Did you know that you can write R and Python code within your T-SQL statements? Machine Learning Services in SQL Server eliminates the need for data movement. Instead of transferring large and sensitive data over the network or losing accuracy with sample csv files, you can have your R/Python code execute within your database. Easily deploy your R/Python code with SQL stored procedures making them accessible in your ETL processes or to any application. You can install and run any of the latest open source R/Python packages to build Deep Learning and AI applications on large amounts of data in SQL Server.

Deep learning (DL) is a form of machine learning that uses hierarchical layers of abstraction to model complex structures. DL requires efficient training strategies and these are at the heart of today's successful applications which range from natural language processing to engineering and financial analysis. While deep learning has been available for several decades there were only a few practical applications until the early 2010s when the field has changed for several reasons. The renaissance is due to a number of factors, in particular 1. Hardware and software for accelerated computing (GPUs and specialized linear algebra libraries) 2. Increased size of datasets (Massive Data) 3. Efficient algorithms algorithms, such as stochastic gradient descent (SGD). The goal of our article is to provide the reader with an overview of computational aspects underlying the algorithms and hardware, which allow modern deep learning models to be implemented at scale. Many of the leading Internet companies employ DL at scale Hazelwood et al. [2017]. The most impressive accomplishment of DL is its many successful applications in research and business.

Existing fuzzy neural networks (FNNs) are mostly developed under a shallow network configuration having lower generalization power than those of deep structures. This paper proposes a novel self-organizing deep fuzzy neural network, namely deep evolving fuzzy neural networks (DEVFNN). Fuzzy rules can be automatically extracted from data streams or removed if they play little role during their lifespan. The structure of the network can be deepened on demand by stacking additional layers using a drift detection method which not only detects the covariate drift, variations of input space, but also accurately identifies the real drift, dynamic changes of both feature space and target space. DEVFNN is developed under the stacked generalization principle via the feature augmentation concept where a recently developed algorithm, namely Generic Classifier (gClass), drives the hidden layer. It is equipped by an automatic feature selection method which controls activation and deactivation of input attributes to induce varying subsets of input features. A deep network simplification procedure is put forward using the concept of hidden layer merging to prevent uncontrollable growth of input space dimension due to the nature of feature augmentation approach in building a deep network structure. DEVFNN works in the sample-wise fashion and is compatible for data stream applications. The efficacy of DEVFNN has been thoroughly evaluated using six datasets with non-stationary properties under the prequential test-then-train protocol. It has been compared with four state-of the art data stream methods and its shallow counterpart where DEVFNN demonstrates improvement of classification accuracy.

Yesterday Amazon announced that it has expanded its Alexa Fellowship to include new programs and fourteen new universities, bringing the total number of participating institutions to 18. Each program has a total of ten universities participating, with Carnegie Mellon University and University of Southern California receiving fellowship for both programs. Amazon is clearly all in on voice. But even one of the largest companies in the world admits it needs help to create better conversational AI solutions. By providing funding, Amazon is giving universities, professors and students the initiative "to solve many hard conversational AI challenges, ranging from automatic speech recognition to natural language understanding to text-to-speech."

Over the last few years, Artificial Intelligence (AI) has been gaining momentum across all industries and all spectrums of the world from consumer solutions such as Siri and Alexa leveraging Machine Learning to disruptive technologies such as Uber and Lyft as well as social media channels such as Facebook, Instagram, Snapchat and Twitter to the extreme side of quantum computing attempting to solve the world's biggest and most complex issues and challenges. Let's consider for a minute Higher-Ed including private and public institutions either for-profit or non-profit undergoing significant challenges in attempting to attract, recruit and ultimately enroll students across the globe in a highly competitive environment including a consumer (student) who is more educated as far as options available thanks to the internet, social media and other factors. This compounded with unsustainable tuition increases performed by some institutions as well as increased educational policy changes and regulations, are creating a challenging landscape for post secondary institutions across the nation. Universities and colleges have traditionally lagged behind the constant evolution and innovation of technology as compared with other industries and sectors. This has begun to change in particular driven by the current mainstream challenges and augmented with new generations such as Millenniums and iGen or Generation Z who have embedded in their DNA a digital world that Higher-Ed has yet to match and deliver on their expectations.

This computational prowess of TPUs was possible mainly because of their three decisive features. One, TPUs eliminated unneeded accuracy while performing training and inference. Three, TPUs assumed a minimal and deterministic design where all unnecessary functions such as caching and branch prediction were removed. Such optimized TPUs are deployed on TPU pods, supercomputers specifically developed for machine learning. In order to ensure high performance of Cloud TPUs -- TPUs that we access through GCP -- a typical Cloud TPUs work with the architecture on the image.

IBM Research and Rensselaer Polytechnic Institute (RPI) are collaborating on a new approach to help students learn Mandarin. The strategy pairs an AI-powered assistant with an immersive classroom environment that has not been used previously for language instruction. The classroom, called the Cognitive Immersive Room (CIR), makes students feel as though they are in restaurant in China, a garden, or a Tai Chi class, where they can practice speaking Mandarin with an AI chat agent. The CIR was developed by the Cognitive and Immersive Systems Lab (CISL), a research collaboration between IBM Research and RPI. CISL researchers demonstrate an AI-assisted Mandarin Chinese language learning aid.

Check out the great series of talks on model lifecycle management at the Strata Data Conference in New York, September 11-13, 2018. In this episode of the Data Show, I spoke with Harish Doddi, co-founder and CEO of Datatron, a startup focused on helping companies deploy and manage machine learning models. As companies move from machine learning prototypes to products and services, tools and best practices for productionizing and managing models are just starting to emerge. Today's data science and data engineering teams work with a variety of machine learning libraries, data ingestion, and data storage technologies. Risk and compliance considerations mean that the ability to reproduce machine learning workflows is essential to meet audits in certain application domains.

No two weeks are the same for Strong Analytics co-founder Jacob Zweig. One week, he's strolling across a food manufacturing floor in lab coat and hard hat, only to receive a crash course in retina scans at a biosecurity firm the next. The two industries could not be more different, but they do have one thing in common: data. Zweig and his co-founder, Brock Ferguson, are working to bring every industry, from retail to manufacturing and IoT, into the data age. To do so, they consult with companies to develop scalable machine learning solutions to long-standing problems.

As machine learning increasingly supports consequential decision making, its vulnerability to manipulation and gaming is of growing concern. When individuals learn to adapt their behavior to the specifics of a statistical decision rule, its original predictive power will deteriorate. This widely observed empirical phenomenon, known as Campbell's Law or Goodhart's Law, is often summarized as: "Once a measure becomes a target, it ceases to be a good measure" [25]. Institutions using machine learning to make high-stakes decisions naturally wish to make their classifiers robust to strategic behavior. A growing line of work has sought algorithms that achieve higher utility for the institution in settings where we anticipate a strategic response from the the classified individuals [10, 5, 14]. Broadly speaking, the resulting solution concepts correspond to more conservative decision boundaries that increase robustness to some form of covariate shift.