Learn AI Image Generation Terms with Claude and ChatGPT

image generated with AI

Last month, I asked ChatGPT to “Explain the top 10 terms in LLMs to a non technical audience with funny examples.”

Since my children have really enjoyed using generative AI for images, I thought I’d try it with Claude and ChatGPT to “Explain the top 10 terms in AI for image generation to a non technical audience with funny examples.”

I hope you enjoy it as much as I enjoyed the LLM terms explained by ChatGPT.

Here’s an explanation of the top 10 terms in AI for image generation, tailored for a non-technical audience with funny examples:

Diffusion Models: Imagine a bunch of tiny, confused artists randomly throwing paint at a canvas. Over time, they start to communicate and work together, refining the image until it looks like something recognizable, like a cat wearing a top hat.

Latent Space: It’s like a secret underground club where all the cool AI models hang out and share their abstract ideas. They don’t invite us humans because we wouldn’t understand their quirky sense of humor.

Generative Adversarial Networks (GANs): Picture two AI models engaged in an intense art competition. One tries to create realistic images, while the other plays the role of a grumpy art critic, constantly judging and forcing the first model to improve its skills.

Style Transfer: It’s like giving your pet rock a makeover by applying the style of a famous painting. Suddenly, your dull gray rock looks like it belongs in a Van Gogh masterpiece.

Convolutional Neural Networks (CNNs): These networks are like a group of nosy neighbors peeking through different windows to gather information about an image. They gossip and share their findings to understand the bigger picture.

Autoencoders: Imagine a machine that loves to play “telephone.” It takes an image, whispers its essence to itself, and then tries to recreate the original image from the whispered message. The results can be hilarious and sometimes eerily accurate.

Variational Autoencoders (VAEs): These models are like abstract artists who create their own interpretations of reality. They learn to compress images into a secret language and then use that language to generate new, dream-like versions of the original.

Transformer Models: Picture a group of robots playing a game of “pass the message.” Each robot adds its own unique twist to the image before passing it along, resulting in a wild and unpredictable final creation.

Upscaling: It’s like using a magic magnifying glass that not only makes an image bigger but also fills in the missing details. Suddenly, your pixelated stick figure drawing turns into a high-resolution masterpiece.

Inpainting: Imagine you have a family photo with your annoying cousin in it. Inpainting is like having a skilled artist magically remove your cousin from the picture and seamlessly fill in the background, as if they were never there.

These AI image generation terms might seem complex, but they all work together in amusing and creative ways to produce stunning and sometimes hilarious visual creations.

I hope this helps to explain some of the the technical terms used in generative AI for images!


Yolanda Lau is an experienced entrepreneurship consultant, advisor, and Forbes Contributor. She is also an educator, speaker, writer, and non-profit fundraiser.

Since 2010, she has been focused on preparing knowledge workers, educators, and students for the future of work.

Learn more about Yolanda here.

Yolanda is also a Founding Board Member of the Hawai’i Center for AI (HCAI), a non-profit organization. HCAI envisions a future in which all of Hawaiʻi’s residents have access to AI technology that effectively and safely serves their individual and collective well-being. Hawai’i Center for AI promotes the beneficial use of AI to empower individuals, communities, and industries throughout Hawai’i. We are committed to understanding the ways AI will help grow the state’s economy, help our institutions evolve, and transform our society. Through collaboration, education, and service, we drive research, innovation, and community partnerships to build a sustainable, prosperous, and policy-driven future for Hawai’i.

Exploring AI as a Lifelong Learner and Entrepreneur

As an entrepreneurship consultant, educator, and serial entrepreneur, I am constantly seeking new ways to stay ahead of the curve and empower those around me. 

My journey has been a winding journey full of twists and turns, with many different stops along the way. 

I’m an MIT-trained chemical engineer and biologist, who has founded and/or worked as an executive in companies spanning real estate, B2B SaaS, sports excitement analytics, fintech, and consulting. Through consulting, I’ve worked in e-commerce, cloud computing, entertainment, health and wellness, retail, edtech, and more. I’ve advised venture capital firms and become a recognized thought leader in the future of work.

My curiosity has led me to study neuroscience, toxicology and environmental health, positive psychology, social sector leadership, East Asian studies, finance, world religions, management, negotiation, conflict resolution, and peace building. I’m even a certified yoga instructor. Many years ago, after I read The Omnivore’s Dilemma by Michael Pollan, I went deep into food, nutrition, and vegetable gardening. But my passion has always been mentoring the next generation of entrepreneurs and leaders. I’ve taught app building, robotics, creative coding, entrepreneurship, and more. 

