Alphabet/Google is now the most innovative company in the world according to Boston Consulting Group (BCG), unseating Apple's 13-year dominance of their annual rankings. These and many other insights are from the Boston Consulting Group's 13th annual report defining the world's most innovative companies in 2019. The Most Innovative Companies 2019: The Rise of AI, Platforms, and Ecosystems is a fascinating glimpse into the rising importance of artificial intelligence (AI) and of platforms that support innovation. What makes this survey noteworthy is how it captures how AI's use is rapidly expanding and how enterprises are relying on platforms to scale their efforts in this area. BCG is providing an Interactive Guide that compares the 50 most innovative companies in the world, sortable by industry, company and year.

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.

KLM Royal Dutch Airlines is the world's oldest international airline still operating under its original name. On its 100th anniversary, FTE spoke to Daan Debie, Director Engineering & Architecture, KLM Royal Dutch Airlines, who outlined how the airline has embraced innovation through its "pioneering and entrepreneurial spirit". Indeed, KLM's vigorous digital transformation strategy is largely due to recognising and leveraging the advantages of modern technology. Debie, who will speak in the Premium Conference at FTE-APEX Asia EXPO 2019 (12-13 November, Singapore), explains: "Digital transformation does not just mean replacing paper with apps. For us it means getting the right information to the right people at the right time to enable well-informed decision-making in an increasingly complex environment, supported by digital tooling. "Key to this is to be truly data-driven, working from a single-source-of-truth and applying cutting-edge technology and algorithms to make sense of the complex operations." KLM is currently investing heavily in building automated decision-making tools to improve operations. In June last year, the airline embarked on a unique partnership with Boston Consulting Group (BCG) which has the potential to "revolutionise global airline operations". The project is a result of a close collaboration between KLM Operations Decision Support and Operations frontline teams, BCG's consulting team, and members of BCG Gamma, an artificial intelligence and advanced analytics entity of data scientists, data engineers and software developers, who have developed a solution based on artificial intelligence, machine learning, and advanced optimisation that addresses all elements of the airline operations, while having a positive impact on customer experience and operating costs. With these tools, KLM and other airlines will be able to tackle the most complex decisions pertaining to fleet, crew, ground services and network, with a focus on breaking down the typical silos across these departments. Earlier this year, Brazilian low-cost carrier GOL became the first airline customer of the KLM-BCG joint venture which will help GOL deliver better on-time performance to its customers while maintaining low costs. As Director Engineering & Architecture for the Department of Operations Decision Support (ODS) at KLM, Debie is responsible for creating and maintaining a cohesive overall architecture and technological vision for the products and platforms developed at ODS, but also for other clients within the partnership between KLM and BCG. "I help teams within ODS and BCG/KLM teams at Partnership clients to build their products in accordance with the architectural vision," he explains. "Additionally, I'm responsible for ensuring that we maintain high engineering standards in our development efforts.

Take advantage of our discount rooms at the conference hotel. We are beginning to announce speakers for 2020. Want to join us as a speaker? Check out our proposals page. Jesse Anderson is a data engineer, creative engineer, and managing director of the Big Data Institute. He works with companies ranging from startups to Fortune 100 companies on Big Data. This includes training on cutting edge technologies like Apache Kafka, Apache Hadoop and Apache Spark. He has taught over 30,000 people the skills to become data engineers.

The Poisson equation is commonly encountered in engineering, including in computational fluid dynamics where it is needed to compute corrections to the pressure field. We propose a novel fully convolutional neural network (CNN) architecture to infer the solution of the Poisson equation on a 2D Cartesian grid of varying size and spacing given the right hand side term, arbitrary Dirichlet boundary conditions and grid parameters which provides unprecendented versatility in this application. The boundary conditions are handled using a novel approach by decomposing the original Poisson problem into a homogeneous Poisson problem plus four inhomogeneous Laplace sub-problems. The model is trained using a novel loss function approximating the continuous $L^p$ norm between the prediction and the target. Analytical test cases indicate that our CNN architecture is capable of predicting the correct solution of a Poisson problem with mean percentage errors of 15% and promises improvements in wall-clock runtimes for large problems. Furthermore, even when predicting on meshes denser than previously encountered, our model demonstrates encouraging capacity to reproduce the correct solution profile.

