Capitalogix

The Evolution of Michael Jackson

This lighthearted post has something to do with artificial ...  but nothing to do with artificial intelligence.

While doing my weekly reading and web browsing (which is how I pick those links you probably think an algorithm selects), I happened upon a post about Michael Jackson on Twitter (now called X), and I enjoyed it (or at least was drawn to click and watch it). 

I grew up a huge Michael Jackson fan. As a kid, I watched the Jackson 5 Saturday morning cartoon show. His albums were the soundtrack to my college years. Later, my first wife and I saw him in concert several times. We shared that love with our youngest son, Zach. 

It's funny to look back on, but Zach used to dance to Michael Jackson's songs on stage at his Elementary School talent shows or at random restaurants. There was no choreography ... but lots of movement. I still smile when I think about it. 

You might smile (or shake your head) while watching this short video chronicling the evolution of  Michael Jackson's face changes from birth to death. 

 

via MikeBeast

It's a staggering difference. I won't pretend to know what led him to make the changes, but they're substantial. 

That being said, his music is both timely and timeless - which is very rare. He managed to make music in each era that fit in with the times but still felt very Michael Jackson. 

 

via MikeBeast

 

Gone too soon!

March Is Always Madness ...

March Madness is in full swing and will have the world's attention for a few more days.  As you can guess, almost no one has a perfect bracket anymore.  Yale beat Auburn, James Madison beat Wisconsin, Michigan State beat Mississippi State, and by the end of day 1, only 2,000 brackets remained intact.  That's .008% of all brackets submitted. 

Before 24/7 sports channels, people watched the weekly show "The Wide World of Sports."  Its opening theme promised "The thrill of victory and the agony of defeat!" and "The human drama of athletic competition." That defines March Madness.

The holy grail is mighty elusive in March Madness (as in most things).  For example, the odds of getting the perfect bracket are 1 in 9,223,372,036,854,775,808 (2.4 trillion based on a Duke Mathematician's formula that takes into account rank).  It's easier to win back-to-back lotteries than picking a perfect bracket.  Nonetheless, I bet you felt pretty good when you filled out your bracket.

 

via Duke University

Here's some more crazy March Madness Stats: 

• 80 million tournament brackets were completed
• Companies expect $4 Billion of corporate losses due to employees tracking games
• The highest-paid NCAA coach, Kansas's Bill Self, makes a salary of $9.6 million
• In 2018, it was estimated that March Madness generated $10 Billion in gambling (twice as much as the Super Bowl)

 

Feeding the Madness

"Not only is there more to life than basketball, there's a lot more to basketball than basketball." - Phil Jackson

In 2017, I highlighted three people who were (semi) successful at predicting March Madness: a 13-year-old who used a mix of guesswork and preferences, a 47-year-old English woman who used algorithms and data science (despite not knowing the game), and a 70-year-old bookie who had his finger on the pulse of the betting world.  None of them had the same success even a year later.

Finding an edge is hard - Maintaining an edge is even harder.

That's not to say there aren't edges to be found. 

Bracket-choosing mimics the way investors pick trades or allocate assets.  Some people use gut feelings, some base their decisions on current and historical performance, and some use predictive models.  You've got different inputs, weights, and miscellaneous factors influencing your decision.  That makes you feel powerful.  But knowing the history, their ranks, etc., can help make an educated guess, and they can also lead you astray. 

The allure of March Madness is the same as gambling or trading.  As sports fans, it's easy to believe we know something the layman doesn't.  We want the bragging rights of that sleeper pick, of our alma mater winning, of the big upset. 

You'd think an NCAA analyst might have a better shot at a perfect bracket than your grandma or musical-loving co-worker.

In reality, several of the highest-ranked brackets every year are guesses. 

The commonality in all decisions is that we are biased.  Bias is inherent to the process because there isn't a clear-cut answer.  We don't know who will win or what makes a perfect prediction. 

Think about it from a market efficiency standpoint.  People make decisions based on many factors — sometimes irrational ones — which can create inefficiencies and complexities.  It can be hard to find those inefficiencies and capitalize on them, but they're there to be found. 

