Deepmind, the artificial intelligence company owned by Google, has posted a huge increase in loss for the full year as its debt level reached more than £1bn. The London-headquartered company reported a loss of £470m in 2018, compared to £302m the previous year, according to its latest accounts. While Deepmind almost doubled its revenue over the year to £103m, this was offset by a near doubling of staff costs to £400m. Deepmind also said it has a debt pile of £1.04bn due for repayment this year, £883m of which is owed to Google's parent company Alphabet. A Deepmind spokesperson said: "Our DeepMind for Google team continues to make great strides bringing our expertise and knowledge to real-world challenges at Google scale, nearly doubling revenues in the past year.

In contrast, after developing an experimental deep learning (neural-network) model using TensorFlow via Cloud Machine Learning Engine, the team achieved 78% accuracy in its predictions. This improvement could give AXA a significant advantage for optimizing insurance cost and pricing, in addition to the possibility of creating new insurance services such as real-time pricing at point of sale. AXA is still at the early stages with this approach -- architecting neural nets to make them transparent and easy to debug will take further development -- but it's a great demonstration of the promise of leveraging these breakthroughs.

Is the Rectified Adam (RAdam) optimizer actually better than the standard Adam optimizer? According to my 24 experiments, the answer is no, typically not (but there are cases where you do want to use it instead of Adam). In Liu et al.'s 2018 paper, On the Variance of the Adaptive Learning Rate and Beyond, the authors claim that Rectified Adam can obtain: The authors tested their hypothesis on three different datasets, including one NLP dataset and two computer vision datasets (ImageNet and CIFAR-10). In each case Rectified Adam outperformed standard Adam…but failed to outperform standard Stochastic Gradient Descent (SGD)! The Rectified Adam optimizer has some strong theoretical justifications -- but as a deep learning practitioner, you need more than just theory -- you need to see empirical results applied to a variety of datasets. And perhaps more importantly, you need to obtain a mastery level experience operating/driving the optimizer (or a small subset of optimizers) as well. If you haven't yet, go ahead and read part one to ensure you have a good understanding of how the Rectified Adam optimizer works. From there, read today's post to help you understand how to design, code, and run experiments used to compare deep learning optimizers. To learn how to compare Rectified Adam to standard Adam, just keep reading! In the first part of this tutorial, we'll briefly discuss the Rectified Adam optimizer, including how it works and why it's interesting to us as deep learning practitioners.

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One of the arguments that is regularly used in favor of machine learning systems is the fact that they can arrive to decisions without being vulnerable to human subjectivity. However, that argument is only partially true. While machine learning systems don't make decisions based on feelings or emotions, they do inherit a lot of human biases via the training datasets. Bias is relevant because it leads to unfairness. In the last few years, there has been a lot of progress developing techniques that can mitigate the impact of bias and improve the fairness of machine learning systems.

KT Corporation and Portugal-based WeDo Technologies have signed a Cooperation Agreement for AI-FMS (Artificial Intelligence based Fraud Management System) development and sales. KT's Deep Learning-based Artificial Intelligence (AI) module has been implemented and tested on WeDo's RAID FMS system. This AI module, trained with KT Big Data, has showed strong results for fraud detection and prevention, and has reportedly proved to be effective for a number of fraud use cases, with a high degree of accuracy. KT and WeDo plan to supply the AI-based International Revenue Share Fraud (AI-IRSF) module with the RAID platform to communication service providers (CSPs) by the end of 2019. KT's DL (Deep Learning) based AI module has been implemented and tested on WeDo's RAID FMS system.

Crowd counting is an active area of research and has seen several developments since the advent of deep learning. In this blog, we'll review in brief the Dense and Sparse Crowd Counting Methods and Techniques which can be used in a wide range of applications in industries, hospitals, crowd gathering events, and many more. Crowd counting is a technique to estimate the number of people in an image or a video. Consider the below image and make a wild guess regarding the number of people in it. There are too many people crammed in this picture which makes it a huge task for our brain to accurately predict the right number.

TensorFlow Probability (TFP) is a Python library built on TensorFlow that makes it easy to combine probabilistic models and deep learning on modern hardware (TPU, GPU). It's for data scientists, statisticians, and ML researchers/practitioners who want to encode domain knowledge to understand data and make predictions with uncertainty estimates. In this talk we focus on the "layers" module and demonstrate how TFP "distributions" fit naturally with Keras to enable estimating aleatoric and/or epistemic uncertainty. See the revamped dev site https://www.tensorflow.org/

Roundup Welcome to this week's machine learning musings. Google has upset city officials by trying to improve its facial recognition technology, and the new TensorFlow 2.0 has been released. Google offered $5 gift vouchers to black homeless people and Atlanta city isn't happy: Facial recognition datasets are unfairly dominated with images of white men, so Google hired third-party contractors to go around recording people's faces by offering them vouchers. The temp agency, Randstad, were told to target people of darker skin, and, unfortunately, some of those people were homeless people living on the streets in Atlanta. The methods used to tempt them were ethically dubious.

Today, we stand at the beginning of the AI computing era, ignited by a new computing model, GPU deep learning. This new model--where deep neural networks are trained to recognize patterns from massive amounts of data--has shown to be deeply effective at solving some of the most complex problems in everyday life. In this context, we are looking for a Rendering & Machine Learning Research Intern. You will be working with us on investigating the application of deep- and other machine-learning techniques to physically based rendering--the synthesis of realistic images by simulating the propagation of light through the virtual world. We leverage machine learning to increase the efficiency and quality of rendering algorithms with the ambitious eventual goal of a photorealistic real-time rendering engine.