Intel on AI

In this Intel on AI podcast episode: Enterprises are in a race to become more agile, nimble, and responsive to remain competitive in today’s fast-changing marketplace. Turning to machine learning (ML) and data science is essential. Today companies can spend millions building their own internal ML pipelines that need ongoing support and maintenance. There are numerous tools that exist for developing traditional web services, but not many tools that enable teams to adopt ML and artificial intelligence (AI). Dillon Erb, CEO at PaperSpace, joins the Intel on AI podcast to talk about how their Gradient solution brings simplicity and flexibility of a traditional platform as a service (PaaS) for building ML models in the cloud. Grandient enables ML teams to deploy more models from research to production because of dramatically shorter development cycles when using the solution. Dillon describes how enterprises can now deploy a mature and robust PaaS within their data center to train and deploy models in a fraction of the time and costs that it previously required. He also discusses how PaperSpace has worked closely with Intel to make it easy for enterprises to use their existing CPU hardware infrastructures to build performant machine learning models with Gradient. To learn more, visit: paperspace.com Visit Intel AI Builders at: builders.intel.com/ai

18 Mai 202015min

In this Intel on AI podcast episode: To remain competitive businesses need to utilize their data to the fullest and make data-driven decisions at all levels. Yet, collecting and analyzing data can be expensive when hiring external expertise or time consuming when training internal teams. Vivek Mishra, Head of Technology at vPhrase Analytics, joins the Intel on AI podcast to talk about their AI-based business intelligence tool automate data analysis and reporting to help any company take advantage of their data. He describes how their solution, Phrazor, transforms complex data into easy-to-understand reports with language-based insights and supports multiple languages. Vivek also highlights how Phrazor gathers data in a structured format and applies language to present the reader with humanized, targeted analysis of their data so that anyone in an organization can analyze and understand it. Phrazor gives any enterprise the ability to both analyze and visualize their data in a single, easy to use platform. Vivek discusses how the vPhrase team worked closely with Intel engineers through the Intel AI Builders program to optimize their solution for Intel Xeon processors and Intel optimized TensorFlow and Python to substantially reduce their training time. To learn more, visit: vphrase.com phrazor.ai Visit Intel AI Builders at: builders.intel.com/ai

13 Mai 202019min

In this Intel on AI podcast episode: Every customer call is an opportunity to gain information about customer preferences and provide a positive experience for callers. Yet, call centers can be expensive to run and maintain, especially in the current environment where many workers are unable to travel to their job due to local and state govt restrictions. Filling the call center agent role with an AI assistant is no simple task, but by utilizing Gnani’s solution, companies can ensure that their customers have a good call center experience while saving costs on the back end. Ganesh Gopalan, Co-founder and CEO at Gnani.ai, joins the Intel on AI podcast to talk about how Gnani’s AI enabled virtual voice assistants integrate real-time analytics with a voice-bot agent to interact with callers. He illustrates how this technology delivers an intelligent, fully automated option for call centers enabling businesses to quickly respond to customer concerns and questions in a scalable way. Ganesh also illustrates how Intel Engineers worked to optimize Gnani’s decoding speed to help address more customer service calls for the same hardware configuration, making the whole solution more viable and efficient from the customer standpoint. To learn more about Gnani.ai and AI enabled voice bot technology, visit: gnani.aignani.ai Visit Intel AI Builders at: builders.intel.com/ai

7 Mai 202012min

In this Intel on AI podcast episode: The Internet of Things (IoT) is producing a tremendous amount of data. But companies need to make sense of the data and AI is a clear answer to analyze and act on that data to deliver the full potential of IoT. Previously, combining AI and IoT was relatively unthinkable. Now it is an incredibly fast growing trend, often referred to as AIoT. Bill Roberts, Senior Director of Global Process, Sensors and Smart Practice at SAS, joins the Intel on AI podcast to discuss how Intel and SAS participated in a survey to discover how organizations are using AIoT today, who within the company realizes and utilizes the value, and where AIoT will grow in the future. He illustrates how an organization’s ability to deliver value from IoT is facilitated by the use of AI. Bill discusses how all of the data being derived by the many IoT sensors and cameras available today need AI to analyze and produce insights from that data. The survey highlights how this convergence of AI and IoT is really beginning to show tremendous value to organizations that are implementing AIoT within their systems. Bill also talks about how SAS themselves have even put their own AIoT system in place measuring the health of bee hives on their North Carolina campus and use the huge amounts of data they derive from their IoT systems and AI analysis to help track, analyze, and predict the health of bees across their campus and even their state. To learn more, visit: sas.com/aiotsolutions Visit Intel AI Builders at: builders.intel.com/ai

