Have you listened to the new Beatles song? It took almost 50 years and new technology to create.
How did this happen? An AI system, made by Peter Jackson, uncoupled the vocals from the piano on a poor-quality tape demo from the 70s. The result – a song that would have never seen the light of day was able to bring John Lennon back from the dead to release new music for a new generation.
Was it a touching tribute and closure to an extraordinary legacy? Does it qualify as AI "art"?
We are seeing a surge in creativity due to the rise of generative AI.
People are doing amazing things with AI ... and it's making entrepreneurship accessible to a new group of people.
AI is exciting, but it is also scary. I would argue that it is a net positive. However, there are also clear drawbacks (and potential risks). For example, there are the obvious ones like deepfakes, art being stolen and fed into models without consent, etc. But, there's one that many aren't talking about...
It's a lack of nuance or understanding of art.
Here is an example of using generative AI to improve a famous art piece.
In my opinion, the creator completely missed the point when they tried to improve Nighthawks by Edward Hopper.
The truth is that I don't know their intent or thought process.
However, Nighthawks is famous for a reason. It shows a patron, presumably at a late-night diner, with a desolate urban streetscape outside. To the right audience, it embodies the isolation of a 24-hour modern society and big cities, and the hidden changes of the 20th century. It is a poignant composition and one of the most famous American art pieces.
First, he had AI parse the image and write a description of it. Then, he had it regenerate the image from the description. The setting became light. He thought he could make it better, so he moved people outside. He parses a description again and creates a new image again. He did this several times.
The result is what you see — a beautifully created composition lacking any depth.
The AI did its job; the human did not.
A better prompt or a more artful process would have had a better result.
But is it art?
Once created, art is in the eye or mind of the perceiver. So, should we care who or what creates it?
Here are some other questions worth pondering. Is AI at its best when it's amplifying human intelligence - rather than replacing it? Or ... is the goal simply to amplify intelligence?
The Universe often gives you increasingly painful chances to learn a lesson.
What do you think we're supposed to take from this?
It is that time of year again. We are in the midst of our annual planning for 2024.
If you haven't started planning for your business (or yourself), now is a great time to start.
The best place to start is to analyze where you are and where you've come from. I like to begin annual planning by reviewing the past year and looking back at where we were three years ago (in order to note direction, progress, and new capabilities).
Then it is time to look forward.
The process is relatively straightforward. We start by deciding what the company's three highest priority goals are. With those goals as the base, each department (and manager) creates a big three representing what they can do to reach the company's big three. From there, we dive into quarterly rocks, SMARTs (goals that are specific, measurable, actionable, relevant, and timed), as well as the explicit tactical steps it will take to accomplish what we set out to achieve. We use the Entrepreneurial Operating System (EOS) to plan and execute our meetings.
The meetings are going well. There is a lot of back-and-forth idea sharing, negotiating, and priority setting.
We've gotten a lot better at dialogue - but for many years, what we thought was a dialogue was often multiple monologues.
The disconnect (or misconnect) was because the participants had fundamental beliefs, at a higher level than we were discussing, that were at odds with each other.
I shot two videos that I think help teams get to alignment.
Thinking About Your Thinking
The first video discusses several techniques to enhance your decision-making.
One of the ideas is something called "Think, Feel, Know." It explains that you have to deal with superficial thoughts before getting to deeper feelings. Then, you must deal with those feelings before you get to "knowing."
Another technique discussed in the video involves adding time to look for "insights" after working on something. Those insights are often the seeds for something greater.
Chunking Higher
The second video is about how to chunk high enough to start from a place of agreement. Exploring distinctions from there is relatively easy.
I'll add one more concept for good measure ... Start with the end in mind. Alignment happens in stages. To get aligned on what to do next, you first have to get agreement and alignment about where you are and where you want to go.
With that said, another important component of meaningful communication is a shared understanding of a common language. Words can mean different things to different people. Simply agreeing on a "word" is different than agreeing on a common meaning.
Innovation means a lot of different things. It changes based on where we are in history, the amount of time we're considering, and the scale.
Language was an innovation, the piece of plastic on the edge of your shoelaces was an innovation (called the Aglet), changing time signatures in music was an innovation in history, and so is artificial intelligence.
Defining and measuring innovation is difficult even in your business ... but the Global Innovation Index attempts to do it globally. It does so by measuring several factors, like:
Knowledge and Technology Outputs - patents & high-tech manufacturing
Human Capital & Research - number of researchers & global corporate R&D investment
Business & Market Sophistication - knowledge-intensive employment & financing/VCs for startups
Creative Output, Institutions, and Infrastructure - trademarks, access to resources, and policy
By this metric, Switzerland and Sweden take the top two spots - followed by the U.S. and the U.K.
