Chatbots can order flowers for you on Facebook. An intelligent assistant can schedule a meeting. Now, a new update for your iPhone will be packed with new automations to make our lives easier and maybe even reduce stress, and it won't cost you a cent when it debuts this fall (unless you need to get a new iPhone). This week at a developer conference in California, Apple announced iOS 10 and focused mostly on how to make your phone "think differently" by thinking for you and saving time. There are some brilliant new updates, but here are the ones that impressed me the most and offer the most automation.

In the future, or so some people think, it will become possible to upload your consciousness into a computer. Software emulations of human brains – ems, for short – will then take over the economy and world. This sort of thing happens quite a lot in science fiction, but The Age of Em is a fanatically serious attempt, by an economist and scholar at Oxford's Future of Humanity Institute, to use economic and social science to forecast in fine detail how this world (if it is even possible) will actually work. The future it portrays is very strange and, in the end, quite horrific for everyone involved. It is an eschatological vision worthy of Hieronymus Bosch.

Everyone seemingly is complaining about the spread of ransomware, and now somebody is trying to do something about it using machine learning-based behavioral analytics techniques to track suspicious behavior on company networks. As the scale of the ransomware threat grows, including ransom payments by hospitals and universities and growing fears that it will soon spread to other sectors, a Silicon Valley security intelligence firm has rolled out an approach for detecting ransomware via machine learning. Exabeam, a specialist in user and "entity" behavior analytics based in San Mateo, Calif., unveiled its analytics approach to detecting ransomware attacks during a security conference this week. The early warning system is touted as being able to spot ransomware activity on corporate networks without relying on third-party security controls. The platform also can spot suspicious activity within cloud services, servers and, increasingly, personal devices connected to corporate and other enterprise IT infrastructure.

In this post you will discover how you can check-point your deep learning models during training in Python using the Keras library. When training deep learning models, the checkpoint is the weights of the model. Checkpointing is setup to save the network weights only when there is an improvement in classification accuracy on the validation dataset (monitor'val_acc' and mode'max'). In this post you have discovered the importance of checkpointing deep learning models for long training runs.

Deep neural networks and Deep Learning are powerful and popular algorithms. And a lot of their success lays in the careful design of the neural network architecture. I wanted to revisit the history of neural network design in the last few years and in the context of Deep Learning. It is the year 1994, and this is one of the very first convolutional neural networks, and what propelled the field of Deep Learning. This pioneering work by Yann LeCun was named LeNet5 after many previous successful iterations since they year 1988! The LeNet5 architecture was fundamental, in particular the insight that image features are distributed across the entire image, and convolutions with learnable parameters are an effective way to extract similar features at multiple location with few parameters. At the time there was no GPU to help training, and even CPUs were slow.

Deep learning models can take hours, days or even weeks to train. If the run is stopped unexpectedly, you can lose a lot of work. In this post you will discover how you can check-point your deep learning models during training in Python using the Keras library. How to Check-Point Deep Learning Models in Keras Photo by saragoldsmith, some rights reserved. Application checkpointing is a fault tolerance technique for long running processes.

The Department of Physics is looking to recruit a Research Associate in Computational Modelling and Materials Data Generation, Curation and Inference. The work will involve data conversion, selection and compression, to develop "Big Data Analytics" protocols for structure and property prediction via machine learning algorithms. This post will be Fixed Term 3 months. This is a Full-time – 100 % full time equivalent. The salary will be paid at Grade 6, 32,600 to 38,896 per annum, plus 2,323 per annum London Allowance.

Apple is stepping up its artificial intelligence efforts in a bid to keep pace with rivals who have been driving full-throttle down a machine learning-powered AI superhighway, thanks to their liberal attitude to mining user data. Not so Apple, which pitches itself as the lone defender of user privacy in a sea of data-hungry companies. While other data vampires slurp up location information, keyboard behavior and search queries, Apple has turned up its nose at users' information. The company consistently rolls out hardware solutions that make it more difficult for Apple (and hackers, governments and identity thieves) to access your data and has traditionally limited data analysis so it all occurs on the device instead of on Apple's servers. But there are a few sticking points in iOS where Apple needs to know what its users are doing in order to finesse its features, and that presents a problem for a company that puts privacy first.

In the age-old tech struggle between open and controlled systems, Apple has realized that when it comes to artificial intelligence, it needs to edge toward open. The computer giant has announced it will be opening up its digital assistant Siri to third-party apps and at the same time has put out an API to its artificial intelligence technology. Realistically, the company has been given little choice: Amazon's Alexa has taken off, in large part due to it opening up to other companies, and Google's artificial intelligence systems have streaked ahead of Apple and Siri because when it comes to such a complex and wide-ranging interplay of information and action, broader is better. Where Siri was once a wonder – it worked where other systems didn't – it risks becoming an also-ran, with only Apple fanbois crowding round it in excitement at the latest nerd joke. Not that Apple is taking the news well.

Bayesian models are rooted in Bayesian statistics, and easily benefit from the vast literature in the field. In contrast, deep learning lacks a solid mathematical grounding. Instead, empirical developments in deep learning are often justified by metaphors, evading the unexplained principles at play. These two fields are perceived as fairly antipodal to each other in their respective communities. It is perhaps astonishing then that most modern deep learning models can be cast as performing approximate inference in a Bayesian setting. The implications of this statement are profound: we can use the rich Bayesian statistics literature with deep learning models, explain away many of the curiosities with these, combine results from deep learning into Bayesian modelling, and much more.