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.

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.

The Enduring Relevance of Coding Skills

In the age of AI, many say that coding is a dying art. But I think it’s more essential than ever.

Ever since ChatGPT was launched in November 2022, it has upended how we work, think, and even play. Generative artificial intelligence (AI), based on large language models (LLMs), have made huge improvements since then. Multi-modal AI — which can process info from multiple modalities such as images, video, text, and audio — makes some time consuming and tedious tasks seem effortless. 

With generative AI, anyone can complete computer programming coding projects. I’ve had many non-coder friends tell me that AI has helped them build their first website, app, or other coding project. For that reason, people keep asking if learning to code has become obsolete. 

I believe learning to code remains highly relevant.

Here are 13 reasons why:

1. Fundamental understanding

Learning to code is like getting a backstage pass to technology. When you understand code, you get to see how everything works behind the scenes. Coding helps people understand the fundamental concepts and principles behind how software and technology work. This understanding allows people to use AI in more powerful ways and helps to interpret AI-generated results. 

2. Problem-solving skills

Coding teaches students how to break down complex problems into smaller, manageable parts and develop step-by-step solutions. These problem-solving skills help people solve problems of all types. Want to solve climate change, the problem-solving skills learned with coding will help you ideate more effective solutions. These skills are valuable in many areas of life and work, regardless of whether the actual coding is done by AI or not.

3. Logical thinking

Programming necessitates a structured approach, where every line of code builds upon the previous one, akin to constructing a building’s foundation before adding floors. This process fosters logical thinking, where individuals learn to anticipate potential outcomes, identify patterns, and discern cause-and-effect relationships. Such skills extend beyond coding, enabling individuals to dissect complex real-world problems and devise systematic solutions. Whether troubleshooting a malfunctioning device or strategizing in business, the ability to think logically is indispensable.

4. Learning to experiment

Learning to code encourages students to embrace experimentation and take risks. Through coding, students can quickly prototype and test their ideas, learning from their failures and iterating on their designs. This process of experimentation fosters creativity and innovation, enabling students to develop unique solutions that push the boundaries of what’s known and possible.

5. Collaboration with AI

As AI becomes more prevalent in programming and software development, people who know how to code can better collaborate with and guide AI systems.  Programmers will need to guide AI by providing context, defining requirements, and validating its generated code (debugging). As a result, educators will need to emphasize problem decomposition, testing, and debugging – skills that are important in collaborating with AI for coding. 

AI is great for data analysis and repetitive tasks, but it lacks human intuition and understanding. Conversely, programmers bring these strengths to the table, ensuring the relevance, accuracy, and ethical considerations of AI-driven solutions. When we work together with AI, we maximize the potential of both and create more robust and nuanced outcomes.  However, students need to be taught to be skeptical of AI-generated results and take ownership of verifying and validating them. If students become over reliant on AI, they’ll short-circuit the important learning process.

AI-generated code can provide a starting point, but it often needs human intervention for refinement. Programmers can evaluate the code, identify areas for improvement, fix errors, and tailor solutions to specific needs. This critical evaluation is essential, especially as technology and user requirements evolve.

6. Computational thinking

Moreover, learning to code develops computational thinking, a fundamental set of skills and thought processes essential for solving complex problems across various disciplines. Computational thinking encompasses problem-solving skills, logical thinking, and other key components such as decomposition, pattern recognition, abstraction, and evaluation. By learning to code, students develop the ability to break down complex problems into smaller, manageable parts (decomposition), identify patterns and similarities within and across problems (pattern recognition), focus on the essential features of a problem while ignoring irrelevant details (abstraction), and assess the effectiveness and efficiency of their solutions (evaluation). 

These skills are invaluable in navigating an increasingly complex and technology-driven world, even as AI advances and automates certain aspects of programming.

7. Foundation for systems thinking

Learning to code also provides a strong foundation for systems thinking, which involves understanding how different components of a system interact and influence each other. Coding exercises, such as debugging and optimizing algorithms, help students anticipate unintended consequences and develop strategies for building more resilient and adaptable systems. By breaking down complex problems into manageable components, identifying patterns and relationships, and understanding the interconnectedness of different elements within a system, students develop a systems thinking mindset that is transferable across many domains, from software development to business management, public policy, and beyond.