Honestly, the list surprised me some. Some names I expected to be on the list - or higher on the list - didn't crack the top ten. Though Switzerland and Sweden have dominated this list for many years.
A topic I'm very passionate about right now is patents - and how valuable they can be to your business. Here's a previous article I wrote on the subject, but I'll revisit it soon with new ideas and distinctions.
In 2016, I wrote a variation of this article focused on trading ... but it's even more relevant today as I spend more time talking with entrepreneurs and AI enthusiasts.
There are many lucky people in the business world. Perhaps they made a good decision at the right time – and are now on top of the world. There's nothing wrong with luck. But, the goal is to make sure your success isn't predicated on it. Why? Because you might get lucky once, but it's unlikely you'll get lucky every time.
Luck favors the prepared ... and those who understand the difference between skill and luck.
First, let's talk about luck. Think about a nationwide coin-flipping contest. Initially, each citizen is paired up with another for a contest. The winner goes on to the next round. Think how many rounds you would need to win to be City Champion, State Champion, Regional Champion, etc.
At the end, someone would have won many coin-flip contests. Assuming they didn't cheat, they were lucky ... but does the winner have an edge? If so, what could it be?
If you followed the contest from beginning to end, I'm sure you could imagine the finalists doing articles or interviews about how their mindfulness practice gives them an edge ... Or, the law of attraction .... Or, how the power of prayer is the difference.
Meanwhile, sometimes, the most straightforward rationale provides the best explanation. Somebody had to win that contest – and luck was the reason.
Finding The Edge
Likewise, just because a product or business makes money doesn't prove it has an edge. For example, at OpenAI's Developers Conference last week, they announced several new models and internally created tools that cannibalize or obsolete many tools or businesses built on their platform. Meanwhile, they also announced several new models and tools that will help create new businesses. But, the app developers who have been made redundant are out of luck.
I saw the same thing with the rush of .com companies in the late '90s. The ones that made it are now the underpinning of a new era, but they climbed out of a sea of failed businesses that might have even been better businesses - they were just unlucky (e.g., Betamax vs. VHS).
Simply relying on whether something is profitable NOW means you have both the chance that you have an edge - and also that you got lucky.
If it isn't just a matter of winning, how do we know if we're skillful? In trading, we would call this alpha. We are searching for clues to help find systems with an edge ... or at least have an edge in certain market conditions.
Unfortunately, I can't give you the one rule to follow to identify skill vs. luck, but it's much easier to find the answer if you're asking yourself the question.
Internally, we've built validation protocols to help filter lucky systems and systems that can't repeat their results on unseen data.
It is exciting as we solve more of the bits and pieces of this puzzle.
What we have learned is that one of the secrets to long-term success is (unsurprisingly) adaptability.
What that looks like for us is a library of systems ready to respond to any market condition - and a focus on improving our ability to dynamically select the systems that are "in-phase". The secret isn't predicting the future, but responding faster - and more reliably - to changing environments.
From a business perspective, this looks like being willing to adapt to and adopt new technologies without losing track of a bigger 'why' like we talked about in last week's article.
A Practical Example
When we first wrote on this, one of Capitalogix's advisors wrote back to see if they understood the coin-flipping analogy.
The odds of flipping a coin and getting heads 25 times in a row is roughly 1-in-33 million. So if we have 33 million flippers and 100 get 25 heads in a row, statistically that is very improbable. We can deduce that group of 100 is a combination of some lucky flippers, but also that some have a "flipping edge." We may not be able to say which is which, but as a group our 100 will still consistently provide an edge in future flip-offs.
Well, that is correct. If we were developing coin-flipping agents, that would be as far as we could go. However, we are in luck because our trading "problem" has an extra dimension, which makes it possible to filter out some of the "lucky" trading systems.
Determining Which are the Best Systems.
There are several ways to determine whether a trading system has a persistent edge. For example, we can look at the market returns during the trading period and compare and contrast that with trading results.
This is significant because many systems have either a long or short bias. That means even if a system does not have an edge, it would be more likely to turn a profit when its bias aligns with the market.
You can try to correct that bias using math and statistical magic to determine whether the system has a predictive edge.
It Is a Lot Simpler Than It Sounds.
Imagine a system that picks trades based on a roulette spin. Instead of numbers or colors, the wheel is filled with "Go Long" and "Go Short" selections. As long as the choices are balanced, the system is random. But what if the roulette wheel had more opportunities for "long" selections than "short" selections?
This random system would appear to be "in-phase" whenever the market is in an uptrend. But does it have an edge?
One Way To Calculate Whether You Have An Edge.
Let's say that you test a particular trading system on hourly bars of the S&P 500 Index from January 2000 until today.
The first thing you need is the total net profit of the system for all its trades.
