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Inside the Algorithm

Inside the Algorithm

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 Website - https://insidethealgorithm.com/ Join host us on a journey into the world of artificial intelligence through our unique interview-style podcast. ”Inside the AI” delves deep into understanding the vast impact AI is poised to have on our world. Each episode features engaging conversations with leading AI experts, exploring innovative perspectives on how AI technologies are shaping our future. But there’s a twist – we don’t just talk about AI; we talk to AI. We conducts interviews and debates with AI systems to get insights directly from the source. Our discussions cover a wide range of current topics, from ethical dilemmas to technological breakthroughs, providing our audience with a front-row seat to the evolving landscape of AI. Subscribe and join us as we explore the fascinating world of AI, one conversation at a time!Copyright 2024 All rights reserved. Sozialwissenschaften Ökonomie
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  • #25 🚀 Revolutionizing AI Automation with Marc Jaffe | Expert Insights on AI in Business 🤖💡

    #25 🚀 Revolutionizing AI Automation with Marc Jaffe | Expert Insights on AI in Business 🤖💡
    Mar 10 2025

    In this episode, we sit down with Marc Jaffe, an expert in AI automation, to explore how artificial intelligence is transforming industries, streamlining business processes, and shaping the future of work. Whether you’re a tech enthusiast, business leader, or AI developer, this conversation will provide insider insights into leveraging AI for efficiency, automation, and scalability.

    📌 Topics Covered:

    ✅ How AI automation is changing the business landscape

    ✅ The future of AI in cybersecurity, data analysis, and decision-making

    ✅ Ethical challenges and AI’s impact on jobs

    ✅ The best AI tools for businesses in 2025

    ✅ Predictions for the next five years of AI innovation

    🔔 Subscribe for more deep dives into AI, automation, and the future of work!

    Don’t forget to like, comment, and share if you found this insightful. Connect with Marc Jaffe https://x.com/AIadvantage25

    🔥 Stay Connected:

    🎙️ Listen to the full podcast on insidethealgorithm https://open.spotify.com/show/0Xz4LbUuKtcTg5Z54PAYFG?si=4602285d3b074b17

    💬 Join the conversation in the comments! What are your thoughts on AI automation? 🔍 #AI #Automation #AIAutomation #MachineLearning #TechPodcast #ArtificialIntelligence #FutureOfWork #AITrends #AI2025 #PodcastInterview
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    38 Min.
  • AI Mini Series: Intro to LLM and Generative AI

    AI Mini Series: Intro to LLM and Generative AI
    Feb 5 2025
    This course material introduces large language models (LLMs), focusing on the transformer architecture that powers them. It explains how LLMs work, including tokenization, embedding, and self-attention mechanisms, and explores various LLM applications in natural language processing. The text also covers prompt engineering techniques, such as zero-shot, one-shot, and few-shot learning, to improve model performance. Finally, it outlines a project lifecycle for developing and deploying LLM-powered applications, emphasizing model selection, fine-tuning, and deployment optimization. Briefing Document: Introduction to Large Language Models and Generative AI 1. Overview & Introduction to Generative AI Core Concept: Generative AI uses machine learning models that learn statistical patterns from massive datasets of human-generated content to create outputs that mimic human abilities.Focus: This course primarily focuses on Large Language Models (LLMs) and their application in natural language generation, although generative AI exists for other modalities like images, video, and audio.Foundation Models: LLMs are "foundation models" trained on trillions of words using substantial compute power, exhibiting "emergent properties beyond language alone" such as reasoning and problem-solving.Model Size: The size of a model, measured by its parameters (think of these as "memory"), directly correlates with its sophistication and ability to handle complex tasks. “And the more parameters a model has, the more memory, and as it turns out, the more sophisticated the tasks it can perform.”Customization: LLMs can be used directly or fine-tuned for specific tasks, allowing for customized solutions without full model retraining. 2. Interacting with Large Language Models Natural Language Interface: Unlike traditional programming, LLMs interact using natural language instructions.Prompts: The text input provided to an LLM is called a "prompt".Context Window: The "context window" is the memory space available for the prompt, typically a few thousand words, but varies by model.Inference & Completions: The process of using the model to generate text is called "inference." The model's output is called a "completion," comprising the original prompt and the generated text. “The output of the model is called a completion, and the act of using the model to generate text is known as inference. The completion is comprised of the text contained in the original prompt, followed by the generated text.” 3. Capabilities of Large Language Models Beyond Chatbots: LLMs are not just for chatbots but can perform diverse tasks, driven by the base concept of "next word prediction."Variety of Tasks: The text details capabilities including:Essay writingText SummarizationTranslation (including between natural language and machine code)Information Retrieval (e.g., named entity recognition)Augmented interaction via connection to external data and APIs.Scale & Understanding: Increased model scale (number of parameters) leads to improved subjective understanding of language, which is essential for processing, reasoning, and task-solving. "Developers have discovered that as the scale of foundation models grows from hundreds of millions of parameters to billions, even hundreds of billions, the subjective understanding of language that a model possesses also increases." 4. The Transformer Architecture & Self-Attention RNN Limitations: Previous models used Recurrent Neural Networks (RNNs), which were limited by computational resources and memory requirements, hindering their ability to capture long-range context. "RNNs while powerful for their time, were limited by the amount of compute and memory needed to perform well at generative tasks."Transformer Revolution: The 2017 "Attention is All You Need" paper introduced the Transformer architecture, which uses an "entirely attention-based mechanism". "In 2017, after the publication of this paper, Attention is All You Need, from Google and the University of Toronto, everything changed. The transformer architecture had arrived."Key Advantages: The transformer architecture allows for efficient scaling, parallel processing of input data, and the ability to "pay attention to the mean," leading to dramatically improved performance in natural language tasks.Self-Attention: The transformer's power stems from self-attention, which allows models to learn the relevance and context of all words in a sentence, not just adjacent words, by learning "attention weights" between each word. "The power of the transformer architecture lies in its ability to learn the relevance and context of all of the words in a sentence...not just to each word next to its neighbor, but to every other word in a sentence."Attention Maps: These visualize the relationships, highlighting word connections and their relevance within the sentence.Multi-Headed Self-Attention: The architecture learns multiple sets of self-attention weights in parallel...
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    17 Min.
  • AI Mini Series: AI Agents: Compound Systems and Agentic Approaches

