The Impact of AI on your Business- Real or Myth?

This episode discusses the impact of AI in business, focusing on its value creation, challenges, and the future of AI-driven enterprises. The hosts, who work for AI-focused companies, share insights on AI trends, business applications, careers, and leadership in AI-enabled workplaces. We explore topics such as code generation, the monetization of AI technologies, when AI goes wrong, the role of large language models (LLMs), and the importance of AI in decision-making and automation. Additionally, we address the need for regulation, the potential of retrieval augmented generation (RAG), and the development of specialized LLMs for enterprise applications. The conversation also touches on the importance of reskilling and becoming a better AI leader.
0:00 - Introductions
0:40 - Host backgrounds
2:14 - What is the value of AI?
4:45 - Who makes money with AI?
9:02 - GenAI revolution from content generation to agents
10:40 - How has a big enough problem to require paid LLMs
11:05 - Making AI perform actions vs telling you how to do them
13:45 - AI Legislation and how to prevent AI to take the wrong action
17:00 - RAGs, finetuning, prompt engineering and other ways to improve GenAI action
19:15 - Smaller specialized LLMs for more AI value and better results
21:40 - How to become a better AI enabled employee and a better leaders for an AI enabled workforce

[@user-ip2gz8in9n]

Very thought provoking. The balance between responsible regulatory postures and innovation support will be critical, and I hope that governments are well advised in navigating this. Arpad's comments about smaller niche players were interesting, as I can see the potential for solving narrower business problem sets with a more focused approach and data. That said, we have seen so many software vendors trying to become everything to everyone. I enjoyed this and will watch for further episodes--Thanks!

[@DigitalValueCreation]

Thanks for the comments. Good point. Focus is a hard think for many software companies.

[@user-qh8mv3tt3p]

In your opinion what do you envision the industry role(s) of engineers who specialize in machine learning will be as the architecture of ML models becomes more standardized and more accessible? I am concerned that my economic prospects as an electrical engineer who specializes in machine learning will be made obsolete very quickly in the AI field due to AI code generation models that can write effective and deployable standardized ML architecture with high efficiency.

[@dvcai]

Good news: As we scale LLMs to enterprise-grade, ML expertise remains not just relevant but critical. The journey to enterprise readiness goes beyond basic model deployment, involving deep optimization across the entire toolchain-from nuanced entity recognition and efficient data extraction and chunking to tuning embedding and indexing, strategic retrieval optimization, advanced prompt engineering, and fine-tuning models using techniques like LoRA. The role of an ML/AI architect evolves into that of a master tuner, harmonizing each element to enhance overall performance significantly.
The challenge ahead is to dive deep into the mechanics of managing and elevating the performance of the end-to-end processing chain. Embrace emerging tools like llamaIndex, PromptFlow, and LangChain, and master tuning methodologies that unlock new levels of effectiveness, improving RAG performance. This journey is not just about maintaining relevance; it's about leading the charge in the transformative use of AI in business, driving innovations that redefine what's possible.
Thank you for the question - we will focus the next episode on AI skills and learning and what it takes to achieve AI leadership.

[@felixlin4279]

sounds like the plotline of the movie Wargames lol