Recent research in AI is focusing towards generating narrative stories about visual scenes. It has the potential to achieve more human-like understanding than just basic description generation of images- in-sequence. In this work, we propose a solution for generating stories for images-in-sequence that is based on the Sequence to Sequence model. As a novelty, our encoder model is composed of two separate encoders, one that models the behaviour of the image sequence and other that models the sentence-story generated for the previous image in the sequence of images. By using the image sequence encoder we capture the temporal dependencies between the image sequence and the sentence-story and by using the previous sentence-story encoder we achieve a better story flow. Our solution generates long human-like stories that not only describe the visual context of the image sequence but also contains narrative and evaluative language. The obtained results were confirmed by manual human evaluation.

Malicious scripts are an important computer infection threat vector. Our analysis reveals that the two most prevalent types of malicious scripts include JavaScript and VBScript. The percentage of detected JavaScript attacks are on the rise. To address these threats, we investigate two deep recurrent models, LaMP (LSTM and Max Pooling) and CPoLS (Convoluted Partitioning of Long Sequences), which process JavaScript and VBScript as byte sequences. Lower layers capture the sequential nature of these byte sequences while higher layers classify the resulting embedding as malicious or benign. Unlike previously proposed solutions, our models are trained in an end-to-end fashion allowing discriminative training even for the sequential processing layers. Evaluating these models on a large corpus of 296,274 JavaScript files indicates that the best performing LaMP model has a 65.9% true positive rate (TPR) at a false positive rate (FPR) of 1.0%. Similarly, the best CPoLS model has a TPR of 45.3% at an FPR of 1.0%. LaMP and CPoLS yield a TPR of 69.3% and 67.9%, respectively, at an FPR of 1.0% on a collection of 240,504 VBScript files.

Like the course I just released on Hidden Markov Models, Recurrent Neural Networks are all about learning sequences - but whereas Markov Models are limited by the Markov assumption, Recurrent Neural Networks are not - and as a result, they are more expressive, and more powerful than anything we've seen on tasks that we haven't made progress on in decades. So what's going to be in this course and how will it build on the previous neural network courses and Hidden Markov Models? In the first section of the course we are going to add the concept of time to our neural networks. I'll introduce you to the Simple Recurrent Unit, also known as the Elman unit. We are going to revisit the XOR problem, but we're going to extend it so that it becomes the parity problem - you'll see that regular feedforward neural networks will have trouble solving this problem but recurrent networks will work because the key is to treat the input as a sequence.

Today's blog post is inspired by PyImageSearch reader Ezekiel, who emailed me last week and asked: I went through your previous blog post on deep learning object detection along with the followup tutorial for real-time deep learning object detection. I've been using your source code in my example projects but I'm having two issues: I would really appreciate it if you could cover this in a blog post. In fact, if you go through the comments section of my two most recent posts on deep learning object detection (linked above), you'll find that one of the most common questions is typically (paraphrased): How do I modify your source code to include my own object classes? Since this appears to be such a common question, and ultimately a misunderstanding on how neural networks/deep learning object detectors actually work, I decided to revisit the topic of deep learning object detection in today's blog post. To learn more about deep learning object detections, and perhaps even debunk a few misconceptions or misunderstandings you may have with deep learning-based object detection, just keep reading. Today's blog post is meant to be a gentle introduction to deep learning-based object detection. I've done my best to provide a review of the components of deep learning object detectors, including OpenCV Python source code to perform deep learning using a pre-trained object detector.

This interactive data visualization shows the potential value created by artificial intelligence and other analytics techniques for 19 industries and nine business functions. This data visualization shows the potential business applications and economic value for a range of analytics and artificial-intelligence (AI) techniques. It is based on a study of more than 400 use cases, covering 19 industries and nine business functions. The use cases are taken from a variety of sources, including thousands of engagements by McKinsey Analytics with clients around the globe. The data incorporate real examples of companies and public-sector organizations using a range of techniques, as well as some more theoretical applications where we assumed that use of analytics in one sector could be applied in another.

Amazon SageMaker, a machine learning development and deployment service introduced at re:Invent 2017, cleverly sidesteps the eternal debate about the "best" machine learning and deep learning frameworks by supporting all of them at some level. While AWS has publicly supported Apache MXNet, its business is selling you cloud services, not telling you how to do your job. SageMaker, as shown in the screenshot below, lets you create Jupyter notebook VM instances in which you can write code and run it interactively, initially for cleaning and transforming (feature engineering) your data. Once the data is prepared, notebook code can spawn training jobs in other instances, and create trained models that can be used for prediction. SageMaker also sidesteps the need to have massive GPU resources constantly attached to your development notebook environment by letting you specify the number and type of VM instances needed for each training and inference job.

"Doctors are already inundated with alerts and demands on their attention -- could models help physicians with tedious, administrative tasks so they can better focus on the patient in front of them or ones that need extra attention? Can we help patients get high-quality care no matter where they seek it? We look forward to collaborating with doctors and patients to figure out the answers to these questions and more," Rajkomar and Oren concluded.

If you have to walk a different route to the shops, it's normally not too much of a stretch to consult our'inner satnav' and chart a new course. That's because the human brain has a range of built-in mechanisms that help you find your way. But the underlying brain computation that goes into even simple navigation, such as planning the most direct route between points A and B, remains pretty murky. A team from Google DeepMind and University College London in the United Kingdom have trained a form of artificial intelligence to traverse a virtual environment from one point to another. The computer program, described in the journal Nature today, developed "neurons" similar to "grid cells", which are the brain cells found in mammals that bestow navigation skills.

The co-founder of Google's DeepMind has slammed self-driving cars for not being safe enough, saying current early tests on public roads are irresponsible. Demis Hassabis has urged developers to be cautious with the new technology, saying it is difficult to prove systems are safe before putting them on public roads. The issue of AI in self-driving cars has flared up this year following the death of a women hit but a self-driving Uber in March. The accident was the first time a pedestrian was killed on a public road by an autonomous car, which had previously been praised as the safer alternative to a traditional car. Speaking at the Royal Society in London, Dr Hassabis said current driverless car programmes could be putting people's lives in danger.

'It's an art and a science,' explains Joshua Staker, a senior scientist at the US software company Schrodinger. He's referring to deep learning – a branch of computer science that looks set to transform how chemists screen molecules and explore chemical behaviour. Over the past few decades, deep learning has entered the public consciousness through projects such as AlphaGo. A landmark in computing, Google's algorithm is able to autonomously learn and play the board game Go – 1050 times more complex than chess – a challenge once thought to be beyond computers. AlphaGo first defeated a human opponent in 2015, and beat the world number 1 in 2017.