Nikhil and Piyush discuss the evolution of digital advertising and the transformative role of AI. We explore the shift from traditional advertising to the sophisticated AI-driven campaigns of today, and how AI has enabled companies like Airbnb to turn advertising from a cost center into a profit making machine. We discuss the importance of measurement and profitability, and how AI is automating sophisticated targeting and bidding strategies to help businesses grow profitably.

=== ⏰ CHAPTERS ===

00:00: Introduction to digital advertising

06:19: The evolution of advertising: from traditional to digital

29:56: How Airbnb turned advertising into a profit center

50:55: Self-driving AI advertising to optimize for profit

=== 🧠 KEY CONCEPTS ===

- Traditional vs. Digital Advertising: The early days of digital marketing replicated traditional advertising models with digital banners and impressions. AI is now transforming these strategies by optimizing for actual conversions and profit, eliminating much of the guesswork.

- AI-Powered Advertising: Google and Meta’s AI platforms are making sophisticated marketing accessible by automating complex processes like bidding, targeting, and creative generation, allowing advertisers to focus on profit-driven goals.

- From Impressions to Conversions: Businesses can now optimize directly for conversions and profit, turning digital marketing into a money-printing machine rather than a cost center.

- Key Metrics and Strategy: The most advanced advertising programs are now focusing on maximizing contribution margin (profitability per product), using advanced tracking and AI-powered optimization.

- Incrementality and Attribution: With tools like incrementality testing, businesses can scientifically measure the true impact of their advertising spend, ensuring they allocate budgets efficiently and profitably.

=== 🔗 LINKS ===

- Homepage: https://artscienceai.substack.com/about

- YouTube: https://www.youtube.com/@ArtScience-AI

- Spotify: https://podcasters.spotify.com/pod/show/art-science-ai

- Linkedin: https://www.linkedin.com/company/art-science-ai

- Facebook: https://www.facebook.com/ArtScienceAI

- Instagram: http://instagram.com/artscienceai/

- Threads: https://www.threads.net/@artscienceai

- Twitter / X: https://x.com/ArtScienceAI

=== 💬 KEYWORDS ===

#AI #GenAI #LLM #ChatGPT #podcast

In this episode Nikhil and Piyush discuss Google's recent AI announcements from the Google Pixel event and the recent antitrust ruling in the US declaring Google to be a monopolist. We discuss new AI capabilities in Google Pixel such as Gemini Live, multimodal AI, and the challenges of integrating LLMs into AI assistants. We then discuss the Google antitrust ruling and its implications for Google and Apple. The conversation also touches on the power dynamics of big tech companies, the future potential of AI to expand human sensory consciousness and the implications for AGI!

=== ⏰ CHAPTERS ===

- 00:00 Google Pixel event: AI features & Gemini

- 07:25 Google Gemini vs. Apple Intelligence

- 27:55 Google antitrust ruling

- 46:54 Future of AI: tech giants vs. startups

=== 🧠 KEY CONCEPTS ===

- AI in Google Pixel: Google announced the launch of new Pixel phones with AI features centered around Gemini Live. However, there was no mention of any third-party app integrations that were announced in Apple Intelligence.

- Gemini Live: Gemini Live allows real-time interaction with the Google AI model, combining voice input with access to the camera, screen, and app information.

- Multimodal AI: Integrating different modalities, such as text, image, audio, and video, into AI models is a complex challenge that researchers are working on. The ultimate goal is to develop multimodal models that can process and generate information in various formats, similar to human sensory consciousness.

- Google's monopoly status: A US judge ruled that Google is a monopolist in an antitrust case, which has implications for Google, Apple, and raises questions about the power dynamics of big tech companies and the future of AI.

=== 🔗 LINKS ===

- Homepage: https://artscienceai.substack.com/about

- YouTube: https://www.youtube.com/@ArtScience-AI

- Spotify: https://podcasters.spotify.com/pod/show/art-science-ai

- Linkedin: https://www.linkedin.com/company/art-science-ai

- Facebook: https://www.facebook.com/ArtScienceAI

- Instagram: http://instagram.com/artscienceai/

- Threads: https://www.threads.net/@artscienceai

- Twitter / X: https://x.com/ArtScienceAI

=== 💬 KEYWORDS ===

#AI #GenAI #LLM #ChatGPT #podcast

Nikhil and Piyush welcome their first guest, Rukma Sen, an AI Product Marketing Manager at Google. Rukma shares her unique insights into how AI is transforming marketing strategies and operations, offering a detailed look at how she coaches AI tools to boost productivity and creativity in her role. From designing custom AI workflows to using cutting-edge tools, Rukma discusses the art and science of integrating AI into her job. We discuss Rukma's content creation workflow using a content creator bot together with a content evaluator bot, how she coaches the LLMs to improve their output and how they form a team to interact with each other. Rukma emphasizes the importance of starting with the end goals in mind when creating marketing content and the importance of human judgment and intuition in conjunction with AI tools to enhance productivity and creativity in a goal-oriented manner.