The latest twist in my journey has been an exploration of artificial intelligence (AI) through graduate-level coursework, hands-on projects, and leading AI workshops and training sessions for adults (and AI-lessons for students grades 4-12).

But why would someone like me, deeply embedded in entrepreneurship, bother diving into the technical aspects of AI? Why not just use and collaborate with generative AI and stop at that? Since these questions keep coming up, I thought I’d write this up to share my why. 

Never Stop Learning

If you’d told me two years ago that I’d be coding GANs, VAEs, RNNs, and LSTM networks, I’d have said you were crazy. Actually, I’d have said I have no idea what those acronyms mean. But yet, here I am learning to code AI and ML (machine learning) projects in Python. 

My curiosity is my competitive advantage. Equipped with deep knowledge across many domains, I can make connections more quickly and see pathways to innovation. Do I remember 100% of everything I’ve studied — of course not. But drinking from the firehose that is MIT (and surviving with not 1 but 2 degrees) taught me how to learn any discipline rapidly and retain just enough knowledge to relearn anything on demand. Learning the technical skills behind AI is just the latest tool I’m adding into my brain’s library. 

Staying Ahead in a World of Accelerated Change

In the fast-paced world of entrepreneurship, innovation is a necessity. By learning the technical aspects of AI, I am staying abreast of the latest technological advancements and trends shaping the entrepreneurial landscape. This enables me to better prepare my students for the future by integrating AI concepts into our curriculum. And with a deeper understanding for custom AI projects, I’m better able to serve my clients in exploring the potential applications of AI for their ventures.

Broadening Horizons and Building Versatility

Entrepreneurship is about adaptability and thinking outside the box. Engaging in AI development projects has not only expanded my technical skill set but also cultivates adaptability and interdisciplinary thinking. And seeing augmented reality (AR), virtual reality (VR), and mixed reality (MR) (together, extended reality or XR) all over several countries in Asia last summer, I’m eager to learn more about XR as well. Through hands-on experimentation and exploration, I am continuing to build on my creativity and critical thinking skills. These are just a few of the skills essential for success in a world that is changing faster than ever. 

Understanding the Impact and Ethical Implications

AI comes with a whole host of ethical issues, particularly in education and in entrepreneurship. By studying AI, I am gaining insights into the ethical implications of AI technologies and their potential impact on various industries. Bias, privacy, and data security are just a few of the ethical issues with AI. This understanding allows me to guide my students and clients in navigating ethical dilemmas to develop responsible AI-driven business models.

Equity and Inclusion

Early reports of AI use indicate that men are using AI much more than women. In addition, more than 80% of leaders in AI companies are white men. And as we’ve seen with many tech products, it’s clear that including more women and minorities in technology leads to improved products that are better for everyone. Diversity of thought helps discover problems that aren’t visible in monocultures. 

One of my favorite examples is forms that only accept last names with 3 or more characters. If there had been even a few Asian people on those teams (who are more likely to be familiar with a few of the very common last names of Wu, Yi, and Li), those decisions would never have been made. Another example is the AI-driven deep fake apps that are disproportionately used to nudify women and children. Those products would never have been released with women on the team. 

I’m investing my time into learning AI to help make sure women and minorities aren’t left behind in the economic benefits that are sure to follow with the advancements in AI. And to reduce the negative impacts on women that we are already seeing. 

Identifying Opportunities and Challenges

Through AI coursework and projects, I am gaining new insights into emerging business opportunities and challenges. From understanding the potential of AI to disrupt traditional industries to recognizing the need for ethical implementation, I feel better equipped to advise my students and clients on identifying entrepreneurial opportunities and developing strategies for success in an AI-driven world.

Empowering Entrepreneurs for the Future

Incorporating AI into entrepreneurship education, and all education, has benefits for students and educators alike. By embracing AI, we can better prepare the next generation of entrepreneurs, leaders, and citizens to thrive in an increasingly AI-driven world. We’ve been using generative AI to teach students how to create their own careers or ventures. We’ve also been working with students to understand how to use AI responsibly and ethically as a copilot — instead of on autopilot. Through exploration, experimentation, and education, we empower tomorrow’s leaders to harness the potential of AI for innovation and positive impact.

