Deep learning methods have shown extraordinary potential for analyzing very diverse biomedical data, but their dissemination beyond developers is hindered by important computational hurdles. We introduce ImJoy ( https://imjoy.io/), a flexible and open-source browser-based platform designed to facilitate widespread reuse of deep learning solutions in biomedical research. We highlight ImJoy's main features and illustrate its functionalities with deep learning plugins for mobile and interactive image analysis and genomics. These and other early advances generate considerable interest and a strong demand by biomedical researchers to apply and adapt deep learning methods to new data sets and questions. However, making full use of recent deep learning approaches faces considerable bottlenecks. A distinctive challenge of machine learning methods arises from their strong reliance on training data. While these tools are useful, they do not allow researchers to retrain models on their own data or on public data sets. Therefore, enabling users to retrain existing models on other data is essential to realizing the full promise of deep learning in biomedical research. Many deep learning approaches provide open-source code, typically written in Python and using libraries such as Tensorflow or Pytorch, which in theory enables retraining.

We present a deep reinforcement learning approach to optimizing the execution cost of computation graphs in a static compiler. The key idea is to combine a neural network policy with a genetic algorithm, the Biased Random-Key Genetic Algorithm (BRKGA). The policy is trained to predict, given an input graph to be optimized, the node-level probability distributions for sampling mutations and crossovers in BRKGA. Our approach, "REINFORCE-based Genetic Algorithm Learning" (REGAL), uses the policy's ability to transfer to new graphs to significantly improve the solution quality of the genetic algorithm for the same objective evaluation budget. As a concrete application, we show results for minimizing peak memory in TensorFlow graphs by jointly optimizing device placement and scheduling. REGAL achieves on average 3.56% lower peak memory than BRKGA on previously unseen graphs, outperforming all the algorithms we compare to, and giving 4.4x bigger improvement than the next best algorithm. We also evaluate REGAL on a production compiler team's performance benchmark of XLA graphs and achieve on average 3.74% lower peak memory than BRKGA, again outperforming all others. Our approach and analysis is made possible by collecting a dataset of 372 unique real-world TensorFlow graphs, more than an order of magnitude more data than previous work.

The ability of reasoning beyond data fitting is substantial to deep learning systems in order to make a leap forward towards artificial general intelligence. A lot of efforts have been made to model neural-based reasoning as an iterative decision-making process based on recurrent networks and reinforcement learning. Instead, inspired by the consciousness prior proposed by Yoshua Bengio, we explore reasoning with the notion of attentive awareness from a cognitive perspective, and formulate it in the form of attentive message passing on graphs, called neural consciousness flow (NeuCFlow). Aiming to bridge the gap between deep learning systems and reasoning, we propose an attentive computation framework with a three-layer architecture, which consists of an unconsciousness flow layer, a consciousness flow layer, and an attention flow layer. We implement the NeuCFlow model with graph neural networks (GNNs) and conditional transition matrices. Our attentive computation greatly reduces the complexity of vanilla GNN-based methods, capable of running on large-scale graphs. We validate our model for knowledge graph reasoning by solving a series of knowledge base completion (KBC) tasks. The experimental results show NeuCFlow significantly outperforms previous state-of-the-art KBC methods, including the embedding-based and the path-based. The reproducible code can be found by the link below.

Over the past few years the Angry Birds AI competition has been held in an attempt to develop intelligent agents that can successfully and efficiently solve levels for the video game Angry Birds. Many different agents and strategies have been developed to solve the complex and challenging physical reasoning problems associated with such a game. However none of these agents attempt one of the key strategies which humans employ to solve Angry Birds levels, which is restarting levels. Restarting is important in Angry Birds because sometimes the level is no longer solvable or some given shot made has little to no benefit towards the ultimate goal of the game. This paper proposes a framework and experimental evaluation for when to restart levels in Angry Birds. We demonstrate that restarting is a viable strategy to improve agent performance in many cases.

