Education Archives - Yolanda Lau

Updated on May 8, 2024

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.

Published on May 8, 2024

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

By Yolanda Lau

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.

Updated on May 12, 2024

Learn AI Large Language Model Terms with ChatGPT

By Yolanda Lau

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.

Updated on February 24, 2024

Embrace Lifelong Learning To Thrive In The Future Of Work

By Yolanda Lau

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.

Interested in the Future of Work? Join the Work of the Future #FutureOfWork Facebook Group.

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.

Updated on February 24, 2024

Hiring For Skills Of The Future: Part Two

By Yolanda Lau

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.

Interested in the Future of Work? Join the Work of the Future #FutureOfWork Facebook Group.

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.

Updated on February 24, 2024

Increasing Mindfulness In The Workplace

By Yolanda Lau

Mindfulness matters. The ability to be present and mindful — to stay focused intentionally without passing judgment — is a 21st-century skill. Businesses with mindful teams are better equipped to compete in today’s ever-changing environment.

Mindfulness At Work

As most of us have experienced firsthand, stress and anxiety can take a significant toll on the mind and body. A study by the Kaiser Family Foundation found that nearly 40% of Americans feel that the stress of the pandemic has negatively affected their mental health. Not only is stress taxing, but it also increases inflammation and can lead to chronic diseases of the brain and heart.

On the other hand, research at companies like Google, Aetna and Intel have shown that increasing mindfulness in the workplace can decrease stress levels while improving focus, thoughtfulness, decision-making abilities, and overall well-being. Mindfulness gives employees permission and space to think — to be present — leading to mental agility, resilience, and self-awareness. In addition, mindfulness can reduce emotional exhaustion, increase openness to new ideas, and develop compassion and empathy.

In this day and age, being able to stay calm and rapidly adapt to shifting circumstances with an open mind is and will continue to be a competitive advantage. Moreover, a mindful workplace can be a powerful tool for recruiting purposes. After all, if given a choice between a company that invests in its employees’ well-being and one that doesn’t, which would you choose? Similarly, increasing mindfulness at work may lead to higher levels of commitment at work and increased engagement, ultimately reducing costly turnover.

Here are a few (perhaps unconventional) tips for increasing mindfulness and wellness in the workplace.

Yoga And Meditation For Mindfulness

In 2018, the “Employer-Sponsored Health and Well-Being Survey” of 163 companies by the National Business Group on Health (NBGH) and Fidelity Investments found that 52% of companies offered mindfulness training that year. While there are many ways to offer mindfulness training, yoga and meditation are some of the more cost-effective methods. Yoga (which I’ve practiced for 25 years) and meditation are good for your mind and body, with benefits including stress management, concentration and focus, self-confidence, and overall fitness.

The past five years have seen an explosion of apps and programs for meditation and yoga: Shine, Meditation Studio, Headspace, Yoga Ed., and Calm are just some of the apps and training programs available for improving wellness and mindfulness. What I particularly like about Yoga Ed. is that it not only equips individuals with yoga and mindfulness tools to enhance their own wellness, but it also improves the lifelong health of the children and teens in their lives.

Moreover, workout apps like Nike Training Club, ClassPass, and Peloton also offer on-demand yoga and/or meditation classes. Most of these apps and programs listed above are relatively inexpensive and easy to implement via corporate partnerships — and certainly cheaper than hiring Jon Kabat-Zinn himself, who pioneered formal mindfulness training in the workplace, to run a corporate mindfulness seminar.

Brain Breaks And Unscheduled Time For Mindfulness

You probably think that long (boring) meditation sessions are necessary to achieve mindfulness. But research out of Wharton has found that even short — seven- or eight-minute — bursts of mindfulness results in more productive, helpful and pleasant employees. Even these short brain breaks have been found to increase rational decision-making skills and may improve attention and focus. Just a few minutes of mindfulness can increase “divergent thinking” to generate new ideas, an extremely valuable skill during times of uncertainty (and also a skill necessary for succeeding in the future of work).

I also recommend purposefully scheduling blocks of unscheduled time. These moments of planned solitude provide the silence needed to focus on higher-level thinking and stimulate creativity while increasing mindfulness. With the frenetic pace of our modern lives, it’s become harder to find quiet moments, hence the need to schedule them into our busy calendars.

Create Time For Mindfulness By Leveraging Automation

To make time for mindfulness, I’ve been relying heavily on automation. Technology is rapidly changing the nature of work, especially as artificial intelligence and machine learning become more sophisticated. These technologies are paving the way for automation of repetitive tasks — a little known cause of employee burnout. Research out of McGill University suggests that repetitive tasks impair judgment, aptitude for goal planning, capacity to focus, and risk assessment abilities.

I recommend taking advantage of the myriad of companies and services that increase automation, allowing your employees to focus on innovative thinking and other work that cannot be replicated by software. In particular, Zapier makes it possible for anyone to create automated workflows without code. I use this service to help automate marketing “busy work,” but there are thousands of use cases for every role and industry.