My journey into AI may have started as a curiosity, but it has become a vital part of my mission to empower and mentor entrepreneurs for success in the 21st century and beyond. As I continue working at the intersections of AI, entrepreneurship, and education, I am excited to see the transformative impact it will have on the future of work and innovation. 

How are you diving into artificial intelligence and why? 


Yolanda Lau is an experienced entrepreneurship consultant, advisor, and Forbes Contributor. She is also an educator, speaker, writer, and non-profit fundraiser.

Since 2010, she has been focused on preparing knowledge workers, educators, and students for the future of work.

Learn more about Yolanda here.

Yolanda is also a Founding Board Member of the Hawai’i Center for AI (HCAI), a non-profit organization. HCAI envisions a future in which all of Hawaiʻi’s residents have access to AI technology that effectively and safely serves their individual and collective well-being. Hawai’i Center for AI promotes the beneficial use of AI to empower individuals, communities, and industries throughout Hawai’i. We are committed to understanding the ways AI will help grow the state’s economy, help our institutions evolve, and transform our society. Through collaboration, education, and service, we drive research, innovation, and community partnerships to build a sustainable, prosperous, and policy-driven future for Hawai’i.

The Entrepreneurial Mindset: Equipping Students for the Future of Work

The Entrepreneurial Mindset: Equipping Students for the Future of Work

Rapid innovation in technology, automation, and artificial intelligence is changing how we work, learn, and play. The pace of change is such that by the time students complete their education, the skills they have learned may no longer be relevant in the job market.  While traditional educational models may struggle to keep up, we have an exciting opportunity to reimagine learning. 

By embracing entrepreneurship and nurturing entrepreneurial ethics, education can equip students with the resilience and adaptability needed to thrive in tomorrow’s diverse and dynamic job market.

The Entrepreneurial Mindset

Entrepreneurship isn’t just about starting businesses.  It’s a way of thinking that empowers individuals to adapt, innovate, and create value in the face of adversity. It cultivates essential skills like problem-solving, critical thinking, adaptability, creativity, learning from setbacks and failures, and calculated risk-taking. These skills are valuable in any career path, empowering individuals to be proactive, resilient, and seize opportunities.

Traditional education often prioritizes comfort and clear answers. Entrepreneurial education flips the script, encouraging students to experiment, embrace challenges, and learn from setbacks. It helps students learn two of the most important skills for the future – learning to be comfortable being uncomfortable and how to be content living with uncertainty. As the world continues to change faster than ever, students must be prepared to navigate ambiguity and embrace the unknown. 

Entrepreneurial education provides a unique opportunity to cultivate this mindset by exposing students to real-world challenges and encouraging them to step outside their comfort zones.

By nurturing an entrepreneurial mindset in students, we help them build the resilience, adaptability, creativity, and emotional intelligence needed to navigate any challenge that comes their way. Whether they choose to start their own ventures or pursue careers in established organizations, students with an entrepreneurial mindset are better equipped to identify opportunities, drive innovation, and create positive change.

Why Entrepreneurial Education Matters

The traditional focus on rote memorization falls short of preparing students for the future. Entrepreneurial education bridges this gap. Here’s how:

  • Problem-solving and critical thinking: Entrepreneurship requires students to identify real-world problems and develop innovative solutions. Doing this helps develop the ability to analyze complex situations and make informed decisions.
  • Creativity and innovation: By encouraging students to think outside the box and explore novel ideas, entrepreneurial education nurtures their creative potential and helps them develop the skills needed to drive innovation in their future careers.  
  • Adaptability and resilience: The entrepreneurial journey is often fraught with challenges and setbacks. By learning to embrace failure as a learning opportunity and persevere in the face of adversity, students develop the adaptability and resilience necessary to thrive in an ever-changing job market.
  • Digital Skills: As technology continues to reshape the world of work, digital skills have become increasingly essential. Entrepreneurial education provides students with opportunities to develop proficiency in areas such as coding, data analysis, digital marketing, and the use of emerging technologies like AI, AR (augmented reality), and VR (virtual reality).
  • Storytelling: Effective communication is key to success. Entrepreneurial programs teach students to craft compelling narratives, present ideas persuasively, and connect with their audience. Sharing your vision can be a powerful tool in any career path, from leadership roles to mission-driven nonprofits.
  • Lifelong Learning: The ability to continuously learn and adapt is paramount. Entrepreneurial education fosters a growth mindset, encouraging students to embrace lifelong learning and stay relevant in an ever-evolving job market.
  • Building Empathy: Every entrepreneur needs empathy to understand their audience’s needs, desires, and pain points. This skill is essential for creating products and services that genuinely address customer needs and for building strong relationships with colleagues, clients, and stakeholders. And like every other entrepreneurial skill, this one is important for overall success. 
  • Resourcefulness: Every entrepreneur learns to create value with limited resources. This entrepreneurial skill is especially important in nonprofit work for addressing social issues.
  • Comfort with uncertainty: In a world characterized by rapid change and disruption, the ability to be comfortable with discomfort and to thrive in the face of uncertainty is a critical skill. Entrepreneurial education helps students cultivate this mindset by exposing them to ambiguity and encouraging them to make decisions with incomplete information.

