Welcome, to AI for Business Newsletter.
3 Topics in 4 min:
1/ Leveraging Your Company Knowledge with AI
2/ The Crisis of Meaning in AI Adoption
3/ Guide to Choosing Between SLMs and LLMs in 2024
Let’s get into it...
The Unique Twist (GPTs)
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The Practical Section
1/ Leveraging Your Company Knowledge with AI
In the evolving landscape of AI, I've found knowledge to be the most intriguing asset. It's not just about data or information, but the depth of understanding within your organization that often goes untapped.
Here's my experience with generative AI and knowledge management:
1. Utilizing Existing Knowledge: I've noticed businesses often overlook the vast knowledge they already possess. For instance, analyzing past social media posts can reveal a lot about your company’s voice and stance. Similarly, sales transcripts and customer support emails are goldmines for customer insights.
2. Creating New Knowledge: I believe in actively documenting and building upon what we already know. Answering FAQs or creating a knowledge base are effective strategies. Every interaction, even a simple ChatGPT conversation, adds layers to your understanding. Capture it, refine it, use it.
3. Combining Knowledge and Data: Integrating data with knowledge enhances its value exponentially. Many are linking AI to data, but adding a layer of knowledge can significantly boost performance.
I'm constantly amazed by the potential of AI in enhancing our knowledge management capabilities. It's about creating a dynamic, evolving understanding of our work.
GPT Quick Dive: 2-Minute Exploration
◢ In what ways can generative AI contribute to the evolution and enhancement of a company's knowledge management? ◤
Find the answer here and also explore other of my AI for Business GPTs.
The Philosophical Section
2/ The Crisis of Meaning in AI Adoption
92% of us believe our work matters. This is true across the board, even in roles like food prep or economics. We find meaning in our tasks, regardless of their nature.
Then comes AI. It's freeing at first, offloading tedious tasks, allowing us to dive into what we love. Studies back this up – people using AI are happier at work, feeling they're truly using their skills.
But there's a catch. When AI takes over tasks that felt meaningful when done by us, like performance reviews, something shifts. Our work enters an AI loop: we create, AI processes, AI responds. The human element? It fades.
Here's the real issue: our sense of purpose in work. If AI does our writing, what's traditionally seen as a measure of effort, where do we stand? Education is already there, with AI-written essays. Other sectors will follow.
Yet, there's hope. The decline of busy-work can be our liberation. It's a chance for leaders to redesign work for an AI era, focusing on what truly adds value.
The potential? Huge. A future where working with AI starts a process, but doesn't define it. That’s the future of work I envision.
GPT Quick Dive: 2-Minute Exploration
◢ Investigate how AI's involvement in traditionally human-centric tasks like writing affects our perception of effort and success in professional environments. ◤
Find the answer here and also explore other of my AI for Business GPTs.
The Technical Section
3/ Guide to Choosing Between SLMs and LLMs in 2024
As we step into 2024, a new player is entering the arena: Small Language Models (SLMs).
Understanding LLMs and SLMs:
Large Language Models (LLMs): These are akin to a Swiss Army knife – versatile and multi-functional. They're great for tasks ranging from content generation to query handling. However, they're not without faults. LLMs feed on vast public data, which can sometimes lead to inaccuracies or even "hallucinations."
Small Language Models (SLMs): Picture these as your specialized toolkit. Unlike LLMs, SLMs are designed for particular tasks within specific domains, often used in enterprise settings. They are trained on more focused datasets, offering higher accuracy for specialized tasks.
Why This Matters to You:
When choosing between Large Language Models (LLMs) and Small Language Models (SLMs), consider:
Accuracy vs. Creativity: LLMs are great for creative, broad tasks but may lack precision. SLMs offer specialized accuracy but with less creative breadth.
General vs. Specialized Knowledge: LLMs cover a wide range but can stumble in specific areas. SLMs excel in their trained domains.
Resource Efficiency: LLMs require more resources; SLMs are more cost-effective for targeted tasks.
Your choice depends on whether you value broad capabilities or precise, specialized expertise.
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Have a great day and until next time.
Roland
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