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Author: fran

The Bitter Lesson in AI

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The Bitter Lesson in AI: Scalability vs. Human Priors

Understanding why scalable methods outperform human-designed solutions in AI development.

“The history of AI shows that scalable learning methods consistently outperform human-designed solutions.” — Richard Sutton

The “bitter lesson” in AI suggests that scalable approaches, powered by massive compute and data, outperform clever human-designed solutions. This article explores how leading models like GPT-4, PaLM, and LLaMA embody this principle and why scalability is the future of AI.

What is the Bitter Lesson?

The bitter lesson, proposed by Richard Sutton, highlights that AI progress comes from scalable learning methods rather than human ingenuity. In practice, this means training large models on diverse data rather than manually crafting task-specific rules.

Why Scalability Matters

Models like GPT-4 and PaLM demonstrate that scaling data, compute, and model size leads to superior performance. Instead of relying on handcrafted features, these models learn complex relationships autonomously.

Real-World Implications

  • Natural Language Processing: Large language models handle diverse linguistic tasks without explicit programming.
  • Healthcare: AI systems analyze vast patient datasets to recommend treatments without predefined medical rules.
  • Robotics: Reinforcement learning enables robots to master complex tasks through trial and error at scale.
  • Finance: Scalable AI predicts market trends from enormous transaction data, surpassing human-designed trading strategies.

Scalable Learning vs. Human Priors

While earlier AI models depended on human priors, scalable approaches like LLaMA and PaLM show that performance improves significantly with more data and compute power, not necessarily more sophisticated algorithms.

Final Thoughts

The bitter lesson emphasizes that scalable learning methods are the key to future AI breakthroughs. As models like GPT-4, PaLM, and LLaMA continue to scale, they will redefine what AI can achieve, leaving human-designed shortcuts behind.

Mixture of Experts

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Mixture of Experts: A Cost-Effective Approach to AI

How models like Switch Transformer, GLaM, and M6-T support scalable AI with efficient resource use.

“The Mixture of Experts architecture reduces training costs by activating only essential parts of AI models.”

As AI models grow larger, the need for efficient training and inference becomes critical. The Mixture of Experts (MoE) architecture addresses this challenge by optimizing resource use without sacrificing performance. This article explores how models like Switch Transformer, GLaM, and M6-T implement MoE for scalable AI development.

What is Mixture of Experts?

MoE models activate only relevant portions of the network for each input, reducing computational overhead. For example, Switch Transformer can scale efficiently by routing tasks to specialized “experts,” making large language models more accessible.

Why MoE Improves Efficiency

MoE reduces training and inference costs by selecting only the necessary parameters for processing. This allows models like GLaM to deliver high performance with fewer active parameters, improving scalability and responsiveness.

Key Applications of MoE Models

  • Language Processing: Enabling large-scale translation and summarization with lower computational loads.
  • Healthcare: Supporting real-time analysis with minimal latency.
  • Finance: Handling large transaction data while maintaining quick response times.
  • Research: Processing complex scientific datasets efficiently.

How MoE Models Compare

While Switch Transformer focuses on large-scale language tasks, GLaM emphasizes multilingual capabilities with fewer active parameters. M6-T combines these efficiencies for real-time content generation.

Final Thoughts

MoE architectures represent a crucial step toward cost-effective, scalable AI. By activating only the most relevant parts of a model, solutions like Switch Transformer, GLaM, and M6-T make high-performance AI accessible across industries.

How AI is Learning to Think

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Reasoning Models: How AI is Learning to Think

Exploring how AI models like GPT-4, Claude, PaLM, and DeepSeek-R1 are evolving to reason and solve complex problems.

“Reasoning models represent a shift in AI, offering logic, transparency, and context-aware solutions across industries.”

Artificial intelligence is advancing from basic pattern recognition to sophisticated reasoning. Models like GPT-4, Claude, PaLM, and DeepSeek-R1 now break down complex problems, explain logic, and provide actionable insights. This article highlights what reasoning models are, their significance, and how they differ from traditional AI.

What Are Reasoning Models?

