A Clearer Understanding of the AI Landscape
It has been quite intimidating for me, as it has for many other people, to wrap my head around all the components of the AI landscape - Acronyms help describe complex systems but for someone who is just getting started, it can feel like learning a foreign language. This conference was a good place to get a better understanding of the AI landscape, what things mean, and some simple ways to get started even as things continue to rapidly change.
Here are some of the main acronyms that are widely used
- LLM (Large Language Model)
- SLM (Small Language Model)
- RAG (Retrieval-Augmented Generation)
- AI agents
One of the standout explanations I heard at the conference about LLMs was simple yet effective: Imagine an LLM as a librarian who helps you find resources based on what already exists. ChatGPT, for example, is an LLM that has evolved into a powerful search engine. LLMs excel at tasks like reasoning, transforming, organizing, summarizing, and expanding data they have access to. For instance, I use ChatGPT to help me research and organize vacation options.
However, while LLMs are powerful search tools, they are not infallible. The saying “garbage in, garbage out” holds true—if an LLM is trained on inaccurate or outdated data, its responses may be flawed or even completely incorrect. This phenomenon is called “hallucinations,” where the LLM creates information that wasn’t part of its training data, leading to errors or fabrications.
To improve the accuracy of results, LLMs can be paired with RAG (Retrieval-Augmented Generation), which pulls in real-time data from more reliable sources. By combining this with contextual prompts, LLMs can deliver more accurate and relevant responses. This capability has particularly accelerated the use of AI-powered chatbots in industries like health tech and fintech, where accuracy and tone are essential for a good user experience.
While LLMs, SLMs, and RAG models are great at retrieving and presenting information, they don’t make decisions for the user. That’s where AI Agents come in. These models can operate independently, making decisions without constant user input. A great example of an AI agent is a self-driving car, which makes autonomous decisions about how to navigate the road.
At the conference, one presenter shared a fascinating example of how AI agents could seamlessly transition between tasks: Imagine reading an article on your device, then automatically having it converted to audio as you head out for a run. If you lose connection while boarding a train, the article could be switched to a PDF for offline reading. This kind of fluid interaction could revolutionize user experience and enhance accessibility across different contexts.
Exciting New AI-Powered Tools
As AI rapidly advances, we’re seeing the emergence of new products that harness these technologies. Here are some tools you might want to explore:
- ChatGPT or DeepSeek: A powerful conversational AI model for answering questions, summarizing content, and assisting with tasks.
- FireFlies.ai: A meeting assistant that automatically records, transcribes, and summarizes meetings, helping teams stay on top of key takeaways.
- Loveable.dev: A platform that lets users quickly build fully functional web applications without needing to write code.
- Pitch Guide: For entrepreneurs: This is an AI-powered coach that helps entrepreneurs and businesses craft and refine their pitch presentations.
- Obot: An administrative assistant designed to automate customer interactions, workflows, and repetitive tasks for businesses, making it easier to scale customer service, lead generation, and sales processes.
The Future of AI: Regulation and Ethical Considerations
With the rapid growth of AI, there is an increasing need for stricter rules and regulations around security, compliance, and ethics. As AI continues to evolve, so too will the challenges of mitigating the risks associated with unethical or malicious uses of the technology. The processes for building AI-powered products and services are more complex and require additional steps in your process to help mitigate risk. These include adding processes such as evaluations, testing, load testing, and red teaming (simulating real-world adversarial behavior to uncover vulnerabilities and weaknesses in AI systems, helping to identify and mitigate potential risks before they can be exploited).
The fields of LLMs, RAG, and AI agents are advancing at breakneck speed. However, it’s AI agents that seem poised for the most disruption, presenting enormous opportunities for innovation that will fundamentally change the way we work and live.
Having a better understanding of what things mean and how they work today together makes engaging with AI tools less intimidating for me to begin my own exploration. There is no question there is a lot to learn but I can at least see a way to get started.
If we can help you navigate your AI intiative, please reach out.