AI Insiders

About AI Insiders

"AI Insiders" positions itself as the go-to podcast for deep, behind-the-scenes insights into the AI industry, offering listeners an insider's perspective on the technology, business, and future of artificial intelligence. TARGET AUDIENCE: - Tech professionals and developers - Business leaders and decision-makers - AI enthusiasts and students - Industry stakeholders - Tech-savvy general audience interested in AI's impact UNIQUE SELLING POINTS: - Expert Access: Featuring interviews with AI researchers, tech leaders, and industry pioneers - Behind-the-Scenes: Exclusive insights into AI companies and breakthrough developments - Real-world Applications: Practical discussions about AI implementation across industries - Future Trends: Analysis of emerging technologies and market directions POTENTIAL SEGMENTS: "The Inside Scoop" - Breaking news and industry updates "Tech Deep Dive" - Technical explanations of new AI developments "Startup Spotlight" - Featuring promising AI startups "AI Ethics Corner" - Discussing ethical implications and challenges "Use Case Breakdown" - Real-world applications and success stories TONE & STYLE: Professional yet accessible Technical but explained clearly Engaging and conversational Thought-provoking and insightful EPISODE FORMATS: 1) Interview Episodes (45-60 mins) - One-on-one conversations with industry experts - Panel discussions on trending topics 2) News & Analysis Episodes (30-45 mins) - Weekly roundup of major AI developments - Expert commentary on industry trends 3) Special Features (60+ mins) - Live event coverage - In-depth technology reviews - Industry conference highlights POTENTIAL EPISODE THEMES: "The Future of Large Language Models" "AI in Healthcare: Revolution or Evolution?" "Building Ethical AI Systems" "Startup to Scale: AI Success Stories" "The Human Side of AI Development" MARKETING TAGLINES: "Where AI Meets Insight" "Your Backstage Pass to the AI Revolution" "AI Understanding, Unlocked" "The Stories Behind the Code"

Publisher

Ronald Soh

Category

business · technology

Language

en

Explicit

No

First episode

13 Nov 2024

Latest episode

29 Mar 2025

Latest episodes

24 episodes in the feed.

1. 

   29 Mar 2025

   Examples as the Prompt: A Scalable Approach for Efficient LLM Adaptation in E-commerce

   This paper addresses the challenges associated with adapting Large Language Models (LLMs) for various tasks within the e-commerce domain using prompting techniques. While prompting offers an efficient alternative to fine-tuning, it often requires significant manual effort from domain experts for prompt engineering and frequent updates to align with evolving business needs. Furthermore, crafting truly unbiased natural language prompts and selecting representative in-context examples remain difficult for humans. The authors propose a novel framework called Examples as the Prompt (EaP). This approach leverages labelled data to enhance prompts by automatically selecting the most representative examples to maximise the few-shot learning capabilities of LLMs. EaP is designed to be efficient due to its unsupervised example selection and adaptive to potential data distribution shifts.

2. 

   28 Mar 2025

   From Demonstrations to Rewards: Alignment Without Explicit Human Preference

   This paper addresses a core challenge in aligning large language models (LLMs) with human preferences: the substantial data requirements and technical complexity of current state-of-the-art methods, particularly Reinforcement Learning from Human Feedback (RLHF). The authors propose a novel approach based on inverse reinforcement learning (IRL) that can learn alignment directly from demonstration data, eliminating the need for explicit human preference data required by traditional RLHF methods. This research presents a significant step towards simplifying the alignment of large language models by demonstrating that high-quality demonstration data can be effectively leveraged to learn alignment without the need for explicit and costly human preference annotations. The proposed IRL framework offers a promising alternative or complementary approach to existing RLHF methods, potentially reducing the data burden and technical complexities associated with preference collection and reward modelling.

3. 

   26 Mar 2025

   Generative AI in Education: Impact Across Grade Levels

   This paper investigates the impact of Generative Artificial Intelligence (GAI), such as ChatGPT, Kimi, and Doubao, on students' learning across four grade levels (high school sophomores and juniors, university juniors and seniors) in six key areas collectively termed LIPSAL: learning interest, independent learning, problem-solving, self-confidence, appropriate use, and learning enjoyment. The study employed a hybrid-survey method combining questionnaires and group interviews. Key findings indicate that GAI has a generally positive impact on all LIPSAL aspects, with the most significant influence on 'appropriate use' and 'independent learning', and the least on 'learning interest' and 'self-confidence'. University students reported a higher level across all LIPSAL aspects compared to high school students. Students hold a positive attitude towards GAI and are willing to use it, recognising its potential while also acknowledging challenges related to accuracy, over-dependence, and ethical considerations.

Podcast Authority Score: 29 / 100

A composite of feed quality, social presence, YouTube performance and engagement. Read the methodology.

Quality

58

Social presence

0

YouTube

0

Engagement

0

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