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Hold onto your job - artificial intelligence (AI) is driving us through a fourth industrial revolution. Up to half our existing jobs will be replaced by robots, nanotechnology and AI within 10 to 15 years and many are warning New Zealand business is woefully unprepared. Sophie is Air New Zealand's face of digital disruption. Soon she may be booking your flight. Sophie was made by New Zealand-based company Soul Machines, which is about to roll out digital employees to some major companies.

Read the full ABC article and watch the video interview to learn more about Tanmay and his work in the field of AI. The Australian Broadcasting Corporation (ABC) recently profiled 13-year-old Canadian tech prodigy Tanmay Bakshi who started using computers at age five, launched his first app at age nine, and has been working with IBM's AI and cognitive APIs for a couple of years now. Tanmay is in a different league from the average pre-teen. In 2013, at age nine, he built "tTables," an app to help kids learn multiplication which Apple's App Store accepted after rejecting it three times. An incredible achievement for a child who loves to code but is largely self-taught.

Telstra has used open source machine learning technology to answer the age-old question that plagues every marketer: how effective is my ad spend? The telco wields one of the biggest marketing budgets in Australia, but that doesn't stop Telstra from wanting to track the performance of every dollar spent. The company previously faced a six-month lag to get visibility into the effectiveness of its marketing spend; that is now down to five weeks using new marketing mix modelling developed in partnership with Accenture, Deakin University and Servian. The telco previously used a traditional econometric model to assess the performance of its marketing spend, pulling together 800 variables – which took two-and-a-half months to assemble – and then modelling this using regression techniques. "Six months after the marketing period had ended I could tell the CMO [chief marketing officer] and the marketers how effective their marketing was... six months ago," Telstra's director of research, insights & analytics Liz Moore told the recent Big Data & Analytics Innovation Summit in Sydney.

In the hope of making drone deliveries even more accurate, Project Wing has started making deliveries directly to people's houses in southeastern Australia. The firm announced that it will deliver food from Mexican food chain, Guzman y Gomez, and medicines from Chemist Warehouse pharmacies to customers in rural areas on the border of the Australian Capital Territory and New South Wales. Project Wing, which is run by Google parent Alphabet, hopes the trials will help to fine-tune how its drones move goods from where they're located to where they're needed. In the hope of making drone deliveries even more accurate, Alphabet's Project Wing has started making deliveries directly to people's houses in southeastern Australia Project Wing's aircraft has a wingspan of approximately 1.5m (4.9ft) and have four electrically-driven propellers. The total weight, including the package to be delivered, is approximately 10kg (22lb). The aircraft itself accounts for the bulk of that at 8.5kg (18.7lb).

Apparently, Project Wing brought airborne burritos to Virginia Tech last year as preparation for something bigger. Alphabet X's experimental project is now dropping burritos (and medicine) from the skies of Australia as part of a series of tests to figure out how to run a drone delivery service efficiently. Project Wing Co-Lead James Ryan Burgess said they've teamed up with Australia Mexican food chain Guzman y Gomez and pharmacy chain Chemist Warehouse to drop off orders to testers living in a rural area. These testers usually have to take a 40-minute round trip by car to get to the nearest grocery or restaurant, making them the perfect subjects for Wing's experiments. Project Wing has to conduct these tests, because while it has a system that can pre-configure routes, its drones rely on on-board sensors to avoid obstacles.

Machines won't become intelligent unless they incorporate certain features of the human brain. Europe's massive €1 billion project has shifted focus from simulation to informatics By Megan Scudellari Large-scale brainlike systems are possible with existing technology--if we're willing to spend the money By Jennifer Hasler Researchers in this specialized field have hitched their wagon to deep learning's star By Lee Gomes Running algorithms that mimic a rat's navigation neurons, heavy machines will soon plumb Australia's underground mines By Jean Kumagai Artificial intelligence might endow some computers with self-awareness.

You can tell a lot about a company's values and its priorities by the way it spends its money. Each October on ZDNet we do a special report on how business leaders are planning to spend their tech budgets for the year ahead. In our survey of 2018 plans, we found that nearly two-thirds of decision makers are increasing their technology budgets in 2018, and almost a quarter of them are boosting their tech spend by 10% or more. SEE: Tech Budgets 2018: A CXO's Guide Aside from specific tech projects, 35% of leaders said they are investing in training existing employees and 29% said they are investing in hiring new employees. We also polled TechRepublic's CIO Jury, where 83% said their IT budgets would increase or stay the same in 2018.

Algorithmic decision making process now affects many aspects of our lives. Standard tools for machine learning, such as classification and regression, are subject to the bias in data, and thus direct application of such off-the-shelf tools could lead to a specific group being unfairly discriminated. Removing sensitive attributes of data does not solve this problem because a \textit{disparate impact} can arise when non-sensitive attributes and sensitive attributes are correlated. Here, we study a fair machine learning algorithm that avoids such a disparate impact when making a decision. Inspired by the two-stage least squares method that is widely used in the field of economics, we propose a two-stage algorithm that removes bias in the training data. The proposed algorithm is conceptually simple. Unlike most of existing fair algorithms that are designed for classification tasks, the proposed method is able to (i) deal with regression tasks, (ii) combine explanatory attributes to remove reverse discrimination, and (iii) deal with numerical sensitive attributes. The performance and fairness of the proposed algorithm are evaluated in simulations with synthetic and real-world datasets.

We are moving rapidly towards a world where robots and artificial intelligence (AI) systems are connected to and influenced by social media, the Internet of Things (IoT) and big data. Technological developments are moving fast, and AI has many governments concerned. Given the pace of technological advancement, how do rule-makers set legislation for AI while allowing the safe evolution of technology? Who thinks about and enforces these guidelines, and what work is being done, or should be done, with governments to craft AI policy? Moves by the European Parliament to consider granting some form of legal status to AI have revived questions of liability and responsibility.