Utilizing Tech - The Podcast Series about New and Emerging Technologies

Developers of AI applications face many obstacles, but the chief challenge is simply that these are different from traditional software development projects. 85% of businesses say they are looking to adopt AI but a similar percentage of data science projects never reach production. Too many organizations approach AI application development similarly to other software projects. Another issue is foc...

Microsoft plays a large role in enterprise IT applications, from the desktop to the datacenter to the Azure cloud, and the company is active in the world of AI as well. But most of Microsoft’s work has gone unnoticed, with high-profile cloud AI and ML applications at companies like Google and Uber getting all the press. In this episode, Steph Locke joins Chris Grundemann and Stephen Foskett to dis...

You might think that 5G and AI are completely unrelated, but these new technologies support each other. Both are expressions of information theory, and both use similar mathematics under the hood. Both 5G and AI are also disruptive to existing business models and enable new applications. EdgeQ develops processors that leverage machine learning to improve customer experience in 5G and enable custom...

Machine learning models have grown tremendously in recent years, with some having hundreds of billions of data points, and we wonder how big they can get. How do we deploy even bigger models, whether it’s in the cloud or using captive infrastructure? Models are getting bigger and bigger, then are distilled and annealed, and then grow bigger still. In this episode, Dennis Abts of Groq discusses the...

AI processing is appearing everywhere, running on just about any kind of infrastructure, from the cloud to the edge to end-user devices. Although we might think AI processing requires massive centralized resources, this is not necessarily the case. Deep learning training might need centralized resources, but the topic goes way beyond this, and it is likely that most production applications will us...

Development of autonomous vehicles is an excellent example of machine learning applied to industrial IoT. In this episode, Alexander Noack of b-plus and Frank Kräemer of IBM Germany join Chris Grundemann and Stephen Foskett to discuss data collection on the road, central processing, and AI model training. Machine learning is part of the development of autonomous vehicle development and is also use...

We are on the cusp of a totally new architecture for enterprise IT, and this change toward composability is being driven by applications like AI. Instead of designing around fixed-configuration servers, disaggregation allows the use of pools of resources, and composability allows dynamic allocation of these resources as needed for different applications. When it comes to AI workloads, organization...

Industrial cameras and sensors are generating more data than ever, and companies are increasingly moving machine learning to the edge to meet it. This is the market for FogHorn, so we invited Co-Founder Sastry Malladi to join Chris Grundemann and Stephen Foskett to discuss the implications of this challenge. Industrial IoT, also called operational technology, is the use of distributed connected se...

AI applications typically require massive volumes of data and multiple devices within the datacenter. Nvidia acquired Mellanox to bring them industry-leading networking products to enable next-generation applications, including artificial intelligence. Kevin Deierling joins Chris Grundemann and Stephen Foskett to discuss the Nvidia vision for a datacenter-wide compute unit with integrated networki...

Training and optimizing a machine learning model takes a lot of compute resources, but what if we used ML to optimize ML? Luis Ceze created Apache Tensor Virtual Machine (TVM) to optimize ML models and has now founded a company, OctoML, to leverage this technology. Fundamentally, machine learning relies on linear algebra, but how should we pick the fastest approach for each model? Today this is do...

AI applications have large data volumes with lots of clients and conventional storage systems aren’t a good fit. In this episode, James Coomer from DDN talks about the lessons they have learned building storage systems to support AI applications. Inferencing requires terabytes or petabytes of data, often large files and streaming data. For example, autonomous driving applications generate hundreds...

AI and analytics needs access to massive volumes of data, but we are constantly reminded of the importance of securing data. How can data be protected at rest and in flight while still enabling access? That’s what Titaniam is enabling, and this episode of Utilizing AI features CEO Arti Raman, who tells us how they are able to provide access to data without leaving it wide open. They provide granul...

Most organizations have a vast amount of so-called unstructured data, and this poses a major risk for operations. But what if there was an AI-powered application that could sift through all this data, categorize it, and determine the risk profile for everything? That’s the promise of Concentric IO, and the premise for this episode of Utilizing AI with their CEO, Karthik Krishnan. The company uses...

