"The eyes are the window of the soul." Unless you're a state-of-the-art set of machine-learning algorithms with the ability to demonstrate links between eye movements and four of the big five personality traits. If that's the case, then Cicero was spot on. A joint project between the University of South Australia and the University of Stuttgart had an artificial intelligence track and monitor the eye movements of 42 human participants using a video-based eye-tracker from SensorMotoric Instruments. Of the "big five" personality traits (openness, conscientiousness, extraversion, agreeableness and neuroticism), the artificial intelligence was able to reliably identify four: neuroticism, extraversion, agreeableness and conscientiousness.

"With joyous gambols gay and still array, no longer when he'twas, while in his day at first to pass in all delightful ways around him, charming, and of all his days." Don't worry: You haven't accidentally clicked on pre-Digital Trends, by mistake. This is part of a Shakespearean sonnet created by deep learning artificial intelligence -- and, shockingly, it's actually pretty good. The bot was created by researchers at IBM Research Australia. Trained on around 26,000 real sonnets, it mimics the iambic pentameter and rhyming pattern of the poems most famously written by ol' Bill Shakespeare himself.

Bestselling author Shep Hypken--the "Chief Amazement Officer" at Shephard Presentations--makes a rock-solid case for why customer experience has advanced to the level of 21st-century table stakes: "New research proves that consumers are expecting, if not demanding, highly personalized experiences," Hypken writes in Forbes. "And the good news for those businesses that can deliver is that customers are typically willing to spend more when they receive such custom-tailored service." Computer vision also comes into play where smart retail stores are concerned.iStock Enter the Internet of Things (IoT), which through interconnected devices and strong data analytics makes an entirely new level of customer surprise, delight and convenience possible. What's more, the IoT brings relevant experiences and information to consumers, whether to facilitate the operation of smart homes or to provide relevant health and wellness data that can be shared with medical professionals.

Deep learning constitutes a recent, modern technique for image processing and data analysis, with promising results and large potential. As deep learning has been successfully applied in various domains, it has recently entered also the domain of agriculture. In this paper, we perform a survey of 40 research efforts that employ deep learning techniques, applied to various agricultural and food production challenges. We examine the particular agricultural problems under study, the specific models and frameworks employed, the sources, nature and pre-processing of data used, and the overall performance achieved according to the metrics used at each work under study. Moreover, we study comparisons of deep learning with other existing popular techniques, in respect to differences in classification or regression performance. Our findings indicate that deep learning provides high accuracy, outperforming existing commonly used image processing techniques.

Obtaining accurate and well calibrated probability estimates from classifiers is useful in many applications, for example, when minimising the expected cost of classifications. Existing methods of calibrating probability estimates are applied globally, ignoring the potential for improvements by applying a more fine-grained model. We propose probability calibration trees, a modification of logistic model trees that identifies regions of the input space in which different probability calibration models are learned to improve performance. We compare probability calibration trees to two widely used calibration methods---isotonic regression and Platt scaling---and show that our method results in lower root mean squared error on average than both methods, for estimates produced by a variety of base learners.

Monitoring of disasters is crucial for mitigating their effects on the environment and human population, and can be facilitated by the use of unmanned aerial vehicles (UAV), equipped with camera sensors that produce aerial photos of the areas of interest. A modern technique for recognition of events based on aerial photos is deep learning. In this paper, we present the state of the art work related to the use of deep learning techniques for disaster identification. We demonstrate the potential of this technique in identifying disasters with high accuracy, by means of a relatively simple deep learning model. Based on a dataset of 544 images (containing disaster images such as fires, earthquakes, collapsed buildings, tsunami and flooding, as well as non-disaster scenes), our results show an accuracy of 91% achieved, indicating that deep learning, combined with UAV equipped with camera sensors, have the potential to predict disasters with high accuracy.

Advances in artificial intelligence (AI) will transform modern life by reshaping transportation, health, science, finance, and the military. To adapt public policy, we need to better anticipate these advances. Here we report the results from a large survey of machine learning researchers on their beliefs about progress in AI. Researchers predict AI will outperform humans in many activities in the next ten years, such as translating languages (by 2024), writing high-school essays (by 2026), driving a truck (by 2027), working in retail (by 2031), writing a bestselling book (by 2049), and working as a surgeon (by 2053). Researchers believe there is a 50% chance of AI outperforming humans in all tasks in 45 years and of automating all human jobs in 120 years, with Asian respondents expecting these dates much sooner than North Americans. These results will inform discussion amongst researchers and policymakers about anticipating and managing trends in AI. This article is part of the special track on AI and Society.

You will learn how to use Python to analyze data (big data analytics), create beautiful visualizations (data visualization) and use powerful machine learning algorithms. You will specifically get to learn how to use NumPy, Seaborn, Matplotlib, Pandas, Scikit-Learn, Machine Learning, Plotly, Tensorflow and more.

Hundreds of companies and thousands of individuals, many of them researchers and engineers prominent in the fields of robotics and artificial intelligence, vowed on Wednesday never to apply their skills toward the creation of autonomous killing machines. Led by the Future of Life Institute, a Boston-based nonprofit, as many as 160 AI-related companies in 36 countries, and 2,400 individuals in 90 countries, signed the pledge stating that autonomous weapons posed a "clear and present danger to the citizens of every country in the world," and that they would not participate in their development. "Artificial intelligence (AI) is poised to play an increasing role in military systems," the pledge states. "There is an urgent opportunity and necessity for citizens, policymakers, and leaders to distinguish between acceptable and unacceptable uses of AI." The signatories, who join 26 United Nations countries that have explicitly called for a ban on lethal autonomous weapons, include DeepMind, Google's top AI research team; the European Association for AI; ClearPath Robotics/OTTO Motors; the XPRIZE Foundation, the Swedish AI Society; and University College of London, among others. Leading AI researchers Demis Hassabis, Stuart Russell, Yoshua Bengio, Anca Dragan, Toby Walsh, and Tesla and SpaceX founder Elon Musk are among the individuals who also signed the pledge.

Humans should get'out of the loop' of artificial intelligence systems, UTS roboticist Professor Mary-Anne Williams argued last week at an Australian Human Rights Commission technology conference in Sydney. AI needn't consult a flesh and blood individual even when making life or death decisions, said Williams, director of The Magic Lab at the university's Centre of Artificial Intelligence. The rise of autonomous weapons systems which operate without human control is being campaigned against around the world. "States must draw the line now against unchecked autonomy in weapon systems by ensuring that the decision to take human life is never delegated to a machine," the Campaign to Stop Killer Robots states. In Australia, 122 AI experts last year signed a letter to Prime Minister Malcolm Turnbull urging him to "take a firm global stand" against weapons systems that remove "meaningful human control" when selecting targets and deploying lethal force.