This year, I'm honored to be the chair of the Artificial Intelligence and Data Science track at the DataWorks Summit in San Jose. Reviewing the submissions and working with the experienced and sharp committee members has been an education in itself, in particular the chance to see what's trending in the open source world. My day-to-day data science work gives me the chance to dig into a few open source projects, but it's hard to find time to get an overview of which topics and projects are hot and worth exploring more deeply. The key topics emerging this year are deep learning, graph-based machine learning, and model inference in production. Not surprisingly, the topics and tools around deep learning (DL) still top the list of big trends, and top-notch research in math and computation are driving progress across vision, speech and text.

I am into my first term of Udacity's Self Driving Car Nanodegree and I want to share my experiences regarding one of my recent projects. The objective of this project is to basically apply the concepts of Deep Learning and Convolutional Neural Networks to teach the machine to drive car autonomously. How is this even possible? First things first, it is not magic but it really feels like magic. With just a bunch of Python libraries, some lines of Code and huge amount of Data we can teach a car to drive itself.

You will learn how to use Python to analyze data (big data analytics), create beautiful visualizations (data visualization) and use powerful machine learning algorithms. You will specifically get to learn how to use NumPy, Seaborn, Matplotlib, Pandas, Scikit-Learn, Machine Learning, Plotly, Tensorflow and more.

Discover how AI and HPC workload convergence running on an Intel -based HPC infrastructure can accelerate your innovation. It starts with Intel's common HPC reference architecture--a combination of high-performance hardware with optimized software that can simultaneously support analytics, simulation and modeling, machine learning, deep learning and other compute- and data-intensive workloads. An ...HPC infrastructure featuring Intel Xeon Scalable processors and an Intel Omni-Path Architecture (Intel OPA) fabric delivers the highly-scalable compute performance AI and analytics applications need.

The creation and development of a robust neural network is a labor-intensive and time consuming endeavor. That's why a team of IBM researchers recently developed a way for AI developers to protect their intellectual property. Much like digital watermarking, it embeds information into a network than can then be triggered for identification purposes. If you've spent hundreds of hours developing and training AI models, and someone decides to exploit your hard work, IBM's new technique will allow you to prove that the models are yours. IBM's method involves embedding specific information within deep learning models and then detecting them by feeding the neural network an image that triggers an abnormal response.

Modern movie trailers are already cynical exercises in attention grabbing (such as the social media-friendly burst of imagery at the start of many clips), but they might be even more calculated in the future. Researchers at 20th Century Fox have produced a deep learning system that can predict who will be most likely to watch a movie based on its trailer. Thanks to training that linked hundreds of trailers to movie attendance records, the AI can draw a connection between visual elements in trailers (such as colors, faces, landscapes and lighting) and the performance of a film for certain demographics. A trailer with plenty of talking heads and warm colors may appeal to a different group than one with lots of bold colors and sweeping vistas. Notably, the deep learning approach already appears to work in real world conditions.

A team of researchers at the University of California has developed a novel kind of neural network--one that uses light instead of electricity to arrive at results. In their paper published in the journal Science, the group describes their ideas, their working device, its performance, and the types of applications they believe could be well served by such a network. Deep learning networks are computer systems that "learn" by looking at many examples of data types and then use patterns that develop as a way to make interpretations of new data. Like all other computers, they run on electricity. In this new effort, the researchers have found a way to create a deep learning network that does not use electricity at all--instead, it uses light.

Malware authors have always been at an advantage of being able to adversarially test and augment their malicious code, before deploying the payload, using anti-malware products at their disposal. The anti-malware developers and threat experts, on the other hand, do not have such a privilege of tuning anti-malware products against zero-day attacks pro-actively. This allows the malware authors to being a step ahead of the anti-malware products, fundamentally biasing the cat and mouse game played by the two parties. In this paper, we propose a way that would enable machine learning based threat prevention models to bridge that gap by being able to tune against a deep generative adversarial network (GAN), which takes up the role of a malware author and generates new types of malware. The GAN is trained over a reversible distributed RGB image representation of known malware behaviors, encoding the sequence of API call ngrams and the corresponding term frequencies. The generated images represent synthetic malware that can be decoded back to the underlying API call sequence information. The image representation is not only demonstrated as a general technique of incorporating necessary priors for exploiting convolutional neural network architectures for generative or discriminative modeling, but also as a visualization method for easy manual software or malware categorization, by having individual API ngram information distributed across the image space. In addition, we also propose using smart-definitions for detecting malwares based on perceptual hashing of these images. Such hashes are potentially more effective than cryptographic hashes that do not carry any meaningful similarity metric, and hence, do not generalize well.

Nowadays, a lot of scientific efforts are concentrated on the diagnosis of Alzheimer's Disease (AD) applying deep learning methods to neuroimaging data. Even for 2017, there were published more than a hundred papers dedicated to AD diagnosis, whereas only a few works considered a problem of mild cognitive impairments (MCI) conversion to the AD. However, the conversion prediction is an important problem since approximately 15% of patients with MCI converges to the AD every year. In the current work, we are focusing on the conversion prediction using brain Magnetic Resonance Imaging and clinical data, such as demographics, cognitive assessments, genetic, and biochemical markers. First of all, we applied state-of-the-art deep learning algorithms on the neuroimaging data and compared these results with two machine learning algorithms that we fit using the clinical data. As a result, the models trained on the clinical data outperform the deep learning algorithms applied to the MR images. To explore the impact of neuroimaging further, we trained a deep feed-forward embedding using similarity learning with Histogram loss on all available MRIs and obtained 64-dimensional vector representation of neuroimaging data. The use of learned representation from the deep embedding allowed to increase the quality of prediction based on the neuroimaging. Finally, the current results on this dataset show that the neuroimaging does affect conversion prediction, however, cannot noticeably increase the quality of the prediction. The best results of predicting MCI-to-AD conversion are provided by XGBoost algorithm trained on the clinical and embedding data. The resulting accuracy is 0.76 +- 0.01 and the area under the ROC curve - 0.86 +- 0.01.

Surface reconstruction is a vital tool in a wide range of areas of medical image analysis and clinical research. Despite the fact that many methods have proposed solutions to the reconstruction problem, most, due to their deterministic nature, do not directly address the issue of quantifying uncertainty associated with their predictions. We remedy this by proposing a novel probabilistic deep learning approach capable of simultaneous surface reconstruction and associated uncertainty prediction. The method incorporates prior shape information in the form of a principal component analysis (PCA) model. Experiments using the UK Biobank data show that our probabilistic approach outperforms an analogous deterministic PCA-based method in the task of 2D organ delineation and quantifies uncertainty by formulating distributions over predicted surface vertex positions.