If you've been following along with the Data-Mania blog recently, then you've learned about the power of branding yourself as a data-driven professional. You've seen why it's important to get active on social media. Now I want to give you a brief heads up on some top tech influencers for data professionals to follow. Unlike all those Twitter popularity contests you see (Read: Who's Who of Twitter bot strategy), this list isn't about promoting businesses or who's got the most followers. These recommendations for top tech influencers for data professionals to follow are all made with YOU in mind.

The world as we know it is moving towards machines big time. But we can not fully utilize the working of any machine without a lot of human interaction. So in order to do that, we needed some kind of intelligence for the machines. Here comes the place for Artificial Intelligence. It is the concept of machines being smart to carry out numerous tasks without any human intervention.

SAS supports the creation of deep neural network models. Examples of these models include convolutional neural networks, recurrent neural networks, feedforward neural networks and autoencoder neural networks. Let's examine in more detail how SAS creates deep learning models using SAS Visual Data Mining and Machine Learning. SAS Visual Mining and Machine Learning takes advantage of SAS Cloud Analytic Services (CAS) to perform what are referred to as CAS actions. You use CAS actions to load data, transform data, compute statistics, perform analytics and create output.

SAS supports the creation of deep neural network models. Examples of these models include convolutional neural networks, recurrent neural networks, feedforward neural networks and autoencoder neural networks. Let's examine in more detail how SAS creates deep learning models using SAS Visual Data Mining and Machine Learning. SAS Visual Mining and Machine Learning takes advantage of SAS Cloud Analytic Services (CAS) to perform what are referred to as CAS actions. You use CAS actions to load data, transform data, compute statistics, perform analytics and create output.

TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization. The system is designed to facilitate research in machine learning, and to make it quick and easy to transition from research prototype to production system. Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and community contributors.

TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization. The system is designed to facilitate research in machine learning, and to make it quick and easy to transition from research prototype to production system. Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and community contributors.

Nvidia this week unveiled its newest AI breakthrough in the form of a mind-blowing computer vision technique that can'inpaint' parts of an image that have been deleted or modified. If you're thinking Photoshop already does this, think again. This is something you have to see to believe. Nvidia's researchers explain the difference between its novel method for inpainting images with deep learning and currently existing tech in a whitepaper published earlier this week: Previous deep learning approaches have focused on rectangular regions located around the center of the image, and often rely on expensive post-processing. The goal of this work is to propose a model for image inpainting that operates robustly on irregular hole patterns, and produces semantically meaningful predictions that incorporate smoothly with the rest of the image without the need for any additional post-processing or blending operation.

The debate on Machine Learning vs. Deep Learning has gained considerable steam in the past few years. The fundamental strength of both these technologies lies in their ability to learn from available data. Though both of these offshoot AI technologies triumph in "learning algorithms," the manner in which Machine Learning (ML) algorithms learn is very different from the learning methods of the Deep Learning (DL) algorithms. While ML directly observes data patterns and establishes correlations, DL algorithms learn progressively from intricate layers of knowledge. DL is considered a subset of ML, where learning happens through a layered network of algorithms commonly known as an Artificial Neural Network (ANN).

In model-based reinforcement learning, generative and temporal models of environments can be leveraged to boost agent performance, either by tuning the agent's representations during training or via use as part of an explicit planning mechanism. However, their application in practice has been limited to simplistic environments, due to the difficulty of training such models in larger, potentially partially-observed and 3D environments. In this work we introduce a novel action-conditioned generative model of such challenging environments. The model features a non-parametric spatial memory system in which we store learned, disentangled representations of the environment. Low-dimensional spatial updates are computed using a state-space model that makes use of knowledge on the prior dynamics of the moving agent, and high-dimensional visual observations are modelled with a Variational Auto-Encoder. The result is a scalable architecture capable of performing coherent predictions over hundreds of time steps across a range of partially observed 2D and 3D environments.

Currently, many critical care indices are repetitively assessed and recorded by overburdened nurses, e.g. physical function or facial pain expressions of nonverbal patients. In addition, many essential information on patients and their environment are not captured at all, or are captured in a non-granular manner, e.g. sleep disturbance factors such as bright light, loud background noise, or excessive visitations. In this pilot study, we examined the feasibility of using pervasive sensing technology and artificial intelligence for autonomous and granular monitoring of critically ill patients and their environment in the Intensive Care Unit (ICU). As an exemplar prevalent condition, we also characterized delirious and non-delirious patients and their environment. We used wearable sensors, light and sound sensors, and a high-resolution camera to collected data on patients and their environment. We analyzed collected data using deep learning and statistical analysis. Our system performed face detection, face recognition, facial action unit detection, head pose detection, facial expression recognition, posture recognition, actigraphy analysis, sound pressure and light level detection, and visitation frequency detection. We were able to detect patient's face (Mean average precision (mAP)=0.94), recognize patient's face (mAP=0.80), and their postures (F1=0.94). We also found that all facial expressions, 11 activity features, visitation frequency during the day, visitation frequency during the night, light levels, and sound pressure levels during the night were significantly different between delirious and non-delirious patients (p-value<0.05). In summary, we showed that granular and autonomous monitoring of critically ill patients and their environment is feasible and can be used for characterizing critical care conditions and related environment factors.