Intel on AI

The Intel on AI podcast is relaunching with New York Times best-selling author Abigail Hing Wen as the new host. Focusing on interviews with the world's most interesting AI experts, the Intel on AI podcast covers a wide range of topics, including applications, strategy, ethics, policy, entertainment, scientific research, and society's future. Previously, the podcast ran for over sixty episodes and featured Intel partners and AI business leaders.

Future episodes will include guests such as: • Andrew Ng, co-founder of Coursera, adjunct professor of computer science at Stanford University, and former head of Baidu AI Group and Google Brain • Pieter Abbeel, professor at Berkeley Artificial Intelligence Research (BAIR) Lab • Ed Hsu, senior adviser of disruptive technologies at ?World Bank Group • Sandra Rivera, executive vice president and chief people officer at Intel

Host Abigail Hing Wen is the author of the New York Times best-selling novel Loveboat, Taipei, a contributor to Forbes and Fortune, and has been seen on Bloomberg, NBC News, and more. She holds a BA from Harvard and JD from Columbia.

Follow Abigail on Twitter at: twitter.com/abigailhingwen

Learn more about the future of AI at: intel.com/ai

In this Intel on AI podcast episode: Enterprises today are investing in machine learning (ML) and artificial intelligence (AI) to transform their business and optimize existing workflows. Yet, knowing how to implement AI and ML in your business can be very challenging. Ali Bajwa, the Director of Partner Solutions Engineering at Cloudera, joins the Intel on AI podcast to discuss how Cloudera has recently launched the Cloudera Data Platform (CDP) which is an integrated analytics and data management platform offering broad data analytics and artificial intelligence functionality along with secure user access and data governance features. Ali describes how CDP can be deployed on cloud services or in private data centers and provides enterprises with powerful features to address almost any of your AI business needs.

Ali also describes how CDP is optimized for Intel architecture including 2nd Generation Intel Xeon Scalable Processors, Intel Optane DC Persistent Memory and Intel Ethernet providing their customers the incredible performance and flexibility that Intel technologies provides.

To learn more, visit: cloudera.com

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In this Intel on AI podcast episode: Organizations want to get insights from their data but face barriers to adopting machine learning (ML) and AI including lack of data science expertise in the global workforce, exorbitant costs, lack of guidance, and time commitments of traditional modeling methods. Ben Taylor, the Chief AI Evangelist at DataRobot, stops by the Intel on AI podcast to discuss how the DataRobot enterprise AI platform enables organizations build and deploy accurate ML models in a fraction of the time needed in comparison with traditional data science methods. He describes his work helping organizations overcome many of the obstacles they face when implementing AI in their business models including identifying the important business problem to solve for an organization rather than the most interesting problem. Ben also talks about a challenge DataRobot recently faced having a limitation on the size of data sets that they could train due to limited memory availability on their platform. DataRobot worked with the Intel AI Builders program to optimize their platform to utilize Intel DC Optane Persistent Memory which enabled DataRobot to provide customers with the ability to train incredibly large data sets up to 100+ gigabytes. This gives businesses the ability to truly unlock the potential of all of their data and not be hindered by smaller training data sets. Ben also talks about DataRobot is working hard to help organizations implement AI in an ethical way and protect against bias in AI algorithms.

To learn more, visit https://www.datarobot.com/ and join the conversation at: community.datarobot.com

Visit Intel AI Builders at: builders.intel.com/ai

In this Intel on AI podcast episode: In Europe, like many locations around the world, there are important efforts around wildlife monitoring and invasive species population control to ensure that endemic species are still able to live in their natural habitats. Yet, it can be very challenging for conservationists and wildlife experts to track and monitor specific species manually. Some digital sensors and tracking systems can be put in place to assist, but often trail cameras and sensors can trigger false positives from unrelated wildlife species.

Jürgen Konetschnig, the Chief Technical Officer at SAIMOS, joins the Intel on AI podcast to talk about how the SAIMOS / ICU Server solution is using AI technology to help wildlife control efforts become more efficient. He illustrates how the solution uses AI to ensure that alarms are not triggered "blindly" and the authenticity of each alarm can be evaluated with a short video-recording or an image. Jürgen highlights how the solution is an integrative platform that links geo-data with data from any other source (eg video + sensor data) and can detect and track the intended wildlife by analyzing the image or video in real time. Jürgen also talks about how SAIMOS utilizes Intel Movidius technology to power their solution has worked with Intel to optimize their solution with Intel Distribution of OpenVINO toolkit enabling them to greatly improve performance. He also describes how SAIMOS is now working to enable all of their solutions to be OpenVINO compatible and will continue to work with Intel to utilize and optimize for Intel architecture.