=== ⏰ CHAPTERS ===

0:00 Introducing Rukma and her role as a Product Marketing Manager

19:06 Coaching and Managing AI assistants

32:52 Multi-agent workflows

42:03 Tools to enhance creativity and productivity

=== 🧠 KEY CONCEPTS ===

- The importance of coaching AI: Rukma emphasizes the importance of treating AI tools as "coachees" that require clear instructions and feedback to improve their outputs. This approach can significantly enhance the effectiveness of AI in various marketing tasks.

- Building a team of AI assistants: Using a team of AI assistants such as a content creator bot and a content evaluator bot can streamline the content creation process and ensure high-quality output.

- Exploring "unknown unknowns": The use of frameworks like the Johari Window helps in uncovering blind spots and unknowns in AI-driven projects, leading to more robust and effective outcomes.

- Understand the end goals: Understanding the target audience, event goals, and competitive presence is crucial when creating marketing content. Human judgment and intuition are essential in conjunction with AI tools to achieve the right goals.

=== 🔗 LINKS ===

Rukma Sen

- Linkedin: https://www.linkedin.com/in/rukmasen/

Art and Science of AI

- Homepage: https://artscienceai.substack.com/about

- YouTube: https://www.youtube.com/@ArtScience-AI

- Spotify: https://podcasters.spotify.com/pod/show/art-science-ai

- Linkedin: https://www.linkedin.com/company/art-science-ai

- Facebook: https://www.facebook.com/ArtScienceAI

- Instagram: http://instagram.com/artscienceai/

- Threads: https://www.threads.net/@artscienceai

- Twitter / X: https://x.com/ArtScienceAI

=== 💬 KEYWORDS ===

#AI #GenAI #LLM #ChatGPT #podcast

In this episode, Nikhil and Piyush delve into the latest happenings in the world of AI focusing on Apple Intelligence, Meta AI, and OpenAI's SearchGPT. We discuss the recent delays in Apple Intelligence, the growing trend of companies over-promising and under-delivering on AI features, and the impact of European Union regulations on tech giants. We explore Meta’s significant advancements in open-source AI with LLAMA 3.1, Meta's strategy of commoditizing AI, and the implications of this shift for the industry. We also discuss the launch of OpenAI's Search GPT and its potential impact on the search industry.

=== ⏰ CHAPTERS ===

00:00: Apple Intelligence delays and impacts

09:29: European Union regulations and impact on AI

20:04: Meta’s Open Source AI and Industry Implications

39:26: OpenAI's SearchGPT and the future of AI search

45:30: The Innovator's Dilemma: Balancing Innovation and Existing Business

=== 🧠 KEY CONCEPTS ===

- Apple AI Delays: Apple’s new AI features, initially planned for the iPhone 15 Pro launch, are delayed due to security and privacy issues, pushing some features to next year.

- European Union excluded from big tech AI launches: The European Union’s stringent regulations, like the Digital Markets Act, are causing tech giants like Apple and Meta to delay or withhold AI features in the region, impacting innovation.

- Meta’s Open Source Move: Meta’s release of LLAMA 3.1 highlights the rapid advancements in open-source AI, challenging the dominance of closed-source models from companies like OpenAI.

- AI Model Commoditization: The competitive landscape suggests that the value of proprietary AI models may diminish as open-source alternatives quickly catch up, pushing companies to focus on unique applications and user experiences.

- Future of AI Search: The introduction of OpenAI’s Search GPT presents a potential disruption to traditional search engines, emphasizing the need for incumbents like Google to adapt to evolving AI-driven search experiences.