In trading, AI and advanced math help remove biases and identify inefficiencies humans miss.

Can machine learning also help in March Madness?

“The greater the uncertainty, the bigger the gap between what you can measure and what matters, the more you should watch out for overfitting - that is, the more you should prefer simplicity” - Tom Griffiths

The data is there.  Over 100,000 NCAA regular-season games were played over the last 25+ years, and we generally have plenty of statistics about the teams for each season.  There are plenty of questions to be asked about that data that may add an extra edge. 

That being said, people have tried before with mediocre success.  It's hard to overcome the intangibles of sports - hustle, the crowd, momentum - and it's hard to overcome 1 in 9.2 quintillion odds. 

Two lessons can be learned from this:

1. People aren't as good at prediction as they predict they are.
2. Machine Learning isn't a one-size-fits-all answer to all your problems.

Something to think about.

 

Feeding the Madness

"Not only is there more to life than basketball, there's a lot more to basketball than basketball." - Phil Jackson

In 2017, I highlighted three people who were (semi) successful at predicting March Madness: a 13-year-old who used a mix of guesswork and preferences, a 47-year-old English woman who used algorithms and data science (despite not knowing the game), and a 70-year-old bookie who had his finger on the pulse of the betting world.  None of them had the same success even a year later.

Finding an edge is hard - Maintaining an edge is even harder.

That's not to say there aren't edges to be found. 

Bracket-choosing mimics the way investors pick trades or allocate assets.  Some people use gut feelings, some base their decisions on current and historical performance, and some use predictive models.  You've got different inputs, weights, and miscellaneous factors influencing your decision.  That makes you feel powerful.  But knowing the history, their ranks, etc., can help make an educated guess, and they can also lead you astray. 

The allure of March Madness is the same as gambling or trading.  As sports fans, it's easy to believe we know something the layman doesn't.  We want the bragging rights of that sleeper pick, of our alma mater winning, of the big upset. 

You'd think an NCAA analyst might have a better shot at a perfect bracket than your grandma or musical-loving co-worker.

In reality, several of the highest-ranked brackets every year are guesses. 

The commonality in all decisions is that we are biased.  Bias is inherent to the process because there isn't a clear-cut answer.  We don't know who will win or what makes a perfect prediction. 

Think about it from a market efficiency standpoint.  People make decisions based on many factors — sometimes irrational ones — which can create inefficiencies and complexities.  It can be hard to find those inefficiencies and capitalize on them, but they're there to be found. 

In trading, AI and advanced math help remove biases and identify inefficiencies humans miss.

Can machine learning also help in March Madness?

“The greater the uncertainty, the bigger the gap between what you can measure and what matters, the more you should watch out for overfitting - that is, the more you should prefer simplicity” - Tom Griffiths

The data is there.  Over 100,000 NCAA regular-season games were played over the last 25+ years, and we generally have plenty of statistics about the teams for each season.  There are plenty of questions to be asked about that data that may add an extra edge. 

That being said, people have tried before with mediocre success.  It's hard to overcome the intangibles of sports - hustle, the crowd, momentum - and it's hard to overcome 1 in 9.2 quintillion odds. 

Two lessons can be learned from this:

1. People aren't as good at prediction as they predict they are.
2. Machine Learning isn't a one-size-fits-all answer to all your problems.

Something to think about.

The First Neuralink Patient's #1 Priority

Neuralink received approval for human trials of its PRIME Brain-Computer Interface in September 2023.

In January, Elon took to Twitter and announced that the first human recipient had received an implant and was showing promising neuron spike detection. 

Neuralink designed PRIME to record and transmit neural data to interpret brain activity into movement intention. The PRIME Brain-Computer Interface empowers disabled individuals by enabling them to communicate and engage with the world in innovative and impactful ways, such as regaining the ability to speak and interact with others. In the future, advancements in the PRIME Brain-Computer Interface could even assist individuals with spinal cord injuries learn to walk again.

