Distributing malware by attaching tainted documents to emails is one of the oldest tricks in the book. It's not just a theoretical risk--real attackers use malicious documents to infect targets all the time. So on top of its anti-spam and anti-phishing efforts, Gmail expanded its malware detection capabilities at the end of last year to include more tailored document monitoring. At the RSA security conference in San Francisco on Tuesday, Google's security and anti-abuse research lead Elie Bursztein will present findings on how the new deep-learning scanner for documents is faring against the 300 billion attachments it has to process each week. It's challenging to tell the difference between legitimate documents in all their infinite variations and those that have specifically been manipulated to conceal something dangerous.

Deep learning is a form of machine learning that models patterns in data as complex, multi-layered networks. Because deep learning is the most general way to model a problem, it has the potential to solve difficult problems--such as computer vision and natural language processing--that outstrip both conventional programming and other machine learning techniques. Deep learning not only can produce useful results where other methods fail, but also can build more accurate models than other methods, and can reduce the time needed to build a useful model. However, training deep learning models requires a great deal of computing power. Another drawback to deep learning is the difficulty of interpreting deep learning models.

To not question things is to agree to stay in the same place. Often during that process, your mind can go to wrong directions. But still, you can learn a lot, during the exploration of uncharted territories of the human potential. Excuse me that I will move away from the main topic for a moment, but first I want to share something. More than 10 years ago I started to learn in a hard way what is the power of continuous effort.

There is no shortage of machine learning algorithms so why should a data scientist gravitate towards deep learning algorithms? What do neural networks offer that traditional machine learning algorithms don't? Another common question I see floating around -- neural networks require a ton of computing power, so is it really worth using them? While that question is laced with nuance, here's the short answer -- yes! The different types of neural networks in deep learning, such as convolutional neural networks (CNN), recurrent neural networks (RNN), artificial neural networks (ANN), etc. are changing the way we interact with the world.

There are many steps in a common machine learning pipeline and much thought that goes into architecting it. There is the problem definition, data acquisition, error detection and data cleaning, etc. In this story, we begin with the assumption that we have a clean and ready to go dataset. With that in mind, we outline the four steps necessary before fitting any machine learning model. We then implement those steps in Pytorch, using a common syntax for invoking multiple method calls; method chaining.

Let's face it, people are spending more time on social media than ever before. In 2018, around 2.65 billion people were using social media worldwide, a number thought to increase to 3.1 billion by 2021. Mosts posts are harmless depictions of life, like status updates, pictures of friends or food, the occasional meme, etc. etc. We give these posts a like, maybe a comment, then keep on scrolling. But sometimes people post about more personal topics and show signs that things in their life are not going well.

In this workshop, our expert guest speaker will show you how to leverage industry-grade image detection models to solve your computer vision problems. Discover how to extract features of your images for traditional machine learning algorithms like random forest or SVM and also how to fine-tune deep learning models like VGG16 or AlexNet. We'll go over a particularly interesting data set for classifying birds inspired by the presenter's recent competition & show you step by step how he approached the problem and repurposed the model. We will be using PyTorch on Google Colab - giving us the power of GPU training without any installation required.

The soccer bot lines up to take a shot at the goal. But instead of getting ready to block it, the goalkeeper drops to ground and wiggles its legs. Confused, the striker does a weird little sideways dance, stamping its feet and waving one arm, and then falls over. It's not a tactic you'll see used by the pros, but it shows that an artificial intelligence trained via deep reinforcement learning--the technique behind cutting-edge game-playing AIs like AlphaZero and the OpenAI Five--is more vulnerable to attack than previously thought. And that could have serious consequences.

Artificial Intelligence (AI) is becoming ubiquitous in both science and industry due to deep learning's superiority in very specific tasks such as image classification¹. Considering this juxtaposition, can we trust deep learning as a scientific tool to explain reality, and in turn exploit these discoveries within industry? Arguably no, as we are blind-sided by hidden risks and explanations that deep learning cannot provide, especially when making predictions beyond training examples. Being able to interpret a prediction, and then explain the problem, is fundamental for the general performance of AI and absolutely crucial for safety, reliability, and fairness². Thus, in order to advance AI we must also advance interpretations of learning.

Adaptive Vision has acquired an online dataset annotation platform, Zillin.io, to further its deep learning image processing offering. The product was developed by QZ Solutions, based in Opole, Poland. The addition of Zillin.io is an important extension of Adaptive Vision's existing portfolio of deep learning products. Zillin will complement the Adaptive Vision deep learning add-on, a set of five ready-made tools for industrial image analysis, along with the Weaver product, an inference engine for anyone's neural networks designed for processing images at high speed. By providing a comprehensive range of products, Adaptive Vision opens itself to wider cooperation with solutions providers who invest in modern technologies and require tools that make their developments more effective, faster and reliable.