8. Emerging technologies

Coding skills are not only relevant to AI but also to other emerging technologies such as AR, (augmented reality), VR (virtual reality), and MR (mixed reality). As these spatial computing technologies continue to advance and gain popularity, there will be an increasing demand for programmers and developers who can create immersive and interactive experiences. Students with coding skills will be well-positioned to contribute to the development of AR, VR, and MR applications, which have the potential to revolutionize various industries, including education, entertainment, healthcare, and more.

9. Data science and analysis

Coding skills are essential for data science and analysis, which play a critical role in today’s data-driven world. Every two days, we create as much data as was created since the dawn of humanity through 2003. That statistic alone should tell you that data science and data literacy are crucial skills. As organizations collect and process vast amounts of data, there is a growing need for professionals who can write code to clean, analyze, and derive insights from complex datasets. Those with coding skills can leverage powerful libraries and frameworks to manipulate and visualize data, build predictive models, and support data-driven decision-making. These skills are invaluable across all industries and functions, including business, retail, healthcare, entertainment, finance, and scientific research.

10. Responsible AI development

As AI-generated code becomes more advanced and widely used, it is crucial for people to understand the code they are working with to prevent unintended consequences. AI systems can sometimes “hallucinate,” generating code that seems plausible but may contain errors, vulnerabilities, or even malicious elements. Without a solid understanding of coding principles and best practices, people may inadvertently release software that is prone to hacks or behaves in unexpected ways. By learning to code, students can develop the skills necessary to critically evaluate AI-generated code, identify potential issues, and ensure the development of safe, secure, and ethical software.

11. Critical thinking skills

Learning to code teaches critical thinking in a different way than writing does. While both coding and writing require logical thinking and problem-solving skills, coding demands a more systematic, step-by-step approach to breaking down complex problems into smaller, manageable parts. Coding also requires students to anticipate and handle potential errors or edge cases, fostering a more rigorous and detail-oriented form of critical thinking. Moreover, coding encourages students to think algorithmically and develop efficient, optimized solutions to problems. These unique critical thinking skills are invaluable not only in programming but also in a wide range of fields and everyday life situations.

12. Tackling complex global challenges 

As we’ve established, learning to code equips students with computational thinking and systems thinking skills. These mindsets and skills are necessary to tackle the world’s most pressing problems. Trying to solve climate change? Tackling the impending water insecurity? Solving the problem of responsible AI implementation? All of the world’s most complex challenges require individuals who can break down problems, identify patterns, and develop innovative solutions. Coding provides a foundation for understanding and leveraging technology to address these challenges, enabling students to become active contributors to a better future.

13. Career opportunities

Even with AI improving rapidly, demand for programmers and software developers is still high. Having coding skills opens up a wide range of career opportunities and allows students to be active participants in shaping the future of technology.

While AI can automate certain aspects of programming and coding, it does not eliminate the need to learn these skills. Coding provides a foundation for understanding technology, develops valuable problem-solving and logical thinking skills, enables collaboration with AI, and opens up career opportunities. 

When the printing press was invented, scribes were not rendered obsolete but adapted to new roles as typographers and printers. The invention of the camera did not eliminate the need for artists but rather opened up new artistic possibilities and genres, such as photography and film. The introduction of the typewriter did not replace the need for writers but instead changed the way they worked and made the writing process more efficient. 

The advent of calculators and computers did not eliminate the need for mathematicians but rather allowed them to tackle more complex problems and develop new mathematical theories. When spreadsheets became commonplace, people theorized that accountants would become irrelevant but accountants simply shifted their work to other areas. The development of computer-aided design (CAD) software did not replace the need for architects and engineers but rather enhanced their ability to design and model complex structures. 

When AI started being used in radiology, people theorized that radiologists would no longer be needed — but instead of becoming obsolete, there is now a shortage of radiologists. In all of these cases, technologies changed how humans worked but did not eliminate the need for those functions. 

Similarly, rather than replacing the need for human coders, AI is likely to change the nature of programming work, requiring programmers to have coding skills and the ability to work effectively with AI tools.​​​​​​​​​​​​​​​​

Preparing for the future

In a world where AI can write essays, create art, and even compose music, it’s fair to wonder if learning to code is still worth it. But as I’ve discussed above, learning to code is absolutely still a worthwhile endeavor. Lest you think I’m not practicing what I preach, I’m actively learning Python for technical AI coding projects. 

As educators, we should integrate coding into our curriculum to prepare students for the future — less for the sake of coding and more for the mindset and frameworks that learning to code develops. This includes emphasizing problem decomposition, testing, debugging, and using AI as a copilot. In many ways, how AI is changing how we teach coding is the same as how AI is changing how we teach anything. We must move from teaching basic skills (in this case, syntax) to higher-order thinking. 