The first patient was 29-year-old Noland Arbaugh, a complete quadriplegic who had lost sensation and suffered paralysis from below the shoulders after sustaining a spinal injury during a diving accident eight years ago.

When we first began receiving updates about him, we were excited to hear that he could use a computer cursor. That was a big step ... and the start of many others. Now, we're being told that he recently used the technology to stay up all night playing a video game called Civilization 6.

https://t.co/OMIeGGjYtG

— Neuralink (@neuralink) March 20, 2024

Similarly, in 2022, a completely paralyzed man used his brand-new brain implant to ask his caregivers for a beer. 

It sounds like a joke, but these are the types of stories that make me optimistic. Both examples highlight a new capability ... but also a deeper purpose, freeing the human to enjoy being human and enhance the quality of their life.

This is a great reminder. Media coverage often focuses on the fear of an increasingly tech-driven world, and what it means for humanity ... but the best uses of technology allow us to be more human. 

What used to be science fiction is becoming reality, and possibilities are becoming inevitabilities. 

Onwards!

Here Are Some Links For Your Weekly Reading - March 31st, 2024

Here are some of the posts that caught my eye recently. Hope you find something interesting.

Lighter Links:

• Gmail Revolutionized Email 20 Years Ago. People Thought It Was Google's April Fool's Day Joke. (APNews)
• 5 Ways AI Will Revolutionize Art, Science and Media — And It's All Happening Right Now. (TomsGuide)
• It's Exactly Nine Years Until the Next U.S. Total Solar Eclipse After April 8. (Forbes)
• Pick a Random Number Between 100. You Probably Chose 37, and There's a Big Reason for That. (Upworthy)
• You May Not Care that the 'Buffett Indicator' is Flashing Red, But It Does Teach a Brilliant Lesson in Leadership and Success. (Inc)

 

Trading Links:

• The Richest Country in the World is Not the US. (24/7 Wall St)
• The Most Important Concept in Finance. (AWealthOfCommonSense)
• This Staggering Visualization Shows the True Power of Elon Musk. (FastCompany)
• Generative AI 'FOMO' is Driving Tech Heavyweights to Invest Billions of Dollars in Startups. (CNBC)
• Report: Microsoft to Build ‘Stargate' Supercomputer With Millions of Chips for OpenAI.(SiliconAngle)

The Jobs Most Impacted By AI

As we talk about the proliferation of AI, it's probably helpful to see where it's predicted to have the most impact. 

via visualcapitalist

These results come from a World Economic Forum report. 

In context, large impact refers to full automation or significant alteration. Small impact refers to less disruptive changes. 

IT and finance have the highest share of tasks expected to be "largely" impacted by AI ... which is unsurprising. 

We've also already seen the impact of LLM and generative AI on customer service and customer care. As these tools improve, more cases will be able to be fully handled by AI. 

This chart isn't meant to make you feel afraid that your industry will be automated—it's meant to help you understand what tasks you should consider automating. 

Here Are Some Links For Your Weekly Reading - March 24th, 2024

I considered using a different cartoon because I was afraid it might offend some readers. Isn't that an interesting commentary in and of itself? 

Here are some of the posts that caught my eye recently. Hope you find something interesting.

Lighter Links:

• 🔥 Unleashing the Power of AI, With Ethan Mollick. (ExponentialView)
• The Quest for a Theory of Everything – Scientists Put Einstein to the Test.(SciTechDaily)
• Mark Zuckerberg is Hot on Something Called the 'Fediverse' After Dumping the Metaverse. (Quartz)
• How “Somatic Markers” Can Transform Your Decision-Making. (BigThink)
• Here's How Much People are Willing to Spend on Weight Loss Drugs, According to a New Survey. (CNBC)

 

Trading Links:

• 'Get Out!': Jerry Seinfeld Is a Billionaire. (Bloomberg)
• China Wants Microsoft and Intel Off Its Computers. (Quartz)
• Apple is Now Worth $540 Billion Less Than Microsoft — A Tesla-Sized Gap.(Insider)
• Why Reddit Founder Made Nothing on $6.4 Billion IPO. (NewsWeek)
• Warren Buffett's Favorite Market Gauge Hit a 2-Year High, Signaling Stocks are Heavily Overvalued and Could Crash. (Insider)

Applications Of Data Analytics & AI For Your Business

It's a common theme in entrepreneurial discussion these days ... AI is coming for your jobs. 

