Domino's has unleashed another set of pizza delivery drones, this time in Germany and the Netherlands. Last year, it worked with Flirtey to drop pizza to customers in New Zealand using unmanned aerial vehicles. For this pilot program, however, it chose to use autonomous rovers developed by Starship Technologies, a company built by two of Skype's founders. Domino's told Engadget that launching this program doesn't mean it has given up on developing its own delivery drones, which it's been doing for a year now. Both this pilot and the one in New Zealand come under the auspices of DRU (Domino's Robotic Unit), the same division that's developing its homegrown machine.

These duties are shared across the organizing team.

In this paper, we introduce transformations of deep rectifier networks, enabling the conversion of deep rectifier networks into shallow rectifier networks. We subsequently prove that any rectifier net of any depth can be represented by a maximum of a number of functions that can be realized by a shallow network with a single hidden layer. The transformations of both deep rectifier nets and deep residual nets are conducted to demonstrate the advantages of the residual nets over the conventional neural nets and the advantages of the deep neural nets over the shallow neural nets. In summary, for two rectifier nets with different depths but with same total number of hidden units, the corresponding single hidden layer representation of the deeper net is much more complex than the corresponding single hidden representation of the shallower net. Similarly, for a residual net and a conventional rectifier net with the same structure except for the skip connections in the residual net, the corresponding single hidden layer representation of the residual net is much more complex than the corresponding single hidden layer representation of the conventional net.

The other day, I went to meet someone in downtown Sydney, Australia. On my way, back on the local train, I looked at my mobile to check my emails and found a message asking me whether I would like to meet the person I had just connected with on my LinkedIn network. So, was this some form of artificial intelligence (AI) at play? We now live in a brave new world where AI is the next frontier. We keep hearing about bots, chatbots, teacherbots, digital assistants, machine learning, deep learning and many more such words and often wonder what do they mean.

This article presents a logical formalization of the emotional appraisal theory, i.e., it formalizes the cognitive process of evaluation that elicits an emotion. This formalization is psychologically grounded on the OCC cognitive model of emotions. More specifically, we are interested in event-based emotions, i.e., emotions that are elicited by the evaluation of the consequences of an event that either happened or will happen. The formal modelling presented here is based on the AfPL Probabilistic Logic, a BDI-like probabilistic modal logic, which allows our model to verify whether the variables that determine the elicitation of emotions achieved the necessary threshold or not. The proposed logical formalization aims at addressing how the emotions are elicited by the agent cognitive mental states (desires, beliefs and intentions), and how to represent the intensity of the emotions. These are important initial points in the investigation of the dynamic interaction among emotions and other mental states.

Representatives from angel investors, venture capital, private equity and corporate venture funds awarded Movus, started by technology strategy consultant Brad Parsons in January 2015, the Investor Award at KPMG Australia's Energise accelerator program last Friday. Having developed an automation monitoring blueprint for BHP Billiton and Aurizon trains and condition monitoring blueprint for Sydney Trains while working for Perth-based Ajilon Australia, Parsons realised the technology was totally applicable for the billions of assets globally that sit below those big heavy assets. Parsons has now developed a condition monitoring sensor that works in the industrial environment picking up the health of machinery using artificial intelligence and a machine learning engine on the backend. It is magnetically attached and can be installed in minutes, the same as a Fitbit for humans. Parsons has already talked about it with Energise sponsors Woodside Petroleum and Chevron Corporation, and will meet Wesfarmers and Western Australia's Water Corporation representatives in Perth in the next fortnight.

Have you ever dreamt of gazing into the eyes of chatbot while explaining your frustration with a service? If so, that would be oddly specific, but you'd also be in luck, thanks to an ambitious New Zealand startup. The company, which has the eerie name of Soul Machines, has created a virtual chatbot that can not only portray human emotion, but also read human facial expressions. The aim is to take chatbot service to the next level by humanizing the interaction between man and machine, basically by making them more like us. Gary Vaynerchuk was so impressed with TNW Conference 2016 he paused mid-talk to applaud us.

Last week the Data Science community had a major event – Strata Hadoop World 2017 at San Jose. The over-arching theme across all talks and the expo was that the focus is increasingly shifting from storing and processing Big Data in an efficient way, to applying traditional and new machine learning techniques to drive higher value from the data at hand. Even as the limelight shifts from Big Data to Machine Learning (and more advanced aspects like Deep Learning), from an implementation perspective, distributed and scalable processing stays dominant as most of the popular ML models have an insatiable appetite for input data and compute power. Mike Olson, CSO and Chairman, Cloudera talked about "The machine-learning renaissance". Most of the fundamental techniques for ML and AI were invented in 1960s and 1970s.

Have you ever dreamt of gazing into the eyes of chatbot while explaining your frustration with a service? If so, that would be oddly specific, but you'd also be in luck, thanks to an ambitious New Zealand startup. The company, which has the eerie name of Soul Machines, has created a virtual chatbot that can not only portray human emotion, but also read human facial expressions. The aim is to take chatbot service to the next level by humanizing the interaction between man and machine, basically by making them more like us. We're inviting 250 to exhibit at TNW Conference and pitch on stage!

Would your banking experience be more satisfying if you could gaze into the eyes of the bank's customer service chatbot and know it sees you frowning at your overdraft fees? Professor and entrepreneur Mark Sagar thinks so. Sagar won two Academy Awards for novel digital animation techniques for faces used on movies including Avatar and King Kong. He's now an associate professor at the University of Auckland, in New Zealand, and CEO of a startup called Soul Machines, which is developing expressive digital faces for customer service chatbots. He says that will make them more useful and powerful, in the same way that meeting someone in person allows for richer communication than chatting via text.