    AI Mini Series: AI Agents: Compound Systems and Agentic Approaches
    Jan 11 2025
    Briefing Document: AI Agents Introduction: This document reviews two sources discussing AI agents. The first source, "Understanding AI Agents," provides a foundational understanding of what constitutes an AI agent, its structure, and different types. The second source, "What are AI Agents?" delves into the practical application of AI agents, highlighting their increasing importance within compound AI systems and contrasting agentic approaches with more traditional programmed systems. Together, these sources offer a comprehensive overview of AI agents, their capabilities, and their future. Key Themes and Ideas: Definition and Core Concepts: AI Agent Defined: An AI agent is an autonomous software entity that interacts with its environment, perceives, reasons, and acts to achieve specific goals. They operate via a cycle of sensing, thinking, and acting.Key Characteristics:Autonomy: Agents operate without direct human intervention.Perception: They gather information from the environment through sensors or data inputs.Action: Agents act upon the environment to achieve their objectives.Goal-Oriented Behavior: They are designed to achieve predefined goals. Structure of an AI Agent: Perception Subsystem: Processes raw data from the environment and transforms it into meaningful information.Decision-Making Engine: Uses reasoning algorithms (rule-based systems, optimization algorithms, machine learning) to determine the best action.Actuator Subsystem: Executes chosen actions to influence the environment.Learning Module (Optional): Enables the agent to learn from past experiences. Types of AI Agents: Simple Reflex Agents: Follow condition-action rules (if-then logic) without internal state. (Example: A thermostat)Model-Based Agents: Use an internal model of the environment to predict outcomes. (Example: Navigation apps)Goal-Based Agents: Take actions that lead to specific goals. (Example: Chess-playing AI)Utility-Based Agents: Optimize actions based on a utility function to quantify the desirability of outcomes. (Example: E-commerce recommendation systems)Learning Agents: Continuously improve performance by learning from past experiences. (Example: Robotic vacuum) Practical Applications of AI Agents: Healthcare: Virtual health assistants, medical image analysis.Finance: Automated trading, fraud detection.Autonomous Vehicles: Self-driving navigation.Customer Service: Chatbots.Gaming: Dynamic and adaptive AI opponents. The Shift from Monolithic Models to Compound AI Systems: Monolithic Models Limitations: Limited by training data, hard to adapt and can give incorrect answers when they don't have access to the appropriate informationCompound AI Systems: Solve problems by building systems around models and integrating them into existing processes with multiple components. Allows for more modular approaches.Example of Compound System: The example given of planning a vacation is that the system would query a database to determine vacation availability, then return that information using an LLM.Benefits of System Design: Allows for breaking down complex tasks, picking the right components (tuned models, large language models, image generation models, programmatic components). Quicker to adapt and easier than tuning a model.RAG as Example: Retrieval Augmented Generation is highlighted as a common example of a compound AI system.Importance of Control Logic: The path to answer a query which is often programmed by the human designing the system. LLM Agents: Shifting Control Logic: Agentic Approach: Puts the large language model in charge of the logic. Leveraging improved reasoning capabilities to develop a plan to tackle a problem and iterate.Thinking Slow vs. Thinking Fast: Shifts system design away from fast, programmed actions towards slower, plan-driven approaches.Capabilities of LLM Agents:Reasoning: LLM at the core of problem-solving, develops a plan.Acting: Uses external programs ("tools") to execute plans. Examples include search, databases, calculators, APIs.Memory: Stores inner logs and conversation history for context and personalization. ReACT Framework: Combines reasoning and acting capabilities. The agent takes a prompt, plans, acts using tools, observes the output, and iterates on the plan as needed. AI Autonomy Spectrum: A sliding scale of autonomy where the trade-offs are considered based on the complexity and narrowness of the tasks.For narrow problems, the programmatic approach can be more efficient than the generic agent route.Agentic approaches are useful for complex tasks with a spectrum of possible queries, where it would be difficult to configure every path in the system. Ethical Considerations: Autonomy vs. Control: Determining the appropriate level of agent autonomy and safeguards against harm.Bias in Decision-Making: Ensuring fair and unbiased decisions in sensitive areas.Transparency: Designing agents that can explain their decisions.Accountability: Establishing who is responsible for agent ...
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    31 Min.
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