Thank you to all the Intel® AI Builders and event attendees who joined us at the O’Reilly Artificial Intelligence Conference in San Jose, CA, where select Intel® AI Builders partners showcased their solutions on Intel AI. The event featured rapid-fire demos across banking and financial services, retail, and healthcare, as well as cross-industry AI solutions. The presentations were followed by a networking cocktail party—an opportunity for attendees and showcase presenters to connect and discuss the possibilities of AI made possible by Intel technology.
Download the partner presentations here.
Partners: Cisco, Cloudera, Dell EMC, Digitate, Gramener, HPE/BlueData, Inspur, InstaDeep Ltd., John Snow Labs, Lenovo, OneClick.ai, Quest Global Digital, Skymind, Stratifyd, Subtle Medical, Wipro
There are more data sources at the edge of the network than in the cloud or data center. In fact, it is easier to move the analytics to the edge than to move the data to the data center, making it critical to be able to support a variety of analytics at the edge. Come to this session to see how Cisco HyperFlex is able to support a variety of analytics, including AI/ML at the edge in a retail environment.
Organizations face challenges in consistently and repeatedly building and deploying machine learning capabilities to drive efficiencies and cost savings at scale. For example, machine learning for predictive maintenance has the potential to dramatically lower costs in many industries, yet businesses still face challenges related to data management, tools and the machine learning lifecycle in their journey to deploying capabilities repeatedly and successfully at scale.
Dell EMC has built AI Ready Solutions that combine Dell infrastructure leadership in I compute, storage and networking with software tools from Intel and the industry to provide an ease-of-use deep learning experience. As model development moves strongly toward Kubernetes container environments, Dell Ready Solution offers an integrated, turn key solution.
Digital transformation is the only way for enterprises today to remain competitive in a fast-evolving economy and keep pace with skyrocketing consumer expectations.
However, exploding data volumes and the growing scale and complexity of enterprise data centers results in unplanned IT downtime. This disrupts mission-critical operations, causes loss of data, and impairs application services – not to mention the inconvenience caused to consumers.
Join us in the Intel AI Builder’s Showcase to hear how award-winning ignio™ combines artificial intelligence and machine learning with Intel® AI hardware and software to help organizations run their enterprise IT more efficiently, improve the customer experience, and increase the agility and stability of IT.
Solutions that can generate accurate estimates of counts are in demand. They come in handy to tally the number of people in a video frame, they can help count animals of an endangered species in the wild, they can be used to estimate the count of any defined shape within a picture. Traditional crowd counting methods and models that use detection or regression-based approaches fall short in such scenarios. They suffer from problems such as occlusion, non-uniform distribution, perspective distortion, camera angles, and background clutter. They are not robust and often fail with even simple changes to the planned settings.
Deep learning-based crowd counting solutions offer an excellent recourse to such problems. Cascaded CNN’s use density-based estimations to preserve the spatial information and can localize the count in addition to estimating the overall tally. Such neural network architectures capture global and local features and have been drastically improved over the past year to achieve remarkable accuracy.
This talk will provide a background of crowd counting and share the pros & cons of the approaches. It will present a real-world application in the biodiversity conservation space: see how AI helped count penguin populations in Antarctica by using time-lapse pictures from camera traps. The implementation challenges faced, and the approach used to address them will also be discussed.
How do you accelerate innovation and deliver faster time-to-value for your AI initiative while ensuring enterprise-grade security and high performance? How do you provide easy access to the tools and data your data science teams need for large-scale distributed ML/DL with greater agility for rapid prototyping, iteration, and deployment?
Nanda Vijaydev will share practical examples of — and lessons learned from — ML / DL use cases in financial services, healthcare, and other industries. You’ll learn how to quickly spin up containerized multi-node environments for TensorFlow* and other ML / DL tools — to train models in a multi-tenant architecture either on-premises, in the cloud, or in a hybrid environment. She will also talk about the challenges and best-practices regarding ML operationalization and model deployment in production.
Inspur is developing smarter, more powerful end-to-end solutions that help businesses leverage and tackle intelligent technologies from cloud to edge, offering comprehensive AI capabilities that span across application, framework, management and hardware platforms. In this presentation, Inspur will introduce a scalable deep-learning inferencing solution using Intel’s Field-Programmable Gate Arrays (FPGAs) supported by Inspur’s hardware and software. Also learn about the Inspur AI appliance designed in collaboration with Intel to provide flexible, efficient, and easy-to-use converged infrastructure for AI users worldwide.
