The industry is on the verge of a seismic, tech-driven shift. A focus on four areas can position carriers to embrace this change. Welcome to the future of insurance, as seen through the eyes of Scott, a customer in the year 2030. Upon hopping into the arriving car, Scott decides he wants to drive today and moves the car into "active" mode. Scott's personal assistant maps out a potential route and shares it with his mobility insurer, which immediately responds with an alternate route that has a much lower likelihood of accidents and auto damage as well as the calculated adjustment to his monthly premium. Scott's assistant notifies him that his mobility insurance premium will increase by 4 to 8 percent based on the route he selects and the volume and distribution of other cars on the road. It also alerts him that his life insurance policy, which is now priced on a "pay-as-you-live" basis, will increase by 2 percent for this quarter.

The industry is on the verge of a seismic, tech-driven shift. A focus on four areas can position carriers to embrace this change. Welcome to the future of insurance, as seen through the eyes of Scott, a customer in the year 2030. Upon hopping into the arriving car, Scott decides he wants to drive today and moves the car into "active" mode. Scott's personal assistant maps out a potential route and shares it with his mobility insurer, which immediately responds with an alternate route that has a much lower likelihood of accidents and auto damage as well as the calculated adjustment to his monthly premium. Scott's assistant notifies him that his mobility insurance premium will increase by 4 to 8 percent based on the route he selects and the volume and distribution of other cars on the road. It also alerts him that his life insurance policy, which is now priced on a "pay-as-you-live" basis, will increase by 2 percent for this quarter.

DeepMind has a new paper where researchers have uncovered two "surpising findings". The paper is described in "Understanding Deep Learning through Neuron Deletion". In networks that generalize well, (1) all neurons are important and (2) are more robust to damage. Deep Learning network have behavior that reminds us of holograms. These results are further confirmation of my conjecture that Deep Learning systems are like holographic memories.

The rapid pace of innovation in the e-commerce sector propelled by the trend'bricks to clicks' is increasingly shifting consumers to online shopping. A major disadvantage for offline retail stores is their lack of knowledge on customers entering their premises. Here, artificial intelligence (AI) opens up a big opportunity to predict the purchasing behaviour of in-store customers. AI through its sub-technologies such as machine learning and deep learning can enable offline retailers to derive actionable insights from consumer data (structured and unstructured) to offer predictive and precise decisions for better customer experience. AI practices incorporated by global offline retailers The global offline retail industry has been moving toward increased automation, cashless transactions and self-checkout stores based on consumer behaviour patterns, and demand for increased convenience.

Editor's note: We sat down with data scientist and artificial intelligence evangelist Ozge Yeloglu to talk about her love of machine learning and how she battles perfectionism. This story is told in her own words. On my first day of college classes at Ege University in Turkey, I sat down in the computer lab and was instructed to insert the floppy disk. I stared blankly at the computer, because I had no idea what a floppy disk was or how to put it in correctly. I quickly looked over at the kids next to me and watched what they did.

How does one teach a machine to see? Seeing AI is an exciting Microsoft research project that harnesses the power of Artificial Intelligence to open the visual world and describe nearby people, objects, text, colors and more using spoken audio. Designed for the blind and low vision community, it helps users understand more about their environment, including who and what is around them. Today, our iOS app has empowered users to complete over 5 million tasks unassisted, including many "first in a lifetime" experiences for the blind community, such as taking and posting photos of their friends on Facebook, independently identifying products when shopping at a store, reading homework to kids, and much more. To learn more about Seeing.AI you can visit our web page here. One of the most common needs of the blind community is the ability to recognize paper currency.

Machine learning models are often susceptible to adversarial perturbations of their inputs. Even small perturbations can cause state-of-the-art classifiers with high "standard" accuracy to produce an incorrect prediction with high confidence. To better understand this phenomenon, we study adversarially robust learning from the viewpoint of generalization. We show that already in a simple natural data model, the sample complexity of robust learning can be significantly larger than that of "standard" learning. This gap is information theoretic and holds irrespective of the training algorithm or the model family. We complement our theoretical results with experiments on popular image classification datasets and show that a similar gap exists here as well. We postulate that the difficulty of training robust classifiers stems, at least partially, from this inherently larger sample complexity.

We introduce the SaaS Algorithm for semi-supervised learning, which uses learning speed during stochastic gradient descent in a deep neural network to measure the quality of an iterative estimate of the posterior probability of unknown labels. Training speed in supervised learning correlates strongly with the percentage of correct labels, so we use it as an inference criterion for the unknown labels, without attempting to infer the model parameters at first. Despite its simplicity, SaaS achieves state-of-the-art results in semi-supervised learning benchmarks.

We propose a tri-modal architecture to predict Big Five personality trait scores from video clips with different channels for audio, text, and video data. For each channel, stacked Convolutional Neural Networks are employed. The channels are fused both on decision-level and by concatenating their respective fully connected layers. It is shown that a multimodal fusion approach outperforms each single modality channel, with an improvement of 9.4\% over the best individual modality (video). Full backpropagation is also shown to be better than a linear combination of modalities, meaning complex interactions between modalities can be leveraged to build better models. Furthermore, we can see the prediction relevance of each modality for each trait. The described model can be used to increase the emotional intelligence of virtual agents.

The continuous dimensional emotion modelled by arousal and valence can depict complex changes of emotions. In this paper, we present our works on arousal and valence predictions for One-Minute-Gradual (OMG) Emotion Challenge. Multimodal representations are first extracted from videos using a variety of acoustic, video and textual models and support vector machine (SVM) is then used for fusion of multimodal signals to make final predictions. Our solution achieves Concordant Correlation Coefficient (CCC) scores of 0.397 and 0.520 on arousal and valence respectively for the validation dataset, which outperforms the baseline systems with the best CCC scores of 0.15 and 0.23 on arousal and valence by a large margin.