The AI solution, created by the London-based Tractable, uses computer vision to look at photos of car damage, making sense of it as a human would, in near-real time. Tokio Marine will use the AI to understand the full range of repair decisions available to it, including recommended repair, paint, and blend operations, as well as the labour hours require, the Japanese insurer says in a statement. Using AI in this way can increase the speed of remotely reviewing claims from days to minutes, removing inefficiencies from the process, helping insurers and repairers to agree on repairs more quickly, getting customers back on the road faster. Tokio Marine has worked with Tractable since 2018, with the ambition of improving appraisal operations that require complex visual assessments with a solution based on computer vision. After successful trials of the AI, Tokio Marine will now use Tractable's technology at one of its claim service centres from this month, with the potential to deploy it across the country. Tokio Marine group deputy general manager Hidenori Kobayashi said: "In Japan, after an accident it can take 2-3 weeks to determine the amount that should be paid.

Whatever it is they're selling, businesses today are under more pressure than ever to provide a five-star experience. Customer service is not a'nice-to-have', it's taken for granted by customers who have plenty of eager competitors at their disposal and a multitude of public forums to share their negative experiences. In times of need, if customers can't open a chatbot or pick up a phone to quickly get resolution to their issues, they'll start shopping around – customer experience is now part of the package. Meeting these real-time demands, then, is a deal-breaker, but there are also rewards. According to McKinsey, 70 percent of buying experiences are based on how the customer feels they are being treated, and if you do it right, they will stick around.

The Department of Defense is racing to test and adopt artificial intelligence and machine learning solutions to help sift and synthesize massive amounts of data that can be leveraged by their human analysts and commanders in the field. Along the way, it's identifying many of the friction points between man and machine that will govern how decisions are made in modern war. The Machine Assisted Rapid Repository System (MARS) was developed to replace and enhance the foundational military intelligence that underpins most of the department's operations. Like U.S. intelligence agencies, officials at the Pentagon have realized that data -- and the ability to speedily process, analyze and share it among components – was the future. Fulfilling that vision would take a refresh.

The wireless home-audio specialist Sonos is launching the first of its next-generation speakers with a new Dolby Atmos voice-controlled soundbar called Arc. Arc replaces the firm's popular Playbar and Playbase as its top-end TV sound system, re-engineered to provide a wider, more powerful sound and built on the new S2 software platform, which is due to roll out to existing speakers soon. The elliptical soundbar contains 11 separate speakers and is designed to sit below, above or in front of a TV. Four woofer speakers face forwards to provide the centre, left and right channels, two point to the side to provide the wide and rear channels, while two more point upwards to provide the height channel that is part of Dolby's Atmos home-cinema sound. Three tweeters provide high notes, while the bar can be wall or table-top mounted, adjusting automatically for the best sound.

The social media site Pinterest has rolled out new features to its app, making it easier for users to manage their boards by letting them add dates and notes to them. Pinterest is an app where users save images – called pins – to collections called boards, with a large base of fashion, travel, home decor and hobbyist users. Adding notes to boards will allow users to annotate things they've saved with personal information, such as adding ingredients to an image of a meal or creating to-do lists for crafts. Users can use dates to track timelines for projects, as well as letting users archive the boards afterwards. Pinterest's other major feature it's introducing is an improvement to its recommendation technology, suggesting sections to organise your boards topics.

This report is part of "The global AI agenda," a thought leadership program by MIT Technology Review Insights examining how organizations are using AI today and planning to do so in the future. Featuring a global survey of 1,004 AI experts conducted in January and February 2020, it explores AI adoption, leading use cases, benefits, and challenges, and seeks to understand how organizations might share data with each other to develop new business models, products, and services in the years ahead. How do executives in the US and Canada see AI playing out in their business? What are the main benefits reaped so far, and what challenges do they face in AI deployment?