Reasoning models process information logically, connecting concepts to deliver structured explanations. Unlike traditional AI that relies on pattern matching, these models solve complex problems and provide coherent, context-aware responses.

For example, when explaining scientific concepts, they break down ideas into components, offering clear, logical connections.

Why Reasoning Matters

Reasoning models enhance AI performance by delivering precise, context-rich responses. In education, they personalize learning with tailored explanations. In healthcare, they analyze patient data to suggest accurate diagnoses. In business, they interpret market trends, supporting strategic decisions with logical insights.

Key Applications of Reasoning Models

  • Education: Customizing content for effective learning.
  • Healthcare: Supporting accurate diagnostics and treatment plans.
  • Business: Analyzing trends for informed decisions.
  • Research: Interpreting complex datasets for scientific breakthroughs.
  • Finance: Providing logical justifications for investment strategies.

How They Differ from Traditional AI

Traditional models offer answers without explaining the logic behind them. Reasoning models provide transparency by showing how conclusions are reached. For example, in financial forecasting, they explain which market factors influence predictions, offering deeper insights for planning.

Final Thoughts

Reasoning models mark a significant leap in AI development. By combining language generation with logical reasoning, they deliver coherent explanations and robust solutions.

As models like GPT-4, Claude, PaLM, and DeepSeek-R1 continue to evolve, they will play a vital role in transforming decision-making, education, research, and business across industries.

AI: Pre-training vs. Post-training

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Pre-training vs. Post-training: AI Development

Exploring how foundational and refinement processes shape powerful AI models like DeepSeek-R1.

“Pre-training builds the knowledge, post-training refines it—together they create intelligent, adaptable AI systems.”

Language models like DeepSeek-R1 rely on two critical processes—pre-training and post-training—to achieve their remarkable capabilities. These stages define how AI systems learn language, interpret context, and deliver accurate responses. This article highlights their significance and how they work together.

What is Pre-training?

Pre-training is the foundational stage where AI models process extensive datasets containing diverse text sources.

This phase allows AI to learn grammar, facts, and contextual relationships by predicting the next word in a sequence, building general knowledge and fluency.

However, pre-training alone does not tailor the model for specific tasks or user preferences.

Why Post-training Matters

Post-training refines the model’s abilities by aligning responses with user needs. Techniques like instruction tuning help the model follow prompts accurately, while preference fine-tuning adjusts outputs based on feedback.

This process transforms general knowledge into task-specific abilities, ensuring coherent and relevant responses.

The Synergy Between Pre-training and Post-training

Pre-training provides a strong knowledge base, while post-training adapts this knowledge for real-world applications.

For instance, in customer service, pre-training enables conversational understanding, and post-training allows the AI to handle company-specific queries accurately.

Real-World Applications

The combination of pre-training and post-training allows AI to excel across industries.

  • Healthcare: Interpreting patient data with context-aware precision.
  • Education: Delivering personalized tutoring tailored to individual learning needs.

This dual-stage process ensures that AI systems are both knowledgeable and adaptable.

Key Takeaway

Pre-training builds the knowledge base; post-training refines and adapts it for real-world impact.

Final Thoughts

Pre-training and post-training together shape AI models that are both intelligent and adaptable.

This synergy ensures language models like DeepSeek-R1 can provide coherent, context-aware, and relevant responses across diverse industries, showcasing the transformative power of well-trained AI systems.

Open Source AI Models

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The Significance of Open Source AI Models

How open-source AI models like DeepSeek-R1 are democratizing AI and driving innovation.

“Open-source AI fosters collaboration, transparency, and accessibility, empowering innovation across industries.”

Open-source AI models are transforming artificial intelligence by making advanced technology accessible to a broader audience. DeepSeek-R1, with its permissive MIT license, exemplifies how open-source AI fosters innovation and transparency.

Why Open Source Matters

Open-source AI enables developers, researchers, and startups to experiment and innovate without relying on large corporations.

By offering transparent access to code and data, models like DeepSeek-R1 promote collaboration and accelerate advancements, ensuring AI development remains inclusive and adaptable.

