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Super Data Science: ML & AI Podcast with Jon Krohn

Jon Krohn
Super Data Science: ML & AI Podcast with Jon Krohn
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5 of 936
  • 935: Global Issues Accelerated by AI (with Solutions), feat. Stephanie Hare
    Jon Krohn speaks to researcher, broadcaster and author Stephanie Hare about how the Hippocratic Oath might apply to artificial intelligence, and a guiding ethos for pushing innovation while protecting users from harm. A code of conduct, she says, could be one approach to ensuring that people are using technology more mindfully and ethically, as well as an opportunity for users to feel that they belong to a wider, global community. Although she sympathizes with people concerned by overregulation undermining innovation, Stephanie also notes that we expect certain standards to be met elsewhere, such as vehicle and drug safety, as well as fair journalistic practices. As Stephanie explains, we need to find a realistic middle ground between innovation and regulation. This episode is brought to you by the ⁠⁠Dell⁠⁠, by ⁠⁠Intel⁠⁠, by Fabi and by Gurobi⁠⁠⁠. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/935⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (01:23) What ‘technology ethics’ is                (14:46) Developing a Hippocratic Oath for tech  (42:32) How to protect against sensationalism           (53:38) How to maintain a balance of growth and infrastructure
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  • 934: Is AI Replacing Junior Workers?
    With the number of jobs dramatically slowing in the last year, many question if this decline is down to companies turning to AI for completing entry-level tasks in particular. Research published earlier this month by Yale University shows no major difference in the types of roles and tasks in so-called `white-collar jobs` since late 2022, an auspicious date that coincides with the launch of ChatGPT. In this week‘s Five-Minute Friday, host Jon Krohn discusses if and when AI will undercut junior-level jobs, particularly in the US. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/934⁠⁠⁠⁠⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.
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  • 933: Future-Proofing Your Career in the AI Era, feat. Sheamus McGovern
    Sheamus McGovern, CEO of Open Data Science, takes Jon Krohn and his listeners on a journey to launching his popular data science and AI conference, now in its tenth year, as well as the great shifts to the fields that he has seen on the way. For Seamus, the growth of his Open Data Science Conference has shown him that an AI engineer is just the beginning of several roles that will emerge from the industry. He asks Jon to consider the breadth of tasks demanded of today’s engineers, from data profiling and transformation to feature engineering, hyper-parameter tuning, and model deployments. Just as the AI engineer emerged from the data scientist role, Seamus expects the industry to respond to the broadening range of projects and tools with new, niche, and dynamic job roles.  This episode is brought to you by the Trainium2, the latest AI chip from AWS, by Gurobi⁠⁠, by ⁠Dell⁠ and by ⁠Intel⁠. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/933⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (02:50) Why Seamus started ODSC (18:27) The differences in AI engineers and data scientists      (24:20) How to keep up with AI’s rapid pace (33:51) How people hire for AI orchestration  (46:26) How companies can get team skillsets right
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  • 932: Should You Build or Buy Your AI Solution? With Larissa Schneider
    Larissa Schneider speaks to Jon Krohn in this Feature Friday about finding the right time to invest in AI solutions, and when it’s better to build them yourself. She discusses her work leading global strategy and operations at Unframe, and how they raised $50 million in venture capital since the company’s launch in March 2025. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/932⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information.
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  • 931: Boost Your Profits with Mathematical Optimization, feat. Jerry Yurchisin
    AI predictions, and how to act on them: Data Science Strategist at Gurobi, Jerry Yurchisin, speaks to Jon Krohn about how mathematical optimization helps enterprises automate decisions for business success and where to find the resources to make it happen.   This episode is brought to you by the ⁠ODSC, the Open Data Science Conference, by Fabi, by ⁠Dell⁠, and by ⁠Intel⁠. Additional materials: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.superdatascience.com/931⁠⁠⁠⁠ Interested in sponsoring a SuperDataScience Podcast episode? Email [email protected] for sponsorship information. In this episode you will learn: (02:34) What mathematical optimization is (13:58) How to get started with mathematical optimization (45:56) Gurobi’s use cases (56:29) Quantum computing and mathematical optimization
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About Super Data Science: ML & AI Podcast with Jon Krohn

The latest machine learning, A.I., and data career topics from across both academia and industry are brought to you by host Dr. Jon Krohn on the Super Data Science Podcast. As the quantity of data on our planet doubles every couple of years and with this trend set to continue for decades to come, there's an unprecedented opportunity for you to make a meaningful impact in your lifetime. In conversation with the biggest names in the data science industry, Jon cuts through hype to fuel that professional impact. Whether you're curious about getting started in a data career or you're a deep technical expert, whether you'd like to understand what A.I. is or you'd like to integrate more data-driven processes into your business, we have inspiring guests and lighthearted conversation for you to enjoy. We cover tools, techniques, and implementation tricks across data collection, databases, analytics, predictive modeling, visualization, software engineering, real-world applications, commercialization, and entrepreneurship − everything you need to crush it with data science.
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