77 episodes

AI is getting real, moving out of academia and hyperscale and into the enterprise. Businesses are adopting AI in strategically, and IT companies are deploying AI technologies in their products. This podcast focuses on practical applications of artificial intelligence and machine learning in the modern enterprise datacenter and cloud infrastructure. Hosted by Stephen Foskett of GestaltIT.com, Chris Grundemann of chrisgrundemann.com, and Frederic Van Haren, of HighFens Inc.

Utilizing AI - The Enterprise AI Podcast Utilizing AI

    • Technology
    • 5.0 • 1 Rating

AI is getting real, moving out of academia and hyperscale and into the enterprise. Businesses are adopting AI in strategically, and IT companies are deploying AI technologies in their products. This podcast focuses on practical applications of artificial intelligence and machine learning in the modern enterprise datacenter and cloud infrastructure. Hosted by Stephen Foskett of GestaltIT.com, Chris Grundemann of chrisgrundemann.com, and Frederic Van Haren, of HighFens Inc.

    3x28: Revisiting Utilizing AI Season 3

    3x28: Revisiting Utilizing AI Season 3

    Frederic Van Haren and Stephen Foskett look back on all the subjects covered during Season 3 of Utilizing AI. The podcast covered many topics, from religious and ethical implications of AI to the technology that enables machine learning, but one topic that stands out is data science. If data is the key to AI, then the collection, management, organization, and sharing of data is a critical element of making AI projects possible. We also continue our “three questions” tradition by bringing in open-ended questions from Rich Harang of Duo Security, Sunil Samel of Akridata, Adi Gelvan of Speedb, Bin Fan of Alluxio, Professor Katina Michael, and David Kanter of MLCommons.

    Three Questions:

    Stephen's Question: Can you think of an application for ML that has not yet been rolled out but will make a major impact in the future?

    Frederic's Question: What market is going to benefit the most from AI technology in the next 12 months

    Rich Harang Senior Technical Lead, Duo Security: In an alternate timeline where we didn't develop automatic-differentiation and put it on top of GUPs do this entire deep learning hardware family that we depend on now never got invented. What would the dominat AI/ ML technology be and what would have been different? 

    Sunil Samel, VP of Pusiness Development, Akriadata: How will new technologies like AI help marginalized members of the communities. Folks like senior citizens, minorities, pepole with disabilities, veterans trying to reenter civilian life?

    Adi Gelvan, CEO and Co-Founder of Speedb: What do you think the risks of AI are and what is your recommended solution?

    Bin Fan, Founding Member, Alluxio: Im wondering if AI can help with a humanitarian crisis happening in the future?

    Katina Michael, Professor, School for the Future of Innovation in Society, Arizona State University: If AI was to self replicate what would be the first thing it would do?

    David Kanter, Executive Director of MLCommons: what s a problem in the AI world where you are held back by the lack of good publicly available data?

    Hosts:

    Frederic Van Haren, Founder at HighFens Inc., Consultancy & Services. Connect with Frederic on Highfens.com or on Twitter at @FredericVHaren. 

    Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.

    Date: 4/25/2022 Tags: @SFoskett, @FredericVHaren,

    • 28 min
    3x27: Benchmarking AI with MLPerf

    3x27: Benchmarking AI with MLPerf

    How fast is your machine learning infrastructure, and how do you measure it? That's the topic of this episode, featuring David Kanter of MLCommons, Frederic Van Haren, and Stephen Foskett. MLCommons is focused on making machine learning better for everyone through metrics, datasets, and enablement. The goal for MLPerf is to come up with a fair and representative benchmark to allow the makers of ML systems to demonstrate the performance of their solutions. They focus on real data from a reference ML model that defines correctness, review the performance of a solution, and post the results. MLPerf started with training then added inferencing, which is the focus for users of ML. We must also consider factors like cost and power use when evaluating a system, and a reliable bench

    Links:


    MLCommons.org
    Connect-Converge.com


    Three Questions:


    Frederic: Is it possible to create a truly unbiased AI?
    Stephen: How big can ML models get? Will today's hundred-billion parameter model look small tomorrow or have we reached the limit?
    Andy Hock, Cerebras: What AI application would you build or what AI research would you conduct if you were not constrained by compute?

    Gests and Hosts


    David Kanter is the Executive Director of MLCommons. You can connect with David on Twitter at @TheKanter and on LinkedIn. You can also send David an email at david@mlcommons.org. 
    Frederic Van Haren, Founder at HighFens Inc., Consultancy & Services. Connect with Frederic on Highfens.com or on Twitter at @FredericVHaren.
    Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.

    Date: 4/12/2022 Tags: @SFoskett, @FredericVHaren, 

    • 43 min
    3x26: DataOps - Putting the Data in Data Science

    3x26: DataOps - Putting the Data in Data Science

    The quality of an AI application depends on the quality of the data that feeds it. Sunil Samel joins Frederic Van Haren and Stephen Foskett to discuss DataOps and the importance of data quality. When we consider data-centric AI, we must consider all aspects of the data pipeline, from storing, transporting, and understanding to controlling access and cost. We must look at the data needed to train our models, think about the desired outcomes, and consider the sources and pipeline needed to get that result. We must also decide how to define quality: Do we need a variety of data sources? Should we reject some data? How does the modality of the data type change this definition? Is there bias in what is included and excluded? Data pipelines are usually simple, ingesting and storing data from the source, slicing and preparing it, and presenting it for processing. But DataOps recognizes that the data pipeline can get very complicated and requires understanding of all these steps as well as adaptation from development to production.

