Big banks and other bill providers have earned big profits for many years from customers who don't examine their payments or search for better deals. However, digital banks -- also known as neobanks and smartbanks -- are set to turn that strategy on its head by automatically finding savings and prompting customers to act. Financial technology infrastructure provider Frankie Financial's CEO, Simon Costello, said consumers would benefit through artificial intelligence and "shiny new tech" providing automatic recommendations and switching. Don't be afraid of the rise of robots to help in managing your bills and bank accounts.Source:Supplied "Neobanks will be able to spot your providers in your direct debits, let you know how much you would save if you switched providers and then offer a single-click switch," he said. "This is very powerful functionality that has the potential to change the way consumers compare, select and change providers not just across the energy sector, but financial, insurance, and health sectors as well."

Apple workers have stopped listening to Siri queries worldwide, the company said this week. Apple plans to bring back human reviews of Siri voice recordings at some unspecified date, but the company said it will only review them when customers specifically opt in to the practice. Separately, Google today confirmed that it recently "paused" human reviews of Google Assistant queries worldwide. Apple's decision to stop having humans listen to Siri queries follows a report last week that contractors who review the recordings for accuracy heard private discussions and even sexual encounters. Apple calls the human reviews of Siri recordings "grading."

Here are the top stories covered by DocWire News recently regarding artificial intelligence (AI) in healthcare.

There are many headline studies about artificial intelligence making strides in medicine, but the reality can be somewhat more prosaic.

--Collaborative filtering is one of the most popular techniques in designing recommendation systems, and its most representative model, matrix factorization, has been wildly used by researchers and the industry. However, this model suffers from the lack of interpretability and the item cold-start problem, which limit its reliability and practicability. In this paper, we propose an interpretable recommendation model called Multi-Matrix F actorization (MMF), which addresses these two limitations and achieves the state-of-the-art prediction accuracy by exploiting common attributes that are present in different items. In the model, predicted item ratings are regarded as weighted aggregations of attribute ratings generated by the inner product of the user latent vectors and the attribute latent vectors. MMF provides more fine grained analyses than matrix factorization in the following ways: attribute ratings with weights allow the understanding of how much each attribute contributes to the recommendation and hence provide interpretability; the common attributes can act as a link between existing and new items, which solves the item cold-start problem when no rating exists on an item. We evaluate the interpretability of MMF comprehensively, and conduct extensive experiments on real datasets to show that MMF outperforms state-of-the-art baselines in terms of accuracy. I NTRODUCTION In recent years, recommendation systems gain increasing interest by both researchers and the industry [1], [2]. The most popular recommendation systems are based on collaborative filtering (CF) technique, which provides recommendations based on other similar users' choice [3]. Matrix factorization (MF) is one of the most common collaborative filtering models, whose main idea is to learn user latent vectors and item latent vectors, so that the inner product of the two vectors can approximate the original matrix with the minimal approximation error. MF has advantages of simplicity and performing well in many domains, such as recommendation systems, computer vision and document clustering [4]-[7]. However, it suffers from two limitations.

Traditionally, music was treated as an analogue signal and was generated manually. In recent years, music is conspicuous to technology which can generate a suite of music automatically without any human intervention. To accomplish this task, we need to overcome some technical challenges which are discussed descriptively in this paper. A brief introduction about music and its components is provided in the paper along with the citation and analysis of related work accomplished by different authors in this domain. Main objective of this paper is to propose an algorithm which can be used to generate musical notes using Recurrent Neural Networks (RNN), principally Long Short-Term Memory (LSTM) networks. A model is designed to execute this algorithm where data is represented with the help of musical instrument digital interface (MIDI) file format for easier access and better understanding. Preprocessing of data before feeding it into the model, revealing methods to read, process and prepare MIDI files for input are also discussed. The model used in this paper is used to learn the sequences of polyphonic musical notes over a single-layered LSTM network. The model must have the potential to recall past details of a musical sequence and its structure for better learning. Description of layered architecture used in LSTM model and its intertwining connections to develop a neural network is presented in this work. This paper imparts a peek view of distributions of weights and biases in every layer of the model along with a precise representation of losses and accuracy at each step and batches. When the model was thoroughly analyzed, it produced stellar results in composing new melodies.

An open question is how to make deep neural networks work in conjunction with existing (and upcoming) video codecs, such as MPEG H.264/A VC, H.265/HEVC, VVC, Google VP9 and AOMedia A V1, as well as existing container and transport formats, without imposing any changes at the client side. Such compatibility is a crucial aspect when it comes to practical deployment, especially when considering the fact that the video content industry and hardware manufacturers are expected to remain committed to supporting these standards for the foreseeable future. We propose to use deep neural networks as precoders for current and future video codecs and adaptive video streaming systems. In our current design, the core precoding component comprises a cascaded structure of downscaling neural networks that operates during video encoding, prior to transmission. This is coupled with a precoding mode selection algorithm for each independently-decodable stream segment, which adjusts the downscaling factor according to scene characteristics, the utilized encoder, and the desired bitrate and encoding configuration. Our framework is compatible with all current and future codec and transport standards, as our deep precoding network structure is trained in conjunction with linear upscaling filters (e.g., the bilinear filter), which are supported by all web video players. Results with FHD (1080p) and UHD (2160p) content and widely-used H.264/A VC, H.265/HEVC and VP9 encoders show that coupling such standards with the proposed deep video precoding allows for 15% to 45% rate reduction under encoding configurations and bitrates suitable for video-on-demand adaptive streaming systems. The use of precoding can also lead to encoding complexity reduction, which is essential for cost-effective cloud deployment of complex encoders like H.265/HEVC and VP9, especially when considering the prominence of high-resolution adaptive video streaming.

These are the things our readers went nuts for last month. If you make a purchase by clicking one of our links, we may earn a small share of the revenue. However, our picks and opinions are independent from USA TODAY's newsroom and any business incentives. When the dog days of summer arrive and the temperature rises, prices across a ton of retailers tend to drop. There's also Prime Day of course, one of the biggest shopping holidays of the year, when Amazon slashes their prices across the board in an attempt to compete with Black Friday sales. Nordstrom happens to use a similar technique to get ahead of the end-of-summer sales curve with their massive Nordstrom Anniversary sale, which features discounts on a ton of fashion, beauty, and home products.