Three Australian startups have been recognised as some of the most disruptive companies in the world with the potential to "influence, change or create new global markets". Twice a year, leading entrepreneurs, investors and experts from the likes of Microsoft Ventures, Silicon Valley Bank, Sky News and IBM curate the Disrupt 100 list, which is published by UK-based Tallt Ventures. Out of more than one million global startups and corporate ventures, Sydney's Eora 3D, Adelaide's Humanihut and Queensland's Go One made the cut. With Asia expected to represent a growing number of listings in the future, Disrupt 100 has highlighted Eora 3D among the many startups in the region leading the way. It's a proud moment for Eora 3D co-founder Rich Boers who says he can't believe his startup has been listed ahead of companies like IBM Watson, a health platform using artificial intelligence to generate insights on unstructured data.

Three years later in 2016, enough time has passed to discern patterns from trends. In that time the industry has experienced seismic shifts and a sweeping wave of consolidation. So let's take another look at the state of the digital nation and why, for the bold, great opportunity lies ahead. There's plenty of additional reading in the links for those who want to go down the rabbit hole, as well as a reference table at the end. Happy to continue the discussion on Twitter using the hashtag #DigitalNation at @ezyjules and @marvelapp. A sweeping wave of acquisitions has decimated the ranks of independent agencies and formed two clashing clans. On the one side are the giants of advertising and marketing and on the other the titans of management consultancy. Meanwhile the market over which they are fighting is in the midst of a multi-faceted existential crisis. Over the last four years the design consultancy industry has experienced an unprecedented period of consolidation, building to a ...

We show that the climate phenomena of El Nino and La Nina arise naturally as states of macro-variables when our recent causal feature learning framework (Chalupka 2015, Chalupka 2016) is applied to micro-level measures of zonal wind (ZW) and sea surface temperatures (SST) taken over the equatorial band of the Pacific Ocean. The method identifies these unusual climate states on the basis of the relation between ZW and SST patterns without any input about past occurrences of El Nino or La Nina. The simpler alternatives of (i) clustering the SST fields while disregarding their relationship with ZW patterns, or (ii) clustering the joint ZW-SST patterns, do not discover El Nino. We discuss the degree to which our method supports a causal interpretation and use a low-dimensional toy example to explain its success over other clustering approaches. Finally, we propose a new robust and scalable alternative to our original algorithm (Chalupka 2016), which circumvents the need for high-dimensional density learning.

Machine learning will have a huge impact on business and society but at present is "still a cottage industry", says Professor Bob Williamson, chief scientist of CSIRO's Data 61 group. There's been a resurgence of interest in machine learning in recent years. Though it's not a new concept, factors like Big Data, the availability of more powerful computational processing and cheaper data storage, means more CIOs are investigating its applications. Speaking at a SAS customer event in Sydney, Williamson outlined the need for those working in the field to better share and standardise their work. There's very little reuse," he explained. "Plenty of my colleagues will do things from scratch.

Apple now has the tech in place to give its digital assistant a big boost thanks to a UK-based company called VocalIQ it bought last year. In fact, it was so impressive that Apple bought VocalIQ before the company could finish and release its smartphone app. After the acquisition, Apple kept most of the VocalIQ team and let them work out of their Cambridge office and integrate the product into Siri. Before Apple bought the company, VocalIQ tested its product against Siri, Google Now, and Cortana, and the results were impressive. Users asked each AI questions using normal language, not the robotic commands you're used to using with digital assistants.

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.

In recent years, big data and artificial intelligence (AI) have received overwhelming attention, however the interesting – even obvious – connection between the two hasn't often been explored. It is the combination of big data and AI working together that is now enabling business leaders to deliver new insights, efficiencies and even new functions that haven't been possible before. This is evident in the increasingly useful role big data and AI are playing in a broad spectrum of traditionally outsourced functions such as recruitment, HR, finance and supply chain, through to security and IT. The combination has already had a major impact on how the stock market works, synthesising more and more data to the point at which some people believe it will eventually be able to accurately predict both market trends and human influences on the market. At a more everyday level, Google uses deep learning to recognise objects in images, AI is the technology behind Facebook's Deep Face friend tagging feature and machine learning is the basis for Amazon's recommendation engine.

In an ongoing effort to extract more useful information and insights from massive volumes of structured and unstructured data, many organizations have turned to cloud based Hadoop big data analytics solutions such as Qubole. And as effective as these solutions are at capturing and analyzing large data volumes, their ability to interact with powerful Business Intelligence (BI) tools such as Machine Learning Libraries (MLL), is taking big data analytics capabilities to a whole new level. What follows is a look at 5 Machine Learning Libraries and the Big Data use case for each. MLlib features a host of common algorithms and data types, all designed to run at speed and scale. This makes MLlib a good fit for network security and other use cases such as predictive intelligence, customer segmentation for marketing purposes, and sentiment analysis.

Related: Robot revolution: rise of'thinking' machines could exacerbate inequality As a senior adviser to Hillary Clinton, Alec Ross travelled the world with the remit of cataloguing the best examples of innovation the human race has to offer. His trips took him to Korea, the Congo and Silicon Valley (and far enough overall he has calculated, to take him from the Earth to the moon twice, with a side trip from the US to New Zealand), and left him with a concern that the rate of change could leave many behind. From robots entering the workforce and leading to the very real prospect of redundancy within a decade for the million employees of Taiwan's electronics manufacturing giant Foxconn to genetic engineering unleashing the possibility of designer babies, the power of technology to reshape the world is reaching historic levels. But the people who have the most to lose from those changes are often the ones who get the least warning. That, says Ross, was his motivation for writing The Industries of the Future, which looks at six of the biggest waves of change about to hit the world.

PAPIs '15, the 2nd International Conference on Predictive APIs and Applications, took place in Sydney, Australia and featured 4 research presentations. The corresponding papers were compiled into proceedings that were published in the Journal of Machine Learning Research (Volume 50 of the Workshop & Conference Proceedings series; you can also download the whole proceedings in a single pdf here). The first paper of these proceedings gives us a behind-the-scenes look at Microsoft Azure ML, an MLaaS environment for authoring predictive models, experimenting with them, running them on a cloud infrastructure and publishing them as web APIs. The Azure ML team presents design principles, challenges encountered and lessons learnt while building the platform. While it is common for ML practitioners to measure models' performance via predictions' accuracy, the second paper of these proceedings by Brian Gawalt of Upwork focuses on concerns of software engineers who are in charge of deploying in production and scalability: models' throughput and response time.