Students, embrace the challenge of learning to code, learn to be comfortable being uncomfortable, and don’t be afraid to make mistakes. 

And to anyone reading this, recognize the importance of coding skills in today’s digital world and take steps to learn — and you’ll develop new ways of thinking that will help you become better problem solvers. 

Together, we can foster a generation of creative, critical thinkers who are equipped to navigate and shape the future.​​ 


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.

Embrace Lifelong Learning To Thrive In The Future Of Work

In the future of work, the critical skills for success are increasingly soft skills like emotional intelligence, adaptability, and resilience. Success in the future of work requires becoming a lifelong learner. The world is changing faster than ever, and only through lifelong learning will we have the capability to adapt along with it.

Here are 11 strategies to develop a habit of lifelong learning.

1. Ask why.

Think back to when you were a child. Chances are you drove your parents a little crazy with all of your questions. Be like a child and always ask why — question everything. But be open to changing your mind. Seek out counter opinions and acknowledge alternative viewpoints — and you’ll learn more from these perspectives and push yourself further.

2. Learn to love challenges.

Challenges stimulate learning and bring a sense of fulfillment. I love challenges and welcome struggles and obstacles as the things most worth doing are often hard. Without challenges, we stagnate. While challenges and bumps in the road can be uncomfortable, these opportunities are the ones from which you will learn the most. Get ready to take risks by setting stretch goals for yourself. A willingness to take risks doesn’t mean you need to take on every challenge — it’s about taking measured risks that push you beyond your current limits.

3. Embrace failure.

Failing is something you do because you’re pushing yourself to do more. Every failure is an opportunity to learn. Don’t be ashamed or afraid of failure. You likely aren’t challenging yourself if you haven’t failed some of the time. I’ve found that failure has frequently been my best teacher, and my successes are a result of growth and learning from past failures and mistakes. Embrace failure and mistakes as opportunities to integrate valuable feedback and information. After all, as Albert Einstein once said, “Failure is success in progress.”

4. Practice mindfulness.

I’ve found that mindfulness is an essential soft skill to learn as it amplifies your other soft skills. Mindfulness can boost your mental agility, self-awareness, and resilience. Plus, taking a mindful brain break can boost your productivity and effectiveness while increasing the “divergent thinking” that results in new ideas. A plethora of apps and programs, such as Headspace, Yoga Ed., and Calm can help you practice mindfulness and build this essential lifelong learning skill.

5. School is only the beginning.

School should function to build a foundation for lifelong learning. Lifelong learners realize that learning doesn’t stop when school ends. Never stop seeking opportunities to learn, prioritizing both surface learning and deep learning. While surface learning is quick and easy, deep learning takes more effort. Both are valuable.

Coursera, Udemy, and EdX are great for consuming content. Cohort-based courses like Maven, On Deck, and Ascend take learning to the next level by bringing together groups of learners.

6. Be open to feedback.

Be proactive about asking for feedback. Surround yourself with mentors, personal advisors, and coaches and be willing to ask for help. I’ve found that having a community and network of peers and advisors has been essential in not only solving day-to-day problems or identifying new opportunities, but also in fueling my personal development. Frequent feedback has helped me to continually grow personally and professionally.

7. Become a polymath.

In the past, it paid to be a specialist — to accumulate as much knowledge as possible in only one area. But in the future of work, polymaths and expert generalists have the advantage. Developing deep knowledge in multiple areas, ideally with cross-disciplinary awareness, makes it easier to uncover unexpected connections and convergences. In a world where data is everywhere, pattern recognition and intuitive thinking have become more important than ever. Being an expert generalist or polymath requires continuous education — lifelong learning.

8. Teaching brings mastery.

In my experience, teaching brings mastery. I’ve been a teacher or teaching assistant for everything from entrepreneurship to ESL, citizenship to physics, biology to coding, sustainability to app building — each teaching opportunity was a window to deepen my understanding. Answering questions on the fly is the quickest way to test your knowledge and learn what you don’t know. Having spent my career advising entrepreneurs and small business owners, I’ve constantly been learning as I teach. I love it when I get to tell a founder, “I don’t know,” as it gives me something to learn.

9. Stay curious.

Talk to strangers. Be present in conversations and look for things that stimulate your curiosity. Pull on those threads and be open to learning from strangers. Follow your curiosity, and you never know where it will lead you. Sign up for that yoga teacher certification course, take that cooking class, try out a new sport, go for that art class. You need to keep that sense of wonder you had as a child to spark inquiry and continual exploration. This curiosity and openness will fuel your lifelong learning.