The entrepreneurial mindset is the foundation for success in any industry, including climate change mitigation, hospitality, and even nonprofit social impact work.

Integrating AI and Emerging Technologies

As artificial intelligence and other emerging technologies continue to transform the job market, it is crucial that entrepreneurial education keeps pace with these developments. By incorporating AI and related technologies into the curriculum, educators can help students understand the ethical implications of these tools and learn how to leverage them to create value and drive innovation.

Like everyone in every industry, educators, school administrators, and staff must learn to use AI to augment themselves. Ask yourself, “What can I uniquely do? And what can AI do reasonably well — to help me spend more of my time doing those things?” By using AI as a teaching aid, educators can augment their own capabilities and free up time to focus on the unique aspects of their role, such as providing personalized guidance and fostering critical thinking skills.

As students become more familiar with AI and its applications, they will be better prepared to navigate the AI-driven changes that are reshaping our society and the world of work. I’ve been using AI with students in grades 4-12 and we’ve had engaging, thought-provoking student-led discussions on how to use AI appropriately — while also leaving students with the confidence to use AI to make the world a better place. My students are using AI to envision bold entrepreneurial futures for themselves. By the time they graduate from high school, every student should know how to responsibly use AI as a copilot. Students can also learn how to leverage AI and machine learning (ML) to create innovative products and services, automate processes, and make data-driven decisions.

Ethics and Responsible Entrepreneurship

While entrepreneurship has the potential to drive positive change, students must learn the importance of ethical and responsible business practices. That’s why we emphasize the triple bottom line (or the three Ps of People, Planet, and Profit) with every student. It’s important to teach kids to understand the broader impact of their actions on stakeholders, communities, and the environment.

By teaching students to prioritize ethics and sustainability alongside financial success, we can help ensure that the next generation of entrepreneurs is equipped to build businesses that not only generate profits but also contribute to the greater good. This focus on responsible entrepreneurship will be particularly important as we face pressing global challenges such as climate change and social inequality.

Revolutionizing Education for the Future of Work

The current educational system is not equipped to meet the changing needs of the job market. Traditional education is often focused on rote memorization — and doesn’t encourage students to think critically or creatively. This approach is poorly suited to the future of work, where thinking outside the box and solving complex problems will be critical for success. 

In contrast, entrepreneurial education encourages students to be proactive, to think critically, and to take risks. It also teaches them to embrace failure as a learning opportunity and to be persistent in the face of obstacles. The entrepreneurial mindset is the foundation for success in any industry, including climate change and nonprofit work. 

Integrating Entrepreneurial Education Across Levels

Entrepreneurial education should be integrated into the curriculum at all levels of education, from primary school to higher education. This can be done through the development of dedicated entrepreneurship courses, the creation of innovation and entrepreneurship programs, and the integration of entrepreneurial skills into existing courses.  Imagine a science class where students develop new sustainable products, or a history class exploring entrepreneurship through the lens of historical events.

Shaping the Future

The future belongs to those who can adapt, innovate, and lead. By embracing entrepreneurial education, we equip students with the skills and mindset to not only survive but thrive in the ever-changing future of work. They’ll become the problem-solvers, innovators, and leaders driving economic growth and positive change in the world.


Yolanda Lau is an experienced entrepreneurship consultant, advisor, and Forbes Contributor. She is also an educator, speaker, writer, and non-profit fundraiser.

Since 2010, she has been focused on preparing knowledge workers, educators, and students for the future of work.

Learn more about Yolanda here.