For example, services such as Coupa, Bill.com, and Liquid streamline accounting through automated payment approvals. Automating your accounts payable processes will not only reduce errors but also increase productivity and the overall well-being of your employees. The more you empower employees to automate their repetitive tasks, the more mindful they can be about the work that matters.

Leading With Mindfulness

Similar to emotional intelligence, increasing mindfulness in the workplace starts from the top down. Lead by example by taking brain breaks and blocking out unscheduled time. Invest in automation software or services. Start with yourself and your executive team and the effects will trickle down.

Bringing mindfulness to the workplace is advantageous on several levels. After all, investing in the well-being and resilience of all employees is simply the right thing to do. But mindfulness is also a sound business investment that pays dividends. It allows businesses to decrease stress, reduce turnover, improve productivity, recruit top talent, and increase innovation.

The future of work is more than remote work. It is human-centered, where workers thrive and mindfulness, wellness, and well-being become more than just buzz words. The human-centered future of work is a movement and it starts with each of us.

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.

Updated on February 24, 2024

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

By Yolanda Lau

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?

Interested in the Future of Work? Join the Work of the Future #FutureOfWork Facebook Group.

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.

Updated on February 24, 2024

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

By Yolanda Lau

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!

Interested in the Future of Work? Join the Work of the Future #FutureOfWork Facebook Group.

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.

Updated on February 24, 2024

Reflecting on my First Seven Jobs

By Yolanda Lau

During college and in high school, I was almost always employed part-time. I rarely worked during the summer, and I liked the challenge of juggling coursework with employment. So it’s no surprise that I was only 19 years old by the time I had worked my #FirstSevenJobs.

Here they are:

English as a second language tutor. 📝 Sometimes, I forget that I started my first venture in middle school. On an external hard drive of archived data, I still have the worksheets I made for my students.
Babysitter. 😊 What teenage girl didn’t babysit? I never babysat actual babies, just children younger than me. I’ve always loved children and I also volunteered in childcare centers and preschools.
Punahou School math tutor. 🔢 Yup, I’m a true nerd. For as long as I can remember, math has been easy for me. My parents signed me up for Kumon in elementary school and I started learning calculus in middle school. I was that annoying kid turning in math tests 15 minutes into the 50-minute period. Being in the honors math track in high school, I had the privilege of spending my free periods sitting in the math tutoring center waiting to help the students enrolled in less advanced math classes.
Punahou School Physics Honors teaching assistant. ⚛ I graded assignments and tutored students. I’m sure I had other responsibilities, but I don’t remember what they were anymore.
SAT-prep tutor. 📓 Yes, I’m proud to admit that I’m a nerd. Back when the SAT had only two sections and a combined score of 1600, I got a near perfect score of 790 on the verbal section and 800 on the math section (see #3 on being a math nerd). Anyway, doing well on standardized tests is a sure-fire way to get tutoring clients. This was my second freelancing gig, not including babysitting.
Retail sales associate at Quiksilver, Newbury Street, Boston. ☀️🏄🏽 I’m from Honolulu. Before attending college in Cambridge, I had never before been to the East Coast. And I had not seen snow fall. So on a cold winter day, when I walked down to Newbury Street from the Boston brownstone I lived in, I was over the moon to find a newly opened surf shop that reminded me of warm days on the beach. In a way, even in this job I was freelancing. I saw the store struggling to penetrate the market, so I took it upon myself to start marketing the store to other homesick students from Hawaii. It would have been a lot easier if Facebook had existed then.
MIT Introductory Biology tutor. I don’t recall how I got this job. 🔬 It must have had something to do with having declared a double-major in Biology and Chemical Engineering. The work was easy, helping students with homework assignments and grading homework assignments (or, problem sets, as we they were called at MIT).

And there it is.

Number 8 was being the MIT Undergraduate Association Office Manager, and number 9 was supporting women’s recruitment at MIT Admissions. And my first “real” job at the MIT Technology Licensing Office, was number 10.

This trip down memory lane has reminded me that I once wanted to be a teacher. I loved children and thought I wanted to be a preschool teacher. Given my math and science aptitude, I later thought it was my duty to become a high school teacher focusing on Advanced Placement courses. And I’ve long wanted to participate in yoga teacher training to get certified to teach yoga, which I’ve been practicing since 1996. But the memory of a Punahou teacher telling my parents that I was too smart to be a teacher is what has always held me back from pursuing that career path.

I’m reminded now that I’ve always been passionate about education, lifelong learning, and helping others. It’s why I volunteered in a childcare center for toddlers from low-income families, and volunteered in a homeless shelter for women and children, and was once a volunteer tutor for immigrants studying to pass the US Citizenship Exam. And it’s the reason I have been an Alumni Mentor for MIT’s 12.000 Solving Complex Problems for the last decade.

And I see that I’ve always been a freelancer and entrepreneur, creating my own career, searching for work-life fit, seeking new challenges and skills, and looking towards the future of work. So it’s no wonder that my path has led me to entrepreneurship and small business consulting. My specialty is creating and improving processes to maximize efficiency, reduce costs, and increase customers and revenue. But my job is really teaching and helping small business owners so they can grow their business.

What were your first seven jobs, and how have they shaped your career? Let me know in the comments or contact me personally!

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.