To learn more, visit: saimos.de/en shop.icuserver.com

Visit Intel AI Builders at: builders.intel.com/ai

In this Intel on AI podcast episode: As customers realize the importance of adopting artificial intelligence (AI) throughout their business there can be many challenges to overcome. Hurdles like identifying the pertinent business problems, defining success criteria, evaluating technologies, implementation techniques, and adoption can all be gating factors to implementing AI.

Potti Ramakrishna, General Manager Head of HOLMES Platform Engineering at Wipro Technologies, joins the Intel on AI podcast to discuss how Wipro HOLMES can help alleviate may of the challenges for enterprise AI adoption and is a powerful suite of automation tools that can help an organization infuse AI into almost any facet of their business. He describes how Wipro and Intel have been collaborating for several years and through the Intel AI Builders program they have been able to optimize their HOLMES platform for Intel architecture and software like the Intel Optimization for Tensorflow and Intel Distribution of OpenVINO Toolkit.

Potti discusses how the Wipro platform can address specific challenges like data curation through their HOLMES Data Labeling Studio that helps reduce data intake and processing efforts. He also highlights how the name HOLMES is an acronym for Heuristics & Ontology-based Learning Machines & Experiential Systems which can help customers address an incredibly broad variety of AI challenges within their business. Potti also emphasizes several other solutions that Wipro has collaborated with Intel on including Wipro Pipesleuth for pipe crack analysis as well as medical imaging solutions that assist in diagnoses of cancer using lung CT scans.

To learn more, visit: wipro.com/holmes

Visit Intel AI Builders at: builders.intel.com/ai

In this Intel on AI podcast episode: Safety and productivity issues are crucial for engineering, manufacturing, and industrial organizations. Ensuring that workers are in their safe and respective area not only helps prevent injuries but also increases productivity and compliance for a company. Apoorva Ankad, Head of Computer Vision and AI Group at Deevia Software, joins the Intel on AI podcast to discuss Deevia's AI powered monitoring system which supports several different industries with AI powered vision solutions. He highlights how Deevia's solution can leverage an organization's existing CCTV (closed circuit television) camera infrastructure and existing Intel-based infrastructure to accomplish their customer's training and inferencing needs. He talks about how Deevia's solution has been used to help detect and analyze factory worker's ergonomic positions to help analyze if workers are conducting their tasks in a safe manner. This can help maintain worker safety by sensing when they are not operating in a safe way and alerting the worker that they need to adjust their work to a more ergonomically correct action. Apoorva also discusses how Deevia has pivoted their technology to help create a social distancing monitoring system where camera sensors can detect if social distancing is correctly happening in a public area. Where a lapse is detected, an audio message can be delivered via speakers as a reminder to adhere to distancing protocols and help keep the general public safe by helping to remind everyone to stay socially distanced.

To learn more, visit: deevia.pw

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In this Intel on AI podcast episode: Industrial operations such as metal manufacturing need to keep track of how well their facilities, time, and materials are being utilized in order to be as productive and profitable as possible. Yet, there can be 100s of sensors in a manufacturing plant and capturing, analyzing, and making predictions based on all that data can be very difficult. Also, ensuring the proper working or equipment can also ensure the safety of industrial workers and minimize wasted materials. Hitesh Mittal, the Director Business Development from Tvarit GmbH, joins the Intel on AI podcast to discuss how Tvarit is working to optimize the business processes of their customers. He described a specific use case where Tvarit worked to optimize operations of a steel manufacturing plant using supervised machine learning algorithms to predict the health of mechanical components. Analyzing and predicting equipment utilization reduces waste & downtime, increases safety and profitability and Hitesh describes how the training and inferencing were done on Intel Xeon Scalable processors. Hitesh also emphasizes how Tvarit works closely with the Intel AI Builders program to optimize their solution for Intel technology and praised the program for the amount of help Tvarit received from the Intel team.

