and its 2015 counterpart conducted by New Zealand's ANCAP. The safety advisory slammed the two vehicles into each other head first. It's impressive to see the difference between 2015 model with its mostly intact cab and the car built in 1998 which is so mangled there's a good chance the person behind the wheel would not have survived. Meanwhile, while you usually associate search and rescue with people in yellow vests and dogs, researchers at UC San Diego have created a tiny robot that walks by inflating and deflating the bladders in its legs.

A future driven by AI technology has just been outlined by the CEO of Google, Sundar Pichai, during his keynote address at Google IO 2017. Pichai stated "the more we democratise access to AI, the sooner everyone will benefit". This vision was backed up by announcing a number of AI driven technologies including improvements to Google Home, Google Assistant and the all new Google Lens -- an object recognition system that uses a smartphone camera and machine learning to interpret the world around you. This is another major step toward a machine driven future -- a movement that has garnered a lot of attention in recent years. AI isn't a new technology and has been applied to plenty of digital systems for decades.

The future is looking bright for New Zealand businesses when they begin integrating artificial intelligence (AI) and new technologies into products, NZTech chief executive Graeme Muller says. A recent United States survey found 80 percent of consumers are excited about the way AI and machine learning will change their lives. US consumers are optimistic about technology developments in smart machines, AI, and robotics. The report from global creative consultancy firm Lippincott surveyed 2000 leading edge US consumers to determine their emotional state around technology, and draw conclusions about what the customer of the future will be looking for from businesses. A number of events are being held this week, as part of NZTech's national Techweek, to discuss AI and its implications.

The automotive industry may have turned its sights to self-driving cars and smart technology, but what really sets today's cars apart from their predecessors is something far more important – they're much, much safer. Cars built in the year 2000 or earlier account for more than a third of fatal crashes, according to a new analysis from the Australasian New Car Assessment Program. As shocking footage from recent crash tests reveals the dramatic difference between older cars and those made in the last few years, experts warn that these at-risk vehicles also tend to be operated by the most at-risk drivers. In the tests, cars were driven toward a head-on collision at 64 kilometers per hour. While newer cars were found to perform'very well,' older vehicles did not hold up well at all, with severe consequences for the dummy driver inside In the tests, cars were driven toward a head-on collision at 64 kilometers per hour.

Adding more ammunition to the grand AI alliance, Intel, Salesforce, eBay, Sony, SAP, McKinsey & Company, Zalando and Cogitai are joining the Partnership on AI, a collection of companies and non-profits that have committed to share best practices and communicating openly about the prospects and challenges of artificial intelligence research. The group also announced a slew of non-profit partners including the Allen Institute for Artificial Intelligence, the AI Forum of New Zealand, the Centre for Democracy & Technology, the Centre for Internet and Society (India), Data & Society Research Institute among others. The new members expand a group that already counts heavyweights like Facebook, Amazon, Google, IBM, Microsoft and Apple. The platform will be hosting a series of AI Grand Challenges to encourage and incentivize researchers working on AI. It has also announced an award for best paper on the topic of "AI, People, and Society" to aid in addressing a similar goal.

Imagine if Siri wasn't just an anonymous voice living inside your iPhone. Instead, what if the dulcet tones of none other than Jeff Goldblum asked "how can I help you?" every time you accidentally press your home button for too long? If Steve Jobs had his way, we'd be living in a world where we'd have the chance to carry around our own little piece of Goldblum in our pockets, everywhere we go. Just imagine if you could hear this all day, whenever you ask your AI assistant for some help. "Steve Jobs called me up a few decades ago to be the voice of Apple," the actor told Richard Wilkins of Australia's Today Show in an interview that was picked up by cNet.

I'm an Airbnb host and while reviewing my most recent Airbnb guest, James from Australia, I saw the section again that said "Anything you would like to tell Airbnb? It'll be private…" and it got me thinking about what happens behind the scenes at any company that receives customer feedback. For me, I've only used that section for extremes. The 90 and the 10. "BEST GUESTS EVER" and "He made me feel icky". I mean why bother the great folks at Airbnb to say "They were great. The stars are there for that.