10. Prioritize process over goals.

Life is not about completing a series of goals. Most of us have had long and winding career paths, which didn’t necessarily make sense at the moment. When you prioritize the process of learning over the goals of completing the class or diploma, you’ll open yourself to new opportunities. Changing your mindset gives you the flexibility to follow your curiosity and may lead to opportunities you would never have otherwise thought of.

11. Give yourself the gift of grace.

But give yourself the gift of grace. It’s okay if you don’t know something. Embrace this as a challenge and as an opportunity to learn something new. It’s also okay to sprint and rest. In fact, giving yourself breaks is the best way to recharge and nurture curiosity. Breaks give your brain space to integrate your learning, developing connections between seemingly unrelated areas.

Become A Lifelong Learner

Lifelong learning isn’t just about preparing for the future of work. Lifelong learning also brings joy and a deep sense of empowerment and fulfillment — making life more meaningful. Grow and succeed professionally and personally by embracing lifelong learning.

This article was originally published in Forbes.


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.

Hiring For Skills Of The Future: Part Two

Whether you’re hiring employees or freelancers, some of the same fundamentals apply. Work has changed and will continue to change. Today, the shelf life of a hard skill — content-based knowledge — is very short. Over half of today’s job activities could become automated by 2055. To succeed in a continuously evolving and changing economy requires highly adaptable workers. Your people are your best asset, and it is crucial to understand each person’s future potential for roles they’ve never done before — instead of hiring them only for what you need today.

In addition to soft skills, here are three additional skills of the future, how to hire for them, and how to teach and/or acquire these skills.

Lifelong Learning And Coachability

A 2017 Deloitte report states that professionals in software engineering, marketing, sales, manufacturing, law, finance, and accounting must update their skills every 12 to 18 months. Time is a scarce commodity, and you don’t have time to hire and train new people with this frequency. One way around this is to hire on-demand workers for specific projects as needed. However, I would argue that even when working with independent contractors, it’s best to work with people who love to learn, who love to receive feedback, and who can quickly get up to speed in changing circumstances.

For workers, I recommend that you ask for feedback from colleagues and supervisors and take action on that feedback. Also, take advantage of the many platforms available to update your skills continuously.

For hirers, there are a few questions I like to ask potential employees and freelance consultants:

  • Are there skills you are working to acquire?
  • Are there random things you would like to learn about?
  • Tell me about the most impactful feedback/constructive criticism you’ve gotten.

These questions help evaluate the degree to which someone is open to feedback and an eager lifelong learner.

Written And Oral Communication Skills

As the world has shifted to remote work during our current crisis, we’ve all seen for ourselves the importance of clear and effective communication. When communicating over Slack, Google Chat, Microsoft Teams, or email, it’s necessary to be able to infer what someone’s unspoken concerns are and to respond appropriately. Listening skills are an essential component of successful communication.

As machine learning and artificial intelligence begin taking over more job functions, skills that are harder for computers to complete effectively become more important. While some AI are capable of writing — and even effective copy for content and ads — effective written and oral communication skills are currently beyond the reach of machines.

For workers, the best things you can do are write more memos instead of having more meetings and practice being aware of how you communicate. Your words, tone, and method of communication affect the outcome you desire.

When hiring potential employees and freelance consultants, I recommend requiring several writing samples. In addition, conduct interviews through short, written messages to mimic communication over Slack as well as interviews over Zoom to evaluate both written and oral communication. I like to ask them the following question: “Can you think of a time you were communicating with someone and they did not understand you? What did you do?” How they respond shows the degree to which they are aware that how we communicate is important and can shift communication styles when appropriate.

Computational Thinking Skills

Computational thinking (or algorithmic thinking) is a phrase that became more widely used since 2006 when computer scientist Jeannette Wing published an essay suggesting that computational thinking is a fundamental skill for everyone, not just computer scientists. I think of computational thinking as the ability to think logically and strategically, work with uncertainty (and a lack of complete data), break down complex issues into smaller pieces, quickly recognize patterns, use patterns to think through potential solutions, manipulate and use data to gain insights and iterate when appropriate. As the world has become increasingly interconnected and complex, this skill has become ever more important.

I’ve found that consultants who lack computational thinking skills require more supervision, generally due to the lack of creativity to complete tasks. A computational thinker is agile, is adaptable, and generally learns quickly.

