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The Interface

Podkast av The Hindu

engelsk

Nyheter og politikk

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Tech is evolving rapidly, reshaping industries with AI, automation, and robotics. The Interface podcast, hosted by John Xavier, The Hindu's tech editor, offers insights on navigating this dynamic landscape and the mindset needed to traverse it.

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25 Episoder

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

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

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

7. april 2025 - 29 min
episode Ep22: Demystifying AI and separating hype from genuine progress cover

Ep22: Demystifying AI and separating hype from genuine progress

In this episode, we look into the inflated claims about artificial intelligence, how to distinguish between predictive AI, which often fails to accurately predict individual behavior due to inherent limitations in forecasting and data quality, and generative AI, which is seen more favorably as it creates useful output rather than attempting future predictions. The conversation also touches upon the rapid advancements and decreasing costs of AI development, particularly highlighting the competitiveness of Chinese AI models despite sanctions, and explores the potential societal impacts of AI, including job displacement and the proposal of a "partial lottery system" to mitigate inequalities in merit-based systems. Technological acceleration is increasing exponentially. Innovations that once took decades are now happening in a matter of years, or even months. AI, automation, and robotics are making jobs and industries obsolete while creating new roles and economic opportunities. To make sense of this acceleration our host John Xavier speaks to scientists, business leaders and policymakers on The Interface. Guest: Sayash Kapoor, co-author of AI Snake Oil and computer science Ph.D. candidate at Princeton University. Host: John Xavier, Technology Editor, The Hindu. Edited by Jude Francis Weston

8. feb. 2025 - 29 min
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