6 Mai 202012min

In this Intel on AI podcast episode: Healthcare workloads, particularly in medical imaging, require more memory usage than other AI workloads because they often use higher resolution 3D images. Deep learning (DL) models developed from these data sets require both high accuracy and high confidence levels to be useful in clinical practice, but this is incredibly data and compute intensive. David Ojika, Research Scientist at the University of Florida, joins the Intel on AI podcast to talk about his research focused on the use of accelerators for machine learning (ML) as well as heterogeneous computing using Intel FPGAs, CPUs, and GPUs for inferencing. He describes a project that he led between Intel and Dell EMC which illustrated how 2nd Generation Intel Xeon Scalable processors with Intel-optimized TensorFlow on a DellEMC PowerEdge server was a very suitable configuration to address 3D models being deployed for medical imaging analytics. David talks about how, with more than 1 TB of system memory available, 2nd Gen Intel Xeon Scalable enable researchers to develop large DL models that can be several orders of magnitude larger than those available on existing DL accelerators. He expresses how this work between the University of Florida, Dell EMC and Intel better enable the use of AI-based medical imaging to help detect and diagnose cancer using MRI and other medical imaging systems and can ultimately help save lives. To learn more, visit: intel.ly/memorybottleneck Visit Intel AI Builders at: builders.intel.com/ai

20 Apr 20208min

In this Intel on AI podcast episode: Today, the deluge of data has made demand for machine learning engineers explode. Also because distributed computing is a challenging and elite subfield of computer programming, finding engineers to address these skill sets can be even more challenging and limit many business from being able to take advantage of advanced technologies like machine learning (ML). Dean Wampler, the Head of Developer Relations at Anyscale, joins the Intel on AI podcast to talk about how the Ray framework, which is heavily developed and supported by Anyscale, enables any developer to easily write distributed applications which are performant, debuggable, and maintainable. He illustrates how Ray helps developers, enterprises and organizations solve their problems without having to worry about scalable infrastructure and without needing to be experts in distributed computing. Dean discusses some of the biggest users of Ray utilize it to support their infrastructure especially during incredibly high traffic volume events to do general processes, payment processing, and fraud detection. He also describes how other companies are using Ray to do reinforcement learning and business process automation. Lastly, Dean talks about how many teams within Intel are leveraging the Ray framework for model training and reinforcement learning and at the same time working together with Anyscale to contribute to Ray and optimize it for Intel architecture. Lastly, Dean mentioned that in light of growing concerns about COVID-19, they have decided to postpone Ray Summit to late Summer or early Fall of 2020. To learn more, visit: anyscale.io Visit Intel AI Builders at: builders.intel.com/ai

9 Apr 202010min

In this Intel on AI podcast episode: Knowing how a product or service makes a customer feel enables companies to make successful products that their customers enjoy. Yet measuring this traditionally takes a lot of time and effort through impact studies and advertising testing. Millions are spent on creating promotional materials that have little to no analytics behind them. The ability to analyze and measure a customer’s emotional reaction in real-time would be an incredibly valuable tool for many companies. Sumit Chauhan, a Data Scientist from Entropik Tech, joins the Intel on AI podcast to talk about how Entropik focuses on emotion AI to create technologies to detect human emotions through the monitoring of brainwaves, facial expressions, and eye tracking. He illustrates how Entropik’s Affect Lab, the Emotion AI platform is an emotionally intelligent consumer research platform that offers brands a chance to preview the performance of their creatives before launch and integrate the results to produce consumer-centric offerings that generate better ROIs. Sumit discusses how Entropik was able to work with Intel to better optimize their workloads to take advantage of the efficient multi-core processing of Intel Xeon Scalable processors, along with Dlib source build and Intel Distribution of Python to achieve significant improvement in Inference performance for their solution. To learn more, visit: entropiktech.com Visit Intel AI Builders at: builders.intel.com/ai

8 Apr 202012min

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

7 Apr 202011min