The second thing you need to calculate is the percentage of time spent long and short during the test period.
Third, you need to generate a reasonably large population of entirely random entries and exits with the same percentage of long/short time as your back-tested results (this step can be done many times to create a range of results).
Fourth, use statistical inference to calculate the average profit of these random entry tests for that same test period.
Finally, subtract that amount from the total back-tested net profit from the first step.
According to the law of large numbers, in the case of the "roulette" system illustrated above, correcting for bias this way, the P&L of random systems would end up close to zero … while systems with real predictive power would be left with significant residual profits after the bias correction.
While the math isn't difficult … the process is still challenging because it takes significant resources to crunch that many numbers for hundreds of thousands of Bots.
The good thing about RAM, CPU cycles, and disk space is that they keep getting cheaper and more powerful.
Conclusion
It is relatively easy to measure the wins and losses (and luck versus skill) of trading systems. It can be complicated, but ultimately, it's just math. The logic of the example also applies to adopting technology, starting a business, or transforming from a product-based to a platform-based business model, etc.
In most situations, the secret is to figure out what data is incumbent to your industry as well as what data you're creating. Figure out how to analyze it. Figure out how to do that consistently, autonomously, and efficiently. Then ... test.
It's not sexy, and it's not complicated.
We live in a ready, fire, aim era. The speed of innovation is staggering, and the capital and energy needed to create an app or start a business is less than ever before ... and a bias for action is powerful.
Luck and a bias for action will take you further than most - but it still won't take you far enough.
So, I'll leave you with the question...
If you're reading this, you've almost certainly been lucky ... but have you been skillful?
I had friends in town for today's Cowboys game against the Giants. If you care, it was a massive win.
We discussed the difference between Gen Z and Millennials on our way back from dinner last night.
During the conversation, my youngest, Zach (who is 30), called to tell me that his face had been sewn back together after a rugby game.
Wonderful.
But, it was a great chance to hear his opinion about the difference between Gen Z and Millennials.
I'm paraphrasing, but he stressed that the main difference was that he lived through a transition of technologies that they didn't experience.
For example, he is old enough to remember cassette tapes, floppy discs, boomboxes, and more. His first computer was an old-school Mac with a black-and-white display (how primitive).
So, though he didn't see the prior shifts that I did (like the invention of the color TV), he is still aware of the shift between the "old world" and the "new world" ... and how radical the difference was.
Meanwhile, Gen Zers were raised with the technology we see today as their only reality.
As a result, they're much more immune to how awkward or cringy it is to share their entire life online, hopping from instant gratification to instant gratification.
We hear a lot of doom and gloom from (and about) Gen Z - which isn't new. The younger generations are always derided ... in part because they're young.
Nonetheless, GenZ still believes the future is bright.
What do you think about Gen Z? And, what differentiates them from Millennials? I'm curious.
In 2018, the local news did a brief story about Capitalogix - centered around finding tech talent ... and how hard it can be.
It has only become harder since then. In part because of the growing demand for tech talent ... and in part because success today requires a higher level of mathematical, statistical, and innovative problem-solving talent than ever before.
And that's only part of the reason that I'm proud of our team!
The robots aren't coming for our jobs. We're creating the robots, the AI, and the automation.
The secret to great AI is that it still has a heartbeat.
It's not enough to invest in the right ideas or technologies. You have to invest in the right people as well.
"Standing still is moving backward ... so you don't only need new technology, you need a new level of data scientists – a new level of professional that can think about what's possible, rather than how to do what we want to do right now."
Even though we've got an incredible edge now. I recognize that edges decay faster than ever. The trick is to stay ahead.
I can predict that the future is bright ... And I know that the best way to predict the future is to create it.
Who's The Most Innovative?
Innovation means a lot of different things. It changes based on where we are in history, the amount of time we're considering, and the scale.
Language was an innovation, the piece of plastic on the edge of your shoelaces was an innovation (called the Aglet), changing time signatures in music was an innovation in history, and so is artificial intelligence.
Defining and measuring innovation is difficult even in your business ... but the Global Innovation Index attempts to do it globally. It does so by measuring several factors, like:
via visualcapitalist
By this metric, Switzerland and Sweden take the top two spots - followed by the U.S. and the U.K.
Honestly, the list surprised me some. Some names I expected to be on the list - or higher on the list - didn't crack the top ten. Though Switzerland and Sweden have dominated this list for many years.
A topic I'm very passionate about right now is patents - and how valuable they can be to your business. Here's a previous article I wrote on the subject, but I'll revisit it soon with new ideas and distinctions.
Posted at 03:32 PM in Business, Current Affairs, Gadgets, Ideas, Market Commentary, Science, Trading Tools, Web/Tech | Permalink | Comments (0)
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