The ambitious plan will be an economic bonanza for the country's technology firms, as the area defined as core AI is expected to be valued at 150 billion yuan by 2020, while AI-related fields are valued at 1 trillion yuan, according to the government's forecast. By 2025, those values will exceed 400 billion yuan and 5 trillion yuan (US$739 billion) respectively. Up to 26 per cent of China's gross domestic products (GDP) could be generated by AI-related industries by 2030, making the country the world's biggest winner from investing in the field, according to a report last month by PricewaterhouseCoopers. Chinese internet companies led by Alibaba Group Holding, Baidu and Tencent Holdings have been investing heavily in AI applications.

The ambitious plan will be an economic bonanza for the country's technology firms, as the area defined as core AI is expected to be valued at 150 billion yuan by 2020, while AI-related fields are valued at 1 trillion yuan, according to the government's forecast. By 2025, those values will exceed 400 billion yuan and 5 trillion yuan (US$739 billion) respectively. Up to 26 per cent of China's gross domestic products (GDP) could be generated by AI-related industries by 2030, making the country the world's biggest winner from investing in the field, according to a report last month by PricewaterhouseCoopers. Chinese internet companies led by Alibaba Group Holding, Baidu and Tencent Holdings have been investing heavily in AI applications.

The ambitious plan will be an economic bonanza for the country's technology firms, as the area defined as core AI is expected to be valued at 150 billion yuan by 2020, while AI-related fields are valued at 1 trillion yuan, according to the government's forecast. By 2025, those values will exceed 400 billion yuan and 5 trillion yuan (US$739 billion) respectively. Up to 26 per cent of China's gross domestic products (GDP) could be generated by AI-related industries by 2030, making the country the world's biggest winner from investing in the field, according to a report last month by PricewaterhouseCoopers. Chinese technology companies including Alibaba Group Holdings, Baidu Inc and Tencent Holdings have been investing heavily into AI applications.

We provide two main contributions in PAC-Bayesian theory for domain adaptation where the objective is to learn, from a source distribution, a well-performing majority vote on a different, but related, target distribution. Firstly, we propose an improvement of the previous approach we proposed in Germain et al. (2013), which relies on a novel distribution pseudodistance based on a disagreement averaging, allowing us to derive a new tighter domain adaptation bound for the target risk. While this bound stands in the spirit of common domain adaptation works, we derive a second bound (recently introduced in Germain et al., 2016) that brings a new perspective on domain adaptation by deriving an upper bound on the target risk where the distributions' divergence--expressed as a ratio-- controls the tradeoff between a source error measure and the target voters' disagreement. We discuss and compare both results, from which we obtain PAC-Bayesian generalization bounds. Furthermore, from the PAC-Bayesian specialization to linear classifiers, we infer two learning algorithms, and we evaluate them on real data.

"How are you doing today?" "What's going on in your world right now?" "How do you feel?" These might seem like simple questions a caring friend would ask. However, in the present day of mental health care, they can also be the start of a conversation with your virtual therapist. Innovative technology is offering new opportunities to millions of Americans affected by different mental health conditions. Advancements in artificial intelligence (AI) are bringing psychotherapy to more people who need it.

It's been eight weeks since we announced our upcoming service MachineLabs to the world. We've opened the gates to apply for the private beta which is just around the corner. Today we would like to reflect on our recent progress and the road ahead. We are super happy to unveil that we just founded MachineLabs, Inc. After we've implemented a first prototype of MachineLabs, we knew we are onto something and decided pretty quickly after that, to bootstrap a new company as soon as possible. Here's our brand new company logo, carefully crafted with 3 by Judith Kutscheid.

Variational inference methods often focus on the problem of efficient model optimization, with little emphasis on the choice of the approximating posterior. In this paper, we review and implement the various methods that enable us to develop a rich family of approximating posteriors. We show that one particular method employing transformations on distributions results in developing very rich and complex posterior approximation. We analyze its performance on the MNIST dataset by implementing with a Variational Autoencoder and demonstrate its effectiveness in learning better posterior distributions.

The world of Artificial Intelligence (AI) is growing at an unprecedented rate. This report provides a broad look at how enterprises leverage AI in meaningful ways. This report includes data from Gigaom's recent AI survey, insights from our recent AI Conference, and personal experience working with corporate enterprises on their AI journey.

Despite being originally inspired by the central nervous system, artificial neural networks have diverged from their biological archetypes as they have been remodeled to fit particular tasks. In this paper, we review several possibilites to reverse map these architectures to biologically more realistic spiking networks with the aim of emulating them on fast, low-power neuromorphic hardware. Since many of these devices employ analog components, which cannot be perfectly controlled, finding ways to compensate for the resulting effects represents a key challenge. Here, we discuss three different strategies to address this problem: the addition of auxiliary network components for stabilizing activity, the utilization of inherently robust architectures and a training method for hardware-emulated networks that functions without perfect knowledge of the system's dynamics and parameters. For all three scenarios, we corroborate our theoretical considerations with experimental results on accelerated analog neuromorphic platforms.

Automation and computer intelligence to support complex human decisions becomes essential to manage large and distributed systems in the Cloud and IoT era. Understanding the root cause of an observed symptom in a complex system has been a major problem for decades. As industry dives into the IoT world and the amount of data generated per year grows at an amazing speed, an important question is how to find appropriate mechanisms to determine root causes that can handle huge amounts of data or may provide valuable feedback in real-time. While many survey papers aim at summarizing the landscape of techniques for modelling system behavior and infering the root cause of a problem based in the resulting models, none of those focuses on analyzing how the different techniques in the literature fit growing requirements in terms of performance and scalability. In this survey, we provide a review of root-cause analysis, focusing on these particular aspects. We also provide guidance to choose the best root-cause analysis strategy depending on the requirements of a particular system and application.