What Exactly Do You Mean By AI?

An excellent staff is not built by bargaining down a potential employee, seeing how little you have to offer to get them to sign on the dotted line. That is almost a sure guarantee of job dissatisfaction and an untimely and probably acrimonious exit. It's also counter-productive to make extravagant claims about your startup to tempt the strongest talent; once you've lost their confidence by overstating the benefits, it's almost impossible to recover it.

Analysis Artificial intelligence and machine learning engines are underpinning many emerging applications and services, from making sense of big data for enterprises, to supporting hyper-personalized consumer content, or virtual reality gaming. The current challenge is to move AI from the supercomputer to the mobile device, supporting technologies like computer vision locally on the handset, car, camera or VR headset. Qualcomm has been a leader here, but the past weeks have seen Intel and its Chinese partner Rockchip invest in chip-level computer vision and AI capabilities, while Apple has acquired machine learning startup Turi, presumably to enhance its AI-driven personal assistant Siri. Rockchip has licensed the XM4 imaging and vision DSP (digital signal processor) design from IP provider CEVA, to enhance these aspects of its system-on-chip (SoC) products. It says it will enable advanced vision features at the low power levels required for mobile devices, supporting digital video stabilization, object detection and tracking, and 3D depth sensing, among others.

It is really fascinating to see how chat bots can automate contextual conversational interactions. According to Casey Newton from The Verge "Bots are here, they're learning – and in 2016, they might eat the web", which seems to be very realistic because there are thousands of bots out there and more are being developed that increase productivity, simplify the user experience, or are just for plain entertainment. The majority of the bots as of today are simple commands to automate daily tasks. However, Artificial Intelligence (AI) powered bots are the future -- you can build natural languages as services to communicate directly with customers to order food for example, and even pay for items entirely through it. AI stuff is bubbling with the explosive growth of social messaging, there is a real opportunity here to wire-up and manifest the bot to ride the bot wave.

We kick off today a month-long focus on trends in the following categories: Manufacturing, Manufacturing Technology, Supply Chain, Logistics, and Transportation Management. This 2016 supply chain trend will continue as best-in-class organizations leverage business networks to create a digital community of partners executing coordinated processes in a more organized and informed way than in the past. This new breed of supply chain is more connected, intelligent, scalable and rapid than traditional supply chain management. In today's global and connected economy, digital supply chains are the on-ramp to innovation and success.

In 1950, computing pioneer Alan Turing posed a heretical question: Can machines think? Some 66 years later, the answer is clear. Evidence of machine learning is all around us. Execute a Google search and you'll reap the benefits of machine learning. When Google presents results to a user, the user votes on those results via a click.

Today, social networking tools and graph technology can accurately map and extract valuable insights from the relationships between various entities in a network. Networks can also be analysed by machine learning, a technique in which a computer can adapt its own algorithms. Modern manufacturing equipment has been advancing rapidly; plants are filled with sensors to monitor equipment performance. The number of sensors that allow devices to connect to the internet is growing and so too is the volume and complexity of data available to plant managers. The collection, storage and analysis of this data is vital in unlocking the benefits big data can provide.

This post is authored by Roope Astala, Senior Program Manager at Microsoft. We are thrilled to announce the support for multiple versions of R and Python in Azure ML.

The genetic cause of autism spectrum disorder is notoriously hard to research. Genetic markers for the disorder are tough to match from patient to patient because they're so rare--one of the most common genetic signifiers is only found in less than one percent of those diagnosed with autism. Even when genetic anomalies are found, they must be checked against family members genomes to ensure it's not attributable to a more commonly inherited mutation that doesn't cause disease. Researchers at Princeton and the Simons Foundation turned the traditional approach on its head, teaching a machine learning algorithm to look for the genetic relationships that could cause autism. The algorithm scoured a digital network of the human genome's interactions, looking for relationships and connections that are similar to those in previously-known markers for autism.

Data scientists and deep and machine learning researchers rely on frameworks and libraries such as Torch, Caffe, TensorFlow, and Theano. Studies by Colfax Research and Kyoto University have found that existing open source packages such as Torch and Theano deliver significantly faster performance through the use of Intel Scalable System Framework (Intel SSF) technologies like the Intel compiler and performance libraries for Intel Math Kernel Library (Intel MKL), Intel MPI (Message Passing Interface), and Intel Threading Building Blocks (Intel TBB), and Intel Distribution for Python (Intel Python). Andrey Vladimirov (Head of HPC Research, Colfax Research) noted that "new Intel SSF hardware and software in combination with code modernization delivered an observed 50x machine learning performance improvement in our case study". In the Colfax Research and Kyoto case studies as well as general Python scientific computing benchmarks, results run up to two orders of magnitude (100x) faster as a result of using Intel SSF technologies. Python is a powerful and popular scripting language that provides fast and fundamental tools for machine learning and scientific computing through popular packages such as scikit-learn, NumPy and SciPy.