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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
The AI Memory Wall: Why LLMs & AI Accelerators Hit a Bottleneck
Learn more about the Memory Wall here → https://ibm.biz/~NjOWdJHJy
More GPU compute doesn't always mean faster AI models.
Kaoutar El Maghraoui explains the memory wall, arithmetic intensity, and…
Learn more about the Memory Wall here → https://ibm.biz/~NjOWdJHJy
More GPU compute doesn't always mean faster AI models.
Kaoutar El Maghraoui explains the memory wall, arithmetic intensity, and why LLM inference can become memory-bound or compute-bound.
Learn how memory bandwidth, batching, and the roofline model impact AI performance.
00:00 Why Faster Chips Don’t Always Make AI Workloads Faster
00:32 The Memory Wall: The Hidden Bottleneck in Modern Computing
01:00 Compute Growth vs Memory Bandwidth Explained
02:39 Arithmetic Intensity: The Metric That Predicts Performance
03:48 Understanding the Roofline Model for AI Optimization
05:27 Compute-Bound vs Memory-Bound Workloads Explained
08:25 LLM Decode vs Prefill: Why Performance Varies
09:48 How Batching Improves AI Inference Efficiency
10:35 Why Leading LLM Serving Systems Rely on Continuous Batching
11:22 Practical Steps to Diagnose and Optimize AI Performance
AI news moves fast. Sign up for a monthly newsletter for AI updates from IBM → https://ibm.biz/~nnWVjebwV
AI was used in the creation of the transcript and metadata for this video.
#aimodels #aiaccelerator #ai #machinelearning
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