This week I attended an "Artificial Intelligence (AI) Roundtable" of leading scientists, entrepreneurs and venture investors. As the discussion focused mainly on basic statistical techniques, I left feeling unfulfilled. My friend, Matt Turck, recently wrote that "just about every major tech company is working very actively on AI," which also means that every startup hungry for capital is purchasing a dot'ai' domain name. As the lines blur between what is and what really isn't, I feel it necessary to provide readers with a quick lens of how to view intelligent agents for mechatronics. For 65 years, The Turing Test remained unsolvable until a computer program called "Eugene Goostman" conquered it in 2014.

Research into artificial intelligence is going gangbusters, and the frenetic pace won't let up for about five years--after which the industry will concentrate around a handful of core technologies and leaders, the head of Intel's new AI division predicts. Intel is keen to be among them. In March, it formed an Artificial Intelligence Products Group headed by Naveen Rao. He previously was CEO of Nervana Systems, a deep-learning startup Intel acquired in 2016. Rao sees the industry moving at breakneck speed.

IBM has made three major announcements that boast IBM Cloud as the cloud for fastest deep learning development and artificial intelligence (AI) implementation. These latest announcements are geared toward enterprises requiring industry-specific compliance that are facing security and regulatory challenges. IBM has launched a private cloud version of its development environment, known as the Data Science Experience. The goal of this project is to aid data scientists in collaboration on analytic models, and the building of intelligent applications. Today, 80% of data is proprietary, and IBM is looking to help provide the correct tools and platforms for the task of leveraging data for business impact.

This post is part of a series on deep learning. This week we will explore the inner workings of a Convolutional Neural Network (CNN). You might be wondering what happens inside these networks? And how do they learn? The teaching philosophy behind the course I'm following is based on a top-down approach.

Today at the 2017 GPU Technology Conference in San Jose, NVIDIA CEO Jen-Hsun Huang announced the new NVIDIA Tesla V100, the most advanced accelerator ever built. From recognizing speech to training virtual personal assistants to converse naturally; from detecting lanes on the road to teaching autonomous cars to drive; data scientists are taking on increasingly complex challenges with AI. Solving these kinds of problems requires training exponentially more complex deep learning models in a practical amount of time. HPC is a fundamental pillar of modern science. From predicting weather, to discovering drugs, to finding new energy sources, researchers use large computing systems to simulate and predict our world. AI extends traditional HPC by allowing researchers to analyze large volumes of data for rapid insights where simulation alone cannot fully predict the real world.

In this series, I will try to give you an overview of the MXnet Deep Learning library: we'll look at its main features and its Python API (which I suspect will be the #1 choice). Later on, we'll explore some of the MXNet tutorials and notebooks available online, and we'll hopefully manage to understand every single line of code! If you'd like learn more about the rationale and the architecture of MXNet, you should read this paper, named "MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems". We'll cover most of the concepts presented in the paper, but hopefully in a more accessible way. I'll go as slow and explain as much as I need to.

IBM claims it has the fastest cloud for deep learning and artificial intelligence (AI) after publishing benchmark tests which show NVIDIA Tesla P100 GPU accelerators on the IBM Cloud can provide up to 2.8 times more performance than the previous generation in certain cases. The tests, when fleshed out, will enable organisations to quickly create advanced AI applications on the cloud. "Deep learning techniques are a key driver behind the increased demand for and sophistication of AI applications," the company noted. "However, training a deep learning model to do a specific task is a compute-heavy process that can be time and cost-intensive." IBM purports to be the first of the large cloud providers to offer NVIDIA Tesla P100 GPUs.

IBM (NYSE: IBM) today announced a significant new release of its PowerAI deep learning software distribution on Power Systems that attacks the major challenges facing data scientists and developers by simplifying the development experience with tools and data preparation while also dramatically reducing the time required for AI system training from weeks to hours. Data scientists and developers use deep learning to develop applications ranging from computer vision for self-driving cars to real time fraud detection and credit risk analysis systems. These cognitive applications are much more compute resource hungry than traditional applications and can often overwhelm x86 systems. "IBM PowerAI on Power servers with GPU accelerators provide at least twice the performance of our x86 platform; everything is faster and easier: adding memory, setting up new servers and so on," said current PowerAI customer Ari Juntunen, CTO at Elinar Oy Ltd. "As a result, we can get new solutions to market quickly, protecting our edge over the competition. We think that the combination of IBM Power and PowerAI is the best platform for AI developers in the market today. For AI, speed is everything --nothing else comes close in our opinion."

AI is becoming a hot topic for technology giants, who are increasingly competing against each other in a number of avenues in order to be the first to create an innovative product or solution for consumers. Back in 2016, Amazon, DeepMind/Google, Facebook, IBM, Apple and Microsoft created a non-profit organisation which will work to advance public understanding of artificial intelligence technologies (AI) and formulate best practices on the challenges and opportunities within the field, according to a press release. Tesla CEO Elon Musk has also launched Open AI, a "non-profit AI research company, discovering and enacting the path to safe artificial general intelligence." Other countries such as Singapore are also investing $150 million into AI within the next five years, signifying the growing appeal for AI within the work and social space. We take a look at how technology companies are working on AI and how users will benefit.

The company also announced its first Volta-based processor, the NVIDIA Tesla V100 data center GPU, which brings extraordinary speed and scalability for AI inferencing and training, as well as for accelerating HPC and graphics workloads. "Artificial intelligence is driving the greatest technology advances in human history," said Jensen Huang, founder and chief executive officer of NVIDIA, who unveiled Volta at his GTC keynote. "It will automate intelligence and spur a wave of social progress unmatched since the industrial revolution. "Deep learning, a groundbreaking AI approach that creates computer software that learns, has insatiable demand for processing power. Thousands of NVIDIA engineers spent over three years crafting Volta to help meet this need, enabling the industry to realize AI's life-changing potential," he said.