The Turing test is a test, developed by Alan Turing in 1950, of a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human. When I joined Slack, my vision was to help developers build bots that pass the Turing test 2–3 times a day. As time passes, I understand the "beer test" might be much more important. Now the Beer Test is much less complex than the Turing Test: When I worked in New Zealand, I was asked to interview several engineers and product managers. In addition to assessing their tech and product skills, I had to answer this question: "Would you go out for a beer with this person?"

With venture capitalists more guarded about where to put their cash amid a global economic slowdown, many technology start-ups are having to shed costs and generate profits sooner than expected. "If you want to become profitable at all costs, you're leaving users and growth on the table," said Farhad Mohit, chief executive of the image-sharing app. His Los Angeles company raised 70 million from investors about a year ago, a big haul that's still fueling the business today. The app enables people to create a slideshow from photos and videos, place samples of popular music in the background, add effects and share it with friends. Flipagram can get a small cut of sales if users buy a full song.

Accurate prediction of suicide risk in mental health patients remains an open problem. Existing methods including clinician judgments have acceptable sensitivity, but yield many false positives. Exploiting administrative data has a great potential, but the data has high dimensionality and redundancies in the recording processes. We investigate the efficacy of three most effective randomized machine learning techniques - random forests, gradient boosting machines, and deep neural nets with dropout - in predicting suicide risk. Using a cohort of mental health patients from a regional Australian hospital, we compare the predictive performance with popular traditional approaches - clinician judgments based on a checklist, sparse logistic regression and decision trees. The randomized methods demonstrated robustness against data redundancies and superior predictive performance on AUC and F-measure. Keywords: Suicide risk, Electronic medical record, Predictive models, Randomized machine learning, Deep learning 1. Introduction Every year, about 2000 Australians die by suicide causing huge trauma to families, friends, workplaces and communities[1].

Technological change has accelerated in the past few decades. The digital revolution has significantly changed manufacturing processes, and the ways in which people work, consume and live. Machines powered by computer programs can be designed to plan, reason, present knowledge and learn human responses. Such machines are called intelligent machines, and the intelligence they possess is known as artificial intelligence or AI. During the industrial revolution of the 19th century, machines replaced skilled weavers in the textile industry.

I've written a few times recently about a number of projects that are using drone technology to monitor vast environments. As you can perhaps imagine, with such endeavors, there is a huge amount of data generated, and while it presents rich pickings from a scientific perspective, nonetheless raises challenges about how that data can be managed. A recent study tested the role automation could play in both easing the burden on research teams and making data analysis more effective. The paper revealed that when teams are looking for optimal speed and accuracy that an approach that combines both machine and human can be the best. The researchers used the analysis of aerial images taken by camera drones in the Kuzikus wildlife reserve as their testing ground. The drones were used to count the wildlife in the park, and generated a huge amount of images over the course of the study.

Using stock market prediction algorithm to forecast energy stocks: This Energy Stocks forecast is designed for investors and analysts who need predictions of the best-performing stocks for the whole Energy Industry (See Industry Package). Package Name: Energy Stocks Forecast Length: 30 Days (03/29/16 – 04/29/16) I Know First Average: 36.82% Cliffs Natural Resources Inc.(CLF) grew by 88.89% in just 1-month, was the top performing stock in the Energy Stocks forecast for that time period. Another top performing stock was DNR that grew by 71.56%, with an astonishing return of ten out of the ten stocks that increased in accordance with the algorithm's prediction. CDE and VALE also offered strong returns of 48.90% and 37.29%, Within the predicted 30-days it performed very well in the Energy Package.

Distributional (or distribution-valued) data are a new type of data arising from several sources and are considered as realizations of distributional variables. A new set of fuzzy c-means algorithms for data described by distributional variables is proposed. The algorithms use the $L2$ Wasserstein distance between distributions as dissimilarity measures. Beside the extension of the fuzzy c-means algorithm for distributional data, and considering a decomposition of the squared $L2$ Wasserstein distance, we propose a set of algorithms using different automatic way to compute the weights associated with the variables as well as with their components, globally or cluster-wise. The relevance weights are computed in the clustering process introducing product-to-one constraints. The relevance weights induce adaptive distances expressing the importance of each variable or of each component in the clustering process, acting also as a variable selection method in clustering. We have tested the proposed algorithms on artificial and real-world data. Results confirm that the proposed methods are able to better take into account the cluster structure of the data with respect to the standard fuzzy c-means, with non-adaptive distances.

Why is statistical inference and machine learning approaches important for analysing Big Data? To answer this question, I want to draw your attention to the world's largest coral reef system, and one of Australia's biggest natural wonders, the Great Barrier Reef. The Great Barrier Reef is composed of over 2900 reefs and 900 islands, spanning over 2300km, and is one of the most diverse ecosystems on the Earth. However, because of its large size, monitoring and predicting different trends in the reef is really difficult. For example, here at QUT we're using machine learning approaches to design robots to seek out and control the damaging crown-of-thorns starfish. In this course we show you how to apply certain predictive analysis, dimension reduction, clustering, and machine learning techniques to analyse big data and make informed decisions.

Another week, another IBM acquisition: This time, a cloud consulting and implementation services specialist called Bluewolf Group. IBM (NYSE: IBM) said Thursday (March 31) the acquisition would help extend its analytics, cloud consulting and "experience design" capabilities. Financial details of the acquisition were not disclosed, but reports pegged the deal at about 200 million. Upon completion of the transaction, which is expected by the end of the second quarter of this year, IBM said Bluewolf would become part of its Interactive Experience unit focusing on offering consulting services for clients adopting Salesforce offerings via the cloud. The deal is intended to boost the IBM unit's customer experience and data integration platforms while adding a cloud consulting capability.

Artificial intelligence and autonomous cars are no longer exclusive to science fiction. Google's self-driving cars have driven more than five million kilometres, while some Teslas can drive themselves under certain conditions, and Singapore could see a fully autonomous taxi hit the streets by the end of the year. While all these initiatives are focused on what's outside the car, on equipping and teaching machines to understand and react to the outside world, it's also important to understand the people inside. The solution they created – a camera that can understand when drivers are fatigued or distracted, has helped save thousands of lives. The company traces its origins to a group of roboticists at the Australian National University in 1997.