Yolanda is also a Founding Board Member of the Hawai’i Center for AI (HCAI), a non-profit organization. HCAI envisions a future in which all of Hawaiʻi’s residents have access to AI technology that effectively and safely serves their individual and collective well-being. Hawai’i Center for AI promotes the beneficial use of AI to empower individuals, communities, and industries throughout Hawai’i. We are committed to understanding the ways AI will help grow the state’s economy, help our institutions evolve, and transform our society. Through collaboration, education, and service, we drive research, innovation, and community partnerships to build a sustainable, prosperous, and policy-driven future for Hawai’i.

Why Students Still Need to Learn to Write in the Age of AI

In an era where machines can write your emails and papers for you, why should students still learn to write?

Yes, generative AI has gotten pretty good at natural language generation. In fact, studies have shown that people tend to prefer AI-generated writing!  Personally, I find off-the-shelf AI-generated writing to be too flowery and kind of stale. But in many cases – especially for lower-skilled writers – AI  pumps out pretty decent written content. 

But I firmly believe that students still need to learn and develop writing skills. 

Here are 9 reasons why:

1. Clear thinking 

Clear writing is a reflection of clear thinking. When someone isn’t able to express their thoughts clearly, I question the depth and clarity of their understanding. The process of writing helps students organize thoughts, identify gaps in their understanding, and think more critically about complex ideas. By learning to write clearly, students learn to think logically, express their ideas coherently, and make well-reasoned arguments. This skill is invaluable in everyday life (not just in school). Thinking clearly and making informed decisions are important in everything we do..

2. Critical thinking and problem-solving skills

Writing is a powerful way to develop critical thinking and problem-solving skills. When writing, we have to analyze information, evaluate arguments, and make informed judgments. This process helps develop the ability to think critically about complex issues, consider multiple perspectives, and construct logical, well-reasoned arguments. In addition, writing often requires students to break down complex problems into manageable pieces, devise creative solutions, and communicate their ideas effectively. These skills are essential skills for tackling complex problems in a world of accelerated change.

3. Creativity and originality

From the earliest days of humanity, we’ve been creative. Humans have been creating songs, stories, dance, art, and more since the time of cave-dwelling. Creativity and storytelling are core to who we are as human beings. 

Human writers bring unique perspectives, experiences, and creativity to their writing, producing original content that goes beyond what AI can currently generate based off of patterns and prediction. I think of the richness of the human experience and what will be lost if humans forget how to use their own words and ideas to express themselves. While I believe the future is a world where humans are augmented by AI, it’s imperative we don’t fully outsource writing and other art forms.

4. Effective communication

Writing is a fundamental form of communication that enables everyone to express their ideas, thoughts, and feelings in a clear and compelling manner. Whether crafting an essay, composing an email, or creating a report, writing helps students develop the ability to convey complex information in a way that is easily understood by others. This skill is essential in personal and professional contexts, where the ability to communicate effectively can be the difference between success and failure. Yes, AI-generated writing can help with this. But students still need to learn what makes communication effective. And the best way to learn that is to do it themselves. 

5. Personal and professional growth

Writing is a skill that requires continuous practice and refinement. Engaging in the writing process helps students develop their voice, style, and confidence as writers. Finding your voice can play a big role in personal and professional growth.

6. Human connection

Writing is often a deeply personal and emotional act. Human-written content can forge connections, evoke empathy, and inspire others in ways that AI-generated text may struggle to replicate. Writing is powerful. Again, while I believe AI-enhanced humans are the future, we cannot fully outsource writing and other art forms that allow us to connect with each other. 

7. Developing empathy and understanding

Writing encourages students to explore and understand diverse perspectives. This teaches empathy and open-mindedness. Fiction has been shown to improve empathy and to increase helpful behaviors. By engaging in writing, students become better people and citizens. They develop a deeper appreciation for the complexity of human experiences and learn to value diverse voices.

8. Inspiring innovation

The awe-inspiring, futuristic worlds imagined by science fiction writers have frequently sparked the curiosity and ambition of scientists, leading to the development of groundbreaking technologies. Star Trek and the Jetsons have inspired the creation of cell phones, laptops, and home cleaning robots. I’ve always loved historical fiction and science fiction. But even I remember being an undergrad at MIT and being astonished by how deeply passionate my classmates were about science fiction. Writing is so powerful that it can literally lead to the future – by inspiring and driving innovation across various fields.

9. Collaboration with AI 

Finally, I’ll address the elephant in the room. Yes, the future is a world where writers use AI to craft better writing. Paradoxically, that makes it more important to be a better writer. Confused?

