Boston Dynamics just released a video of their Atlas robot doing an impressive gymnastics routine. Comparing it to their videos from 2009 shows how insane the progress is.
You see the fear of Skynet-esque advanced AI … but Terminator-style robots may be a more immediate threat.
On the one hand, Boston Dynamics makes robotics look cute but there's promise and peril. For example, Syria is using autonomous killer drones in Turkey.
Any tool can be used for good or evil, there's no inherent morality in a tool, but we're certainly good at finding ways to push the boundaries of their uses.
“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 those. On top of the sheer number 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 only just now developing enough data and enough compute power to change deep learning from a thought experiment to providing 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 for 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 a lot of 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 "curiousity"[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!
When I think about the invention of the wheel, I think about cavemen. But that isn't how it happened.
Lots of significant inventions predated the wheel by thousands of years. For example, woven cloth, rope, baskets, boats, even the flute were all invented before the wheel (and apparently not invented by cavemen).
While simple, the wheel worked well (and still does). Even now, the phrase "reinventing the wheel" is used derogatorily to depict needless or inefficient effort. 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.
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.
Football season is officially underway! In honor of that, here's a look at each position's composite player!
As you might expect, different sports have a different ratio of ethnicities. For example, you might expect more Pacific Islanders in Rugby or Asians in Badminton.
The same is true for different positions on a football team. Apparently, 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.
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). 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 Part 2, I talked about normalizing your habits and picking consistent, normalized metrics. This doesn't just work at the gym; it applies to life and business as well.
Today, I want to explain how and why this helps. To do so, we will talk about controlling your arousal states.
Chemically, most arousal states are the same. Meaning, the same hormones and neurotransmitters that make you feel fear also can make you feel excited. They affect your heart rate, respiration, etc. … Though, the outside stimuli you experience likely determines how you interpret what is happening.
In most situations, a heart rate of 170 beats per minute is an indicator of extreme danger (or an impending toe-tag). If I felt my heart racing like that in a meeting, it might trigger a fight or flight instinct. I prefer conscious and controlled responses. So, I train myself to recognize what I can control and to respond accordingly.
One way I do that is by being mindful of heart rate zones during exercise.
My goal is to get as close to 170 bpm as I can, then stay in that peak zone for as long as possible.
Here is a chart showing a Fitbit readout of an exercise session.
As you can see, every time I reach my limit … I get my heart rate back down. It becomes a conscious and controlled learned behavior.
Here is a different look that shows effort based on my maximum heart rate. It is from an app called Heart Analyzer.
Recognizing what this feels like is a form of biofeedback; it's not only gotten me better at controlling what happens after my heartrate reaches 170 but at identifying when I'm close – even without a monitor.
Now, when my heart rate is at 170 bpm (regardless of the situation), I don't feel anxious … I think about what I want to do.
I currently use an Apple watch with the HeartWatch app to measure heart rate during the day. The Oura Ring is what I use to measure sleep and readiness.
These are useful tools.
It's the same with trading … Does a loss or error harsh your mellow – or is it a trigger to do what you are supposed to do?
Getting used to normalized risk creates opportunity.
When you are comfortable operating at a pace, or in an environment, that others find difficult – you have a profound advantage and edge.
When your doctor tells you that you are fat, it is easy to discount (because you pay them to tell you that). When your massage therapist tells you that you are getting fat, you've got to listen (because they're trying to be nice to get a better tip).
Well, for the past two months, I've been getting back into fitness.
I used to be a competitive athlete. In the past, for me, exercise was about gaining an edge and competing better. In a sense, that is still true (just on a different field). Now, I work out to stay healthy, fit, and vital while managing the challenges of running a company, navigating an overbooked calendar, and traveling every week.
This is about focusing on the right things so you can best measure progress.
Normalizing your habits and picking the right metrics isn't just a habit for the gym. It's a habit you should pick up in life. If you don't set the right measuring stick you'll always be unhappy or underperform.
Plan forward – but measure backward … you have to make sure you're not so focused on the horizon that you don't track what you've accomplished.
Normalizing the result makes this easier and better.
In running, for example, it is the time it takes me to finish one mile, while never going above 170 heartbeats per minute.
Meanwhile, in trading, we do this by comparing different opportunities based on a constant risk level (for example, the expected return for the next day of $1M, assuming a 2% maximum drawdown). It doesn't matter what market we trade, or how many trades the system makes … we can make a fair comparison and get better insights about performance.
In July, immigration topped the list, presumably by people on one side of the political spectrum, followed by the government (likely by people on the other side of the spectrum).
Surprisingly to me, the economy and the trade war were nowhere to be found.
Whether or not we have to worry, they seem fairly germane issues. I started writing an article on yield curves and the fear of recession before realizing our Chief Investment Officer, John DeTore, was already writing his own letter on it. So, I'll deal with some of the surrounding issues here.
To start, the U.S. imports nearly 4.5x what it exports to China.
While the USA's GDP is the highest globally, China is gaining quickly.
Meanwhile, China is still the biggest holder of U.S. debt – and is currently allowing the Yuan to weaken to try and make its products more competitive and offset tariffs.
It feels like we're losing some ground. But, everybody seems to feel that right now. The next chart shows that sentiment about the economic situation has been globally gloomy.
Everything seems different, yet everything seems the same. Markets tend to climb a wall of worry before falling off a cliff … and then repeating the cycle.
Trends continue until broken.
Seems like a time for caution. But, historically, times like these seem to have great opportunities hidden in them.
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