What’s the difference between reasoning and traditional AI models? Why is inferencing becoming cheaper? What’s next in AI? (Part 2)

What’s the difference between reasoning and traditional AI models? Why is inferencing becoming cheaper? What’s next in AI? (Part 2)

Author: The Hindu April 7, 2025 Duration: 29:48
Taking the cue from the previous episode on the history of AI, all the way to ChatGPT, this episode looks into the concept of multi-modal AI. We explore how this technology integrates text, images, and audio to mimic human brain processing. We discuss fusion mechanisms that combine these modalities, allowing AI models to comprehend and respond to complex inputs. These mechanisms are crucial for practical applications, such as extracting information from PDFs or answering questions about images. Subsequently, we transition to reasoning models that can be prompted to provide sequential reasoning. Reasoning models, like DeepSeek’s r1, are designed to automatically reason through problems and manage the reasoning effort based on complexity. This approach distinguishes itself from prompting techniques such as “let’s think step by step” or “chain of thought,” which aim to enhance accuracy through structured reasoning. Group Relative Policy Optimization (GRPO) emerges as a reinforcement learning method employed to train models like DeepSeek R1. GRPO incentivizes model improvement through rewards, such as correct answers in mathematical problems. This approach facilitates self-supervised training without human intervention, enabling the emergence of extended thinking chains and enhanced responses. In the concluding segment of the discussion, we address the reduction in training and inference costs, even as companies invest substantial resources in GPUs for training large models efficiently. Algorithmic advancements and hardware improvements facilitate the training of smaller models, thereby increasing AI’s accessibility to enterprises and startups. Agentic AI, model context protocols, and smaller language models represent emerging trends that will shape the future of AI. These advancements will render AI more practical and efficient for real-world applications. Produced by Sharmada Venkatasubramanian

John Xavier, the tech editor at The Hindu, hosts The Interface, a podcast that sits at the crossroads where emerging technologies meet daily life and global business. Rather than just reporting on the latest gadgets or software updates, the conversations here dig into the tangible effects of AI, automation, and robotics-how these forces are actively reshaping entire industries and, by extension, the choices we all face. Each episode is built on the premise that understanding the trajectory of tech is less about predicting the future and more about developing a flexible, informed mindset for the changes already underway. You’ll hear thoughtful analysis on the implications behind the headlines, exploring not just what is happening, but why it matters for policy, work, and society. This isn't a series of breathless hype cycles; it's a grounded guide from a seasoned editor, offering clarity on a dynamic landscape that often feels overwhelming. Tune in for a consistently insightful perspective that helps make sense of the rapid evolution happening just beyond the screen.
Author: Language: English Episodes: 26

The Interface
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