The Reinforcement Learning system demonstrates an agent which learns to pack boxes of different sizes efficiently in containers while respecting multiple operational constraints, e.g. preventing items from overlapping, the need for physical support, weight distribution, etc. The agent is trained using reinforcement learning to minimise the wasted space. Without any human knowledge, the agent achieves super-human performance and outperforms commercial optimisation software. The system was trained on an Intel multi-core system that helped to parallelise simulations and generate data for the agent to accelerate the learning process.
John Snow Labs
Companies who deploy & operate ML/AI models in production, scale to multiple data science teams and workflows, and need a reproducible & collaborative experimentation environment, must often invest substantial effort in assembling their internal AI platforms. This is harder for high-compliance industries – where personal, medical, financial, or educational data must be used for both model training and inference – while maintaining privacy & compliance requirements. This demo shows a turnkey, complete, scalable, and highly secure AI platform that is delivering this functionality to Fortune 500s today.
QuEST Global Digital
QuEST Global has established an Intel AI Lab for developing and scaling Intel AI-based solutions globally and has been collaborating with the Intel AI team to bring Intel® AI/Vision/OpenVINO™ -based solutions in different verticals including industrial, manufacturing, healthcare, banking and financial sectors. QuEST Global will showcase some of their Intel AI-based solutions in these verticals and will discuss the Intel AI value proposition for solutions in different verticals.
Skymind is the company behind Eclipse Deeplearning4j, the most widely-used, open-source, distributed deep-learning library for the JVM. With native support for big data infrastructures such as Hadoop* and Spark*, Skymind brings AI to legacy enterprise environments, both on-premise and cloud, for use on distributed Intel® CPUs.
In collaboration with Intel, Skymind supports Intel® Xeon® Scalable Processors and MKL-DNN to accelerate machine learning workloads from conception to deployment. As a result, organizations can obtain the flexibility and the speed required to deliver faster results in their AI journey. Supported use cases include natural language processing (e.g. chatbots), computer vision (e.g. object detection), and many others.
Stratifyd's AI platform is an end-to-end customer engagement solution that analyzes, categorizes, and visualizes omni-channel customer feedback in real time, providing clients – including Fortune 500 companies such as Kimberly-Clark and Prudential Financial, and brands such as Etsy – with intelligence on both macro and micro levels.
Subtle Medical develops FDA-cleared AI software products to speed up and improve the quality of radiology exams. There are, however, a number of obstacles to deploying and implementing AI solutions within hospital settings due to unique IT requirements and limited computational resources. In this presentation, we will show how Subtle Medical deploys its AI software in an efficient and seamless approach to enable fast inference times powered by the Intel® Distribution of OpenVINO™ toolkit.
Regular inspection of underground water/sewage pipelines helps prioritize periodic maintenance tasks that help avoid pipe leakage, breakage, and blockage, reducing property damage or safety hazards. NASSCO’s PACP (Pipeline Assessment Certification Program) is the North American Standard for pipeline defect identification and assessment, providing standardization and consistency to the methods by which pipeline conditions are identified, evaluated and managed.
Videos of pipeline are generated by a camera-mounted rover, maneuvered by an operator into the underground pipelines. The operator typically notes the observations and reports any problems discovered and their locations. The process of manually reviewing these data is time-consuming and error-prone. Wipro’s Pipe Sleuth is an approach to automate pipeline inspection from the recorded videos using techniques in computer vision and deep learning.
Wipro’s Pipe Sleuth solution (in collaboration with its customer DC Water), automates the process of identification of defects in pipeline videos, annotation of pipeline defects in the videos, scoring/grading of pipeline health, and generation of reports as per PACP standards. The deep learning and CV-based solution has been optimized for running on Intel platforms (CPUs) using Intel® Xeon® Scalable processors and Intel® Distribution of OpenVINO™ toolkit for optimization of the solution for Intel CPUs. The presentation will walk through the Pipe Sleuth solution, which has been optimized to run on Intel platforms at acceptable levels of accuracy and performance.
More information about the event can be found on the AI in the Enterprise: The Intel® AI Builders Showcase Event page. We look forward to seeing you there.