3 Online Resources to Help You See Life Through a New Lens

The pandemic has caused isolation and grief but has also given us time to pause and reflect. Here are 3 free online courses that have helped me through this past year.

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“Change is situational, transition is psychological.”

My heart was pounding out of my chest. I wasn’t being chased by a lion or going on stage for a big presentation. I was sitting in my kitchen, dialling into a company-wide Zoom social event. My heart kept racing for no reason over the next three weeks and my EKG results seem completely normal.

“It’s probably just stress.” The doctor’s conclusion puzzled me. I’ve been in more stressful situations and never had these symptoms. This and several other experiences during the outbreak have given me a chance to re-evaluate many aspects of my life. I went through over 60 books and 25 online courses with the aim to become less anxious, more mindful, and happier. Along the way, I stumbled upon three online courses that have made me see life differently.

The Art of Self-Coaching (public course) (Ed Batista)

Professor Ed Batista has been teaching “The Art of Self-Coaching” at Stanford Graduate School of Business for years. Last year, he experimented for the first time teaching this course online and opened it to the public. Professor Batista blended authentic personal story sharing with lectures, researches, and coaching conversations. There are also lots of reading materials and exercises for people who aren’t satisfied with 90-minute weekly webinars.

Students are suggested to find partners to have coaching conversations together. Personally, I think these conversations are really the meat of the course. Even if you don’t have a partner, I’d still recommend going through the prompts by yourself (e.g., Traveling back to a source of suffering, what did you learn? What are the things you worry about right now? Think about something worse that might happen to you? And something even worse?). These questions will help you put things into perspective.

I also found the assigned exercises helpful. Two of my favourites are 1) activity fit diagnostic from Sonja Lyubomirsky’s book, The How of Happiness: this exercise helps you discover what activities (e.g., gratitude, relationship, and savouring) most effectively help you boost your happiness, and 2) VIA survey of character strengths: character strengths are positive parts of your personality that make you feel authentic and engaged. This exercise is not about your talent or skill but helps you uncover your likely sources of meaning and purpose. I took the same survey two years ago so I got to see which of my strengths stay consistent while others changed. For example, “love of learning” has been consistently one of my top signature strengths. I often seek learning opportunities to increase my happiness, reduce anxiety, and build meaning and a sense of purpose. What are your character strengths? And how will you use your strengths to build a better life?

“What we call obstacles are really the way the world and our entire experience teach us where we’re stuck.” ― Pema Chödrön

Buddhism and Modern Psychology (Princeton University) (Robert Wright)

I was sceptical at first. I Thought Buddhism and science are almost mutually exclusive. But if the pandemic has taught me one thing, that is to do things that the old me wouldn’t do. And I am glad I did. Professor Robert Wright made a compelling case that Buddhism and science are deeply compatible, as the Dalai Lama once said.

As he carefully examines the Buddhist descriptions of the mind and human condition in light of evolutionary psychology, he unpacked some of the deepest questions in life. I had to contemplate some of the highly counterintuitive claims including “the self doesn’t exist” and “much of perceived reality is in some sense illusory” for weeks. You don’t have to agree with him to benefit from thinking deeply about these fundamental questions about life.

Professor Wright uses both ancient wisdom and modern psychology to remind us what truly matters in life: The impermanence of everything teaches us to be present. Just because there’s no single author of our actions, that doesn’t signify that life has no meaning. It simply tells us: there’s no part of our minds that we need to own. We can let go of anything. No self is the most empowering message to us. We can still fully enjoy our journeys even if there’s no single driver in the driver’s seat. If you are curious about how no self and other claims are proven by scientists, check out Incognito: The Secret Lives of the Brain or Introduction to Neuroeconomics on Coursera.

I like his narrative contrasting Darwinism with Buddhism — Evolution has programmed us to be biassed, distorted, and dissatisfied. And he considers Buddhism enlightenment a solution to these flawed human features. This has made me wonder: why do we do the things we do? To look for the answers, I turned to one of my favourite professors, Robert Sapolsky.

“The mind is everything. What you think you become.”

Human Behavioral Biology (Stanford University) (Robert Sapolsky)

This course was at least 10-year-old but the contents are refreshing and more relevant than ever. Watching the introduction, I was immediately captivated by Professor Sapolsky’s breadth of knowledge, humour, and wisdom. He went through factors that influence human behaviours through the lens of neurobiology, endocrinology, culture, evolution, and beyond. His book, Behave: The Biology of Humans at Our Best and Worst, is the most fascinating book I’ve read in years. It’s a crash course of neuroscience, biology, and life.

In this online course, Sapolsky covers various buckets (conceptual filter) of science that apply to human behaviours, examining how they are interconnected. For example, he investigates how emotions impact body reactions and vice versa. Because of the complexity of human behaviour, we think about things in categories and therefore suffer from limited and distorted thinking. He demonstrated how our viewpoints change as we look at a topic from a different lens and warned us about the dangers of getting too wrapped up in preconceived notions. This course doesn’t just teach you about biology and neuroscience. It will also change the way you think. As Professor Sapolsky urges, “Resist the temptation to think only within one bucket or to look for the answer. There are no real buckets.”

“When you’re focused on the categorical boundaries, you don’t see the big picture.” — Robert Sapolsky

Before you go, I am super curious about what you are learning or reading every day and how it changed you? So leave your comments below and share this newsletter with friends who might find it helpful. I look forward to hearing from you.