The more nuanced statement is that AI isn't going to take your job - but someone using AI better might. 

Recently, Andrej Karpathy, ex-director of AI at Tesla and founding member of Open AI, posted a great tweet about how software engineering will be automated.  He compared it to automated driving. 

With automated driving:

1. first, the human performs all driving actions manually
2. then, the AI helps keep the lane
3. then, it slows for the car ahead
4. then, it also does lane changes and takes forks
5. then, it also stops at signs/lights and takes turns
6. eventually, you take a feature-complete solution and grind on the quality until you achieve full self-driving.

The progression is similar for software engineering (and, you guessed it, your business as well)

1. first, the human writes the code manually
2. then, GitHub Copilot autocompletes a few lines
3. then, ChatGPT writes chunks of code
4. then, you move to larger and larger code diffs
5. then, a tool starts coordinating other tools (a terminal, browser, code editor, etc.)

You get the point.  Human oversight begins to move towards increasingly higher levels of abstraction and management. 

If you think about it, this parallels a pretty generic path that a typical employee might take in your business.  A junior employee can't handle any ambiguity.  As they move up, a mid-level employee can probably handle some mild ambiguity ... they need to know where they're headed, but they don't need hand-holding on how to implement it.  A senior employee needs to know what problems they need to tackle, and then you get to entrepreneurs, and they don't even need to know what problems to tackle ... they'll find some. 

This suggests a pretty solid modus operandi for the coming years.  If you're worried about being replaceable, focus on higher-level behaviors. 

AI empowers businesses to do more with less.  Early adopters of AI will gain a significant competitive advantage by automating tasks, enhancing customer experiences with personalized recommendations, and making data-driven decisions that lead to cost savings and increased revenue.  Integrating AI into your business will propel your organization forward by unlocking new levels of efficiency, effectiveness, and certainty.  If you're steering the ship, you don't need to be as afraid of the waves. 

Here is a framework I created to identify the path to some not-so-easy wins that lead to sustainable business growth and progress: 

• Create Process Playbooks that leverage automation and AI to help businesses exceed standards both front-stage and backstage.  This class of solutions improves practical and business outcomes and helps avoid errors, omissions, and discretionary mistakes.
• Use Outcome Integrity Trackers to log decisions, actions, and results, hopefully improving and standardizing processes and outcomes.  This capability will evolve into the ability to measure the difference between skill and luck reliably and to the creation of accurate recommendation engines with real-time expectancy scoring.
• Capture, Calculate, and Curate Custom Metrics.  Much of what happens each day is lost.  Finding a way to save this data creates, expands, and augments a valuable new asset that is valuable itself, helps solve complex problems, and leads to new products, services, and solutions.
• Curate a Single Integrated Source of Trusted Data that is accurate, complete, and up-to-date.  Together, that data becomes the foundation for building new models, metrics, validations, certification, and compliance solutions.

Developing a Comprehensive AI Strategy is Crucial for Business Success

Businesses that don't adapt to changing landscapes fail. Having a roadmap, centered on what doesn't change is a reliable life support. Change doesn't have to be dramatic to be valuable. Just by taking these little steps and asking the right questions, you can make a big impact. I hope you're finding way to reap the rewards of these transformations, not just surviving them. 

Message me if you want to talk more about this.

Overhyped Technologies (Or Not)

Just because something is overhyped doesn’t mean it’s bad.

Gartner’s Hype Cycle is a great example of this concept.  It highlights the likely cycle of inflated expectations, disillusionment, and, ultimately, utility.

The key takeaway from the Hype Cycle model is that much of what happens is predictable ... and that a significant portion of the extreme swings are based on human nature rather than technical merit.

