In this Intel on AI podcast episode: Manually gathering, processing, and analyzing unstructured data is extremely effort and time intensive. For industries such as insurance or finance this is a big issue and can cost an organization much time and money to address. Antonio Filho, Head of Machine Learning at Nuveo, joins the Intel on AI podcast to discuss how the Nuveo Ultra OCR (Optical Character Recognition) solution eliminates the bureaucracy enabling companies to process documents and payments through an automated system saving time and money. He illustrates how their solution enables computer systems to rapidly classify image files and extract useful metadata for export to a spreadsheet or database effectively unlocking the information trapped in a PDF or TIF image. This alleviates manual data entry by letting the computer read all the characters in a document. Antonio also emphasizes how Nuveo's solution saw a performance beyond their expectations upon optimizing the inference with Intel optimized tools running on systems powered by Intel Xeon Scalable processors.

To learn more, visit: nuveo.ai

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In this Intel on AI podcast episode: Gauging prospects and predicting the direction of the fashion industry is incredibly difficult. Businesses and investors hypothesizing the success of rising stars in the industry have to make real-time decisions to stay ahead of the curve in such a fast-paced industry. Previously, trying to analyze market data to predict a model's success could take weeks. Frédéric Godart, Co-CEO and Head of Industry at IFDAQ (International Fashion Digital Automated Quantification), joins the Intel on AI podcast to discuss how IFDAQ is redefining the intelligent insights and real-time predictive analytics for the fashion and luxury industry enabling organizations to identify fashion trends, highlight opportunities, and guide investors by measuring the effective market value based on the relative performance in the industry. IFDAQ is an artificial intelligence (AI) system that provides quantitative market values for anyone and everything in the fashion and luxury industry drawing data from numerous sources including; industry publications, social media, corporate financial data, casting value of models appearing in fashion shows and many more. Frédéric describes how this solution can predict the real market value and influence of everything that counts in fashion and enables retailers to make smart decisions on their portfolio, helps brands hire fashion models that will have the best impact on their brand image, or enables fashion models determine where they will have the most success. He also describes how working with the Intel AI Builders program has provided great value to IFDAQ and their customers through significant performance increases.

To learn more, visit: ifdaq.com research.ifdaq.com/cities

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In this Intel on AI podcast episode: One of the biggest challenges manufacturing operations face when adopting digitalization and intelligence is the cost and complexity to instrument existing machines, connect them to a network, and deploy relevant software. This is especially costly with legacy equipment that is not enabled with the necessary sensors, intelligence, or ability to communicate with plant infrastructure. Anand Deshpande, the Founder and CEO of Asquared IoT (A2IoT), joins the Intel on AI podcast to talk about how the Equilips 4.0 solution from A2IoT enables businesses to measure overall equipment effectiveness and provide insight into manufacturing operations right from the site of measurement. He explains how Equilips 4.0 is a completely non-invasive and non-touch device that analyzes sounds from industrial machines, welders, and other operations to provide real time feedback on the health and functionality of these operations. Equilips runs on Intel architecture and performs all of the computing at the edge, eliminating the need for a network or cloud and enabling usage in remote and rugged environments. Anand talks about how Equilips is able to transform legacy machines into AI enabled smart operations and highlights how A2IoT worked with Intel to greatly increase their performance by utilizing Intel Distribution of Python, Intel Optimizations for TensorFlow and Intel MKL-DNN.

To learn more, visit: a2iot.com

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In this Intel on AI podcast episode: When making car insurance claims it can take a lot of time to have a claims adjuster inspect the damage to your car and then get the estimate from a body shop reviewed and approved by your insurance company. This process is costly and complicated for both the insurance company and consumers and can be a pain point for all parties involved. Vladimir Starostenkov, a Machine Learning Architect at Altoros, joins the Intel on AI podcast to discuss how the Altoros Car Parts Identification Solution allows users to upload photos of damaged vehicle on location and uses a machine learning (ML) algorithm to assess the vehicle body to provide a real-time estimate on the damage. He points out that this solution can not only help consumers have a better experience when assessing car damage, but that it can save insurance companies, body shops, and consumers an incredible amount of time and money. Vladimir also describes another solution that Altoros provides that automates discovery and derivation of information from PDF documents using techniques like PDF parsing and natural language processing. He highlights how Altoros has worked with Intel to help optimize their solutions using the Intel distribution of OpenVINO toolkit to provide greater value and performance to their customers.

To learn more, visit: altoros.com cardamage.altoros.com

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In this Intel on AI podcast episode: Today, social media is among the primary business and communication platforms for modern organizations, yet, social media networks are incredibly large platforms with some of the most complex security challenges. Increasingly attackers hide attacks with embedded images and video manipulation which evade traditional detection methods and are very difficult for untrained systems to detect, let alone to be detectable by human beings. Matt Price, Principal Research Engineer at ZeroFOX, joins the Intel on AI podcast to discuss how ZeroFox is using machine learning and artificial intelligence to detect deepfake technology being used on social media platforms today. He talks about the challenges that occur when ingesting differently structured data from various social media platforms and how the ZeroFox platform is able to parse and categorize relevant content or types of media to be used in their data models. Matt highlights how utilizing the Intel Distribution of OpenVINO toolkit has allowed ZeroFOX to greatly increase their object detection model performance by taking advantage of the CPU optimizations within the toolkit. He also discusses how ZeroFOX works on threat intelligence, impersonation remediation, financial fraud detection and many other services with their technology.

