Відео

Emerging Roles in the AI and Robotics Era 1. Technology-Oriented Positions: • AI Integration Managers: Oversee the seamless incorporation of AI systems into existing business processes, ensuring alignment with organizational objectives. • AI Product Managers: Lead the development and deployment of AI-driven products, balancing technical capabilities with market needs. 2. Management and Strategic Roles: • Chief AI Officers: Drive AI strategy and implementation across the enterprise, fostering innovation and competitive advantage. • Digital Transformation Leaders: Guide organizations through technological shifts, integrating AI and robotics to optimize business models. • AI Ethics Officers: Ensure responsible AI deployment, addressing ethical considerations and regulatory compliance. Actionable Steps for Career Transition 1. Skill Development: • Technical Proficiency: Acquire knowledge in AI frameworks, machine learning algorithms, and data analytics. • Strategic Acumen: Understand the business implications of AI technologies to align them with organizational goals. 2. Educational Resources: Online Courses: Platforms like Coursera offer specialized programs in AI and robotics. 3. Practical Experience: • Pilot Projects: Engage in small-scale AI initiatives within your organization to demonstrate capability and drive innovation. • Cross-Functional Collaboration: Work with diverse teams to integrate AI solutions, enhancing both technical and managerial skills. 4. Networking: • Professional Associations: Join groups like the Association for the Advancement of Artificial Intelligence (AAAI) to stay informed and connected. • Industry Conferences: Attend events to learn about the latest trends and expand your professional network. Hope this helps - hood luck with your career transition.

We meant to put this in the shownotes. There are 2 ways to look at AI impact on jobs. 1) which new jobs are in high demand and 2) which jobs are safest from AI automation. There are many such lists out there. Here are 2 that are well researched New AI Jobs: www.techtarget.com/whatis/feature/Top-AI-jobs Jobs safest from AI automation: www.uscareerinstitute.edu/blog/65-jobs-with-the-lowest-risk-of-automation-by-ai-and-robots

@@DigitalValueCreation thank you

Glad you liked it!

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.

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

Great points. There are benefits of repeating patterns that worked for us in the past. It gets reinforced throughout our careers. It is hard to admit in a crisis that we and others have no clear playbooks any more. We are all trying different ones…