Computers aren't going to replace creative pros -- but machine learning and artificial intelligence can be powerful tools in the storytelling process. The 60-second spot was directed by Oscar-winner Kevin Macdonald, working from a script that was developed by IBM's Watson AI system. To produce the spot for the Lexus ES executive sedan launching in Europe, the automaker enlisted its creative agency, The&Partnership London, along with technical partner Visual Voice. The agencies collaborated with the IBM Watson team to use AI to analyze 15 years' worth of footage, text and audio for car and luxury brand campaigns that have won Cannes Lions awards for creativity, as well as a range of other external data. Watson identified elements common to award-worthy commercials that were "both emotionally intelligent and entertaining," according to IBM.

Representation learning is a central challenge across a range of machine learning areas. In reinforcement learning, effective and functional representations have the potential to tremendously accelerate learning progress and solve more challenging problems. Most prior work on representation learning has focused on generative approaches, learning representations that capture all underlying factors of variation in the observation space in a more disentangled or well-ordered manner. In this paper, we instead aim to learn functionally salient representations: representations that are not necessarily complete in terms of capturing all factors of variation in the observation space, but rather aim to capture those factors of variation that are important for decision making -- that are "actionable." These representations are aware of the dynamics of the environment, and capture only the elements of the observation that are necessary for decision making rather than all factors of variation, without explicit reconstruction of the observation. We show how these representations can be useful to improve exploration for sparse reward problems, to enable long horizon hierarchical reinforcement learning, and as a state representation for learning policies for downstream tasks. We evaluate our method on a number of simulated environments, and compare it to prior methods for representation learning, exploration, and hierarchical reinforcement learning.

New Zealand, 26 October 2018 – Yesterday at Microsoft's inaugural AI event, Future Now, real-life applications of how AI will help shape New Zealand were brought to life demonstrating that AI is an important driver of new solutions to address some of our biggest societal challenges. Damon Kelly, Enlighten Designs CEO and Microsoft Partner showcased an exciting new initiative with Sustainable Coastlines aiming to make citizen scientists of us all by arming us with right data and insights to help keep our country clean, green and beautiful. Kelly said, "We have partnered with Sustainable Coastlines and Microsoft's Azure platform to develop an AI-powered tool that empowers Kiwis in cleaning up New Zealand's beaches. The tool is the first of its kind in New Zealand and uses Microsoft's Cognitive Services, alongside a United Nations Environment Programme methodology, to help communities around the country capture and categorise what litter is on our beaches." Working alongside the Ministry for the Environment, Department of Conservation and Statistics New Zealand, data collected from Sustainable Coastlines' new tool will be used to help establish a New Zealand-first national litter database.

We propose a multi-armed bandit algorithm that explores based on randomizing its history. The key idea is to estimate the value of the arm from the bootstrap sample of its history, where we add pseudo observations after each pull of the arm. The pseudo observations seem to be harmful. But on the contrary, they guarantee that the bootstrap sample is optimistic with a high probability. Because of this, we call our algorithm Giro, which is an abbreviation for garbage in, reward out. We analyze Giro in a $K$-armed Bernoulli bandit and prove a $O(K \Delta^{-1} \log n)$ bound on its $n$-round regret, where $\Delta$ denotes the difference in the expected rewards of the optimal and best suboptimal arms. The main advantage of our exploration strategy is that it can be applied to any reward function generalization, such as neural networks. We evaluate Giro and its contextual variant on multiple synthetic and real-world problems, and observe that Giro is comparable to or better than state-of-the-art algorithms.

The paper proposes an on-line monitoring framework for continuous real-time safety/security in learning-based control systems (specifically application to a unmanned ground vehicle). We monitor validity of mappings from sensor inputs to actuator commands, controller-focused anomaly detection (CFAM), and from actuator commands to sensor inputs, system-focused anomaly detection (SFAM). CFAM is an image conditioned energy based generative adversarial network (EBGAN) in which the energy based discriminator distinguishes between proper and anomalous actuator commands. SFAM is based on an action condition video prediction framework to detect anomalies between predicted and observed temporal evolution of sensor data. We demonstrate the effectiveness of the approach on our autonomous ground vehicle for indoor environments and on Udacity dataset for outdoor environments.

ABSTRACT We consider technology-assisted mimicry attacks in the context of automatic speaker verification (ASV). We use ASV itself to select targeted speakers to be attacked by human-based mimicry. We recorded 6 naive mimics for whom we select target celebrities from VoxCeleb1 and VoxCeleb2 corpora (7,365 potential targets) using an i-vector system. The attacker attempts to mimic the selected target, with the utterances subjected to ASV tests using an independently developed x-vector system. Our main finding is negative: even if some of the attacker scores against the target speakers were slightly increased, our mimics did not succeed in spoofing the x-vector system. Interestingly, however, the relative ordering of the selected targets (closest, furthest, median) are consistent between the systems, which suggests some level of transferability between the systems.

Applying for life insurance is a long and often frustrating process. Thousands of questions on seemingly every medical condition ever suffered – except yours. "We've had multiple occurrences where people answer no to all the [medical] questions, then they come to the'other' box at the end and they'll go – 'oh yeah I've had X'. And that question is actually back there, but they didn't understand it so they defaulted to'other' and started writing chapter and verse about their medical condition," explains ANZ OnePath's chief underwriter Peter Tilocca. Whenever answers are given free form, typically the application will require the scrutiny of an underwriter.

We derive upper bounds on the generalization error of learning algorithms based on their \emph{algorithmic transport cost}: the expected Wasserstein distance between the output hypothesis and the output hypothesis conditioned on an input example. The bounds provide a novel approach to study the generalization of learning algorithms from an optimal transport view and impose less constraints on the loss function, such as sub-gaussian or bounded. We further provide several upper bounds on the algorithmic transport cost in terms of total variation distance, relative entropy (or KL-divergence), and VC dimension, thus further bridging optimal transport theory and information theory with statistical learning theory. Moreover, we also study different conditions for loss functions under which the generalization error of a learning algorithm can be upper bounded by different probability metrics between distributions relating to the output hypothesis and/or the input data. Finally, under our established framework, we analyze the generalization in deep learning and conclude that the generalization error in deep neural networks (DNNs) decreases exponentially to zero as the number of layers increases. Our analyses of generalization error in deep learning mainly exploit the hierarchical structure in DNNs and the contraction property of $f$-divergence, which may be of independent interest in analyzing other learning models with hierarchical structure.

A new app using artificial intelligence might be able to help detect autism in children earlier in their lives. A duo of lecturers at the Manukau Institute of Technology (MIT) have created the app that uses deep learning technology to identify autistic traits. Autism is a neurodevelopmental condition that affects cognitive, sensory, and social processing, changing the way people see the world and interact with others. Dr Fadi Fayez says the Autism AI (artificial intelligence) app asks 10 questions across four age groups - toddlers, children, adolescents and adults. READ MORE: * Artificial intelligence knows what you'll choose before you've made up your mind * Artificial intelligence is changing our lives and now is the time to decide how * This optical illusion could help diagnose people with autism * Women are being diagnosed with autism later in life An artificial neural network method processes the answers and shows whether to pursue a formal clinical diagnosis.

A global team of scientists have discovered AI can tell an individual's personality traits by analysing their eye movements. The scientists used machine learning to discover a link between eye movements and the personality of a person earlier this year. Armed with this information, the researchers deployed AI to analyse the eye movements of 42 students. The results were announced last week. In psychology, there are five basic personality traits: Extraversion, Agreeableness, Openness, Conscientiousness, and Neuroticism.