The true value of the Internet of Things is when it is used in conjunction with autonomous devices and machine learning, according to SAS' chief analytics officer for Australia and New Zealand, Evan Stubbs. Speaking at the SAS Analytics Insights event in Sydney yesterday, he said that IoT is just a small part of the "bigger revolution that's going on", which includes big data, analytics, machine learning and automation. "The Internet of Things in isolation is really boring at the end of the day," he said. "The exciting thing about the Internet of Things is the fact that it can create an entire army of autonomous devices linked to a central intelligence that can make decisions for us. "So the fact that your toaster has an IP address – who cares?

Any-angle pathfinding is a fundamental problem in robotics and computer games. The goal is to find a shortest path between a pair of points on a grid map such that the path is not artificially constrained to the points of the grid. Prior research has focused on approximate online solutions. A number of exact methods exist but they all require super-linear space and pre-processing time. In this study, we describe Anya: a new and optimal any-angle pathfinding algorithm. Where other works find approximate any-angle paths by searching over individual points from the grid, Anya finds optimal paths by searching over sets of states represented as intervals. Each interval is identified on-the-fly. From each interval Anya selects a single representative point that it uses to compute an admissible cost estimate for the entire set. Anya always returns an optimal path if one exists. Moreover it does so without any offline pre-processing or the introduction of additional memory overheads. In a range of empirical comparisons we show that Anya is competitive with several recent (sub-optimal) online and pre-processing based techniques and is up to an order of magnitude faster than the most common benchmark algorithm, a grid-based implementation of A*.

Even in the 1990s, when Australian banks copped a lot of flak for closing branches, consumer behaviour was changing. The banks may not have undertaken their branch network rationalisations in the most amenable fashion for the wider community but even then, as data from the Australian Prudential Regulation Authority show, actual points of representation didn't change as much as the headlines suggested. Indeed, points of representation actually increased for 11 straight years from 2001, the first year APRA started compiling proper data. The shift from traditional branch banking and physical currency has been immense. But the next generational shift in financial services – and services more generally – will be even more confronting.

They say that kids aren't born sexist or racist--hate is taught. Artificial intelligence is the same way, and humans are fabulous teachers. ProPublica reported, for example, that an algorithm used to to predict the likelihood of convicts committing future crime tends to tag black folks as higher risk than whites. Despite the oft-repeated claim that such data-driven approaches are more objective than past methods of determining the risk of recidivism or anything else, it's clear that our very human biases have rubbed off on our machines. Consider the case of Microsoft's simple Tay bot, which sucked up all the slurs and racist opinions that Twitter users threw at it and ended up spouting Nazi drivel.

IMAGE: Dr. Ben Choi, associate professor of computer science at Louisiana Tech University, will present his research on a groundbreaking new technology that has the potential to revolutionize the computing industry... view more RUSTON, La. - Dr. Ben Choi, associate professor of computer science at Louisiana Tech University, will present his research on a groundbreaking new technology that has the potential to revolutionize the computing industry during a keynote speech next month at the International Conference on Measurement Instrumentation and Electronics. Choi will present on a foundational architecture for designing and building computers, which will utilize multiple values rather than binary as used by current computers. The many-valued logic computers should provide faster computation by increasing the speed of processing for microprocessors and the speed of data transfer between the processors and the memory as well as increasing the capacity of the memory. This technology has the potential to redefine the computing industry, which is constantly trying to increase the speed of computation and, in recent years, has run short of options. By providing a new hardware approach, the technology will push the speed limit of computing using a progressive approach which will move from two values to four values, then to eight values, then to 16 values, and so on. Future computers could be built using this many-valued approach.

Power BI is the self-service BI engine of Microsoft, this service provides ability for business users to work with data with easier tools and be able to do changes or build their own models on top of one or more data sources. This is a full day of Power BI with many live demos. Training starts with Power BI and its components (Power Query, Power Pivot, Power View, Power Map, and Power Q&A). Course continues with real world demos of fetching data and transforming it with Power Query, loading and Modeling in Power Pivot and DAX, Visualizing it in Power BI Desktop and enhanced usage of Power Q&A. Many tips and best practices for using each component will be explained as well.

I gave a talk on Design Thinking for Data Scientists at the O'Reilly Strata Conference in February 2015. The talk was pretty much orthogonal to every other talk at the conference. But it was well received, so I thought I'd share the transcript with you (or your can watch the video). I'd be delighted to hear your thoughts and experiences around this topic of increasing the business impact of Data Scientists working in industry. This talk is primarily directed towards Data Scientists, especially early career Data Scientists, but its also highly relevant to those who manage Data Scientists. I'm using the term Data Scientist in a very broad sense, to mean anyone who is using data plus statistics plus programming to do something new, something that's never been done before in their organization. I'm going to talk about how and why Intuit is transforming it's Data Scientists into Design Thinking leaders.

In a first, a team of physicists is using artificial intelligence (AI) to run a complex experiment to create an extremely cold gas trapped in a laser beam known as a Bose-Einstein condensate -- thus replicating the experiment that won the 2001 Nobel Prize. Bose-Einstein condensates are some of the coldest places in the universe -- far colder than outer space and typically less than a billionth of a degree above absolute zero. They can be used for mineral exploration or navigation systems as they are extremely sensitive to external disturbances, which allows them to make very precise measurements such as tiny changes in the Earth's magnetic field or gravity. Indian physicist Satyendra Nath Bose, along with German-born theoretical physicist Albert Einstein, founded the basis for Bose-Einstein statistics. It describes the statistical distribution of identical particles with integer spin, now called subatomic particle or the "God particle" Boson.

The race is on for artificial intelligence in cybersecurity, empowering computer solutions with the ability to understand threats and respond immediately to them without (or with reduced) human intervention. Will it will survive the hype? A lot depends on how well the cybersecurity industry draws on the lessons we learned in trying to implement artificial intelligence in legal applications. IBM recently announced its foray into the cybersecurity–artificial intelligence arena using its flagship technology Watson. Watson has famously demonstrated its remarkable versatility already; so far it is has won the game show Jeopardy against two former champions and released its own cookbook (admittedly with mixed success from those who have tried the recipes). While it is a remarkable piece of technology, that doesn't necessarily equate to success in cybersecurity.

Grain storage operators are always trying to minimize the cost of their supply chain. Understanding relationship between receival, outturn, within storage site and between storage site movements can provide us insights that can be useful in planning for the next harvest reason, estimating the throughput capacity of the system, relationship between throughout and inventory. This article explores the potential of scanner data in advance analytics. Combination of these two fields has the potential to be useful for grain storage business. The study describes Grain storage scenarios in the Australian context.