As AI writing tools become more common, students who have strong writing skills can better collaborate with and guide these tools. As AI thought leader Ethan Mollick wrote in Co-intelligence, writers are often the best at working with AI to create writing. They can provide the necessary context, specify requirements, and edit AI-generated content to ensure it meets their intended purpose and audience. 

Because generative AI currently creates writing by guessing what the next probable word is, it often creates generic, inaccurate, one-dimensional text. Writers who can describe the effects they want the words to create are able to use AI to create more powerful prose. With their editing skills, good writers are able to guide the AI to improve their writing. And those who are familiar with a variety of different tones and styles can use that knowledge to prompt the AI more effectively.

In short, good writers are better at reviewing, editing, and adapting AI-generated text to fit specific needs or preferences.

Writing in the Age of AI

While AI can assist with writing tasks, it does not eliminate the need for students to learn and develop their writing skills. The process of writing fosters critical thinking, creativity, effective communication, personal growth, empathy, and the ability to inspire innovation. 

Rather than replacing human writers, AI writing tools are changing the nature of writing work, requiring students to have both strong writing skills and the ability to collaborate with AI.

As is true of many aspects of life, process is equally important as outcome. In teaching writing, process is more important than outcome.


Yolanda Lau is an experienced entrepreneurship consultant, advisor, and Forbes Contributor. She is also an educator, speaker, writer, and non-profit fundraiser.

Since 2010, she has been focused on preparing knowledge workers, educators, and students for the future of work.

Learn more about Yolanda here.

Yolanda is also a Founding Board Member of the Hawai’i Center for AI (HCAI), a non-profit organization. HCAI envisions a future in which all of Hawaiʻi’s residents have access to AI technology that effectively and safely serves their individual and collective well-being. Hawai’i Center for AI promotes the beneficial use of AI to empower individuals, communities, and industries throughout Hawai’i. We are committed to understanding the ways AI will help grow the state’s economy, help our institutions evolve, and transform our society. Through collaboration, education, and service, we drive research, innovation, and community partnerships to build a sustainable, prosperous, and policy-driven future for Hawai’i.

Learn AI Large Language Model Terms with ChatGPT

Last month, I read Nichole Sterling’s LinkedIn post where Kavita Tipnis-Rasal prompted ChatGPT to “Explain the top 10 terms in LLMs to a non technical audience with funny examples” and laughed out loud. The explanations created by AI about AI were great for a non-technical audience.

So, I fed the same prompt into ChatGPT. I ran it three times. Then, I curated, edited, and combined it into this list of terms used in Large Language Models (LLMs). I hope you enjoy it as much as I enjoyed what Tipnis-Rasal shared via Sterling.

Large Language Models (LLMs): Imagine you have a super-smart friend who knows everything about a wide range of topics, from ancient history to modern technology. Now, imagine if you could shrink your friend down and put them inside your computer. That’s essentially what a Large Language Model (LLM) is—a super-smart program trained on massive amounts of text data to understand and generate human-like language.

Natural Language Processing (NLP): Imagine you have a magical translator that can instantly convert your dog’s barks into human language, allowing you to understand exactly what they’re saying. NLP is like that magical translator, but for computers—it helps them understand, interpret, and generate human language, enabling tasks like translation, sentiment analysis, and chatbots to communicate with us in a way that feels natural.

Tokenization: Imagine you’re making a sandwich, but instead of cutting it with a knife, you break it into smaller, manageable pieces with your hands. Tokenization does something similar with text, breaking it down into smaller units, like words or even smaller parts.

Embedding: This is like putting different ingredients into your sandwich to give it flavor. Embedding takes words or phrases and converts them into numerical representations, which helps the model understand their meaning and context.

Attention Mechanism: Picture a teacher in a classroom paying extra attention to some students while explaining a lesson. Similarly, an attention mechanism in LLMs helps them focus on different parts of the text, giving more weight to important words or phrases.

Fine-tuning: Imagine you’ve mastered the art of making a grilled cheese sandwich, but now you want to tweak it a bit by adding different cheeses or toppings. Fine-tuning in LLMs is like adjusting the model’s parameters or training it on specific data to improve its performance for a particular task, like translation or summarization.

Transformer Architecture: Think of a group of robots working together to assemble a giant puzzle. In LLMs, the transformer architecture organizes layers of neural networks to efficiently process and understand large amounts of text data.

