IBM is hosing its annual THINK conference to packed halls in Las Vegas this week. Given how important its cloud business has become to its bottom line, it's no surprise that this event features its fair share of cloud news. This comes a day after Google also confirmed that it is using these processors in its data centers, too. These servers are designed around the recently launched Power9 RISC processor (which are themselves the latest generation of the PowerPC processors Apple once used) and Nvidia Tesla V100 GPUs. Thanks to their use of the high-speed NVLink interface, these machines are especially powerful when it comes to training machine learning models.

Have you noticed that interest in artificial intelligence (AI) has really taken off in the last year or so? A lot of that interest is fueled by deep learning. Deep learning has revolutionized the way we use our phones, bringing us new applications such as Google Voice and Apple's Siri, which are based on AI models trained using deep learning. Deep learning is a new machine learning method based on neural networks that learns and becomes more accurate as we feed the model more data. A widely-accepted principle of deep learning is shown on the left-hand side of the chart below: deep learning–based AI models have much higher accuracy than traditional machine learning methods, but require much more data to train to achieve that accuracy.

We are really pleased to announce the date of our first Meetup for the IBM PowerAI Frankfurt group. Get together with IBM, NVIDIA and INS and learn about Deep Learning and PowerAI. Listen to INS how they are using Deep Learning as part of their daily business and how NVIDIA and IBM are working together to build a Deep Learning perspective. Explore the benefits of using open source frameworks together with IBM PowerAI tools to enable a complete user-friendly Deep Learning system. Everything running on IBM's state-of-the-art platform based on Power CPU's and NVIDIA GPU's.

The OpenPOWER workshop on PowerAI hosted by the NHCE on 19th of December 2017. The Program, led and managed by Ganesan Narayanasamy introduced a wide range of specialist topics ranging from IBM powerAI, deep learning, machine learning, tensorFlow frameworks, Image classification with example. In this session an introduction to OpenPower foundation was delivered.This included an overview of the cooperation of over 300 institutions ranging from academia to industry as well as a more in depth look at some of the success and developments currently underway within the OpenPOWER framework. The Oak Ridge Leadership Computing Facility provides the open scientific community access to America's fastest, most powerful supercomputer and is a key member of the OpenPOWER Founation. Also included an outline of the conventionally used qubit technologies as well as an indication of the current status of the quantum computer projects underway at some of the lead player institutions including IBM, Microsoft and NASA.

IBM has unveiled next-generation Power Systems Servers incorporating its newly designed Power9 processor, built specifically for compute-intensive AI workloads. Tthe new Power9 systems are capable of improving the training times of deep learning frameworks by nearly 4-times, allowing enterprises to build more accurate AI applications, faster. The new Power9 -based AC922 Power Systems are the first to embed PCI-Express 4.0, next-generation NVIDIA NVLink and OpenCAPI, which combined can accelerate data The system was designed to drive demonstrable performance improvements across popular AI frameworks such as Chainer, TensorFlow and Caffe, as well as accelerated databases such as Kinetica. As a result, data scientists can build applications faster, ranging from deep learning insights in scientific research, real-time fraud detection and credit risk analysis. Power9 is at the heart of the soon-to-be most powerful data-intensive supercomputers in the world, the US Department of Energy's "Summit" and "Sierra" supercomputers, and has been tapped by Google.

At the time of writing this post, most of the big tech companies (such as IBM, Google, Microsoft, and Amazon) have easy-to-use visual recognition APIs. Some smaller companies also provide similar offerings, such as Clarifai. But none of them offer object detection. The following images were both tagged using the same Watson Visual Recognition default classifier. The first one, though, has been run through an object detection model first.

IBM has spent the past several years putting a laser focus on what it calls cognitive computing, using its Watson platform as the foundation for its efforts in such emerging fields as artificial intelligence (AI) and is successful spinoff, deep learning. Big Blue has leaned on Watson technology, its traditional Power systems, and increasingly powerful GPUs from Nvidia to drive its efforts to not only bring AI and deep learning into the cloud, but also to push AI into the enterprise. The technologies are part of a larger push in the industry to help enterprises transform their businesses to take advantage of such trends as the rise of the cloud, the increasing use of mobile technologies and the skyrocketing growth of data that is being generated by these companies and needs to be processed and analyzed. Much of the work with AI, deep learning and analytics have been done in the cloud, promoted by hyperscale cloud providers like Amazon Web Services (AWS), Microsoft Azure and Google Cloud. IBM also has put many of its capabilities into its own cloud.

New AI technologies like machine learning and deep learning are fitting ever more snugly into the shifting enterprise landscape. Deep learning in particular is being adopted by an increasing number of enterprises for expanded insights and with the aim to better serving their clients. Thanks to more powerful systems and graphics processing units (GPUs), we are able to train complex AI models that enable these insights. IBM has long been one of the leaders in analytics and over the last year or two introduced two key new products, Data Science Experience and IBM PowerAI, designed to enable enterprises to more easily start using advanced AI technologies. Today we're unveiling that we are bringing these two key software tools for data scientists together.

New AI technologies like machine learning and deep learning are fitting ever more snugly into the shifting enterprise landscape. Deep learning in particular is being adopted by an increasing number of enterprises for expanded insights and with the aim to better serving their clients. Thanks to more powerful systems and graphics processing units (GPUs), we are able to train complex AI models that enable these insights. IBM has long been one of the leaders in analytics and over the last year or two introduced two key new products, Data Science Experience and IBM PowerAI, designed to enable enterprises to more easily start using advanced AI technologies. Today we're unveiling that we are bringing these two key software tools for data scientists together.

Deep learning has taken the world by storm in the last four years, powering hundreds of consumer web and mobile applications that we use every day. But the extremely long training times in most frameworks present a hurdle that's curtailing the broader proliferation of deep learning. It currently may take days or even weeks to train large AI models with big data sets to get the right accuracy levels. At the crux of this problem is a technical limitation. The popular open-source deep-learning frameworks do not seem to run as efficiently across multiple servers.