Quantum Simplex | Dr. Michael Wu
As data scientists, we often treat the data given to us as the raw input. We seldom question where the data came from and how it was collected. Where did these pre-existing inherited biases come from, and can they be eliminated?
In the midst of today’s turbulent geopolitical dynamics and the ever-fluctuating landscape of macroeconomics, emerging technologies such as ChatGPT have sparked a …more
Emergent biases are not inherited from the training data. That means these biases can still emerge even when we have trained the machine-learning (ML) algorithms with a cleansed data set with no known biases.
AI bias problem is currently being thought of as a data problem. And to a large extent, if we can fix the biased data, we would’ve addressed most of the AI biases. However, not all biases are bad. In fact, biases are often introduced in training data to improve the performance of the trained model.
Today, most AI practitioners in the industry are treating AI bias as a data problem. To a large extent, if we can fix the biased data, we fix the AI bias problem. However, fixing the biased data is not the only way to address the problem.
If you’ve been following me on social media, you know that I’ve started blogging, and I’ve been publishing on my blog – …more
If you were asked to pick a single word to describe the world over the past few years, what would it be? …more
On the surface, the airline industry couldn’t be more different from foodservice. Airlines are B2C, primarily selling directly to passengers, but foodservice …more
PROS has fortified its market position through a combination of common moats. With a renowned brand, proprietary AI, strategic customer relationships, and robust R&D investment, we stand as a formidable player. But PROS also has a very distinctive moat – the unique synergy between our Travel and B2B.
The simple and fundamental truth is that not all moats are created equal. The traditional barriers to entry, such as brand recognition, network effects, and economies of scale, while effective in their own right, are still vulnerable to the relentless march of time and competition. Nvidia and TSMC had developed highly unique moats.
Most successful companies that stand the test of time usually have some kind of moat. Cost (economy of scale, efficient supply chain, low-cost supplier, efficient operation), intangible assets (brand, regulation, IPs), and switching cost (network effect, integration complexity) are some common moat strategies.
The interests and investments in AI have skyrocketed since the introduction of ChatGPT. Meanwhile, many businesses are still wrangling with the shift …more
Have you ever experienced a time when you were so immersed in what you were doing that you forgot about your physical feelings and the passage of time? This highly rewarding mental state is known as flow, and it is studied and characterized by a renowned psychologist Mihaly Csikszentmihalyi.
Almost half a decade ago, I concluded my Outperform keynote with the following 2 slides. Was I being too optimistic? In the …more
Last month, I had the pleasure of talking to Hamish Taylor who is the former Head of Brands at British Airways, CEO …more
The interest level for Generative AI (GenAI) has exploded over the last few months. Ever since Outperform with PROS, I’ve spoken to …more
Last week, I shared my general keynote at Outperform on generative AI. This week, I want to focus on the airline industry …more
If you attended Outperform 2023 with PROS, you probably heard my keynote below. I know some of you have been asking and …more
By now, it’s no secret that AI is going to revolutionize business as usual and disrupt every part of our work and …more
Disruptions at the global scale seem to be a norm today. We are seeing everything from the pandemic, leading to supply chain …more
Last week, our first post-pandemic in-person Outperform conference concluded with spectacular success. We had 800+ registered with our highest-ever attendance (over 85% …more
As more and more products get commoditized, customer experience (CX) will be the new battleground as brands attempt to differentiate themselves. But …more
The global airline industry is in recovery mode. Some are further along than others. The number of international flights being booked for …more
Last week I shared the first part of a podcast where we discuss the future of travel with AI. We have been …more
I’ve already shared a lot of content on AI and ML in general here on the Quantum Simplex. So for today, I …more
Ever since the launch of ChatGPT in Nov 2022, many people have asked what I think about this innovation. My response has …more
After ~3 years of sitting behind your cold and lonely monitor, aren’t you getting sick and tired of virtual conferences? Well, I …more
Today, XAI is proving that the infamous “black box problem” is really not a problem after all. Business leaders who continue to scapegoat the use of AI due to its black-box nature are essentially foregoing an efficient and reliable way to optimize their business decisions for something that is only a problem of the past.
Barry hosts a popular podcast where he interviews thought leaders, industry experts, and successful entrepreneurs, delving into a wide range of topics related to leadership, innovation, and personal growth. Today, I’m one of the few lucky ones who had the opportunity to talk to Barry and share some of my own unlearning moments.
What makes a black box model uninterpretable is just complexity, nothing more! And the black box problem is an inherent inability of our human brain to distill those complex models down to something simple enough for us to explain in English.