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The gap between the supply and demand for generative AI-literate employees can be attributed to several factors: ### **Reasons for the Gap** 1. **Rapid Advancement of Technology**: Generative AI has evolved at a breakneck pace, and many education systems and training programs haven't kept up with the speed of change. 2. **Specialized Knowledge Requirements**: Generative AI involves complex concepts such as neural networks, prompt engineering, large language models (LLMs), and domain-specific adaptations, which require a strong foundation in mathematics, programming, and machine learning. 3. **Limited Expertise Pool**: The field of AI is relatively new, and there are fewer professionals with advanced expertise in generative AI as compared to traditional software development or data science roles. 4. **High Demand Across Industries**: As more industries recognize the transformative potential of generative AI, demand for these skills has skyrocketed, leading to competition for the limited available talent. 5. **Education Lag**: Academic programs and certifications often take time to develop and adapt, meaning there are fewer graduates with direct generative AI training. --- ### **How Organizations Can Address This Gap** 1. **Invest in Upskilling Current Employees**: - **Workshops and Bootcamps**: Conduct intensive training programs focused on generative AI tools, technologies, and practical applications. - **Online Learning Platforms**: Encourage employees to complete courses on platforms like Coursera, Udemy, and edX, which offer specialized AI tracks. - **Internal Mentorship**: Create mentorship programs where experienced AI professionals within the organization can train less experienced staff. 2. **Foster a Learning Culture**: - Encourage experimentation with generative AI tools like ChatGPT, DALL·E, or MidJourney for day-to-day tasks to build familiarity. - Provide incentives for employees to innovate and explore AI applications relevant to their roles. 3. **Partner with Academic Institutions**: Collaborate with universities and research institutions to offer customized training programs or internships that align with organizational needs. 4. **Leverage No-Code and Low-Code Platforms**: Provide employees with access to user-friendly AI tools that don’t require deep technical expertise, allowing non-technical staff to integrate generative AI into their work. 5. **Cross-Disciplinary Training**: Since generative AI intersects with various fields, encourage employees from diverse backgrounds (e.g., marketing, HR, and design) to understand how generative AI can apply to their domains. 6. **Build AI Awareness at All Levels**: Offer high-level sessions for leadership and strategic teams to understand the potential and limitations of generative AI, enabling better decision-making and strategic alignment. By adopting a multifaceted approach, organizations can close the skills gap and build a workforce capable of leveraging the full potential of generative AI.

The course is primarily delivered through video content, with a few read-only modules. Most videos consist of slide presentations narrated by an off-screen voice (likely AI-generated) covering a wide range of topics. These range from high-level overviews to more detailed explanations, though the content remains quite general throughout. A small number of videos feature real instructors explaining concepts on a whiteboard. These were the most engaging parts of the course, and it’s unfortunate that there weren’t more of them. The course also includes lab activities that involve interacting with Generative AI platforms. These labs are structured around specific topics and allow learners to explore how Generative AI can be applied in various scenarios. Overall, I didn’t find the course particularly enjoyable. The delivery felt monotonous, similar to reading company policy slides on your first day at work. However, the course did achieve its main objective: providing a broad understanding of AI, including its core concepts such as machine learning, deep learning, generative AI, practical use cases, business applications, AI agents, and ethical considerations.

Once again, an entire course on AI has NO MENTION AT ALL about the training datasets using already copywrited or trademarked intellectual property. These companies will go to ANY length to avoid mentioning what they have done: stealing millions of images from hard-working creative people to train their models. They perform incredibly delicate, elaborate dances around the subject, even within their own AI modules on the ethics of AI (!!) supposedly to address those ethical concerns, but they frame it as "who owns the images AI produces?" instead of asking "should these AI companies recompense artists for using their works without their permission?" This dance is sickening to me -- late-stage capitalism at its absolute worst. Shame. Shame on IBM.

Had good foundational concepts about AI, but I think too many conversational videos with IBM engineers presenting their personal opinions about the future of AI. I found most of them to be overly opiniated with elementary analogies and some even with almost no relevance or value to actual learning about AI. For example "comparing AI > to the > Horse & Buggy" come on really, how about using the "Computer" with modern relevance? Highly recommend replacing one or two of these all-talk videos with some real cool and interesting use case examples showing AI in action. A practical and applied presentation in real world scenarios like technical medicine, industrial production, scientific experiment, etc.

a bit too much marketing, filled with ibm motherhood about productivity, efficiency, and improved customer service. Some technical descriptions failed to properly differentiate between traditional application development and AI application development. Also much of the youve got to have ai misses the point that business use applications and processes to improve performance, these can be implemented manually, traditionally programmed, purchased off the self, or developed with AI. It is the process improvement that matters, alternative implementations need to be evaluated. AI can help with some, not others, but you never choose AI just because it is AI.