Big data really wasn't all that big until modern analytics and machine learning applications appeared, but now storage solutions have to scale capacity and performance like never before. In this episode, Brad King, Co-Founder of Scality, joins Chris Grundemann and Stephen Foskett to discuss this new demand for scalable storage by AI applications. Applications like autonomous driving, log analysis,...

When it comes to AI, it's garbage in, garbage out: A model is only as good as the data used. In this episode of Utilizing AI, Ayodele Odubela joins Chris Grundemann and Stephen Foskett to discuss practical ways companies can eliminate bias in AI. Data scientists have to focus on building statistical parity to ensure that their data sets are representative of the data to be used in applications. We...

Biases can creep into any data set, and these can cause trouble when this data is used to train an AI model. Alf Rehn, Professor of Innovation, Design, and Management at the University of Southern Denmark, joins Andy Thurai and Stephen Foskett to discuss the lessons he has learned about algorithmic bias based on his work with the Velux Foundations Algorithms, Data and Democracy project. Society is...

Productive use of AI requires the application of existing models to new applications through a process called transfer learning. In this episode, High-Performance Computing and AI Expert Frederic van Haren joins Stephen Foskett to discuss the topic of transfer learning and what it means, from voice recognition to autonomous driving and enterprise applications. Transfer learning is analogous to the...

BrainChip is developing a novel ultra low power “neuromorphic” AI processor that can be embedded in literally any electronic device, rather than centralizing learning in high performance processors. Today’s edge devices are applying exiting models to process inputs but can’t actually learn in the field, but on-chip learning and inference could radically alter the capabilities of devices in automot...

AI is impacting IT operations more quickly than expected, and companies like Splunk are leveraging it to augment staff capabilities. Josh Atwell joins Andy Thurai and Stephen Foskett to discuss practical application of AI to help keep IT operations from drowning in data as applications are distributed in containers and the cloud. The key to using AI for operations is to leverage it to assist staff...

Ken Grohe of Weka discusses various business use cases for AI-enabled applications with Chris Grundemann and Stephen Foskett. AI is coming into practical use right now in applications like autonomous vehicles, drug development and healthcare, and retail. High-performance scalable storage is necessary for many ML training applications, and can be key to advanced applications in life sciences and ot...

Per Nyberg of Stradigi AI discusses "blue collar" AI applications with Stephen Foskett.  What problems can businesses solve with AI technology? Machine learning can find anomalies and outliers in manufacturing and finance, look for relationships in data, and cutting through the complexity of multi-disciplinary data. Consider customer churn: Machine learning can discover features in profiles t...

There are many “last mile” items on the enterprise checklist, and companies are struggling to connect everything together. In this episode, Monte Zweben, CEO of Splice Machine, discusses feature stores with Andy Thurai and Stephen Foskett. Data engineers maintain data pipelines, data scientists maintain the data store, and machine learning engineers are trying to create models and package them so...

AI will be part of everything we do in the future, not replacing us but augmenting our work, and this is especially true in information security. In this, the first episode of Season 2 of Utilizing AI, Steve Salinas joins Chris Grundemann and Stephen Foskett to discuss AI as a “co-pilot.” Enterprise security saw an explosion of threats in the last decade, outstripping the ability of information se...

Just as data analytics transformed business intelligence so is artificial intelligence transforming data science. In this episode, Mel Greer of Intel joins Chris Grundemann and Stephen Foskett to discuss this transformation, which is impacting business of all sorts including Intel itself. Intel's strategy has evolved, and their hardware platforms are following, with the company developing hardware...

In this episode, we ask Red Hat about the platform requirements for AI applications in production. What makes AI applications special and how does this change the infrastructure required to support these? The demand for flexibility, scalability, and distribution seems to match the capabilities of a hybrid cloud, and this is emerging as the preferred model for AI infrastructure. Red Hat is supporti...