To learn more, visit: tvarit.com

Visit Intel AI Builders at: builders.intel.com/ai

In this Intel on AI podcast episode: The COVID-19 coronavirus, since its initial outbreak, has quickly become a global pandemic and the inadequate amount of lab tests available for people suspected of infection have posed serious risks to public health and efforts in containing the virus. Jingwen Jia (Wendy), the Assistant General Manager at Huiying Medical (HYHY), joins the Intel on AI podcast to discuss how the HYHY imaging diagnostic solution assists healthcare providers to detect and diagnose potential COVID-19 infections by analyzing computed tomography (CT) chest scans with AI-powered algorithms. She discusses how the HYHY solution is a complementary tool to help doctors make diagnosis quickly by analyzing the ground-glass opacity (GGO) and other indicators revealed in lung CT imagery. The HYHY solution has already been deployed in 30+ hospitals throughout China. Wendy emphasizes how, with the help of AI, the HYHY solution can help doctors detect the virus quickly, helping patients receive the care they need faster and assisting with the tracking and containment of the COVID-19 virus. She also discusses how HYHY has collaborated with Intel through the Intel Capital and Intel AI Builders programs to optimize their solutions to run on Intel architecture and the Intel Distribution of OpenVINO toolkit to help give healthcare providers a powerful tool to help combat the COVID-19 pandemic around the world.

To learn more, visit: en.huiyihuiying.com

Visit Intel AI Builders at: builders.intel.com/ai

In this Intel on AI podcast episode: Retail shoppers around the world are always looking to have a more personalized, convenient, and overall better shopping experience when they visit stores. With the advent of advanced cameras, sensors, and AI technology, smart stores are eliminating the need for customers to wait in line, scan their purchases, or even complete a payment transaction with a cashier. Also, during the worldwide pandemic it can be difficult for retail staff to limit their exposure to potentially infected people throughout the course of their workday. For consumers as well, maintaining safe distances can be nearly impossible when at a busy store or when interacting directly with a store cashier. Mark Perry, the Global Business Director at Cloudpick, joins the Intel on AI podcast to talk about how Cloudpick's AI-powered smart store solution is providing customers with enhanced shopping experiences while also working to help keep them safe. He describes how Cloudpick's system can automatically recognize a customer when they enter the store as well as the products that the customer gathers and automatically charge costs to the customer's account without having to interact with a cashier or scan their items. Mark also talks about how during the Covid-19 pandemic, retail stores being able to limit staff interactions with customers and allowing customers to avoid touching checkout machines helps customers staff avoid potential exposure. He also discusses how optimizing the Cloudpick solution for Intel architecture and using the Intel distribution of OpenVINO has helped enable Cloudpick's solution to operate fater and provide consumers with better, safer shopping experiences.

To learn more, visit: cloudpick.com

Visit Intel AI Builders at: builders.intel.com/ai

In this Intel on AI podcast episode: As wind turbines proliferate and grow in size and complexity, the biggest challenge to Wind Energy is the high cost of Operations and Maintenance. Manual inspection and maintenance is dangerous and expensive. With the advent of drones, gathering maintenance footage has become much easier, but without the use of AI technology, inspecting tons of footage and data is time consuming, expensive, ineffective. One defect can potentially incapacitate an entire turbine, however automation of maintenance can significantly improve the value and cost of wind energy. Alberto Gutierrez Ph.D., Chief Data Scientist at HCL America, joins the Intel on AI podcast to talk about HCL's deep learning (DL) based wind turbine defect detection solution and how it is helping to drive sustainable energy today.

He illustrates how HCL's solution enables wind energy operators to utilize drone technology to capture images of turbines and use deep neural network (DNN) computer vision algorithm to find potential defects in those turbines. Some of the defects that are often detected include visible defects on blade surfaces like missing teeth in VG (Vortex Generator) or panel and blade edge corrosion. Alberto describes how using AI and drones to address this workload is ultimately a safer and less expensive option that helps make wind energy cheaper and more attractive as an alternative, clean energy source. He also discusses how HCL has collaborated closely with the Intel AI Builders program to optimize their solution's DL model using the Intel Distribution of OpenVINO toolkit to process video stream, image segmentation and object detection.

To learn more, visit: builders.intel.com/ai/membership/hcl

Visit Intel AI Builders at: builders.intel.com/ai