=== 🔗 LINKS ===

- Homepage: https://artscienceai.substack.com/about

- YouTube: https://www.youtube.com/@ArtScience-AI

- Spotify: https://podcasters.spotify.com/pod/show/art-science-ai

- Linkedin: https://www.linkedin.com/company/art-science-ai

- Facebook: https://www.facebook.com/ArtScienceAI

- Instagram: http://instagram.com/artscienceai/

- Threads: https://www.threads.net/@artscienceai

- Twitter / X: https://x.com/ArtScienceAI

=== 💬 KEYWORDS ===

#AI #GenAI #LLM #ChatGPT #podcast

In this episode, Nikhil and Piyush discuss the fear of AI leading to mass unemployment. We consider historical precedents such as the Industrial Revolution and the advent of Personal Computers to understand how past technological revolutions led to both job loss as well as new job creation. We consider whether this time is truly different with the AI revolution, the societal implications of these changes, and the importance of jobs as not only a source of economic value, but also as a key source of meaning in the modern world.

= ⏰ CHAPTERS =
00:00: Introduction
03:32: Historical parallel 1 - the Industrial Revolution
09:36: Historical parallel 2 - the Personal Computer revolution
17:54: AI's impact on the future of work
28:35: Societal implications

= 🧠 KEY CONCEPTS =

= 🔗 LINKS =

= 💬 KEYWORDS =
#AI #GenAI #LLM #ChatGPT #podcast

In this episode, Piyush and Nikhil explore the AI value chain. We explore the different components that go into delivering an AI application from start to finish, including the compute layer, the foundational model layer, and the application layer. Drawing analogies to traditional supply chains, we discuss how companies like NVIDIA, OpenAI, Google, Microsoft, etc. fit into this ecosystem. Tune in to understand the intricacies of AI development and the roles various players have in this rapidly evolving field.

=== ⏰ CHAPTERS ===

00:00: Introducing the concept of a Value Chain

08:37: The Value Chain of traditional software applications

25:41: Value Chain of AI: compute, models, and applications

44:04: Broader considerations in the value chain: environmental impact, closed-source vs. open-source, etc.

=== 🧠 KEY CONCEPTS ===

- The AI Value Chain: The value chain for AI includes three main layers: compute (hardware and cloud), foundational models, and applications. Each layer involves different companies and technologies that add value to the end product.

- Compute Layer: NVIDIA GPUs play a crucial role in AI model training due to their efficiency in neural computing. Cloud companies like AWS, Google Cloud, and Microsoft Azure provide the infrastructure for training and deploying these models.

- Foundational Models: Training AI models is resource-intensive, requiring vast amounts of data and computational power. There is a distinction between open source and closed source models, impacting how developers can use and customize them.

- Application Layer: Applications built on AI can either integrate their own models or rely on existing ones. The value of AI in applications depends on the balance between computational cost and the value added to the user experience.

- Energy Consumption: AI's growing energy demands pose environmental challenges, potentially driving innovations in sustainable energy solutions, including nuclear energy.

=== 🔗 LINKS ===

- Homepage: https://artscienceai.substack.com/about

- YouTube: https://www.youtube.com/@ArtScience-AI

- Spotify: https://podcasters.spotify.com/pod/show/art-science-ai

- Linkedin: https://www.linkedin.com/company/art-science-ai

- Facebook: https://www.facebook.com/ArtScienceAI

- Instagram: http://instagram.com/artscienceai/

- Threads: https://www.threads.net/@artscienceai

- Twitter / X: https://x.com/ArtScienceAI

=== 💬 KEYWORDS ===

#AI #GenAI #LLM #ChatGPT #podcast

In this episode we discuss how to go beyond mastering ChatGPT to automating your tasks using AI APIs. We introduce the concept of APIs (Application Programming Interfaces) and how they can be used to automate tasks to help you free up your time and scale up your productivity. Nikhil gives a live demo of an AI automation tool he built to help automate repetitive parts of Piyush's workflow as a sales executive, where he has to manually conduct research on each new client to prepare for sales meetings. The automation transforms a labor-intensive process into a seamless workflow, which saves time and improves productivity for sales executives like Piyush. We explore the technical aspects of API integration and discuss the broader implications of AI in personal productivity. Whether you're in sales, tech, or just curious about AI, this episode offers valuable insights into making AI work for you.

=== ⏰ CHAPTERS ===

00:00: Benefits of automation

07:59: Understanding APIs and the AI ecosystem

24:33: Automating Piyush's sales research

38:04: Exploring Use Cases for AI and Automation

51:38: AI automation for personal productivity

=== 🧠 KEY CONCEPTS ===

- Importance of Automation: Automation frees up time and increases productivity by enabling repetitive tasks to be done in the background

- Importance of APIs (Application Programming Interfaces): APIs allow different applications to interact with each other without human intervention.