An example of a question to ask a potential hire might be something like: “How many tennis balls does it take to fill an SUV? And how did you arrive at your answer?” There’s no “right” answer, but asking a question like this allows you to evaluate how a candidate breaks down a complicated problem, makes assumptions, works through potential solutions, gut-checks their answer, and iterates if necessary.

Real-World, Project-Based Learning Experiences

Apprenticeships, internships, fellowships, course work or independent work focused on complex real-world problem-solving are a few ways to gain experience. Experiential learning is the surest way to gain the skills needed for success. When hiring employees or freelancers right out of school, I look for people who have had real-world, project-based learning experiences.

Prepare Your Team For The Future Of Work

As companies work to become more agile and adaptable in their business strategies, it’s essential that they are hiring workers with the skills needed for the ever-changing future of work. HR leaders are rethinking their roles and talent strategies as they prepare for the future of work with a blended workforce model. Building and growing a team able to meet the opportunities and challenges ahead requires life-long learners who embrace feedback, communicate effectively, and fuel creativity with computational thinking skills. By hiring workers with these three critical skills (and with soft skills), your team will be ready for the future of work.

This article was originally published in Forbes.


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.


FlexTeam  is  a mission-based micro-consulting firm, co-founded by Yolanda Lau in 2015, that matches talented mid-career women with meaningful, challenging, temporally flexible, remote project-based work opportunities. FlexTeam’s clients are businesses of all sizes across all industries and sectors. FlexTeam’s most requested projects are competitor / market research, financial models, and investor decks. FlexTeam is also the team behind Liquid.

Soft Skills Are Essential To The Future Of Work: Hiring for Skills of the Future, Part One

Whether you are hiring employees, independent contractors, or a blended workforce, we all know that the world is changing rapidly and how work gets done is evolving. As a result, how we screen and hire employees and freelancers has changed too. Soft skills — such as empathy, emotional intelligence, kindness, mindfulness, adaptability, integrity, optimism, self-motivation, grit, and resilience — have become crucial success factors.

Why Soft Skills Have Become More Important

As more and more job activities become automated, soft skills, which cannot yet be replicated by machines, have become more important. In 2017, Deloitte also reported that “soft skill-intensive occupations will account for two-thirds of all jobs by 2030” and that hiring employees with more soft skills could increase revenue by more than $90,000.

Empathy And Emotional Intelligence

The importance of empathy and social-emotional skills cannot be overstated. Emotionally intelligent teams have a competitive advantage, and I have found that empathy is one of the most important skills to hire for. Caring about how your teammates and customers feel and sensing their unspoken feelings is a true skill that I believe increases productivity and revenue. Empathy and emotional intelligence require self-awareness and enable better listening, leading to improved communication.

When screening potential employees and freelancers, I like to ask if there are charities or causes they care about. This gives me insight into whether they care enough about others to take action. I also like to ask this question: “Can you think of a time when you worked with someone difficult to get along with — how did you handle interactions with that person?” This shows me whether their empathy and emotional intelligence enabled them to not only defuse a challenging situation but turn it into a win.

Integrity And Ethical Responsibility

Billionaire Warren Buffett is famously credited with calling integrity the most important trait to look for when hiring. I agree that this character trait is critical to long-term success. I’ve found that my most successful employees and contractors are those who are ethical, take responsibility for their successes and mistakes, have humility, respect other people’s time, give others credit and take full ownership of their work — especially for losses. When someone tells me they’ve made a mistake and how they intend to fix it, I know I can trust them. In today’s fast-paced world, integrity is even more critical. It’s easy to take shortcuts and show short-term gains, but it’s harder to do things right to set yourself up for long-term success.

In the days of in-person interviews, I liked to ask the receptionist how applicants treated them (and if a meal was involved, how the applicant treated the wait staff). In our remote work world, ask admin assistants how applicants treat them over email. How people treat others reflects their true character.

To encourage a culture of integrity, I own up to mistakes and encourage others to do the same. To screen for this, ask potential workers to explain an incident that occurred in their life that didn’t go as expected and how they resolved it. How they respond usually shows whether they are capable of taking responsibility when things go wrong.

Adaptability And Resilience

As technological advances come more rapidly, hiring for adaptability and resilience is critical. You need open-minded people who can shift gears and take on different responsibilities as needed, adapt their behaviors to their teammates’ needs, manage uncertainty and find the positive when things go wrong. Agility and flexibility — which go hand in hand with adaptability — allow workers to bring and implement fresh ideas.