To learn more, visit: zerofox.com

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In this Intel on AI podcast episode: The process of diagnosing a patient with chest abnormality is done by radiologists and doctors with a lot of experience and expertise. This involves looking for the presence of foreign bodies, infiltrates, and other information to determine the type of infection so that proper medication can be suggested for a cure. This process can be challenging for providers with heavy workloads and sometimes expertise may not be available in remote areas. Alberto Gutierrez Ph.D. Chief Data Scientist, Analytics COE for HCL America, joins the Intel on AI podcast to talk about how HCL's Diagnostic Decision Support for Medical Imaging (DDSM) solution utilizes the power of deep learning to detect the presence of thoracic disease in patients Chest X-ray. He highlights how using the Intel Distribution of OpenVINO toolkit enables HCL to deliver optimized image processing to their customers driving clear ROI in processing and accurate image detection for patients. Alberto describes how this heightened performance assists radiologist to classify the type of infection present in the patient's chest X-ray, both saving waiting time and improving accuracy in patient diagnoses. He also talks about how HCL has worked closely with the Intel AI Builders program to utilize Intel support and software to achieve incredible performance improvements and provide greater value to their customers.

To learn more, visit: hcltech.com builders.intel.com/ai/solutions

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In this Intel on AI podcast episode: Recording live at the Intel AI Summit event in San Francisco California, Ben Taylor the Chief Data Officer of Zeff, joins the Intel on AI Podcast to discuss deepfake technology and risks that deepfakes can present to elections, banking, security, and many other sectors. A deepfake is the use of AI or machine learning techniques to take an existing image or video and superimpose or imitate someone's likeness in that media. Ben talks about how a previous indicator that was used to detect deepfakes was analyzing the pattern of an individual's eye blink rate in a video but because deepfake technology has increasingly become more complex, Zeff now uses techniques like blood flow analysis to identify them. He highlights that while previously Zeff used GPUs to run their workloads, because of batch size and memory constraints Zeff is using Intel Xeon Scalable processors to overcome these limitations and drive better performance in their workloads. Ben also discusses how, in addition to detecting deepfakes, Zeff has been working to transform businesses in a wide array of ways by using AI including smart home technology, gameshow prediction, and many others.

To learn more, visit: zeff.ai

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In this Intel on AI podcast episode: Recording live at the Intel AI Summit event in San Francisco California, Ray Pang Head of Technology Enablement at Supermicro, joins the Intel on AI Podcast to talk about the long term collaboration between Intel and Supermicro. He explains how, in addition to hardware, Supermicro is providing many solutions to their customers including their Resource Savings architecture which allows customers to reuse components in their server systems. This architecture enables customers to upgrade their compute and memory in server systems as advances become readily available while keeping the still viable sub-systems like power, cooling and cabling intact in the server. This reduces TCO (total cost of ownership), lowers acquisition costs, and overall reduces IT waste to help the environment. Ray also describes how Supermicro is working with Intel to support their customers to better take advantage of the AI technology that Intel has been innovating by creating efficient power and cooling systems as well as their AI and Machine Learning Ready Platform to allow their customers to take advantage of state of the art processors from Intel for AI. Lastly, he also highlights how AI and 5G are coming together at the edge and that Supermicro is helping to enable this trend by providing a very broad product portfolio that addresses the different density, power and cooling needs for edge deployments.

To learn more, visit: supermicro.com

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In this Intel on AI podcast episode: Before an MRI technologist can scan a patient, they manually specify the slices they want the MRI to acquire. This can take several minutes of tweaking, leaving a patient waiting anxiously in the MRI scanner and adding unnecessary steps to set up each scan. It can also introduce inconsistencies into images taken over time if parameters or positioning are slightly different each time a patient gets scanned, making it challenging to accurately monitor disease progression or treatment. Recording live at the Intel AI Summit event in San Francisco California, Matthew DiDonato Director of Product and AI at GE Healthcare, joins the Intel on AI Podcast to talk about GE Healthcare's AIRx solution. He highlights how AIRx uses deep learning to automatically identify anatomical structures to prescribe slice locations, and angle of those slices for neurological exams, delivering consistent and quantifiable results. Matthew explains how AIRx enables consistent, repeatable scan alignment to help physicians better monitor a patient across longitudinal studies and also reduces the amount of time a patient has to wait and spend during their MRI treatment. He also talks about how working with the Intel Distribution of OpenVino enabled GE to achieve a significant reduction in processing time to enable more efficient healthcare and better patient outcomes when using AIRx. Matt also talks about how GE Healthcare and Intel are working together on a number of other projects based on the GE Edison AI platform and achieving amazing result with Intel AI technology.

To learn more, visit: gehealthcare.com

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In this Intel on AI podcast episode: Recording live at the Intel AI Summit in San Francisco California, Deepak Dinkar Senior Practice Manager at Wipro Technologies, joins the Intel on AI Podcast to discuss how Wipro is working with Intel AI technologies to drive a wide array of innovative solutions. Deepak discusses how the Wipro Pipe Sleuth solution uses artificial intelligence (AI) to automatically process video scans of municipality sewer and water pipes to identify and mitigate pipe leakage, breakage, and blockage, which could result in property damage or safety hazards. He mentions how DC Water in Washington DC is already seeing benefits in the reduction of time it takes to inspect and maintain their piping infrastructure by using Pipe Sleuth. Deepak also highlights other innovations from Wipro including their surface crack detection solution which uses AI to identify potential defects in concrete structures enabling inspectors to more rapidly address safety concerns. He also outlines the Wipro medical imaging solution which utilizes the Intel Distribution of OpenVINO toolkit to speed up analysis of medical images helping to diagnose diseases from CT and x-ray scans. Lastly, Deepak discusses how being a member of the Intel AI Builders program has helped Wipro address different customers and verticals to create new and innovative solutions.

To learn more, visit: wipro.com

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