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IBM Technology explores AI through explanations of how systems work, what can go wrong and how organisations put them to use. Model developments share space with cybersecurity, data handling and…
IBM Technology explores AI through explanations of how systems work, what can go wrong and how organisations put them to use. Model developments share space with cybersecurity, data handling and software workflows. The channel is particularly relevant to viewers who want to connect AI news with technical questions about trust, integration and the systems that support an application. Its subjects span both introductory concepts and issues faced when technology is used in practice.
Security is a recurring thread. One episode looks inside AI-powered cyberattacks, while another asks why a chatbot can be deceived. Coverage of ransomware detection considers the role of storage in identifying an attack. Together, these topics move beyond whether a model can produce a useful answer to questions about the wider environment in which it operates. Data exposure and the tension between model capability and safety extend that line of inquiry. Explanatory questions provide an accessible entry into subjects that also matter to developers and technology teams.
The application side includes a look at agents, language models and APIs using real-time data at the US Open, as well as discussion of the path from a coding prompt to production software. Model-news roundups sit alongside an introduction to Jev, framed as a model that does not generate text. These formats give the channel room to address both a specific technical concept and a collection of industry developments. For learners, developers and people assessing AI at work, the result is a set of explanations that link capabilities with implementation and security considerations. The recurring question is how to understand what a system is doing well enough to judge where and how it can be useful.
Latest episode
OpenAI's always-on agents, 700+ math manuscripts & HackerRank's AI interviewer
Visit Mixture of Experts podcast page to get more AI content → https://ibm.biz/~qrhY7KEYZ
Can AI take mathematics beyond human understanding?
This week on Mixture of Experts, host David Zax is…
Visit Mixture of Experts podcast page to get more AI content → https://ibm.biz/~qrhY7KEYZ
Can AI take mathematics beyond human understanding?
This week on Mixture of Experts, host David Zax is joined by Kush Varshney, Skyler Speakman, and Utsav Dholakia. First, OpenAI introduces Dots at DevDay, and Utsav shares his firsthand impressions as the panel discusses whether persistent AI agents can truly work independently. Next, OpenAI releases more than 700 AI-generated math manuscripts, raising questions about the future of mathematics and whether humans can keep up with AI-generated math. Then, HackerRank’s Chakra brings AI to job interviews, prompting a debate about bias, access to opportunity, and how we evaluate candidates.
Finally, Reflection AI unveils Beam, giving the panel an opportunity to explain mixture of experts models and the difference between open-weight and open-source AI.
All that and more on Mixture of Experts.
00:00 – Intro
00:58 – Inside OpenAI DevDay and Dots
9:08 – AI generates 700+ math manuscripts
16:03 – HackerRank’s AI interviewer
24:18 – Mixture of experts models, explained
"The opinions expressed in this podcast are solely those of the participants and do not necessarily reflect the views of IBM or any other organization or entity. AI tools may be used to transcribe this episode and support selected stages of the production process. All AI-assisted content is reviewed by the production team before publication."
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AI was used in the creation of the transcript and metadata for this video.
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