One question I like to ask potential employees and independent contractors to look for adaptability is, “What’s the most stressful situation you have handled, and what was the outcome?” I also look for people who have combined working part-time during college or graduate school or taken on different roles and responsibilities. To build adaptability and resilience, challenge yourself to be comfortable in unfamiliar environments and situations.

Self-Motivated And Self-Directed

Self-motivated workers, people who have intrinsic motivation, need less oversight and management. Self-motivation and self-direction enables people to take initiative and ownership of their work, set achievable goals against a schedule and take steps accordingly and adapt their plans as necessary. In a future where things are constantly changing, these skills are paramount to success. While I’ve found these skills difficult to develop, helping connect employees to find intrinsic motivation in their work can help.

One question I like to ask potential employees and freelancers is “Tell me about a time when you set a goal for yourself and what you did about it.”

Mindfulness

Mindfulness is a soft skill that builds on other skills. Those who are mindful tend to be more emotionally intelligent, adaptable, and forthright. Mindful people stay more focused during difficult situations. Mindfulness is the amplifier of all other soft skills as it cultivates the awareness and discretion to know how to respond in a centered, balanced way across diverse situations.

While I don’t have a secret for hiring for mindfulness, I believe in mindfulness training. Companies can support developing mindfulness by offering perks like a subscription to Headspace or Calm. Or, if you want to maximize the benefits of mindfulness, a subscription to Yoga Ed. so your employees and their families can benefit from on-demand mindfulness and yoga practice. (Full disclosure, I’m an investor in Yoga. Ed.)

Hiring For The Future Of Work

Assessing soft skills should be an essential part of your hiring process for potential employees and contractors. Soft skills strengthen other skills and abilities, and teams with these skills will be equipped to adapt more quickly and easily as the future of work continues to evolve.

Next time, I’ll share additional skills required for success in the future of work, how to hire employees and freelancers with these skills, and to develop these skills with your teams.

This article was originally published in Forbes.


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.


FlexTeam  is  a mission-based micro-consulting firm, co-founded by Yolanda Lau in 2015, that matches talented mid-career women with meaningful, challenging, temporally flexible, remote project-based work opportunities. FlexTeam’s clients are businesses of all sizes across all industries and sectors. FlexTeam’s most requested projects are competitor / market research, financial models, and investor decks. FlexTeam is also the team behind Liquid.

Musings on Entrepreneurship and Education: Reflections Inspired by MIT’s new College of Computing

Last week, MIT celebrated the new College of Computing with a multi-day conference filled with talks by luminaries like Sir Tim Berners-Lee, Joi Ito, Henry Kissinger, Megan Smith, Thomas Friedman, and many more. When Eric Schmidt (Technical Advisor to Alphabet and former Executive Chairman of Google) opened the panel for Entrepreneurship and AI, I was struck by his comments that the world needs more entrepreneurs; that the world needs more of us to become entrepreneurs to solve the problems of our times. I’d read about Schmidt’s previous calls for more entrepreneurs in 2016, but hearing him reiterate the point felt like a call to action.

MIT was founded in April 1861, two days before the start of the civil war. It was founded before we knew of the existence of DNA or atoms, before cars, before telephones, before the internet. And yet it has endured and innovated to stay at the forefront of technology, while becoming a model for college level entrepreneurship education.

Today, it’s become commonplace for universities to nurture entrepreneurs and to teach entrepreneurial skills. But few high schools, middle schools, or elementary schools incorporate entrepreneurship into their curriculum. MIT has done its part to inspire high school student entrepreneurs with the spinoff of LaunchX (originally started as a program of MIT called MIT Launch) and by the relatively new creation of a world education lab dedicated in part to reinventing preK-12 education. And there are many other programs here and there for high school student entrepreneurs.

But I believe that we need to do more to empower our children (including younger children) to become the next generation of innovators and entrepreneurs. To help them become change-makers who will make “a better world” (to borrow the name of MIT’s $6 billion campaign). Moreover, I am confident that an entrepreneurial education gives students the skills to succeed in any career or workplace.

What do I mean by an entrepreneurial education?

First, there’s the obvious — supporting students of all ages to turn their ideas into companies. But to me, it’s more than that. It’s giving students low stakes opportunities to fail. It’s showing students how to find joy in challenging themselves and to get comfortable with being uncomfortable. It’s helping students apply their learnings to real world problems. It’s nurturing their natural creativity, curiosity, and ability to find patterns and make connections. It’s providing opportunities to pursue interests and passions, and to collaborate and work in teams. It’s grounding education in ethics, empathy, and compassion so that they can prevent biases. And it’s teaching skills that are crucial to success in entrepreneurship, skills that are transferable to other careers and to life in general.

