Demystifying AI: What Are Foundation Models (and How to Use Them), with Tom Chant
Dec 13, 2023
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Tom Chant, a Scrimba instructor and AI engineer, discusses AI foundation models and their impact on front-end applications. They explore practical applications, GPT versions and capabilities, prompt engineering skills, and the significance of social media support.
Foundation models enable front-end developers to create features previously only accessible to larger companies with more resources.
AI has revolutionized industries like home assistants, education, and commerce, creating opportunities for innovative ideas and benefits for all companies.
Fine-tuning and retrieval-augmented generation (RAG) are essential concepts in AI applications, allowing customization and incorporation of specific knowledge or data into foundation models.
Deep dives
Foundation models revolutionizing front-end applications
Foundation models, like GPT and Llama, are revolutionizing front-end applications by enabling the creation of features that were previously only accessible to larger companies with more resources. These models provide a fundamental shift in the capabilities and user experience of front-end applications, making it essential for front-end developers to understand and utilize them. Incorporating foundation models can fundamentally change the way front-end applications are built and increase the demand for developers familiar with these technologies.
The power and potential of AI in various industries
The power and potential of AI can be seen in various industries. For example, AI has transformed home assistants like Alexa, Siri, and smartphones, making them more conversational and context-aware. In education, AI-driven apps like ElsaSpeak are revolutionizing language teaching by providing native speaker quality pronunciation feedback. AI is also being used in commerce to target consumers with highly specific product promotions, leading to increased revenue and demand for AI engineers. AI offers opportunities for innovative business ideas, while also providing benefits to every company, regardless of its size.
Understanding the concept and purpose of foundation models
Foundation models are large-scale machine learning models that have been trained on massive datasets. These models, like GPT and Dali, have the ability to adapt to a wide range of tasks. They involve complex neural networks that enable tasks like pattern recognition, decision-making, and prediction. While the exact workings of these models are not fully understood even by the scientists who created them, they provide a powerful tool for creating applications and features in various domains. Foundation models can be used to build applications that integrate with chatbots, generate speech or images, or enable interactive web experiences.
The role of fine-tuning and RAG in AI applications
Fine-tuning and retrieval-augmented generation (RAG) are important concepts in AI applications. Fine-tuning allows customization of foundation models to specific styles or formats, enabling developers to create applications with unique characteristics. For example, fine-tuning can be used to optimize text generation models for specific writing styles, resulting in outputs that align with specific brand voices or user preferences. On the other hand, RAG allows the incorporation of specific knowledge or data into the model, ensuring accurate and specific responses. RAG is especially useful when it comes to creating chatbots or applications that require domain-specific information or context.
Exploring the potential of open-source AI models and self-hosting
Apart from commercial offerings like OpenAI's models, there are also open-source AI models available, such as those provided by Hugging Face. These models can be an alternative to OpenAI's models, offering more cost-effective options for developers. While open-source models may require more fine-tuning and may not match the quality of OpenAI's models out of the box, they provide opportunities to explore AI capabilities and self-host models. Self-hosting models can reduce costs and provide more control over the AI infrastructure, enabling developers to build applications with specific requirements and maintain performance even when cloud-based AI services face downtime.
Meet Tom Chant 🇬🇧! Tom is a Scrimba instructor who is a part of our in-house team that brought you a brand new career path available on Scrimba.com - the AI engineer Path.
In this episode, we're diving into the world of AI foundation models: what are they, how do they work, and how can you use them to build front-end applications that you, until recently, couldn't even think of unless you were a big company with loads of resources.
AI is fundamentally changing the features and user experience of front-end applications. In this episode, you'll learn how to use different foundation models out there (so, not just OpenAI) for your own projects.
This is the second episode of our series on AI engineering, introducing Scrimba's AI Engineer Path. This path is your gateway to unlocking the full potential of AI for your projects.
If you enjoyed this episode, please leave a 5-star review here and tell us who you want to see on the next podcast. You can also Tweet Alex from Scrimba at @bookercodes and tell them what lessons you learned from the episode so that he can thank you personally for tuning in 🙏 Or tell Jan he's butchered your name here.
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