Adversarial robustness of the neural network is a significant concern when it is applied to security-critical domains. In this situation, adversarial distillation is a promising option which aims to distill the robustness of the teacher network to improve the robustness of a small student network. Previous works pretrain the teacher network to make it robust against the adversarial examples aimed at itself. However, the adversarial examples are dependent on the parameters of the target network. The fixed teacher network inevitably degrades its robustness against the unseen transferred adversarial examples which target the parameters of the student network in the adversarial distillation process. We propose PeerAiD to make a peer network learn the adversarial examples of the student network instead of adversarial examples aimed at itself. PeerAiD is an adversarial distillation that trains the peer network and the student network simultaneously in order to specialize the peer network for defending the student network. We observe that such peer networks surpass the robustness of the pretrained robust teacher model against adversarial examples aimed at the student network. With this peer network and adversarial distillation, PeerAiD achieves significantly higher robustness of the student network with AutoAttack (AA) accuracy by up to 1.66%p and improves the natural accuracy of the student network by up to 4.72%p with ResNet-18 on TinyImageNet dataset. Code is available at https://github.com/jaewonalive/PeerAiD.

Algorithmic fairness is a motif that plays throughout our podcast series: as we look to AI to help us make consequential decisions involving people, guests have stressed the risks that the automated systems that we build will encode past injustices and that these decisions may be too opaque. In episode twelve of the Intel on AI podcast, Intel AI Tech Evangelist and host Abigail Hing Wen talks with Alice Xiang, then Head of Fairness, Transparency, and Accountability Research at the Partnership on AI--a nonprofit in Silicon Valley founded by Amazon, Apple, Facebook, Google, IBM, Intel and other partners. With a background that includes both law and statistics, Alice's research has focused on the intersection of AI and the law. "A lot of the benefit of algorithmic systems, if used well, would be to help us detect problems rather than to help us automate decisions." Algorithmic fairness is the study of how algorithms might systemically perform better or worse for certain groups of people and the ways in which historical biases or other systemic inequities might be perpetuated by AI.

When it comes to artificial intelligence (AI), few people can match Andrew Ng's breadth and depth of experience, combining research in a world-leading academic environment, accelerating AI education (one of his courses really helped me when I was breaking into AI myself) with founding AI teams at some of the most successful tech companies. Ng lead the Stanford AI Lab, was a founding leader at Google Brain where he worked with legendary engineer Jeff Dean, lead AI at Baidu, and currently serves as an adjunct professor in Computer Science at Stanford University. Among Andrew's other pursuits: being the founder of deeplearning.ai, In a recent episode of the Intel on AI podcast, Ng and host Intel's Abigail Hing Wen discuss why most of the important work yet to be done with AI is in industries outside of Silicon Valley, such as manufacturing, agriculture, and healthcare. Ng sees AI as driving huge growth for successful adopters, to the point where he sees a need for society to be prepared to offer additional support to workers in disrupted industries.

Almost three years ago, Carnegie Mellon University Prof. Dave Andersen and I announced the Intel Science and Technology Center for Visual Cloud Systems (ISTC-VCS) at the 2016 NAB Show. Along with Prof. Kayvon Fatahalian at Stanford, Dave has led the center and collaborated closely with other academic and Intel Labs researchers to push the boundaries in visual cloud systems. We set out to study and find solutions for some of the key problems with gathering, storing and analyzing video data in large scale distributed environments. With the completion of the center now drawing near, it's time to take stock of the results and to talk of work yet to be done. The center's approach has been to bring together systems researchers and computer vision, AI and graphics researchers to create prototype systems that allow investigation of these topics.

Businesses are increasingly seeing the need for simpler, faster ways to harness large data sets and extract useful insights at scale. Thanks to advancements in artificial intelligence (AI), we are moving from experimentation to production. Companies from General Electric to Walmart are using AI and analytics to recognize the power of the data they and their customers generate. The business benefits can be massive, but implementation is a huge and complex undertaking that requires an end-to-end data analytics pipeline. At the O'Reilly AI Conference in Beijing, Intel showed its continued commitment to solving real business problems across multiple market segments with solutions built on modern, scalable hardware and software architectures. A unified, Intel-based architecture also provides IT staff with the ability to harness familiar software and advanced toolkits that support business growth without requiring excessive investments in single-purpose products.

The exponential growth of data is creating scaling and cost challenges. Not being able to scale storage and compute resources independently results in suboptimal resource utilization of data center infrastructure investments. Customers need to solve for this while controlling their licensing costs. They also need to perform real-time analytics on their ever-growing data sets. With Intel's Data Management Platform (DMP), you can build an infrastructure that allows you to operate on petabyte-scale data and harness the power of that data.

Video content will become richer and more data-intensive as it evolves from HD to 4K to 360 and even 8K. Companies are moving these visual workloads to the cloud and edge in order to keep up with capacity, growth and service demands. With the emergence of edge computing and cloudified, 5G networks, organizations have an opportunity to deliver insights through artificial intelligence (AI) that complement new user experiences and are adaptable to the complexities of delivering video content to a global audience. Companies need a visual cloud and media analytics platform that is flexible enough to support changing business models and deployment options, software that enables rapid innovation, and hardware that can scale to provide a range of performance, all while being able to lower total cost of ownership and grow profitability. Intel launched the Intel Select Solutions for Visual Cloud to give companies an easier path towards successful content creation and delivery starting with the Intel Select Solution for Simulation and Visualization and Intel Select Solution for Visual Cloud Delivery Network.

Communication service providers (CommSPs) are already saving money and generating revenue from network transformation investments. There is an expectation these benefits will continue to increase as NFV functions scale across the various elements of the infrastructure--enterprise, radio access network, wireless core, cable and cloud. New 5G and edge computing use cases promise to deliver new revenue along with even more data that must be moved, stored, processed and analyzed. The industry is looking to Artificial Intelligence (AI) and Machine Learning (ML) to enable CommSPs to solve problems and unlock value for their own business operations and their customers. As an example, distributed AI based on reinforcement learning will play a key role in building automated and self-managed networks.

Training an effective deep neural network is one thing, but deploying it in a way that keeps up with customer demand and is both performant and cost-efficient is hard. We've combined a heavily optimized software stack with deep learning-enabled hardware to fix that. There's an exciting change in the mix of problems that machine learning folks talk about. Teams have found their groove with data management and model training, and now have rapidly expanding user-bases. Of course, as great as it is to see your user graph go vertical, success comes with new problems.

Artificial Intelligence (AI) is by no means a new concept. The idea has been around since Alan Turing's publication of "Computing Machinery and Intelligence" in the 1950s. But until recently, the computing power and the massive data sets needed to meaningfully run AI applications weren't easily available. Now, thanks to developments in computing technology and the associated deluge of data, researchers in government, academia, and enterprise can access the compute performance they need to run AI applications that further drive their mission needs. Many organizations that already rely on a high-performance computing (HPC) infrastructure to support applications like modeling and simulation are now looking for ways to benefit from AI capabilities.