What I Learned from Completing DeepLearning.AI’s Short Course on Semantic Kernel
As a someone who has navigated the realms of banking, management consulting, and conversational AI, my journey into the world of Data Science and AI has been nothing short of exhilarating. My latest endeavor, the DeepLearning.AI short course on Semantic Kernel, offered a deep dive into semantic kernel that seamlessly blends AI with practical applications. Here’s a glimpse on some fascinating insights I gained.
Unraveling the Semantic Kernel
The course introduced the Semantic Kernel, a brainchild of Microsoft’s deputy CTO, Sam Scalace. This open-source toolkit is not just a set of tools; it’s a paradigm shift in how we use large language models (LLMs) in applications. The Semantic Kernel serves as the central computational system, orchestrating a symphony of AI functions with finesse and precision.
Completion and Similarity Engines
A pivotal moment in the course was understanding the completion and similarity engines. The completion engine, compared to what we see in LLMs like GPT, completes text based on input. The similarity engine, however, takes a leap further by finding unexpected similarities in data. As someone who’s worked with conversational AI, this was a revelation — the ability to grasp and compare meanings in data can revolutionise how we interact with AI.
Semantic Functions: The Building Blocks
Semantic functions, as explained in the course, are about breaking down tasks into smaller, specialised functions that can be combined in myriad ways. This resonated with me, reflecting the modular approach I’ve appreciated in management consulting — breaking down complex problems into manageable parts.
Vector Memory and Planner Module
The concepts of vector memory and the planner module were particularly intriguing. Vector memory likely refers to vector space models in NLP, transforming words into vectors for analysis. The planner module seemed like an advanced orchestration tool, essential in complex AI systems — a nod to the intricate financial models I’ve encountered in banking.
Practical Business Applications
What truly stood out was the application of these technologies in business contexts. Conducting a SWOT analysis using AI, or finding the most similar items to a query, showed how AI is not just a theoretical marvel but a practical tool for real-world business solutions.
Embracing AI in Strategy and Operations
The course emphasized the role of AI in business strategy and operations. The integration with Azure OpenAI and Hugging Face models highlighted the importance of interoperability and diverse AI models in the current landscape.
The Future: AI Planning and Automation
The course’s discussion on AI automation, particularly in automating the sifting through plugins and generating plans, was an eye-opener. It showcased an advanced level of AI automation, where the system intelligently selects and utilizes various components to achieve specific objectives.
Conclusion
Completing the DeepLearning.AI course on Semantic Kernel was more than an educational experience; it was a journey into the future of AI and its applications in business and beyond. It bridged my past experiences in finance and consulting with my current foray into AI, offering a glimpse into a future where AI is not just an assistant but an integral part of decision-making and strategy.
