Robots that can think and feel like people could soon be living among us, according to one expert. An AI engineer, who has invented a'virtual nervous system', believes it is only a matter of time before robotics hardware catches up to his software. He says responsive robots could be a common sight in businesses and homes around the world within the next ten years. Robots that can think and feel like people could soon be living among us, according to one expert. An AI engineer, who has invented a'virtual nervous system', believes it is only a matter of time before robotics hardware catches up to his software (stock) Dr Mark Sagar is the CEO of Soul Machines, an AI company in New Zealand that specialises in creating hyper-realistic 3D avatars.

While his feet may have been in Sydney, Microsoft CEO Satya Nadella's head was firmly in the clouds. In Australia for a Microsoft Developers conference, Nadella laid out his main theories for the digital future: Mobile-first and cloud-first. SEE ALSO: Inside Microsoft's plan to bring 3D to everyone "We have a distinctive point of view when we say'mobile first,'" he said. "That's what the cloud enables." With echoes of Mark Zuckerberg's bot evangelism at April's F8, and Nadella's own remarks at the Microsoft's annual developers conference in March, it was a bot-heavy message sent Wednesday.

Based in the Big Apple itself (New York), Synechron is one of the fastest-growing digital, business consulting & technology services providers. Since they opened their doors in 2001, Synechron has been expanding at a rapid rate. They now operate in 18 countries around the the world, and has a marked presence in the US, Australia, Canada, UK, Japan, The Netherlands, Hong Kong, Singapore, UAE, Ireland, Germany, Switzerland, Luxembourg, Italy, France, and India. With the AI Summit London drawing ever closer (it's only one week away!), we spoke to Ben Musgrave, who is Synechron's Business Development Manager, in order to understand how one of the event's key sponsors is deploying AI today and how they plan to in the future. We started off our conversation with Musgrave – who'll be delivering a keynote speech at the AI Summit London – how they are currently involved in the AI-space.

By the year 2021, there will be more AI-powered digital assistants installed on devices than there are people in the world, according to new research from Ovum. The install base will be higher than 7.5 billion by that time, which is greater that the planet's population as recorded by the US Census Bureau on May 1, 2017. Google Assistant will be the most installed assistant, accounting for 23.3% of the market, the report said. The next most popular assistant will be Samsung Bixby, with 14.5% market share. SEE: Why robots and AI won't replace most jobs any time soon "Ultimately, a digital assistant is just another user interface. It will only be as good as the ecosystem of devices and services that it is compatible with. Partnerships between tech giants and local service providers will therefore be key differentiators," said Ronan de Renesse, practice leader for Ovum's Consumer Technology team and author of the report, in the release.

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.

Insurtech -- AKA insurance technology -- is the catch-all term to describe the new wave of startups and innovation that are changing the decades-old sector. Developments such as the Internet of Things (IoT) connecting millions of devices and artificial intelligence (AI) in particular are two of the major trends that are making headway in insurance. Insurtech has been named the new fintech in part due to the amount of funding the new sector has started attracting. Research by Willis Towers Watson Securities, the investment banking boutique, found that tnsurtech startups attracted $238m in investment in the first quarter of 2017 alone, showing that the technology is starting to take off. Technology now has the ability to change, or at least improve, the customer relationship and AI is at the centre of it.

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.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next two months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. As part of DARPA's ALIAS program, this robot arm was able to help land a Boeing 737 in a simulator: The only reason this works at all is because of how heavily automated the aircraft already is. It makes me wonder what the point of the robot arm is at all: Why not just build this stuff into the existing autopilot already, you know?

This is the 2-part blog version of a talk I've given at DOAG Conference this week. I've also uploaded the slides (no ppt; just pretty R presentation;-)) to the articles section, but if you'd like a little text I'm encouraging you to read on. That is, if you're in the target group for this post/talk. For this post, let me assume you're a SQL girl (or guy). With SQL you're comfortable (an expert, probably), you know how to get and manipulate your data, no nesting of subselects has you scared;-).

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.