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So, you are heads down working on customer development, and you take a walk, listen to the latest ALL-IN podcast, as you hear the team define your customer development. What they call TAC 2.0 is what we are building here at AI Data CO-OP. You focus on engaging your community and building content, we'll focus on packaging that unique training data and monetize it, giving you the gains. Now that's a good day. Back to building. #ai #ethicalai #datarights #dataprotection #techinnovation #datamanagement #aicommunity #aiforgood Chamath Palihapitiya, Jason Calacanis, David O. Sacks

All machine learning models are best expressed as graphs. Efficient processing of graph-based networks involves large sparse data structures that consist of mostly zero values, and next generation architectures should avoid unnecessary processing. Graph processing is the key to unlocking new AI hardware architectures, as much as new architectures can boost execution of graph-oriented workloads. Hosted by ZDNET's Tiernan Ray with panelists from some of the most groundbreaking AI hardware companies, this panel explores the interrelationship between graph processing and novel AI hardware architectures. #EmergingTech #AI #AIChips #DeepLearning #MachineLearning #Analytics #Hardware #DataScience #Podcast https://lnkd.in/d9z6WCUU

What do graphs have to do with novel hardware architectures for AI workloads? The Connected Data podcast is back featuring a panel that explores the interrelationship between graph processing and novel AI hardware architectures. Hosted by ZDNET's Tiernan Ray with panelists from some of the most groundbreaking AI hardware companies: Blaize, Determined AI, acquired by Hewlett Packard Enterprise company in 2021, Graphcore, and SambaNova Systems. Graph processing is the key to unlocking new architectures, as much as new architectures can boost execution of graph-oriented workloads. As machine learning-powered applications are proliferating, the workloads that are created in order to serve their requirements are taking up an ever increasing piece of the compute pie. An IDC study found that Data Management, Application Development & Testing, and Data Analytics workloads represented more than half of all IaaS and PaaS spending already in 2018. IDC notes that this was driven in part by initial adoption of artificial intelligence and machine learning capabilities. The rise of generative AI means that as adoption grows, data and AI workloads will dominate. This is why we see NVIDIA earnings skyrocket, as well as a renaissance of novel hardware architectures designed from the ground up to serve the needs of data and AI workloads. More specifically for data analytics, understanding relationships among data points is a challenging but essential capability. Graph analytics has emerged as an approach by which analysts can efficiently examine the structure of the large networks and draw conclusions from the observed patterns. This is why DARPA set out to develop a graph analytics processor with the HIVE Project. Furthermore, all machine learning models are best expressed as graphs. This is how machine learning libraries such as TensorFlow work. Efficient processing of graph-based networks involves large sparse data structures that consist of mostly zero values, and next generation architectures should avoid unnecessary processing. #EmergingTech #AI #AIChips #DeepLearning #Analytics #Hardware #DataScience #Podcast Carlo Luschi Evan Sparks Raghu Prabhakar https://lnkd.in/gSPAq4uW

Our first installment of The Innovation Engine for 2024 looks at key trends in the Financial Services industry from 2023 and how those trends will shape the year ahead. Tune in for insights from Rob Murray, Jennifer Mun, and Albert Thibault. They discuss how FinServ companies can tackle technical debt, develop winning customer experiences in an era where consumers are increasingly cashless and even cardless, and harness the coming wave of AI and machine learning. Watch or listen today! https://buff.ly/3TSXyxK #innovation #podcast #FinancialServices

Riyaz Kasmani, our Senior Director of Data Science and head of the Finance AI Innovation Center, describes how his team is thinking about and evaluating AI use cases for finance teams of the future on #WDAYPodcasts: #workday

Riyaz Kasmani, our Senior Director of Data Science and head of the Finance AI Innovation Center, shares how his team is thinking about and evaluating AI use cases for finance teams of the future on #WDAYPodcasts. #TeamWDAY

Riyaz Kasmani, our Senior Director of Data Science and head of the Finance AI Innovation Center, shares how his team is thinking about and evaluating AI use cases for finance teams of the future on #WDAYPodcasts. #TeamWDAY

Riyaz Kasmani, our Senior Director of Data Science and head of the Finance AI Innovation Center, shares how his team is thinking about and evaluating AI use cases for finance teams of the future on #WDAYPodcasts. #TeamWDAY

Riyaz Kasmani, our Senior Director of Data Science and head of the Finance AI Innovation Center, shares how his team is thinking about and evaluating AI use cases for finance teams of the future on #WDAYPodcasts. #TeamWDAY

Riyaz Kasmani, our Senior Director of Data Science and head of the Finance AI Innovation Center, shares how his team is thinking about and evaluating AI use cases for finance teams of the future on #WDAYPodcasts. #TeamWDAY