Data-space inversion (DSI) and related procedures represent a family of methods applicable for data assimilation in subsurface flow settings. These methods differ from model-based techniques in that they provide only posterior predictions for quantities (time series) of interest, not posterior models with calibrated parameters. DSI methods require a large number of flow simulations to first be performed on prior geological realizations. Given observed data, posterior predictions can then be generated directly. DSI operates in a Bayesian setting and provides posterior samples of the data vector. In this work we develop and evaluate a new approach for data parameterization in DSI. Parameterization reduces the number of variables to determine in the inversion, and it maintains the physical character of the data variables. The new parameterization uses a recurrent autoencoder (RAE) for dimension reduction, and a long-short-term memory (LSTM) network to represent flow-rate time series. The RAE-based parameterization is combined with an ensemble smoother with multiple data assimilation (ESMDA) for posterior generation. Results are presented for two- and three-phase flow in a 2D channelized system and a 3D multi-Gaussian model. The RAE procedure, along with existing DSI treatments, are assessed through comparison to reference rejection sampling (RS) results. The new DSI methodology is shown to consistently outperform existing approaches, in terms of statistical agreement with RS results. The method is also shown to accurately capture derived quantities, which are computed from variables considered directly in DSI. This requires correlation and covariance between variables to be properly captured, and accuracy in these relationships is demonstrated. The RAE-based parameterization developed here is clearly useful in DSI, and it may also find application in other subsurface flow problems.

We introduce an optimized physics-informed neural network (PINN) trained to solve the problem of identifying and characterizing a surface breaking crack in a metal plate. PINNs are neural networks that can combine data and physics in the learning process by adding the residuals of a system of Partial Differential Equations to the loss function. Our PINN is supervised with realistic ultrasonic surface acoustic wave data acquired at a frequency of 5 MHz. The ultrasonic surface wave data is represented as a surface deformation on the top surface of a metal plate, measured by using the method of laser vibrometry. The PINN is physically informed by the acoustic wave equation and its convergence is sped up using adaptive activation functions. The adaptive activation function uses a scalable hyperparameter in the activation function, which is optimized to achieve best performance of the network as it changes dynamically the topology of the loss function involved in the optimization process. The usage of adaptive activation function significantly improves the convergence, notably observed in the current study. We use PINNs to estimate the speed of sound of the metal plate, which we do with an error of 1\%, and then, by allowing the speed of sound to be space dependent, we identify and characterize the crack as the positions where the speed of sound has decreased. Our study also shows the effect of sub-sampling of the data on the sensitivity of sound speed estimates. More broadly, the resulting model shows a promising deep neural network model for ill-posed inverse problems.

MineRL 2019 competition challenged participants to train sample-efficient agents to play Minecraft, by using a dataset of human gameplay and a limit number of steps the environment. We approached this task with behavioural cloning by predicting what actions human players would take, and reached fifth place in the final ranking. Despite being a simple algorithm, we observed the performance of such an approach can vary significantly, based on when the training is stopped. In this paper, we detail our submission to the competition, run further experiments to study how performance varied over training and study how different engineering decisions affected these results.

Restricted Boltzmann Machine (RBM) is an energy based, undirected graphical model. It is commonly used for unsupervised and supervised machine learning. Typically, RBM is trained using contrastive divergence (CD). However, training with CD is slow and does not estimate exact gradient of log-likelihood cost function. In this work, the model expectation of gradient learning for RBM has been calculated using a quantum annealer (D-Wave 2000Q), which is much faster than Markov chain Monte Carlo (MCMC) used in CD. Training and classification results are compared with CD. The classification accuracy results indicate similar performance of both methods. Image reconstruction as well as log-likelihood calculations are used to compare the performance of quantum and classical algorithms for RBM training. It is shown that the samples obtained from quantum annealer can be used to train a RBM on a 64-bit `bars and stripes' data set with classification performance similar to a RBM trained with CD. Though training based on CD showed improved learning performance, training using a quantum annealer eliminates computationally expensive MCMC steps of CD.