Mitek Systems, Inc., a leader in digital identity verification solutions, announced that it has acquired A2iA, a leader in artificial intelligence (AI) and image analysis. The deal is for €42.5 million in cash and shares of Mitek's common stock. Mitek software is used in 6,100 U.S. banks, including all of the top 10 largest U.S. financial institutions. "The acquisition of A2iA combines two market leaders in image recognition and processing, creating a powerful force with a deep expertise in image analytics," industry expert Bob Meara, senior analyst at Celent said in a press release. A2iA uses AI and machine learning to create proprietary algorithms that process millions of checks, IDs and documents each day for banks, retailers, insurance companies, mobile operators, healthcare providers and governments in more than 42 countries and 11 languages.

We describe an adversarial learning approach to constrain convolutional neural network training for image registration, replacing heuristic smoothness measures of displacement fields often used in these tasks. Using minimally-invasive prostate cancer intervention as an example application, we demonstrate the feasibility of utilizing biomechanical simulations to regularize a weakly-supervised anatomical-label-driven registration network for aligning pre-procedural magnetic resonance (MR) and 3D intra-procedural transrectal ultrasound (TRUS) images. A discriminator network is optimized to distinguish the registration-predicted displacement fields from the motion data simulated by finite element analysis. During training, the registration network simultaneously aims to maximize similarity between anatomical labels that drives image alignment and to minimize an adversarial generator loss that measures divergence between the predicted- and simulated deformation. The end-to-end trained network enables efficient and fully-automated registration that only requires an MR and TRUS image pair as input, without anatomical labels or simulated data during inference. 108 pairs of labelled MR and TRUS images from 76 prostate cancer patients and 71,500 nonlinear finite-element simulations from 143 different patients were used for this study. We show that, with only gland segmentation as training labels, the proposed method can help predict physically plausible deformation without any other smoothness penalty. Based on cross-validation experiments using 834 pairs of independent validation landmarks, the proposed adversarial-regularized registration achieved a target registration error of 6.3 mm that is significantly lower than those from several other regularization methods.

Building on its strategy to develop innovations that drive retail transformation for brands, retailers and manufacturers, PLM leader Centric Software announces the unveiling of its first artificial intelligence-based PLM module. Centric Software provides the most innovative enterprise solutions to fashion, retail, footwear, outdoor, luxury and consumer goods companies to achieve strategic and operational digital transformation goals. The term'artificial intelligence' dates back to the 1950's when computer scientist John McCarthy coined the phrase to describe the potential'thinking machines' of the future. Today, artificial intelligence (AI) tools are systems modeled on the problem-solving abilities of the human brain, breaking complex problems down into different layers of information comprised of many smaller problems. Applications vary considerably ranging from virtual assistants like'Alexa' and'Siri' to Netflix viewing recommendations to Amazon recommending things we might like to buy.

News Blog China's Perfect Storm AI Moment May 22, 2018 Robin Raskin, Founder, Living in Digital Times In July of 2017 the Chinese government issued a development plan to make the country the world leader in artificial intelligence by 2030. So far things are going better than pretty well. The government's commitment to AI dominance, the sheer amount of data that a country as large as China can feed to its AI learning systems, a mobile-dominated infrastructure, and an edge on chip manufacturing for AI-intensive activities like facial and image recognition contribute to China's AI success story. Deep machine learning, the ability for machines to ingest data, learn from it and then make predictions from it requires lots of data. "There are 160 cities in China with over 1 million people; there are 10 in the U.S., says Deborah Weinswig, Senior Analyst for Fung.

CAMPBELL, Calif., May 24, 2018 – Building on its strategy to develop innovations that drive retail transformation for brands, retailers and manufacturers, PLM leader Centric Software announces the unveiling of its first artificial intelligence-based PLM module. Centric Software provides the most innovative enterprise solutions to fashion, retail, footwear, outdoor, luxury and consumer goods companies to achieve strategic and operational digital transformation goals. The term'artificial intelligence' dates back to the 1950's when computer scientist John McCarthy coined the phrase to describe the potential'thinking machines' of the future. Today, artificial intelligence (AI) tools are systems modeled on the problem-solving abilities of the human brain, breaking complex problems down into different layers of information comprised of many smaller problems. Applications vary considerably ranging from virtual assistants like'Alexa' and'Siri' to Netflix viewing recommendations to Amazon recommending things we might like to buy.