- The role of programming: No-code platforms such as Zapier and Make enable users to create automation workflows without any programming knowledge. But these platforms are limited, and having some basic programming knowledge enables you to leverage the full power of AI and APIs for automation.

- Use cases for automation: Nikhil gives a live demo of an AI automation tool he built to help automate repetitive parts of Piyush's workflow as a sales executive, where he has to manually conduct research on each new client to prepare for sales meetings. The same concept can be extended to automate a wide range of workflows.

=== 🔗 LINKS ===

- Homepage: https://artscienceai.substack.com/about

- YouTube: https://www.youtube.com/@ArtScience-AI

- Spotify: https://podcasters.spotify.com/pod/show/art-science-ai

- Linkedin: https://www.linkedin.com/company/art-science-ai- Facebook: https://www.facebook.com/ArtScienceAI

- Instagram: http://instagram.com/artscienceai/

- Threads: https://www.threads.net/@artscienceai

- Twitter / X: https://x.com/ArtScienceAI

=== 💬 KEYWORDS ===

#AI #GenAI #LLM #ChatGPT #podcast

This episode covers practical applications of AI in everyday life, from simple email drafting to sophisticated company research. We discuss different prompting techniques such as few-shot prompting and chain of thought prompting to improve the quality and accuracy of AI-generated responses. We also discuss custom instructions, custom GPTs, and explore the powerful potential of AI in automating repetitive tasks. Tune in to discover how you can harness the power of AI to enhance productivity and streamline your workflows.

=== ⏰ CHAPTERS ===

00:00: Introduction

08:02: Getting Started with AI and Identifying Tasks and Workflows

13:51: General-Purpose AI vs. Purpose-Built AI

27:30: Examples of ChatGPT usage

31:20: Prompt engineering

48:10: Custom instructions and custom GPTs

=== 🧠 KEY CONCEPTS ===

- Identifying AI Use Cases: Look for tasks in your life that are repeatable, time-consuming, and frustrating to see where AI can help.

- General Purpose AI vs. Purpose-Built AI: General purpose AI (e.g. ChatGPT) is great for a wide range of tasks, while purpose-built AI (e.g. Adobe Acrobat AI) can be more efficient for specific tasks and workflows.

- Prompt Engineering: The quality of AI output is significantly improved with well-crafted prompts. Use the few-shot prompting technique by giving examples to guide the AI model's response.

- Chain of Thought Prompting: Encouraging AI to think out loud and show its reasoning can lead to more accurate and thoughtful responses.

- Custom GPTs: Creating custom GPTs tailored to specific tasks can save time and increase efficiency. This involves setting custom instructions, uploading relevant files, and enabling specific capabilities.

=== 🔗 LINKS ===

- Homepage: https://artscienceai.substack.com/about

- YouTube: https://www.youtube.com/@ArtScience-AI

- Spotify: https://podcasters.spotify.com/pod/show/art-science-ai

- Linkedin: https://www.linkedin.com/company/art-science-ai

- Facebook: https://www.facebook.com/ArtScienceAI

- Instagram: http://instagram.com/artscienceai/

- Threads: https://www.threads.net/@artscienceai

- Twitter / X: https://x.com/ArtScienceAI

=== 💬 KEYWORDS ===

#AI #ArtificialIntelligence #GenerativeAI #GenAI #PromptEngineering #LLM #MachineLearning #ML #tech #podcast

Your one-stop-shop for all things Apple Intelligence! In this episode, we discuss the Apple Intelligence announcements from WWDC 2024. We discuss new AI capabilities in Apple’s apps and in Siri, the all-new AI assistant that can perform actions across multiple apps. We deep-dive into the architecture of Apple Intelligence, with a focus on privacy, and we discuss the implications of Apple Intelligence for users, developers, and the overall AI ecosystem.

In this episode we discuss the hype around AI and the challenges in achieving its full potential in 2024. The last 10% of solving problems with AI has proven to be difficult due to LLM hallucinations and reliability challenges. We discuss how this problem can be addressed by grounding LLMs with a knowledge base via the paradigm of Retrieval Augmented Generation (RAG). We discuss the different approaches to working with language models, including training from scratch, fine-tuning, and using RAG, and the opportunities for entrepreneurs in the AI space.

Takeaways