    Three Questions:


    Frederic: Do you think we should expect another AI winter?
    Stephen: When will we see a full self-driving car that can drive anywhere, any time?
    Mike O'Malley, Seneca Global: Can you give an example where an AI algorithm went terribly wrong and gave a result that clearly wasn’t correct?

    Gests and Hosts


    Sunil Samel, VP of Products at Akridata. Connect with Sunil on LinkedIn or email him at sunil.samel@akridata.com.
    Frederic Van Haren, Founder at HighFens Inc., Consultancy & Services. Connect with Frederic on Highfens.com or on Twitter at @FredericVHaren.
    Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.

    Date: 3/29/2022 Tags: @SFoskett, @FredericVHaren

    • 37 min
    3x25: The Unique Challenges of ML Training Data with Bin Fan

    3x25: The Unique Challenges of ML Training Data with Bin Fan

    Machine learning is unlike any other enterprise application, demanding massive datasets  from distributed sources. In this episode, Bin Fan of Alluxio discusses the unique challenges of distributed heterogeneous data to support ML workloads with Frederic Van Haren and Stephen Foskett. The systems supporting AI training are unique, with GPUs and other AI accelerators distributed across multiple machines, each accessing the same massive set of small files. Conventional storage solutions are not equipped to serve parallel access to such a large number of small files, and they often become a bottleneck to performance in machine learning training. Another issue is moving data across silos, storage systems and protocols, which is impossible with most solutions.

    Three Questions:


    Frederic: What areas are blocking us today to further improve and accelerate AI?
    Stephen: How big can ML models get? Will today's hundred-billion parameter model look small tomorrow or have we reached the limit?
    Sara E. Berger: With all of the AI that we have in our day-to-day, where should be the limitations? Where should we have it, where shouldn't we have it, where should be the boundaries?

    Gests and Hosts


    Bin Fan, Founding Member of Alluxio Inc. Connect with Bin on LinkedIn and on Twitter @BinFan.
    Frederic Van Haren, Founder at HighFens Inc., Consultancy & Services. Connect with Frederic on Highfens.com or on Twitter at @FredericVHaren.
    Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.

    Date: 3/15/2022 Tags: @SFoskett, @FredericVHaren, @BinFan, @Alluxio

    • 35 min
    3x24: Challenges of Building Successful ML Programs

    3x24: Challenges of Building Successful ML Programs

    With so many AI tools available, it can be a challenge to integrate everything into a productive platform. Orly Amsalem of cnvrg.io joins Frederic Van Haren and Stephen Foskett to discuss the challenges of managing data and resources for AI training, development, management, and deployment. Orly discusses her journey from software development to AI and the challenges people face. Many in the AI community are following the same path, and are looking for tools like cnvrg to help them bring AI to their day to day work. AL blueprints, provided by cnvrg and the community, can help developers and data scientists get started with AI projects. In a recent survey, only 10% of developers said training was their main challenge; nearly every one said that deploying a model to production was the biggest. Orly then discusses the main bottlenecks to MLOps in production and how to break through and normalize AI in the enterprise.

    Links:


    "Five Ways to Shift to AI-First"

    Three Questions:


    Frederic: When do you think AI will diagnose a patient as accurately as (or better than) a human doctor?
    Stephen: Is MLOps a lasting trend or just a step on the way for ML and DevOps becoming normal?
    Eitan Medina, Habana Labs: If you should choose something for AI to do for you in your day-today life, what would it be?

    Gests and Hosts


    Orly Amsalem, VP of AI Innovation & Business Development at cnvrg.io. Read "Five Ways to Shift to AI-First" here. 
    Frederic Van Haren, Founder at HighFens Inc., Consultancy & Services. Connect with Frederic on Highfens.com or on Twitter at @FredericVHaren.
    Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.

    Date: 3/01/2022 Tags: @SFoskett, @FredericVHaren, @cnvrg_io

    • 38 min
    3x23: How Algorithmic Bias in ML Affects Marketing

    3x23: How Algorithmic Bias in ML Affects Marketing

    As machine learning is used to market and sell, we must consider how biases in models and data can impact society. Arizona State University Professor Katina Michael joins Frederic Van Haren and Stephen Foskett to discuss the many ways in which algorithms are skewed. Even a perfect model will produce biased answers when fed input data with inherent biases. How can we test and correct this? Awareness is important, but companies and governments should take active interest in detecting bias in models and data.
    Links:


    "Algorithmic bias in machine learning-based marketing models"

    Three Questions:


    Frederic: When will AI be able to reliably detect when a person is lying?
    Stephen: Is it possible to create a truly unbiased AI?
    Tom Hollingsworth of Gestalt IT: Can AI ever recognize that it is biased and learn how to overcome it?

    Gests and Hosts


    Katina Michael, Professor in the School for the Future of Innovation in Society and School of Computing and Augmented Intelligence at Arizona State University. Read here paper here in the Journal of Business Research. You can find more about her at KatinaMichael.com. 
    Frederic Van Haren, Founder at HighFens Inc., Consultancy & Services. Connect with Frederic on Highfens.com or on Twitter at @FredericVHaren.
    Stephen Foskett, Publisher of Gestalt IT and Organizer of Tech Field Day. Find Stephen’s writing at GestaltIT.com and on Twitter at @SFoskett.

    Date: 2/21/2022 Tags: @SFoskett, @FredericVHaren

    • 39 min

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