Haters are going to hate, and sometimes a fad is more than a fad.  For example, here is a front-page article from the New York Times in 1879.  It questions the utility of electric lights as a replacement for gas-powered lighting.  In case you were wondering, that one might have been a bright idea.

 

 

The point is that humans have proven themselves to be pretty bad at exponential thinking.  We’re not bad at recognizing periods of inflection, but we often have trouble recognizing the consequences of the change (and the consequences of those consequences) and predicting who the winners and losers will be as a result of those regime changes. 

 

 

There are countless examples.  Here’s a funny one from Maximum PC Magazine in 2008.  It shows that hype isn’t always a sign of mistaken excess.  This list purported to show things that were getting too much attention in 2008.  Instead of being a list of has-beens and failures, many of these things rightfully deserved the attention and hype they were getting.

 

 

It’s been over 15 years since this came out.  How did the predictions hold up?

Apple has become one of the world’s biggest and most successful companies (with a market cap approaching 3 Trillion dollars).  The iPhone has sold over 2.2 billion phones and accounts for over half of Apple’s total revenue.  Meanwhile, Facebook has become Meta and is also one of the biggest and most successful companies in the world (with a market cap of well over a Trillion dollars).  And the list keeps going: HD video, 64-bit computing, downloading movies from the internet, and multiple GPU video cards. 

Take just that last one. Nvidia has been the primary beneficiary of GPU growth, and it is one of the highest-performing stocks of the past few decades (with a market cap of well over 2 trillion dollars). 

It’s hard to believe how poorly this image aged. 

Remember that the trend is your friend while it continues.

Just because something is overhyped - doesn’t mean you shouldn’t be excited about it. 

The key is to stop thinking about the thing that’s being hyped and, instead, to start thinking about how to use things like that to create what you really want.

Onwards!

Here Are Some Links For Your Weekly Reading - March 17th, 2024

Here are some of the posts that caught my eye recently. Hope you find something interesting.

Lighter Links:

• The Nine Books Sam Altman Recommends Everyone Should Read. (Blinkist)
• Why Memory is More About Your Future Than Your Past. (BigThink)
• The Most Important Skill in the 21St Century. (TheAlgorithmicBridge)
• Experts War-Gamed What Might Happen If Deepfakes Disrupt the 2024 Election. Here are the Results. (NBCNews)
• New Airline Offering Private Jet Experience for Insanely Low Prices — Earning Them Plenty of Industry Critics. (NYPost)

 

Trading Links:

• Bernie Sanders Pushes Bill to Establish a Four-Day Workweek. (NBCNews)
• These Brands Have the Most Momentum Among U.S. Consumers. (VC Posts)
• The Hottest Ticket in American Dining: An Applebee's Weekly Pass. (Wall Street Journal)
• 20 Lessons From 20 Years of Managing Money. (AWealthOfCommonSense)
• Who Do You Trust More With TikTok — China, or American Billionaires? (RobertReich)

 

Velocity Versus Speed

Recently, I've been thinking a lot about how businesses scale and technology adoption accelerates.

For example, consider how fast AI is improving and transforming business.

Last year, I shared a short video called Speed Matters. It includes some thought-provoking ideas. You can view it by clicking here.

 

 

While speed matters, faster is not always better.

As you focus on doing more things faster, it becomes more essential to give people room to do important things slower.

You've almost certainly heard the phrase, "It's better to measure twice and cut once."  It's much easier to do something the right way from the beginning rather than trying to fix it after you mess it up. 

Activity does not create progress if it doesn't move you in the right direction.

This reminds me of a distinction my friend Nic Peterson makes. What you want in your business is velocity rather than speed. Velocity implies a vector (and preferably only one vector). You want to move fast in the desired direction, not fast towards distractions, mistakes, or money down the drain. 

To add one more layer to this, there's an "almost true" axiom in technology: you can only have two out of these three things: a project done fast, done right, and done cheaply.

But it's only almost true for a big reason. If you've already built the team and put in the work, replicating that work for new projects can be done fast, right, and comparatively cheaper. 

Are you just moving fast, or do you have velocity in your business?

Something to think about ...