What is worth noting, however, is that while you may be thinking "this player must be unstoppable" … statistically, he's average.
The "composite" NBA player would be the 112th best player in the league. He's the fourth best starter on an okay team.
We found the same thing with our trading bots. The ones that made it through the most filters weren't the 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). The survivors were generalists, not specialists.
The reminder is that you won't find exceptional specialists if your focus is on generalized safety.
But, being less digitized than competitors, alone, isn't enough to indicate it's ready for disruption:
How Regulated Is The Industry?
Heavily regulated industries mean that any company that can find a way around current regulations and tax structures can become an effective competitor fast … i.e Lyft and Tesla
Is There Too Much Competition Already?
How many competitors are in the space? Is there spare capacity, or does the current model waste resources? Think taxis waiting for customers or empty seats on airplanes.
Can it Be Automated?
Lot's of industries and tasks are ready and waiting to be automated – and only haven't been due to the cost of switching to new technology.
To put it another way – are you solving a customer headache while lowering costs for you and the customer?
The only constant is change, and digitization and automation are only increasing the rate of change. The type, quality, and the number of jobs being offered are changing rapidly.
As digitization increases so does adoption of AI-related technologies.
They're helping you pick out your next gift on Amazon, controlling what you find on Google, they're suggesting new music for you on Spotify, and they're doing their best to keep you on their website.
When trying to explain how machines learn we tend to try and describe it in human terms. Unfortunately (or fortunately) machine learning isn't based on how we teach humans.
The video is a bit simple in its explanations, but it describes some important concepts. So, watch how machines learn.
The video focuses on Genetic Algorithms, which is one type of machine learning, and neglects some of the other more complicated approaches.
As machine learning gets more complicated and evolved, it gets harder for a human to understand what makes it good … and that's okay.
It's human nature to feel safer when we understand something. It's human nature to envision machines as making human-like decisions, just faster.
Of course, just because it suits human nature to believe something, that doesn't make it true.
Part of what makes machine learning exciting is that it can do a lot of things well that humans are really bad at.
In reality, it doesn't matter why a bot is making a decision, or what inputs the bot is making the decision on. What matters is the performance and level of decision-making in relation to itself and to other options.
One of the lessons I teach when I speak publically is that in trading, caring about the markets is a weakness.
It's a distraction.
I don't care how markets are doing.
I care how my systems are doing. I care how my portfolio is doing.
There's too much data for me to try and care about anything else.
It's a brave new world, and not only is big brother watching, but algorithms are too.
I'm sure we'll talk more about Bitcoin … but, more importantly, Blockchain (which is the technology that Bitcoin and other cryptocurrencies were built upon).
I am currently investigating and planning how we'll use Blockchain.
Human's can't do a lot of things. Honestly, the fact that we're top of the food-chain is pretty miraculous.
We're slow, we're weak, and we're famously bad at understanding large numbers and exponential growth.
Our brains are hardwired to think locally and linearly.
It's a monumental task for us to fathom exponential growth … let alone its implications.
Think how many companies have failed due to that inability … Radioshack couldn't understand a future where shopping was done online and Kodak didn't think digital cameras would replace good ol' film. Blockbuster couldn't foresee a future where people would want movies in their mailboxes, because "part of the joy is seeing all your options!" They didn't even make it long enough to see "Netflix and Chill" become a thing.
Innovation is a reminder that you can't be medium-obsessed. Kodak's goal was to preserve memories. It wasn't to sell film. Blockbuster's goal wasn't to get people in their stores, it was to get movies in homes.
Henry Ford famously said: “If I had asked people what they wanted, they would have said faster horses.” Steve Jobs was famous for spending all his time with customers, but never asking them what they wanted.
Two of our greatest innovators realized something that many never do. Being conscientious of your consumers doesn't necessarily mean listening to them. It means thinking about and anticipating their wants and future needs.
Tech and A.I. are creating tectonic forces throughout industry and the world. It is time to embrace and leverage what that makes possible. History has many prior examples of Creative Destruction (and what gets left in the dust).
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