Fintech startups and banks have always been at the forefront of tech adoption, and they've been curiously following the growth and development of AI for many years. And there's a good reason for it -- we, the consumers of their services, want to have access to cutting-edge technology while dealing with our finances, as well as making sure that the companies dealing with our savings be equipped with the best of what tech can offer. AI and ML have recently moved from the realm of futurism to the very crux of the conversation in the Fintech sector, and many aspiring businesses have started integrating it into their services. In this article, we wanted to touch on the ways various Fintech businesses and startups implement this technology in the services they provide their customers with and how it benefits their users. Let's dive right in, shall we?

This paper addresses a boosting method for mapping functionality of neural networks in visual recognition such as image classification and face recognition. We present reversible learning for generating and learning latent features using the network itself. By generating latent features corresponding to hard samples and applying the generated features in a training stage, reversible learning can improve a mapping functionality without additional data augmentation or handling the bias of dataset. We demonstrate an efficiency of the proposed method on the MNIST,Cifar-10/100, and Extremely Biased and poorly categorized dataset (EBPC dataset). The experimental results show that the proposed method can outperform existing state-of-the-art methods in visual recognition. Extensive analysis shows that our method can efficiently improve the mapping capability of a network.

Clustering is a fundamental unsupervised learning approach. Many clustering algorithms -- such as $k$-means -- rely on the euclidean distance as a similarity measure, which is often not the most relevant metric for high dimensional data such as images. Learning a lower-dimensional embedding that can better reflect the geometry of the dataset is therefore instrumental for performance. We propose a new approach for this task where the embedding is performed by a differentiable model such as a deep neural network. By rewriting the $k$-means clustering algorithm as an optimal transport task, and adding an entropic regularization, we derive a fully differentiable loss function that can be minimized with respect to both the embedding parameters and the cluster parameters via stochastic gradient descent. We show that this new formulation generalizes a recently proposed state-of-the-art method based on soft-$k$-means by adding constraints on the cluster sizes. Empirical evaluations on image classification benchmarks suggest that compared to state-of-the-art methods, our optimal transport-based approach provide better unsupervised accuracy and does not require a pre-training phase.

This paper proposes a new evaluation metric and boosting method for weight separability in neural network design. In contrast to general visual recognition methods designed to encourage both intra-class compactness and inter-class separability of latent features, we focus on estimating linear independence of column vectors in weight matrix and improving the separability of weight vectors. To this end, we propose an evaluation metric for weight separability based on semi-orthogonality of a matrix and Frobenius distance, and the feed-backward reconstruction loss which explicitly encourages weight separability between the column vectors in the weight matrix. The experimental results on image classification and face recognition demonstrate that the weight separability boosting via minimization of feed-backward reconstruction loss can improve the visual recognition performance, hence universally boosting the performance on various visual recognition tasks.

A recent trend of fair machine learning is to define fairness as causality-based notions which concern the causal connection between protected attributes and decisions. However, one common challenge of all causality-based fairness notions is identifiability, i.e., whether they can be uniquely measured from observational data, which is a critical barrier to applying these notions to real-world situations. In this paper, we develop a framework for measuring different causality-based fairness. We propose a unified definition that covers most of previous causality-based fairness notions, namely the path-specific counterfactual fairness (PC fairness). Based on that, we propose a general method in the form of a constrained optimization problem for bounding the path-specific counterfactual fairness under all unidentifiable situations. Experiments on synthetic and real-world datasets show the correctness and effectiveness of our method.

The Artificial Intelligence Robots Market report is a complete overview of the market, covering various aspects product definition, segmentation based on various parameters, and the prevailing vendor landscape. Analysis and discussion of important industry trends, market size, market share estimates are mentioned in the report. Artificial Intelligence Robots Market report includes historic data, present market trends, environment, technological innovation, upcoming technologies and the technical progress in the related industry. The Global Artificial Intelligence Robots Market accounted for USD 3.0 billion in 2017 and is projected to grow at a CAGR of 30.1% forecast to 2025. Some of the major countries covered in this report are U.S., Canada, Germany, France, U.K., Netherlands, Switzerland, Turkey, Russia, China, India, South Korea, Japan, Australia, Singapore, Saudi Arabia, South Africa and Brazil among others.

BMO Capital Markets is a trade name used by BMO Financial Group for the wholesale banking businesses of Bank of Montreal, BMO Harris Bank N.A. (member FDIC), Bank of Montreal Europe p.l.c, and Bank of Montreal (China) Co. Ltd and the institutional broker dealer businesses of BMO Capital Markets Corp.

By Howard Solomon A new report highlights a growing fear among Canadians that's tied to the rapid advancement of artificial intelligence. An online study conducted by Environics Research Group revealed that 77 per cent of Canadians are concerned that AI is advancing too quickly to properly understand its potential risks. The survey of 1,200 Canadians was sponsored by TD Bank back in May, and also indicated a growing concern around biases in how the technology is developed. Additionally, sixty per cent of Canadians worry about a lack of diversity in the growing field of AI. The results don't shock Tomi Poutanen, chief AI officer for TD and co-founder of Layer 6, but he said they do signal a growing awareness of AI's transformative capabilities, and people are looking to banks to validate its adoption.

EPAM Systems, Inc. (NYSE: EPAM), a leading global provider of digital platform engineering and software development services, today announced that it has been named a Diamond Global Business Partner of UiPath, an enterprise Robotic Process Automation (RPA) software company. EPAM and UiPath's partnership will enable its joint customers to increase efficiencies and improve customer experience by leveraging intelligent automation (IA) solutions and UiPath's RPA platform. Forrester recently predicted that, in 2019, automation would become the tip of the digital transformation spear. While early automation implementations focused on cost optimization, this new wave will achieve multiple goals, including driving both customer and employee experience, changing the nature of work, and even empowering the next generation of startup companies. With more than 10 years of business process management, robotics and cognitive expertise, EPAM has over 100 certified UiPath advanced developers as part of its team of more than 700 machine learning and RPA engineers.

One of the technologies we are seeing being trialled and deployed in airports is robotic assistants. The humanoid robots are positioned around the airport terminal assisting passengers with queries and information. By making use of Artificial Intelligence (AI) and Machine Learning, the robots can process large amounts of data, with real-time updates to enable them to provide the latest information to passengers. This technology is starting to be used in some select airports but for different functions. Munich Airport in Germany is using robotic assistants primarily for information.