Artificial neural networks have exceeded human-level performance in accomplishing several individual tasks (e.g. voice recognition, object recognition, and video games). However, such success remains modest compared to human intelligence that can learn and perform an unlimited number of tasks. Humans' ability of learning and accumulating knowledge over their lifetime is an essential aspect of their intelligence. Continual machine learning aims at a higher level of machine intelligence through providing the artificial agents with the ability to learn online from a non-stationary and never-ending stream of data. A key component of such a never-ending learning process is to overcome the catastrophic forgetting of previously seen data, a problem that neural networks are well known to suffer from. The work described in this thesis has been dedicated to the investigation of continual learning and solutions to mitigate the forgetting phenomena in neural networks. To approach the continual learning problem, we first assume a task incremental setting where tasks are received one at a time and data from previous tasks are not stored. Since the task incremental setting can't be assumed in all continual learning scenarios, we also study the more general online continual setting. We consider an infinite stream of data drawn from a non-stationary distribution with a supervisory or self-supervisory training signal. The proposed methods in this thesis have tackled important aspects of continual learning. They were evaluated on different benchmarks and over various learning sequences. Advances in the state of the art of continual learning have been shown and challenges for bringing continual learning into application were critically identified.

In wondering what can be done to steer civilization away from the abyss, I confess to being increasingly puzzled by the central enigma of contemporary cognitive psychology: To what degree are we consciously capable of changing our minds? I don't mean changing our minds as to who is the best NFL quarterback, but changing our convictions about major personal and social issues that should unite but invariably divide us. As a senior neurologist whose career began before CAT and MRI scans, I have come to feel that conscious reasoning, the commonly believed remedy for our social ills, is an illusion, an epiphenomenon supported by age-old mythology rather than convincing scientific evidence. If so, it's time for us to consider alternate ways of thinking about thinking that are more consistent with what little we do understand about brain function. I'm no apologist for artificial intelligence, but if we are going to solve the world's greatest problems, there are several major advantages in abandoning the notion of conscious reason in favor of seeing humans as having an AI-like "black-box" intelligence. To believe that we can accurately determine whether or not consciousness contains causal properties is sheer folly. But first, a brief overview as to why I feel so strongly that purely conscious thought isn't physiologically likely.

The key question on the mind of policymakers now is whether Artificial Intelligence would be able to deliver on its promises instead of entering another season of scepticism and stagnation. The quest for Artificial Intelligence (AI) has travelled through multiple "seasons of hope and despair" since the 1950s. The introduction of neural networks and deep learning in late 1990s has generated a new wave of interest in AI and growing optimism in the possibility of applying it to a wide range of activities, including diplomacy. The key question on the mind of policymakers now is whether AI would be able to deliver on its promises instead of entering another season of scepticism and stagnation. This paper evaluates the potential of IA to provide reliable assistance in areas of diplomatic interest such as in consular services, crisis management, public diplomacy and international negotiations, as well as the ratio between costs and contributions of AI applications to diplomatic work.

Quantizing large Neural Networks (NN) while maintaining the performance is highly desirable for resource-limited devices due to reduced memory and time complexity. NN quantization is usually formulated as a constrained optimization problem and optimized via a modified version of gradient descent. In this work, by interpreting the continuous parameters (unconstrained) as the dual of the quantized ones, we introduce a Mirror Descent (MD) framework (Bubeck (2015)) for NN quantization. Specifically, we provide conditions on the projections (i.e., mapping from continuous to quantized ones) which would enable us to derive valid mirror maps and in turn the respective MD updates. Furthermore, we discuss a numerically stable implementation of MD by storing an additional set of auxiliary dual variables (continuous). This update is strikingly analogous to the popular Straight Through Estimator (STE) based method which is typically viewed as a "trick" to avoid vanishing gradients issue but here we show that it is an implementation method for MD for certain projections. Our experiments on standard classification datasets (CIFAR-10/100, TinyImageNet) with convolutional and residual architectures show that our MD variants obtain fully-quantized networks with accuracies very close to the floating-point networks.

Observations of traffic participants and their environment enable humans to drive road vehicles safely. However, when being driven, there is a notable difference between having a non-experienced vs. an experienced driver. One may get the feeling, that the latter one anticipates what may happen in the next few moments and considers these foresights in his driving behavior. To make the driving style of automated vehicles comparable to a human driver in the sense of comfort and perceived safety, the aforementioned anticipation skills need to become a built-in feature of self-driving vehicles. This article provides a systematic comparison of methods and strategies to generate this intention for self-driving cars using machine learning techniques. To implement and test these algorithms we use a large data set collected over more than 30000 km of highway driving and containing approximately 40000 real world driving situations. Moreover, we show that it is possible to certainly detect more than 47 % of all lane changes on German highways 3 or more seconds in advance with a false positive rate of less than 1 %. This enables us to predict the lateral position with a prediction horizon of 5 s with a median error of less than 0.21 m.