Beam Search: Imagine you’re exploring a maze with multiple paths, trying to find the quickest way out. Beam search in LLMs is like looking ahead and considering several possible sequences of words to generate the most coherent and accurate output.

Loss Function: This is like a scoreboard that tells you how well you’re doing in a game. In LLMs, the loss function measures the difference between the predicted output and the actual output, helping the model learn and improve over time.

Gradient Descent: Picture a hiker trying to find the quickest route down a mountain by following the steepest slope. Gradient descent in LLMs is an optimization algorithm that adjusts the model’s parameters in the direction that reduces the loss, helping it converge towards better performance.

Overfitting: Imagine a tailor making a suit that fits one person perfectly but doesn’t look good on anyone else. In LLMs, overfitting occurs when the model learns to perform well on the training data but struggles to generalize to new, unseen data.

Bias-Variance Tradeoff: Think of Goldilocks trying different bowls of porridge—not too hot, not too cold, but just right. In LLMs, the bias-variance tradeoff involves finding the right balance between flexibility (variance) and simplicity (bias) to build a model that generalizes well to new data without overfitting.

Fine-tuning: Suppose you’ve mastered the art of baking cookies, but now you want to experiment with new flavors like bacon or wasabi. Fine-tuning in LLMs is like tweaking a pre-trained model to specialize in specific tasks or domains, much like adding unique ingredients to a familiar recipe.

Pre-training: Imagine you’re a student preparing for a big exam by studying a wide range of topics beforehand. Pre-training in LLMs involves training the model on vast amounts of text data, teaching it general language understanding skills before fine-tuning it for specific tasks like translation or summarization.

Attention Mechanism: Think of attention as a spotlight on a stage, highlighting different actors as they perform. Similarly, an attention mechanism in LLMs directs the model’s focus to relevant parts of the input text, helping it understand context and relationships between words more effectively.

Generative Models: Picture a magician pulling rabbits out of a hat, except instead of rabbits, they’re generating realistic-looking images or text. Generative models in LLMs create new content based on patterns learned from the training data, allowing them to generate coherent paragraphs, poems, or even stories.

Perplexity: Imagine trying to decipher a secret code written in a language you don’t understand. Perplexity in LLMs measures how well the model predicts the next word in a sequence, with lower perplexity indicating better performance, much like cracking a code with fewer guesses.

Backpropagation: Picture a team of detectives trying to solve a crime by retracing their steps and identifying clues along the way. Backpropagation in LLMs is an algorithm that calculates how much each parameter in the model contributed to its error, allowing it to adjust and improve its predictions over time, similar to detectives refining their investigation based on new evidence.

Long Short-Term Memory (LSTM): Imagine you have a forgetful friend who struggles to remember things from the past. Now, picture giving them a magical notebook that helps them selectively remember important information and forget irrelevant details. LSTMs are like that magical notebook for language models, allowing them to maintain long-term context and selectively retain or forget information, making it easier to understand and generate coherent text.

Neural Network: Picture a bustling city with interconnected roads and highways. Now, imagine each intersection is a neuron, and the roads are the connections between them. A neural network in LLMs is like this city, with layers of interconnected nodes working together to process and analyze text data, much like how traffic flows through the city to reach its destination. The more complex the network (or city), the more sophisticated the language understanding and generation capabilities of the model.

I hope these playful examples help demystify the technical jargon surrounding Large Language Models!


Yolanda Lau is an experienced entrepreneurship consultant, advisor, and Forbes Contributor. She is also an educator, speaker, writer, and non-profit fundraiser.

Since 2010, she has been focused on preparing knowledge workers, educators, and students for the future of work.

Learn more about Yolanda here.

Yolanda is also a Founding Board Member of the Hawai’i Center for AI (HCAI), a non-profit organization. HCAI envisions a future in which all of Hawaiʻi’s residents have access to AI technology that effectively and safely serves their individual and collective well-being. Hawai’i Center for AI promotes the beneficial use of AI to empower individuals, communities, and industries throughout Hawai’i. We are committed to understanding the ways AI will help grow the state’s economy, help our institutions evolve, and transform our society. Through collaboration, education, and service, we drive research, innovation, and community partnerships to build a sustainable, prosperous, and policy-driven future for Hawai’i.

Transforming Tomorrow: Harnessing the Future of Work

By Yolanda Lau

The future of work unfolds with boundless potential and transformative opportunities awaiting. As we stand on the cusp of exponential change, a new era emerges in which the skills imperative for success diverge from those of the 20th century. The rise of automation, artificial intelligence, and the gig economy is changing how we work. To thrive, companies and individuals must be proactive in preparing themselves for the future of work.