These skills are innumerable. But as a start, they include empathy, persistence, grit, confidence, self-awareness, communication, collaboration, curiosity, prioritization, flexibility in thinking, integrity, computational thinking, creative thinking, resourcefulness, optimism, conflict resolution, story telling, and so much more. It may sound old-fashioned, but I believe character education and ethics are also central to nurturing tomorrow’s entrepreneurs. Social emotional and ethical learning (SEEL) is not an over-hyped buzzword; this kind of education is critical for success in today’s hyper-connected world. As every job function becomes augmented with automated computing, it’s “soft skills” that will give our kids an edge. Moreover, we’ve all read about the misdeeds of Facebook and other Silicon Valley start-ups, and it seems clear to me that empathy and ethics could have prevented some problems.

As the world becomes more complex and interconnected, so does the work people do. As machine learning and artificial intelligence begin to do more of our work, it will become more important for people to do work that machines find it harder to do. An entrepreneurial mindset will help our students to succeed in work of the future. And it is imperative that tomorrow’s entrepreneurs are fundamentally ethical and trained to recognize and overcome biases.

MIT’s celebration left me feeling hopeful and inspired.

Hopeful that we can give tomorrow’s innovators, thinkers, doers, and leaders the ethically-grounded education that will allow them to use machine learning, artificial intelligence, data science, and other tools that have yet to be developed for the good of the world.

And inspired to help make that future a reality.

What are your thoughts on entrepreneurship and education, and the future of education?


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.

Skills for the Future of Work: what I’ve learned about people while building FlexTeam

I started FlexTeam in 2015 with two other MIT alums. In the early days, we all worked on everything: project scoping, operations, operations strategy, people ops, staffing, business development / sales, marketing, customer success, engagement management, project management, community management, content creation, quality control, copyediting, product development, consultant training & education, social media management, invoicing, and all the other things that come with running a startup or small business.

But as we’ve grown, we’ve all narrowed our focus a bit. My focus now lies mostly with our consultants — onboarding, education, training, learning & development, community building, best practices & processes for projects, project placement, etc.

My personal interest in FlexTeam has always been our consultants.

I’ve long thought that project-based work was the key to finding work-life fit, and once I became a mother I began dreaming about creating a mom micro-consulting firm to help women stay as engaged professionally outside of the traditional workforce.

So when we started FlexTeam, I was the one who sent out our first call for consultants. We started with a simple email to our sorority list (yes, I was in a sorority at MIT). The subject line was “remote / work-from-home opportunities,” the body of the email was five sentences long (plus our contact information) and included a link to a google form to sign up to work “as a freelancer remotely for FlexTeam.” That google form got 30+ responses within a few days.

That was 2015.

Today, we have hundreds of independent consultants in our database and a long wait-list of women who want to join us. Our consultants are alums of MIT, HBS, Wharton, Stanford, Princeton, McKinsey, Goldman Sachs, Bain, Merrill Lynch, & many more elite organizations, who reclaim their time by working with us on challenging projects for our clients. Our consultants work with FlexTeam to help them create their own work-life fit. And our clients get access to highly experienced, highly educated women that they wouldn’t otherwise be able to hire (whether on a project, part-time, or full-time basis).

So what have I learned about people and career success?

First, computational / algorithmic thinking is fundamentally important to being successful as a management consultant working remotely and independently.

What is computational thinking?

Computational thinking is a term that has been used for decades. The phrase computational thinking popularized by an essay by computer scientist Jeannette Wing. Wing suggested that thinking computationally was a fundamental skill for everyone (not just computer scientists). I think of it as the ability to solve problems algorithmically and logically:

  • the ability to break down a problem into its component parts;
  • analyze and organize data;
  • recognize patterns (within the problem and with past problems);
  • identifying, analyzing, and implementing potential solutions;
  • and iterating when feasible.

I think the ability to work with uncertainty is also part of computational thinking.

Why is computational thinking an important skill?

As the world becomes more complex and interconnected, so does the work people do. More importantly, as machine learning and artificial intelligence begin to do more of our work, it will become more important for people to do work that machines find it harder to do.

But for FlexTeam, I’ve found that solving client’s problems requires consultants (or, at the very least the project manager, who supervises other consultants) to be able to think computationally. Our clients expect the work to get done; but they don’t want to spend time telling us how to do the work. That’s why they’re paying us — to get it done without having to expend additional resources or brainpower to it.

