It was the deadliest string of shark attacks the world has ever seen. In 2011, sharks in Réunion, a beautiful island, way out in the Indian Ocean started biting people way more than ever before and with lunatic violence. The epidemic forced local surfers, politicians, and business owners into a proxy war with ocean lovers and conservationists worldwide, where long simmering tensions boiled over. Réunion: Shark Attacks in Paradise is the story of what happened on this beautiful island, and t ...
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Innhold levert av Intel Corporation. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av Intel Corporation eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.player.fm/legal.
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Breaking Barriers Deploying AI at the Edge - CitC Episode 275
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Manage episode 330873821 series 1180916
Innhold levert av Intel Corporation. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av Intel Corporation eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.player.fm/legal.
Daniel Chang, CEO of ENERZAi, joins host Jake Smith to discuss ENERZAi’s vision of delivering the best AI experience on everything for everyone, and how they are doing this by overcoming the constraints that edge devices have through AI models. He highlights how ENERZAi’s Automated Model Compression Optimization Toolkit enables AI models to maintain high accuracy, while minimizing latency, size, power consumption for successful Edge deployment, as proven in their recent collaboration with Intel. Daniel further illuminates how their collaboration through the Intel AI Builders program optimized their state-of-the-art 3D hand pose estimation model for Intel Xeon processors achieved incredible performance results. Utilizing the Intel OpenVINO toolkit to improve the latency and inference times while not compromising the model’s accuracy. This optimization project also helped pave the way for customers aiming to use ENERZAi’s 3D hand pose estimation solution for their driver monitoring systems, AR/VR systems or other systems on Intel processors in an incredibly performant way. Jake and Daniel also chat about a customer use case where ENERZAi was able to help a SAAS customer migrate their solution from expensive GPU instances to more cost efficient Intel CPU instances while preserving the model accuracy. Lastly they both dive into discussing the future of AI and how it can solve the constraints that edge devices face to truly enable AI to be deployed everywhere in the world. For more information, visit: https://enerzai.com/ Follow Jake on Twitter at: https://twitter.com/jakesmithintel
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296 episoder
MP3•Episoder hjem
Manage episode 330873821 series 1180916
Innhold levert av Intel Corporation. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av Intel Corporation eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.player.fm/legal.
Daniel Chang, CEO of ENERZAi, joins host Jake Smith to discuss ENERZAi’s vision of delivering the best AI experience on everything for everyone, and how they are doing this by overcoming the constraints that edge devices have through AI models. He highlights how ENERZAi’s Automated Model Compression Optimization Toolkit enables AI models to maintain high accuracy, while minimizing latency, size, power consumption for successful Edge deployment, as proven in their recent collaboration with Intel. Daniel further illuminates how their collaboration through the Intel AI Builders program optimized their state-of-the-art 3D hand pose estimation model for Intel Xeon processors achieved incredible performance results. Utilizing the Intel OpenVINO toolkit to improve the latency and inference times while not compromising the model’s accuracy. This optimization project also helped pave the way for customers aiming to use ENERZAi’s 3D hand pose estimation solution for their driver monitoring systems, AR/VR systems or other systems on Intel processors in an incredibly performant way. Jake and Daniel also chat about a customer use case where ENERZAi was able to help a SAAS customer migrate their solution from expensive GPU instances to more cost efficient Intel CPU instances while preserving the model accuracy. Lastly they both dive into discussing the future of AI and how it can solve the constraints that edge devices face to truly enable AI to be deployed everywhere in the world. For more information, visit: https://enerzai.com/ Follow Jake on Twitter at: https://twitter.com/jakesmithintel
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296 episoder
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