Let’s connect! If you enjoy reading this article, please subscribe to my newsletter: Humans, Minds, Machines here! And I will share with you human learning x machine learning, the future of life & work, technology x business, and other things I am curious about.

The Future of Work After COVID-19

Three things I’ve learned about career planning from the 2008 financial crisis that can help us navigate the current recession

In 2020, a black swan crashed the global economy — Coronavirus. Just as the outbreak in China appeared to be easing, the number of infections has surged in Europe and the United States. Millions of Americans and Europeans have been forced to work from home.

Government officials and academics have revised expectations and warned that unemployment would rise to 7.4 percent by the end of 2020. The U.S. Secretary of the Treasury committed to doing everything in his power to prevent the unemployment rate from rising to 20 percent.

All this is reminiscent of the global financial crisis that happened a decade ago. The crisis started in 2007 as the subprime mortgage market in the United States depreciated. Hundreds of thousands of Americans lost their jobs every month, with the unemployment rate eventually climbing to 10.2%, the highest point in 26 years.

Unemployment has also risen in Spain, the U.K. and many more countries in Asia. Against the backdrop, some of the new graduates of the time chose to continue their studies, to pursue an advanced program, to study abroad or exchange students, while some struggled to find jobs.

What can we learn from past experiences to help us navigate the recession around the corner? And what’s different this time?

Continue to read…

4 Reasons why AI/ML is not the new SaaS?

AI is predicted to be one of the top segments in cloud computing. However, AIaaS and SaaS could not have been more different.

* Hey all, I will be running a couple of “remote AI/ML Masterminds” for people in AI/ML to exchange thoughts and stay connected. I will share more information in the newsletter. Sign up here if you haven’t.

Over 40% of companies plan to deploy AI solutions by the end of this year, according to the recent Gartner Survey. However, the high infrastructure costs and needs for AI/ML expertise are daunting for most organizations. That’s why AIaaS (AI-as-a-service) or MLaaS began to emerge.

Over 40% of companies plan to deploy AI solutions by the end of this year — Gartner.

Popularized by Salesforce, SaaS refers to a licensing model in which software is centrally hosted for customers to access via a browser. SaaS companies also sometimes offer free trials (freemium) to encourage adoption.

Recurring revenue and decreasing costs make the SaaS business model more predictable, profitable and scalable. Therefore, it has become an extremely attractive model for both entrepreneurs and investors.

Since Salesforce went public in 2004, there have been over 70 SaaS IPOs. On average, SaaS companies have significantly outperformed the market. SaaS companies tend to have higher gross margins and lower R&D expenses because they don’t need to support multiple versions or technology stacks.

Similarly, AIaaS allows companies to utilize off-the-shelf AI solutions instead of building their own teams and infrastructures from scratch. AIaaS provides better scalability and flexibility to users by minimizing the upfront investment.

AI will increase 5X from 2019 by the year 2023.

AI enthusiasts also expect that AIaaS business model can help drive adoption and profitability. Gartner’s prediction that cloud-based AI will increase 5X from 2019 by the year 2023 seems to confirm the trend.

Companies from tech giants like Amazon and Microsoft to startups started to offer AIaaS including chatbots, digital assistants, cognitive APIs, and machine learning frameworks. Leveraging these services can simplify the complicated process and lessen the computation burden of deploying AI.

AIaaS seems to be a magic pill to solve all your problems. It enables a rapid and cost-saving AI deployment so companies don’t need to depend on in-house AI experts inhouse who are currently lacking everywhere. Hosting models in the cloud so they can continuously improve with more data also brings out the biggest benefits of ML.

However, the reality is, deploying AIaaS poses many more challenges than SaaS. And here’s why.

Will This Crisis Help set Autonomous AI on the Right Course?

The COVID-19 pandemic serves as a wake-up call to all AI, robotics, and driverless car startups: stop building eye-dazzling demos and talking about the future possibility of general-use AI. Instead...

Millions of Americans have started to work from home amidst the current pandemic. Retailers have struggled with supply while nervous consumers are hoarding everything from toilet paper to hand soap.

Across the globe, Chinese e-commerce giant JD began testing a level-4 autonomous delivery robot in Wuhan and running its automated warehouses 24 hours a day to cope with a surge in demand.

Suddenly, autonomous machines need to be better than just proof of concept. They can no longer depend on onsite engineering support for edge cases. They must be robust enough to work independently across various real-life situations.

In some ways, the epidemic accelerates an automated future that’s already on its way. It has exposed problems that have long existed in the AI venture scene: buzzwords and hype cloud people’s judgment, making it difficult to see real progress.

Continue to Read

The Single Biggest Reason Why AI/ML Companies Fail to Scale?

And Three Things You Can do to Avoid it Happening to you.

“What’s the accuracy of this machine learning (ML) model?”

“How long is the training time?”

“How much training data do you need?”

Working for a company that builds machine learning software for robotics, I hear these questions every day. Machine learning has become a shiny object that everyone wants to pursue. Over 80% of the companies are looking into at least one AI project.

Users generally want to know how long it would take to onboard a new item and how well the models perform or generalize. They want a way to measure the overall cost against performance. However, answers to the above questions don’t give you a full picture. Even worse, they are misleading.

Continue to read…