In this blog I aim to provide an introduction to TensorFlow and the SAP HANA integration, give you an understanding of the landscape and outline the process for using External Machine Learning with HANA. There's plenty of hype around Machine Learning, Deep Learning and of course Artificial Intelligence (AI), but understanding the benefits in an enterprise context can be more challenging. Being able to integrate the latest and greatest deep learning models into your enterprise via a high performance in-memory platform could provide a competitive advantage or perhaps just keep up with the competition? With HANA 2.0 SP2 onwards we have the ability to call TensorFlow (TF) models or graphs as they are known. HANA now includes a method to call External Machine Learning (EML) models via a remote source.

Check out the session "Get Your Hard Hat: Intelligent Industrial Systems with Deep Reinforcement Learning" at the AI Conference in Beijing, April 10-13, 2018. Best price ends January 26. The flurry of headlines surrounding AlphaGo Zero (the most recent version of DeepMind's AI system for playing Go) means interest in reinforcement learning (RL) is bound to increase. Next to deep learning, RL is among the most followed topics in AI. For most companies, RL is something to investigate and evaluate but few organizations have identified use cases where RL may play a role. As we enter 2018, I want to briefly describe areas where RL has been applied.

Deep learning, meet brick and mortar. This is a real time demonstration of our autonomous checkout system, running at 30 FPS. This system includes our models for person detection, entity tracking, item detection, item classification, ownership resolution, action analysis, and shopper inventory analysis, all working together to visualize which person has what item in real time. We're excited to demonstrate more features for our zero friction checkout experience. Stay tuned to see the future of retail.

Rapid advances in AI have spawned a number of recent initiatives that aim to convince engineers, programmers, and others to prioritize ethical considerations in their work--but almost all of them have originated in rich Western countries. An effort from the huge engineering association IEEE is now trying to change that, with its own AI ethics proposal that it says will be a global, multilingual collaboration. In the past two years alone, a raft of new efforts to explore ethics in AI have launched, including the Elon Musk–backed nonprofit OpenAI, the corporate alliance Partnership on AI, Carnegie Mellon University's AI ethics research center, and the Ethics & Society research unit at Google's AI subsidiary DeepMind. But most of these projects are based in the U.S. or U.K., are led by a small group of researchers, and issue updates only in English, which could limit their ability to foster AI that benefits all of humanity, not just those in developed countries. Since 2016, a group called the IEEE Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems has been writing a document called "Ethically Aligned Design" that recommends societal and policy guidelines for technologies such as chatbots and home robots. This week, the group unveiled an updated version of the document that integrates feedback from people in East Asia, Latin America, the Middle East, and other regions.

Based on two R packages for neural networks. In this article, I compare two available R packages for using neural networks to model data: neuralnet and deepnet. Through the comparisons I highlight various challenges in finding good hyperparameter values. I show that some needed hyperparameters differ when using these two packages, even with the same underlying algorithmic approach. Both packages can be obtained via the R CRAN repository (see links at the end). I will focus on a simple time series example, composed of two predictors and the performance of the packages to predict future data after being trained on past data using a simple 5-neuron network. Note that most of what you read about in deep learning with neural networks are "classification" problems (more later); nonetheless such networks have promise for predicting continuous data including time series. Briefly, a neural network (also called a multilayer-perceptron etc.) is a connected network of neurons as shown here. An example neural network (generated using neuralnet). Note that except for the input layer (where the predictor values are fed in), the inputs to a neuron have weights specific to that neuron, so the output of a neuron is "re-used" as input to all neurons in the next layer, with unique weights. Before moving on to a brief description of how neural networks compute predictions, it is worth reflecting on the number of independent parameters in neural network models as compared to, for example, linear regression.

This is a long overdue blog post on Reinforcement Learning (RL). You may have noticed that computers can now automatically learn to play ATARI games (from raw game pixels!), they are beating world champions at Go, simulated quadrupeds are learning to run and leap, and robots are learning how to perform complex manipulation tasks that defy explicit programming. It turns out that all of these advances fall under the umbrella of RL research. I also became interested in RL myself over the last year: I worked through Richard Sutton's book, read through David Silver's course, watched John Schulmann's lectures, wrote an RL library in Javascript, over the summer interned at DeepMind working in the DeepRL group, and most recently pitched in a little with the design/development of OpenAI Gym, a new RL benchmarking toolkit. So I've certainly been on this funwagon for at least a year but until now I haven't gotten around to writing up a short post on why RL is a big deal, what it's about, how it all developed and where it might be going. It's interesting to reflect on the nature of recent progress in RL. Similar to what happened in Computer Vision, the progress in RL is not driven as much as you might reasonably assume by new amazing ideas. In Computer Vision, the 2012 AlexNet was mostly a scaled up (deeper and wider) version of 1990's ConvNets. Similarly, the ATARI Deep Q Learning paper from 2013 is an implementation of a standard algorithm (Q Learning with function approximation, which you can find in the standard RL book of Sutton 1998), where the function approximator happened to be a ConvNet. AlphaGo uses policy gradients with Monte Carlo Tree Search (MCTS) - these are also standard components.

I have to predict the performance of an application. The inputs will be time series of past performance data of the application, CPU usage data of the server where application is hosted, the Memory usage data, network bandwidth usage etc. I'm trying to build a solution using LSTM which will take these input data and predict the performance of the application for next one week. I'm able to build a solution which takes one input, ie past performance data of the application. I'm currently stumbled at the part where I have to pass these multiple inputs.

Elon Musk has hired a new director of AI research at Tesla, and it may signal a plan to rethink the way its automated driving works. This week, Musk poached Andrej Karpathy, an expert on vision, deep learning, and reinforcement learning, from OpenAI, a nonprofit that Musk and others are funding that's dedicated to "discovering and enacting the path to safe artificial general intelligence." Karpathy, who will apparently report directly to Musk, is a rising star in the world of AI, having studied at Stanford with Fei-Fei Li, a leading AI expert who is now the chief scientist of Google Cloud. Li is famous in tech circles for having developed a data set of images that helped inspire a breakthrough in machine vision. Many have pointed to Karpathy's expertise in computer vision as a key asset for Tesla, and that's true.

It was a memorable year, to be sure, with plenty of drama and unexpected happenings in terms of the technology, the players, and the application of big data and data science. As we gear up for 2018, we think it's worth taking some time to ponder about what happened in 2017 and put things in some kind of order. Here are 10 of the biggest takeaways for the big data year that was 2017. Teradata, for instance, found that 80% of enterprises are already investing in AI, which backed similar findings from IDC. Nevertheless, the same old challenges that kept big data off Easy Street also emerged to cool some of the heat emanating from AI. Over the summer, Databricks' CEO, Ali Ghodsi, warned about "AI's 1% problem."

Deep learning has given us tremendous new powers to spot patterns hidden in great globs of data. For some challenges, neural networks can even outperform top human experts. However, despite all the progress the new approach represents and the hope that it will lead us to actual artificial intelligence, there are big limits on the practical application of deep learning. Deep learning has emerged as the latest "easy button" for big data analytics. The thinking seems to go like this: Got a lot of data to analyze?