An agent's assessment of its trust in another agent is commonly taken to be a measure of the reliability/predictability of the latter's actions. It is based on the trustor's past observations of the behaviour of the trustee and requires no knowledge of the inner-workings of the trustee. However, in situations that are new or unfamiliar, past observations are of little help in assessing trust. In such cases, knowledge about the trustee can help. A particular type of knowledge is that of values - things that are important to the trustor and the trustee. In this paper, based on the premise that the more values two agents share, the more they should trust one another, we propose a simple approach to trust assessment between agents based on values, taking into account if agents trust cautiously or boldly, and if they depend on others in carrying out a task.

Similar to the growth in the number of vehicles in an urban area, the number of aircraft and the passengers they ferry, are in a phase of constant growth. Globally, the number of aircraft is expected to double from the base year of 2015 up to 2035. Since there are only limited number of airports and limited amount of space in each airport, this implies that each aircraft movement on the ground needs to be efficiently handled for faster turnaround. Faced with the pressure of managing multiple cost heads, airlines are now outsourcing their airport ground handling and cargo management services to specialist companies, and focusing on their core competence. While growth trends in passenger volumes tend to follow macroeconomic fundamentals, the growth in aircraft turnarounds are more immune to such highs and lows.

Emirates NBD will also utilize AWS data analytics, Internet of Things (IoT), Natural Language Processing (NLP), and other advanced technologies as part of its ongoing efforts to better engage with customers and simplify banking. A front-runner in retail banking innovation, Emirates NBD is working with AWS because of its broad and deep portfolio of cloud services and the increased security and control Emirates NBD can achieve in the cloud, and is continuing to invest in AWS as its preferred provider for machine learning workloads. With AWS, Emirates NBD will take further advantage of AWS artificial intelligence and machine learning services including Amazon SageMaker, a fully managed machine learning service for building, training, and deploying machine learning models to provide relevant real-time banking experiences. To create a more rewarding and customer-centric banking experience, Emirates NBD is also leveraging Amazon Personalize, an AWS machine learning service that enables the development of individualized recommendations to launch new personalized retail banking applications. One of the first of these applications is a personal finance manager that uses an automated, self-learning system to deliver a highly personalized banking experience to customers in order to predict what each individual customer needs and match this with the most appropriate solution. To support this work, Emirates NBD is using Amazon Polly, a cloud service that uses advanced deep learning technologies to convert written content into human-like speech, in its automated call center to further enhance customer interactions by delivering lifelike voice banking experiences.

The Commonwealth Bank of Australia (CBA) has announced a trial of a redesigned banking app that it says has been backed by "world-leading" machine learning, data analytics, and behavioural science. The bank said its app boasts 5.3 million unique users and more than 6.5 million log-ons per day. It expects the redesign will provide the "first completely personalised and smart digital banking experience in Australia, backed by world-leading application of machine learning technology". According to chief digital officer Pete Steel, CBA has a "unique ability to use technology and innovation capabilities to support good financial habits". "We're using a combination of cutting edge machine learning technology, data analytics, and behavioural science to develop smart banking features to create a highly personalised digital banking experience," he said.

We propose a set of compositional design patterns to describe a large variety of systems that combine statistical techniques from machine learning with symbolic techniques from knowledge representation. As in other areas of computer science (knowledge engineering, software engineering, ontology engineering, process mining and others), such design patterns help to systematize the literature, clarify which combinations of techniques serve which purposes, and encourage re-use of software components. We have validated our set of compositional design patterns against a large body of recent literature.

Music has the power of inducing emotions, and human beings exploit such a phenomenon in order to empower a variety of mental states and activities, both positively and negatively. The study of emotions and music has a long and still vibrant tradition. New findings and changes of perspective in the field are not uncommon. More recent is the field investigating music emotion recognition through computational means. Music emotion recognition (MER) is an emerging and cross-disciplinary field spanning information retrieval (audio, symbolic and metadata) and machine learning, on a strong backing of music cognition (semiology of music and psychology) and music theory.