The Power of Open Weights

Open weights give users direct control over AI models, allowing for customization to meet specific needs. Unlike proprietary models restricted by APIs, open-source AI can be run locally, improving security and efficiency.

Open Source vs. API-Driven Models

While API-driven models offer convenience, they often limit transparency and customization. Open-source models overcome these challenges by providing full access for auditing and modifications, enhancing security and reducing long-term costs.

Real-World Impact

Open-source AI has already improved accessibility by enabling regional language adaptations and advancing scientific research through tailored tools.

Moreover, open-source competition pushes proprietary providers to enhance transparency and performance, benefiting users globally.

Key Takeaway

Open-source AI offers more than code—it provides the freedom to innovate, adapt, and advance AI responsibly.

Final Thoughts

Open-source AI models like DeepSeek-R1 represent a paradigm shift in AI development. By offering transparency, flexibility, and cost-effectiveness, they empower users and foster global collaboration.

As AI continues to evolve, open-source models will play a pivotal role in ensuring that technological benefits are accessible to all, pushing the boundaries of what AI can achieve.

DeepSeek AI: V3 vs R1

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DeepSeek AI: Key Insights into V3 and R1 Models

Exploring how DeepSeek’s latest models are shaping the future of AI reasoning and applications.

“Artificial intelligence is the new electricity.” – Andrew Ng

Artificial intelligence continues to transform technology, with language models like DeepSeek AI’s V3 and R1 leading the charge. While these models share foundational technology, they differ in architecture, training, and applications. This article highlights their core features, training processes, and practical uses, with a comparison to Meta’s Llama model.

Core Features and Differences

DeepSeek V3 is a generalist model, handling a broad range of topics with fluency. It is ideal for conversational AI, customer support, and content creation.

R1 excels in reasoning and analytical tasks. It breaks down complex problems and explains them logically, making it suitable for applications like education and healthcare, where structured analysis is critical.

Core Principle: Reasoning Capabilities

While V3 offers broad conversational capabilities, R1’s standout feature is its ability to provide detailed, step-by-step analyses, making it invaluable for tasks requiring logical explanations.

Training Process Simplified

Both models undergo pre-training and post-training. Pre-training allows them to absorb language structures and context from extensive datasets. Post-training refines these skills, aligning responses with user preferences.

R1’s post-training is especially focused on enhancing reasoning capabilities, enabling it to provide logically coherent explanations.

Important Distinction

R1’s ability to reason logically and provide coherent explanations gives it an edge in industries requiring precise analytical skills.

Comparing V3, R1, and Llama

Meta’s Llama model is efficient and accessible but lacks the reasoning depth of R1. While V3 offers broad conversational capabilities similar to Llama, R1 stands out with its ability to provide detailed, step-by-step analyses.

For example:

  • V3: Summarizes a scientific concept quickly and fluently.
  • R1: Explains the same concept comprehensively, highlighting logical connections and providing in-depth insights.

These distinctions make R1 better suited for use cases that require thorough understanding and deep reasoning.

Key Takeaway

R1’s unique ability to reason and provide structured analyses sets it apart in scenarios where understanding complex relationships is crucial.

Real-World Applications

R1’s analytical precision is valuable in education, acting as a tutor offering tailored explanations.

In healthcare, it can assist clinicians by analyzing patient data and providing logical recommendations.

V3’s conversational strengths make it ideal for customer engagement and content creation, ensuring smooth and relevant interactions.

These applications highlight how DeepSeek’s models address diverse industry needs, from complex analytical tasks to engaging conversational AI experiences.

Key Takeaway

V3 enhances conversational experiences, while R1 excels in analytical reasoning. Together, they cover a wide range of AI applications.

Final Thoughts

DeepSeek AI’s V3 and R1 models represent significant advancements in language modeling. V3 offers broad knowledge for diverse applications, while R1 introduces reasoning capabilities essential for complex problem-solving.

Together, they signal a future where AI not only generates language but also reasons and explains, enhancing decision-making and supporting meaningful insights across various industries.

