Artificial Intelligence will be central to solving humanity's grand challenges Imagine a world void of antibiotic-resistant superbugs, where those once-invincible critters were no longer given a chance to grow because infection patterns in patients were identified and treated before any symptoms appeared. Or what if safety-critical industries like healthcare or transportation were virtually zero risk, thanks to automated decision making and effective pattern recognition by robotic systems? What if cancer found its cure, crime was accurately intercepted, and democracy was given the tools to empower each and every citizen? And what if this was just a fraction of what AI will offer humanity down the line? The promise of AI is the promise of transformative technology which has unfathomable potential to dramatically improve the lives of millions, if not billions, of humans on planet earth.

Which AI applications are playing a role in automation or augmentation of the retail process? How are retail companies using these technologies to stay ahead of their competitors today, and what innovations are being pioneered as potential retail game-changers over the next decade? Innovation is a double-edged sword, and as with any innovation results are a mixed bag. While many AI applications have yielded increased ROI--this case study of AI in retail marketing segmentation is one example--others have been tried and failed to meet expectations, shining a light on barriers that still need to be overcome before such innovations become industry drivers. Below are 10 brief use cases across five retail domains or phases.

Social robot company Jibo is sadly running on fumes after burning through nearly $73 million in funding. In a story first reported by BostInno and since confirmed by The Robot Report, Jibo has laid off the majority of its workforce to enable "additional time to secure additional funding or pursue an exit." Jibo was once heralded as "the first social robot for the home." Founded in 2012 by famed MIT roboticist Cynthia Breazeal, Jibo successfully raised over $3.5 million when its Indiegogo campaign ended in 2014. At the time, Breazeal promised to usher in a new age of social robotics.

"The only thing constant in life is change," said 17th century French thinker François de La Rochefoucauld. The Internet of Things explained: What the IoT is, and where it's going next. That goes for double in the tech industry. By 2006, the people of the world were doing 100 million Google searches every day--which, by the way, we thought was an enormous number at the time. Now, we're doing 4.5 billion searches a day--45 times as many--which shows just how far and wide the digital revolution is still sweeping the planet and embedding itself deeper and deeper into our everyday lives.

"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.