A consultant lacking in computational thinking skills is able to get the work done, but requires attention from others to figure out a plan of action. More than that, she needs help with gut checks (does what I’ve produced make sense in real life?), has difficulty coming up with recommendations (a key component of FlexTeam’s offerings), and she sometimes lacks creativity to get the job done.

The computational thinker is more adapatable, agile, and able to manage time and priorities. And as they are self-motivated and curious, they find joy in solving problems.

Communication skills are also important

Since we work remotely (our consultants are all over the United States, with a few spread out across the globe), written communication skills are obviously important to us — our consultants communicate with our clients via chat on project pages, and our consultants communicate internally with each other via Slack. Also, most of our projects require us to deliver a report or memo of some sort to the client, so it’s important to write clearly, effectively, and precisely.

But we think that effective oral and written communication skills are important to succeed in any career these days.

Again, as machine learning and artificial intelligence begin to do more of our work, it will become more important for people to do work that machines find it harder to do — effective communication is one such task. Computers can surely put together pieces of writing, but understanding nuances of communication are best left to humans.

Can a computer take a client’s message, and tease out what the client really means? Can it tailor their message (whether oral or written) to the audience? Can it read the audience to know how best to phrase their message?

I think not.

Our best consultants are able to intuit what a client’s main concerns are, even if they are unspoken. They are able to intuit how frequently a client wants to be updated, and how much detail the client wants. They are able to communicate effectively to other consultants what work needs to get done and when, and knows how to motivate them when necessary.

You can certainly get by without superior oral and written communication skills, but you’ll be more successful if you excel at those skills.

As Jason Fried and David Heinemeier Hansson say in Rework, “Hire the better writer.”

“Soft skills” make all the difference in career success

So far we’ve learned that computational thinking and writing skills are important to career success. Obviously, some core competency in knowledge is also important. But it may surprise you to hear that “soft skills” like grit, resilience, persistence, being a good listener, empathy, a desire to learn, a cooperative attitude, resourcefulness, kindness, a “always do your best” attitude, optimism, ability to deal with difficult personalities, and manage conflict (among many others) are just as important.

The benefits of soft skills can be hard to measure, but new research reveals that training employees in soft skills can bring substantial return on investment to employers while also benefiting employees.

In fact, we’ve found that consultants who lack these “soft skills” typically produce work that client’s are less satisfied with. These “soft skills” enable consultants to go above and beyond for our clients. And the truth is that having soft skills like emotional intelligence usually correlates with computational thinking abilities and writing skills. These skills build on each other!

So what?

If you are looking for a job, assess your computational thinking abilities, writing skills, and “soft skills”. Where can you improve? How can you work toward improvement? Where do you excel? How can you highlight those skills in your resume, cover letter, and LinkedIn profile?

If you are hiring, recognize that at some point there is a level of technical capability or job function competence that is sufficient. After that, the person who is the better writer, who is better at computational thinking, and who has better “soft skills” is going to get you more productivity than someone who is simply more technically brilliant. He or she will be more eager to learn, more eager to work, and simply achieve more. She’ll get more done and go above and beyond.

If you work in education, think about how you are teaching these skills to your students. Whether you are a kindergarten teacher, or a college professor, what can you do to help your students learn these skills? Read Mitchel Resnick’s Lifelong Kindergarten: Cultivating Creativity through Projects, Passion, Peers, and Play — this book talks about the importance of computational thinking and creativity in the future of work, and discusses how to teach and cultivate it. Read Dr. Tony Wagner’s book The Global Achievement Gap: Why Even Our Best Schools Don’t Teach the New Survival Skills Our Children Need and What We Can Do About It — published in 2008, the 21st century skills listed in this book are still relevant. In fact the “7 survival skills” are traits that most of our best consultants at FlexTeam excel at.

And if you’re a busy business person looking to get more done, think about working with FlexTeam. Our top consultants excel at all of these skills and are ready to help you achieve more.

Let me know what other skills you think are important to career success!


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.


FlexTeam  is  a mission-based micro-consulting firm, co-founded by Yolanda Lau in 2015, that matches talented mid-career women with meaningful, challenging, temporally flexible, remote project-based work opportunities. FlexTeam’s clients are businesses of all sizes across all industries and sectors. FlexTeam’s most requested projects are competitor / market research, financial models, and investor decks. FlexTeam is also the team behind Liquid.