AI Explored: Custom AI Models vs ChatGPT – A Guide to Private Large Language Models

Episode Release Date: December 24, 2024
Host: Michael Stelzner, Social Media Examiner
Guest: Yash Gad, AI Strategist and CEO of Ringer Sciences

Introduction

In this enlightening episode of AI Explored, host Michael Stelzner engages in a deep dive with Yash Gad, an accomplished AI strategist and the founder of Ringer Sciences. The conversation centers around the burgeoning field of custom AI models versus widely recognized platforms like ChatGPT. Aimed at marketers, creators, and business owners, this episode unpacks the nuances of private large language models (LLMs) and their strategic advantages in safeguarding proprietary data and enhancing specific business functions.

Guest Background: Yash Gad's Journey into AI

Yash Gad introduces himself as a biophysicist specializing in computational neurosciences, holding a Ph.D. Initially entrenched in developing neural network models to study eye movement control, his trajectory took a pivotal turn in 2013. Joining a marketing and PR agency, Yash discovered the vast potential of AI in natural language processing and behavior analysis, propelling him towards founding Ringer Sciences in 2018. His transition from academia to the agency world underscores a commitment to leveraging AI for broader, practical applications beyond traditional research.

Yash Gad [02:42]: "When I joined a marketing and PR agency, I saw how neural networks could accelerate our understanding of natural language processing and network effects."

The Rise of Custom AI Models

Yash articulates the shift from utilizing general AI models like ChatGPT to developing custom language models tailored to specific business needs. This transition is driven by concerns over data security, intellectual property, and the necessity for models that can handle domain-specific tasks with higher accuracy.

Yash Gad [07:19]: "We developed a niche around custom language models, moving away from simply inputting all data into a model like ChatGPT. Instead, we focus on building secure, specialized models that excel at specific tasks essential to our clients."

Benefits of Custom AI Models

The discussion highlights several key advantages of custom AI models:

1. Enhanced Security and Data Privacy: Custom models operate within secure infrastructures, ensuring proprietary data remains confidential and isn't exposed or used to train other models.

2. Task Specialization: These models are fine-tuned to perform specific functions exceptionally well, unlike generalized models that may lack precision in niche areas.

3. Prompt Analytics: Understanding how users interact with AI through prompt analytics can unlock valuable insights into customer behavior and generate unique intellectual property for businesses.

Yash Gad [08:54]: "Custom models can be dialed into very specific tasks, ensuring relevancy and maintaining the privacy of sensitive information critical to a business."

Understanding Custom AI Models

Yash breaks down the framework of custom language models into four core components:

1. Secure Data Storage: Whether on-premises or in the cloud, data must be stored securely and structured meticulously.

2. Model Selection: Choosing the right AI model (e.g., Llama 3, Mistral) based on its training data and suitability for the intended tasks.

3. User Interface: Developing a front end tailored to the specific use case, which may or may not include a chat interface.

4. Analytics Layer: Tracking interactions and managing prompt analytics to refine and protect the generated responses.

Yash Gad [10:31]: "A custom language model framework includes secure data storage, the appropriate AI model, a user-specific interface, and an analytics layer to monitor interactions."

Creative Applications: Case Studies

Yash shares practical applications of custom AI models, illustrating their transformative impact on businesses:

1. Branding Exercise: By synthesizing data from past press releases and branding documents, a custom model generated mission and vision statements swiftly, reducing what traditionally took months to just two weeks. The results were so accurate that minimal human edits were required.

   Yash Gad [19:07]: "We cranked out drafts quickly and presented them to the C-suite, who only needed minor tweaks. This efficiency replaced months of manual effort."

2. Social Media Report Normalization: For a client overwhelmed with disparate media reports, Yash's team built a model that ingested various formats and normalized the data into a cohesive Excel sheet. This automation eliminated the tedious manual process of sifting through hundreds of presentations.

   Yash Gad [22:00]: "Our AI framework normalized data from hundreds of presentations into structured, usable Excel sheets, streamlining the client's reporting process."

Implementing Custom AI Models: Steps to Success

Yash outlines a strategic approach for businesses eager to adopt custom AI models:

1. Define the Use Case: Clearly identify the problem or task the AI model will address.

2. Gather and Structure Data: Collect relevant data and ensure it is securely stored and properly tagged for the model's intended functions.

3. Model Selection: Choose an appropriate AI model that aligns with the business's specific needs and ensures compatibility with existing infrastructure.

4. Develop the Interface: Create a user-friendly interface tailored to the use case, facilitating seamless interaction with the AI model.

5. Integrate Analytics: Implement systems to monitor and analyze user interactions, enhancing the model's effectiveness and safeguarding intellectual property.

Yash Gad [24:18]: "Start by defining your use case, gather and secure the necessary data, select the right model, develop a tailored interface, and integrate analytics to maximize the model's value."

Addressing Security Concerns

A significant portion of the conversation addresses the critical issue of data security. Yash emphasizes that custom models ensure data never leaves the organization's controlled environment, mitigating risks associated with data breaches and unauthorized access.

Yash Gad [27:39]: "By building walled-off models, we ensure that data never leaves your IT infrastructure, maintaining tight control over sensitive information."

Selecting the Right AI Model

The episode delves into various AI models, contrasting open-source options like Mistral and Llama 3 with proprietary models such as ChatGPT and Claude. Yash explains the criteria for selecting models based on factors like training data, performance, deployment flexibility, and community support.

1. Mistral: Praised for its superior performance in specific tasks and faster deployment compared to other open-source models.

2. Llama 3: Recognized as a robust generalist model, suitable for a wide range of applications with strong community support through platforms like Hugging Face.

3. ChatGPT and Claude: While powerful, these models are considered "black boxes" due to their opaque training data and potential security vulnerabilities when handling proprietary information.

Yash Gad [39:44]: "Llama 3 is embraced for its general-purpose capabilities, while Mistral offers better performance in specific, open-ended tasks."

Future Outlook: The Evolution of AI Models

Yash anticipates that open-source models like Mistral and Llama 3 will continue to close the performance gap with proprietary models like ChatGPT. He foresees increased adoption as these models become more sophisticated and easier to integrate, offering businesses enhanced flexibility and control over their AI solutions.

Yash Gad [41:59]: "Over the next six months to a year, open models will catch up significantly, providing robust alternatives to proprietary models like ChatGPT."

Conclusion

The episode concludes with Yash Gad sharing avenues for further engagement, including his website ringersciences.com and LinkedIn profile. Listeners are encouraged to connect for more insights into custom AI models and their applications.

Yash Gad [44:25]: "Connect with me on LinkedIn and visit ringersciences.com to explore how we can help your business harness the power of custom AI models."

Key Takeaways:

• Custom AI Models Offer Superior Security: By operating within secure, controlled environments, businesses can protect proprietary data from exposure and misuse.

• Specialization Enhances Functionality: Tailored models excel at specific tasks, providing more accurate and relevant outputs than generalized models.

• Scalability and Flexibility: Custom models can be designed to be hot-swappable, allowing businesses to integrate new models seamlessly as they evolve.

• Practical Applications Demonstrate Value: Real-world case studies underscore the efficiency and effectiveness of custom AI models in streamlining complex business processes.

For marketers, creators, and business owners striving to leverage AI securely and effectively, this episode offers invaluable insights into the strategic advantages of custom language models over generalized platforms like ChatGPT.