On July 31, 2019, DeepMind published a research letter in Nature (via AI in Healthcare) about a clinically applicable approach to the continuous prediction of acute kidney injury (AKI). The predictive model was developed on a longitudinal dataset comprised of 703,782 adult patients across 172 inpatient and 1,062 outpatient sites. It "predicted more than half (55.8%) of all inpatient episodes of acute kidney injury and 90.2% of all acute kidney injuries that required dialysis. The lead-up time was 48 hours, and the model had a ratio of two false alerts for every true alert." TechCrunch reported on the study in an article titled "DeepMind touts predictive health care AI'breakthrough' trained on heavily skewed data."

This paper proposes a fully convolutional architecture to address the problem of estimating the depth map of a scene given an RGB image. Modeling of the ambiguous mapping between monocular images and depth maps is done via residual learning. The reverse Huber loss is used for optimization. The model runs in real-time on images or videos. The approach proposed in this paper uses a CNN for depth estimation.

When it comes to weather events that may affect operations, today's enterprises have great insights into the future -- thanks to satellites and advanced forecasting systems that continue to advance technologically. The same holds true for sales and revenue forecasting, as companies leverage sophisticated predictive analytics to gain a clearer view of their financial future. Now, enterprises are taking their predictive capabilities to new heights, thanks to the power of artificial intelligence applications driven by high performance computing systems. This new breed of predictive applications is a cornerstone to making better business decisions, keeping systems and equipment in top shape, understanding the movement of markets and much more. In many cases, these forward-looking applications are both predictive and prescriptive, meaning they tell you what's likely to happen and recommend steps you can take to address emerging issues and influence outcomes.

Machine learning is fundamentally different from all the tech that came before it in one major way: As time passes, it gets smarter. It's the opposite of a static solution because it actually provides more value with age. Unsurprisingly, companies in all industries have been eager to embrace machine learning in recent years, and that shows no signs of slowing. We've already seen machine learning applications enter the mainstream via web search algorithms and product recommendations. Transformative though they may be, they're just the inaugural applications.

Artificial intelligence has been rapidly gaining attention for this decade. In the current trends, artificial intelligence mainly means machine learning techniques including neural networks, i.e., deep learning, and probabilistic generative models, i.e., Bayesian models. Robotics is clearly the representative of the targets of the application of artificial intelligence. Robots need to deal with uncertainty in the real world and learn knowledge from the daily environment including human users. Machine learning methods enable robots adapt to the real-world environment by dealing with uncertainty in a statistical manner.

Microsoft is nowadays one of the major providers for AI powered cloud services. In fact, according to a RightScale's survey carried out in 2018, Microsoft Azure Cloud services are currently second just to Amazon AWS (Figure 1). In this article, I will be considering Microsoft as case study as Microsoft CEO Satya Nadella recently shared Microsoft interest to make AI a vital part of their business [1]. I will now introduce you to some of the different Microsoft tools which are currently available and some alternatives provided by the completion. Finally, we will focus on what are going to be next steps in research.

I'm going to lead with the punchline on this one. I believe that LPI should invest in providing a certification path for some kind of machine learning, specifically geared to open source development in artificial intelligence. Whatever you may think about automation and artificial intelligence from the perspective of what it will eventually mean for humanity, there's no question that some form of artificial intelligence is present in every aspect of our lives. Those of us who own one or more Google Home or Alexa speakers know full well how much AI touches our lives. Smart systems like Google's Assistant are built using TensorFlow ( https://tensorflow.org), an open source programming library that has become a kind of goto set of tools for anyone building machine learning, deep learning, natural language processing (as in your smart speaker), or neural network based applications.

Cities around the world have an increasing number of inhabitants, and when the number of people in an area increases also the production of garbage is increased. This created the dilemma of collecting this garbage. By making this process more efficient the garbage will be on city streets for a shorter duration and thus have less negative effects on the environment. When garbage can be detected in a timely manner this will allow for a more efficient reaction from the local government, which in turn can deploy the correct resources to solve the problem. For example a truck that can pick up bulky waste or enforcers that can enforce when local regulations are broken.

Founded by world-renowned deep learning expert and recipient of the prestigious 2018 Turing Prize Yoshua Bengio, Mila is an epicenter of artificial intelligence and machine learning research, seeking to create a space for innovation in artificial intelligence and technology while fostering strong synergy and knowledge exchange between academic research and innovators in society. As the leading provider of AI-powered automotive assistant technologies to automakers worldwide, Cerence's global research and development organization is more than 700 strong, with a tenured and experienced team dedicated to building human-like, innovative in-car experiences with their foundations in AI-powered speech recognition; natural language understanding and generation; and multi-modal technologies like gaze, gesture and emotion recognition.

Artificial intelligence (AI) and machine learning (ML) are here to stay. Besting humans in complex games such as Go and Poker was just the beginning. Today, companies in fields as diverse as life and medical sciences, telecommunications, energy management, security, and manufacturing are seeing the benefits that artificial intelligence and machine learning can bring. This impact is reflected both in the number of scientific publications in the field (over 1.6 million and counting) and also in the number of patent filings (nearly 340'000 worldwide to date). The pace shows no signs of slowing down: patent filings in deep learning (an area of AI) experienced an average annual growth rate of 175% between 2013 and 2016.