Embracing Automation

The rise of automation is one of the most significant changes in the future of work. Automation encompasses various technological advancements aimed at reducing or replacing human labor with machinery or software — and is expected to reshape almost every industry. Many low-skilled jobs, such as assembly line work or data entry, will likely be automated, leading to a transformation in job roles and skill requirements. Automation isn’t about replacing humans entirely — instead, it’s about augmenting human capabilities and efficiency. Companies must invest in training and development programs to ensure their employees have the skills required to adapt to this evolving landscape. While automation may displace some jobs, it will also create new opportunities for those who can design, build, and manage automated systems.

Integrating Artificial Intelligence

In tandem with automation, artificial intelligence (AI) is revolutionizing the future of work. AI technologies — including machine learning, natural language processing, computer vision, and robotics — are increasingly integrated into various aspects of business operations. While many fear that AI will lead to widespread job loss, the reality is more nuanced. AI has the potential to enhance productivity, streamline processes, and unlock new possibilities. However, individuals who resist or fail to adapt to AI may find themselves at a disadvantage in the job market. Companies must invest in AI education and training to empower their workforce to leverage AI technologies effectively. Every senior executive should be thinking, “How can my team use AI to augment themselves?” By embracing and integrating AI, companies can gain a competitive edge and drive innovation in the evolving landscape of work.

Navigating the Gig Economy

Simultaneously, the gig economy continues to grow, redefining traditional notions of employment. More people are taking side gigs, to hedge against potential layoffs and to sharpen and learn new skills. And more individuals are gravitating towards freelance or contract engagements, thanks to the rise of online platforms facilitating flexible work arrangements. To adapt, companies must embrace the liquid workforce — and learn to cultivate and work with a virtual talent bench engaged in project-based work.

Shifting towards Project-Based Work

The future of work will also see a shift towards project-based work. This trend is driven by the need for agility — and for organizations to constantly be responsive to changing market conditions. Project-based work allows companies to quickly assemble a team of experts with the necessary skills to complete a specific project, rather than maintaining a large permanent workforce. Companies must invest in project management and collaboration tools — and create a documentation-first culture — to ensure that their employees and contract workers can work effectively in project-based teams.

Cultivating Skills and Adaptability

To prepare for the future of work, companies must be proactive in developing their employees’ skills and abilities. This can be achieved through a variety of methods, such as on-the-job training, online courses, and formal education programs. Companies must also invest in leadership and management development programs to ensure their employees have the leadership skills required to succeed in a rapidly changing workplace.

Individual Agency in Career Development

Individuals must take ownership of their career development and professional growth — honing their skills through lifelong learning and side gigs. By actively cultivating adaptability and resilience, individuals can position themselves as indispensable assets in the dynamic landscape of the future workplace. Individuals must also be proactive in building their personal brand and cultivating professional networks. Today, everyone is a brand — and individuals must curate their online presence and narrative to authentically reflect their values, expertise, and aspirations. Doing this stratgeically can help find uncover new opportunities. When combined with a strong network of weak ties, individuals can leverage diverse connections to achieve their career goals.

The Imperative of Work-Life Fit

The future of work is also likely to see a greater emphasis on work-life balance, or as I prefer to call it, work-life fit. We’re already seeing this with some countries exploring a four-day work week and others have made it illegal for companies to contact employees outside of the workday. Executives, policymakers, and workers are realizing that work-life fit is essential for both individual well-being and organizational success. Companies must adopt flexible work arrangements, offering employees the ability to work from home or on flexible schedules. They must also invest in creating a workplace where mindfulness, compassion, and grace are commonplace.

Take Action to Transform Your Future

The future of work is here — and it’s teeming with promise and transformation. The time for action is now. Whether you’re a company leader or an individual contributor, the future of work awaits. Embrace change, augment yourself, invest in growth, and seize every opportunity that comes your way. Together, we can shape a future where innovation thrives, and success knows no bounds. The journey starts today — let’s make it count.


Yolanda Lau is an experienced entrepreneurship consultant, advisor, and Forbes Contributor. She is also an educator, speaker, writer, and non-profit fundraiser.

Since 2010, she has been focused on preparing knowledge workers, educators, and students for the future of work.

Learn more about Yolanda here.