As AI continues to evolve, models like V3 and R1 pave the way for more intelligent, context-aware, and reasoning-capable systems that can transform industries and everyday life alike.

Learning SQL

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Lesson One: Learning SQL – Note to Self

Getting started on my SQL journey and setting up my development environment on my Mac.

Welcome to Lesson One of my SQL journey! In this lesson, I’ll be setting up my development environment on my Mac to learn SQL in a practical, hands-on way. Here’s my plan:

Code Editor

I’ll be using Visual Studio Code as my primary code editor. It’s lightweight and has excellent extensions that will help me work with SQL and Jupyter Notebooks.

Interactive Notebooks

I plan to install Jupyter Notebook along with the ipython-sql extension. This setup will allow me to write and execute SQL queries interactively right from my notebooks—perfect for experimenting with queries and seeing results immediately.

Database Platform

I’ll use Google’s database services for practice. Specifically, I might start with Google Cloud SQL to create a small SQL instance—or even explore BigQuery if I want to work with larger datasets. This will let me run real SQL queries against a cloud-hosted database.

Showcasing Projects

As I build my SQL projects, I’ll document them in Jupyter Notebooks and host the code on GitHub. I also plan to use Binder so that visitors can launch interactive sessions directly from my portfolio website.

My Workflow Will Include:

  1. Installing and setting up Visual Studio Code, with extensions for SQL and Jupyter.
  2. Installing Jupyter Notebook and the ipython-sql extension.
  3. Creating and connecting to a Google Cloud SQL instance.
  4. Practicing SQL queries interactively in Jupyter.
  5. Documenting my projects on GitHub and using Binder to make them interactive on my website.

Final Thoughts

This lesson is all about getting my tools and environment in order so that I can dive into SQL with confidence. I’m excited to see where this learning journey takes me, and I plan to share all my projects and insights on my website!

How to replicate LinkedIn Account IQ

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How to Replicate LinkedIn Sales Navigator Account IQ Using Custom ChatGPT Prompts

LinkedIn Sales Navigator Account IQ provides detailed insights into target accounts by aggregating company data, engagement trends, and relationship signals. This powerful feature enables sales teams to make informed decisions and refine their outreach strategies. In this post, you’ll learn how to replicate this feature using a custom ChatGPT prompt.

The Issue

LinkedIn Sales Navigator Account IQ isn’t available for every company—many small-to-medium businesses and non-US/UK companies have this feature disabled.

The Workaround

I have coded a solution that lets me easily achieve “almost” the same results as Account IQ by using a custom prompt and feeding it to ChatGPT.

Custom ChatGPT Prompt Development

I developed the prompt by reverse-engineering the ABC Ltd. research report template and applying advanced machine learning techniques. The process involved four key steps:

  1. Data Collection: Retrieve the most recent (within the last 12 months) and reputable data from major business publications, industry analysis sites, and official company releases. All sources are verified and clearly cited.
  2. Draft Creation: Construct a detailed draft report with clearly delineated sections, including:
    • How your product can help [COMPANY NAME]
    • How [COMPANY NAME] makes money
    • Strategic priorities
    • Business challenges
    • Competitive landscape
    • Headcount insights
  3. Iterative Refinement: Use recursive self-review, iterative improvement, and reinforcement learning from human feedback (RLHF) to conduct at least three internal review iterations. This process ensures that every detail is accurate, clear, and of high quality.
  4. Final Verification: Confirm that every section includes clearly labeled source references, meets the detailed depth required, and adheres to a high standard of accuracy and clarity.

Generating a Custom Research Prompt with Code (Python & JS)

I’ve developed functions in JavaScript and Python that customize research prompts for any company. Simply enter the company name, click “Generate Prompt,” and copy the output. Then paste it into your preferred LLM to get a detailed company analysis instantly.

Final Thoughts

By following this method, you can reliably produce a detailed, high-quality research report for any company using a custom ChatGPT (or other LLMs) prompt that mirrors the capabilities of LinkedIn Sales Navigator’s Account IQ feature.

Customized ChatGPT Prompt: