Football season is officially underway! In honor of that, here's a look at each position's composite "player" (as of 2019).
As you might expect, different sports have different ratios of ethnicities. For example, you might expect more Pacific Islanders in Rugby or Asians in Badminton.
The same is true for various positions on a football team. Offensive linemen are more likely to be white – while running backs are more likely to be black.
Here is a visualization that shows what happens when you average the top players' faces in various positions.
While you may be thinking "this player must be unstoppable" ... statistically, he's average.
The "composite" NFL player would be the 848th best player in the league. He's not a starter, and he plays on an average team. You probably don't know his name if you don't root for his team.
We found the same thing with our trading bots. The ones that made it through most filters weren't star performers. They were the average bots that did enough not to fail (but failed to make the list as top performers in any of the categories). Meaning, the survivors were generalists – not specialists.
In an ideal world, with no roster limits, you'd want the perfect lineup for each granular situation. You'd want to evaluate players on how they perform under pressure, on different downs, against other players, and with different schemes.
That's what technology lets you do with algorithms. You can have a library of systems that communicate with each other ... and you don't even have to pay their salary (but you will need data scientists, researchers, machines, data, alternative data, electricity, disaster recovery, and a testing platform).
You won't find exceptional specialists if your focus is on generalized safety. Generalists are great, but you also have to be able to respond to specific conditions.
In a prior post, we looked at the Global GDP in 2021. Now, let's look at US Revenue vs. Expenditures in 2021.
So, from the start, we can see a 2,770-billion-dollar deficit last year. The #1 expenditure was income security. For those who don't know, income security is an extremely broad spending category. It covers everything from tax credits and unemployment to housing assistance, foster care, and many other welfare programs. It's somewhat of a catch-all for services that help people get necessities.
Surprisingly, the US pays more per person for healthcare than countries with nationalized healthcare.
Looking at 2021 isn't the best indicator of America's spending history as a whole; there were a lot of one-time events - and a pandemic. Usually, the deficit isn't that staggering.
While the deficit may grow out of control, debt is a powerful tool - not just a liability. Nonetheless, given our current economic situation, inflation, and rising interest rates, the strategy that got us here might not be the best strategy to get us where we want to go.
There's a lot of fear from workers about a future where their roles are taken. Gartner recently surveyed workers on what tasks they wanted AI to handle.
According to VentureBeat, some survey respondents did not want to use AI at work at all. Their reasons were privacy and security concerns.
I think one of the tasks that will be thrust upon AI companies is to help workers understand that AI is not meant to replace or take over their jobs, but to help workers be more effective and focused on higher-value tasks.
“Nobody phrases it this way, but I think that artificial intelligence is almost a humanities discipline. It's really an attempt to understand human intelligence and human cognition.” —Sebastian Thrun
We often use human consciousness as the ultimate benchmark for artificial exploration.
The human brain is ridiculously intricate. While weighing only three pounds, it contains about 100 billion neurons and 100 trillion connections between them. On top of the sheer complexity, the order of the connections and the order of actions the brain does naturally make it even harder to replicate. The human brain is also constantly reorganizing and adapting. It's a beautiful piece of machinery.
We've had millions of years for this powerhouse of a computer to be created, and now we're trying to do the same with neural networks and machines in a truncated time period. While deep learning algorithms have been around for a while, we're just now developing enough data and computing power to change deep learning from a thought experiment to a real edge.
Think of it this way, when talking about the human brain, we talk about left-brain and right-brain. The theory is that left-brain activities are analytical and methodical, and right-brain activities are creative, free-form, and artistic. We're great at training AI for left-brain activities (obviously with exceptions). In fact, AI is beating us at these left-brain activities because a computer has a much higher input bandwidth than we do, they're less biased, and they can perform 10,000 hours of research by the time you finish this article.
It's tougher to train AI for right-brain tasks. That's where deep learning comes in.
Deep learning is a subset of machine learning based on unsupervised learning from unstructured/unlabeled data. Instead of asking AI a question, giving it metrics, and letting it chug away, you're letting AI be intuitive. Deep learning is a much more faithful representation of the human brain. It utilizes a hierarchy of convolutional neural networks to handle linear and non-linear operations so it can think creatively to better problem-solve on potentially various data sets and in unseen environments.
When a baby is first learning to walk, it might stand up and fall down. It might then take a small stutter step, or maybe a step that's much too far for its little baby body to handle. It will fall, fail, and learn. Fall, fail, and learn. That's very similar to the goal of deep learning or reinforcement learning.
What's missing is the intrinsic reward that keeps humans moving when the extrinsic rewards aren't coming fast enough. AI can beat humans at many games but has struggled with puzzle/platformers because there's not always a clear objective outside of clearing the level.
A relatively new (in practice, not in theory) approach is to train AI around "curiosity"[1]. Curiosity helps it overcome that boundary. Curiosity lets humans explore and learn for vast periods of time with no reward in sight, and it looks like it can do that for computers too!
Soon, I expect to see AI learn to forgive and forget, be altruistic, follow and break rules, learn to resolve disputes, and even value something that resembles "love" to us.
While simple, the wheel worked well (and still does). Consequently, the phrase "reinventing the wheel" often is used derogatorily to depict needless or inefficient efforts.
But how does that compare to sliced bread (which was also a pretty significant invention)?
Despite being a hallmark of innovation, it still took more than 300 years for the wheel to be used for travel. With a bit more analysis, it makes sense. In order to use a wheel for travel, it needs an axle, and it needs to be durable, and loadbearing, requiring relatively advanced woodworking and engineering.
