Mr. Robot writer Kor Adana's in-season schedule seems tough. In addition to some pivotal plot-making in the writers' room, Adana does appearances on the The Verge's Digital After Show, conducts regular post-episode Q&As with The Hollywood Reporter, and famously leads the charge on the show's obsession with technical detail. Each bit of screen real-estate that appears in an episode--URLs, code, IP addresses, etc.--has to pass through Adana and the team. But throughout all of that, one constant Mr. Robot quest stays in the back of the mind. The execution, however, left a lot to be desired.

So I stumbled across a weird effect on mistake when training some CNNs on the MNIST dataset. I had implemented the gradient of the softmax layer incorrectly (I was multiplying it by an additional output * (1 - output)), but the odd thing was that I was getting better testing predictions. So comparing using a gradient on my softmax layer of ex vs ex * ex * (1 - ex), the latter was actually doing a fair bit better on final testing predictions. Which when used basically forces the weights away from classifications that are too accurate, which I imagine does a pretty decent job of preventing overfitting. The first one does train quicker, and does reach much lower error rates, however does a worse job of predictions on the test set.

As hundreds of members of the journalism-tech community descend on Jacksonville for the National Institute for Computer-Assisted Reporting conference, we wanted to share results from a recent community survey. Affectionately referred to as "news nerds," 514 folks who work at the intersection of journalism and technology responded to a survey last year to help us get a better sense of who we are, what the heck we call this work, and what we do. Thank you to everyone who took part in the survey–the data gathered has given us a baseline of understanding about this community, so we can hold ourselves accountable to goals on inclusion, representation, and what it takes to do amazing journalism together. Before we dive into the data, I want to share a little bit about how we got here. Nearly two years ago, Brian Hamman of the New York Times and Scott Klein of ProPublica reached out with an idea to organize a SRCCON session as a way to do a census of this community.

Ms Perkins told BBC Newsnight's Emily Maitlis that while the film maker is now seen as a "repulsive monster", at the time he was "an extremely exciting person to be around".

The experts realized the transformative power of A.I., and robotics, while it also seems to be willing to tackle ethical concerns, legal issues around these new technologies and their consequences on the labor market.

A unique opportunity to see the state-of-the-art in artificial intelligence and machine learning at one of the world's great universities, and meet Oxford's AI experts one-to-one. This event will show you the reality of AI today: what is possible and where the technology is going.

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The purpose of Aboleth is to provide a set of high performance and light weight components for building Bayesian neural nets and approximate (deep) Gaussian process computational graphs (and more). We aim for minimal abstraction over pure TensorFlow, so you can easily assign parts of the computational graph to different hardware, use your own data feeds/queues, and manage your own sessions etc. Have a look at the docs for more information!

Millions of fans all over the world eagerly anticipated this week's release of Star Wars: The Last Jedi, the eighth in the series. At last we will get some answers to questions that have been vexing us since 2015's The Force Awakens. Throughout the franchise, the core characters have been accompanied by a number of much-loved robots, including C-3PO, R2-D2 and more recently, BB-8 and K2-SO. While often fulfilling the role of wise-cracking sidekicks, these and other robots also play an integral role in events. Interestingly, they can also tell us useful things about automation, such as whether it poses dangers to us and whether robots will ever replace human workers entirely.

In the information industry and at Thomson Reuters, AI and machine learning (ML) are already driving innovation and transformation. They are embedded in how we sift through large volumes of data and content, and how we enhance, organize, connect, and deliver content and information. They are the engines underlying many of our products and services. In the long term, our objective is to build personal digital assistants for knowledge workers. Its purpose is not to replace you, but to augment you, to scale you, and to help you focus on more interesting tasks.