CAMPBELL, CA, USA, May 24, 2018 – Building on its strategy to develop innovations that drive retail transformation for brands, retailers and manufacturers, PLM leader Centric Software announces the unveiling of its first artificial intelligence-based PLM module. Centric Software provides the most innovative enterprise solutions to fashion, retail, footwear, outdoor, luxury and consumer goods companies to achieve strategic and operational digital transformation goals. The term'artificial intelligence' dates back to the 1950's when computer scientist John McCarthy coined the phrase to describe the potential'thinking machines' of the future. Today, artificial intelligence (AI) tools are systems modeled on the problem-solving abilities of the human brain, breaking complex problems down into different layers of information comprised of many smaller problems. Applications vary considerably ranging from virtual assistants like'Alexa' and'Siri' to Netflix viewing recommendations to Amazon recommending things we might like to buy.

Law enforcement agencies in Oregon and Orlando are using facial recognition tools provided by Amazon. This is how it works. EDITOR'S NOTE: Amazon owner and CEO Jeffrey P. Bezos owns The Washington Post.

Training large-scale image recognition models is computationally expensive. This raises the question of whether there might be simple ways to improve the test performance of an already trained model without having to re-train or even fine-tune it with new data. Here, we show that, surprisingly, this is indeed possible. The key observation we make is that the layers of a deep network close to the output layer contain independent, easily extractable class-relevant information that is not contained in the output layer itself. We propose to extract this extra class-relevant information using a simple key-value cache memory to improve the classification performance of the model at test time. Our cache memory is directly inspired by a similar cache model previously proposed for language modeling (Grave et al., 2017). This cache component does not require any training or fine-tuning; it can be applied to any pre-trained model and, by properly setting only two hyper-parameters, leads to significant improvements in its classification performance. Improvements are observed across several architectures and datasets. In the cache component, using features extracted from layers close to the output (but not from the output layer itself) as keys leads to the largest improvements. Concatenating features from multiple layers to form keys can further improve performance over using single-layer features as keys. The cache component also has a regularizing effect, a simple consequence of which is that it substantially increases the robustness of models against adversarial attacks.

The application uses machine learning and, by extension, artificial intelligence, to properly identify people's faces and surface relevant factoids. Users can access Who's Who either through Sky News's mobile app or its website. The idea is to provide digital onlookers second-screen content to fill in gaps during the event. The app will, in real time, identify the faces of people at the wedding. According to a press release, the app uses Amazon Rekognition tools to name people in the crowd and then surface biographical information about them. As Sky News explains it, Who's Who will "[name] wedding guests as they arrive for the ceremony and tells people about their connection to the royal couple."

An explorative data analysis system should be aware of what the user already knows and what the user wants to know of the data: otherwise the system cannot provide the user with the most informative and useful views of the data. We propose a principled way to do explorative data analysis, where the user's background knowledge is modeled by a distribution parametrised by subsets of rows and columns in the data, called tiles. The user can also use tiles to describe his or her interests concerning relations in the data. We provide a computationally efficient implementation of this concept based on constrained randomisation. This is used to model both the background knowledge and the user's information request and is a necessary prerequisite for any interactive system. Furthermore, we describe a novel linear projection pursuit method to find and show the views most informative to the user, which at the limit of no background knowledge and with generic objective reduces to PCA. We show that our method is robust under noise and fast enough for interactive use. We also show that the method gives understandable and useful results when analysing real-world data sets. We will release, under an open source license, a software library implementing the idea, including the experiments presented in this paper. We show that our method can outperform standard projection pursuit visualisation methods in exploration tasks. Our framework makes it possible to construct human-guided data exploration systems which are fast, powerful, and give results that are easy to comprehend.