All the aforementioned products created before the wheel (except for the flute) were necessary for survival. That's why they came first.
As new problems arose, so did new solutions.
Necessity is the mother of invention.
Unpacking that phrase is a good reminder that inventions (and innovation) are often solution-centric.
Too many entrepreneurs are attracted to an idea because it sounds cool. They get attracted to their ideas and neglect their ideal customer's actual needs. You see it often with people slapping "AI" on to their product and pretending it's more helpful.
If you want to be disruptive, cool isn't enough. Your invention has to be functional, and it has to fix a problem people have (even if they don't know they have it.) The more central the complaint is to their daily lives the better.
Henry Ford famously said: “If I had asked people what they wanted, they would have said faster horses.”
Innovation means thinking about and anticipating wants and future needs.
Your customers may not even need something radically new. Your innovation may be a better application of existing technology or a reframe of best practices.
Uber didn't create a new car, they created a new way to get from where you want with existing infrastructure and less friction. Netflix didn't reinvent the movie, they made it easier for you to watch one.
As an entrepreneur, the trick is build for human nature (meaning, give people what they crave or eliminate the constraint they are trying to avoid) rather than the cool new tech that you are excited about.
Human nature doesn’t seem to change much … Meanwhile, the pace of innovation continues to accelerate.
The challenge is to focus on what people want rather than the distraction of possibility.
According to Mohamed El-Erian, from Queens College at Cambridge University, we're experiencing stagflation - which is when inflation is high but growth is slowing significantly. Theoretically, that leads to recession.
The Consumer Price Index has also grown by over 8% in the past year, so the American household is facing financial threats from many angles.
Many feel that the Fed has responded disappointingly recently, and their response (or lack thereof) will be a major dictator of whether we enter a recession.
I believe that emotions play a role too. When people are afraid, they spend less and hoard what they can to save themselves from an unknown future. They feel anticipatory grief. And their fear, uncertainty, and doubt ripple through society and our lives.
Personally, I've weathered my heaviest storms by sailing toward the future regardless of the threats. An abundance mindset is a powerful tool, and as more people feel confident it becomes a macroeconomic trend with real influence.
I'd encourage you to think about what opportunities there are and will be. There are always seasons of change … Winter eventually comes – and goes. Nevertheless, winter can be a great opportunity to plan your next moves and build the infrastructure to sow more seeds in the coming spring.
As well, unlike nature, you can personally have springtime while the majority are in winter. We're currently in an A.I. springtime - and I believe that will continue regardless of economic trends.
Happy to talk about this … Let me know what you are thinking and feeling!
Stocks, bonds, crypto, and more, all saw rapid declines in the first half of 2022.
It's hard to pin the blame on any one factor. Was it the result of the actions we took to stabilize the economy during the throes of Covid? Is it the runaway inflation, Russia, or the continuing supply-chain issues?
It feels like everything that could go wrong – is going wrong. I mean, how often do stocks and bonds fall at the same time?
On a positive note, bear markets are often much shorter than bull markets. Also keep in mind that while global growth is slowing, innovation isn't!
Don't forget, “Winter” for the economy or a particular industry doesn't have to be winter for you.
Intellectual Property is an important asset class in exponential industries.
Why? Because I.P. is both a property right (that increases the owner's tangible and intangible value) and a form of protection.
They say good fences make good neighbors. But you are also more willing to work to build an asset if you know that your right to use and profit from it is protected.
As a result of that thinking, Capitalogix has numerous patents - and we're developing a patent strategy that goes far into the future. So, it's a topic that's front of mind for me.
Consequently, this visualization of which companies got the most patents last year caught my eye. In 2021, the U.S. granted over 327,000 patents. Here is who got them.
While IBM isn't the public-facing industry leader they once were, they've been topping the list for most patents for the past three decades. Their patents this past year cover everything from climate change to energies, high-performance computing, and A.I..
What ideas and processes do you have that are worth patenting? And, what processes are worth not patenting - to keep from prying eyes?
The Average NFL Player (By Position)
Football season is officially underway! In honor of that, here's a look at each position's composite "player" (as of 2019).
As you might expect, different sports have different ratios of ethnicities. For example, you might expect more Pacific Islanders in Rugby or Asians in Badminton.
The same is true for various positions on a football team. Offensive linemen are more likely to be white – while running backs are more likely to be black.
Here is a visualization that shows what happens when you average the top players' faces in various positions.
osmutiar via Reddit
Composites are interesting.
While you may be thinking "this player must be unstoppable" ... statistically, he's average.
The "composite" NFL player would be the 848th best player in the league. He's not a starter, and he plays on an average team. You probably don't know his name if you don't root for his team.
We found the same thing with our trading bots. The ones that made it through most filters weren't star performers. They were the average bots that did enough not to fail (but failed to make the list as top performers in any of the categories). Meaning, the survivors were generalists – not specialists.
In an ideal world, with no roster limits, you'd want the perfect lineup for each granular situation. You'd want to evaluate players on how they perform under pressure, on different downs, against other players, and with different schemes.
That's what technology lets you do with algorithms. You can have a library of systems that communicate with each other ... and you don't even have to pay their salary (but you will need data scientists, researchers, machines, data, alternative data, electricity, disaster recovery, and a testing platform).
You won't find exceptional specialists if your focus is on generalized safety. Generalists are great, but you also have to be able to respond to specific conditions.
Onwards.
Posted at 03:21 PM in Business, Current Affairs, Games, Ideas, Just for Fun, Market Commentary, Science, Sports, Trading Tools | Permalink | Comments (0)
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