We often talk about Artificial Intelligence's applications – meaning, what we use it for – but we often forget to talk about a more crucial question:
How do we use AI effectively?
Many people misuse AI. They think they can simply plug in a dataset, press a button, and poof! Magically, an edge appears.
Most commonly, people lack the infrastructure (or the data literacy) to properly handle even the most basic algorithms and operations.
That doesn't even touch machine learning or deep learning (where you have to understand math and statistics to make sure you use the right tools for the right jobs).
Even though this is the golden age of AI ... we are just at the beginning. Awareness leads to focus, which leads to experimentation, which leads to finer distinctions, which leads to wisdom.
Do you remember Maslow's Hierarchy of Needs? Ultimately, self-actualization is the goal ... but before you can focus on that, you need food, water, shelter, etc.
In other words, you most likely have to crawl before you can walk, and you have to be able to survive before you can thrive.
Artificial Intelligence and Data Science follow a similar model. Here it is:
First, there's data collection. Do you have the right dataset? Is it complete?
Then, data flow. How is the data going to move through your systems?
Once your data is accessible and manageable you can begin to explore and transform it.
Exploring and transforming is a crucial stage that's often neglected.
One of the biggest challenges we had to overcome at Capitalogix was handling real-time market data.
The data stream from exchanges isn't perfect.
Consequently, using real-time market data as an input for AI is challenging. We have to identify, fix, and re-publish bad ticks or missing ticks as quickly as possible. Think of this like trying to drink muddy stream water (without a filtration process, it isn't always safe).
Once your data is clean, you can then define which metrics you care about, how they all rank in the grand scheme of things ... and then begin to train your data.
Compared to just plugging in a data set, there are a lot more steps; but, the results are worth it.
That's the foundation to allow you to start model creation and optimization.
The point is, ultimately, it's more efficient and effective to spend the time on the infrastructure and methodology of your project (rather than to rush the process and get poor results).
If you put garbage into a system, most likely you'll get garbage out.
There are a lot of commonalities between successful tech companies. Pretty much all of them attempt to leverage software to do jobs that would be too expensive, too boring, or virtually impossible to do otherwise.
Obviously, technology is a huge force factor impacting the success of a company. As a result, there is an arms race to get to "next" ...
Research and Development has always been a key in growing companies. Now, that is truer than ever. As tech improves faster and more dramatically, technologies become relevant and irrelevant faster than ever.
Challenging ... sure, but a great problem to have.
What a great time to be an entrepreneur!
Good luck with your 2018 plans. I'd love to hear what you are focusing on next year.
I was lucky enough to spend time with family and friends watching the Cowboys make the Los Angeles Chargers look good.
My mother always used to lead the table in answering "What are you thankful for?" After the groaning subsided, we would answer. Typically, the focus was predictable – family, togetherness, and health.
Those answers neglect a pivotal part of our happiness. People often view happiness or gratitude as a consequence of their surroundings ... of good things happening in the world around them.
A different, but potentially related question is ... does money buy happiness?
I asked my son, Zach, to answer ... Here is what he compiled.
Happiness is a complex issue. It differs from culture to culture, and more granularly from person to person.
One of the more basic questions is, where are the happiest people?
Unfortunately, finding out where the happiest people are doesn't tell us how to be happy. In fact, there's a whole section of economics called Happiness Economics that strives to figure it out.
Most studies show that money doesn't buy happiness ... above a certain point.
The studies show that there's an income level (relative to your cost of living) where increased money does not increase happiness. For example, in Hawaii, it would be $122,175, but in Mississippi, the threshold is only $65,850. Essentially, past subsistence, other factors matter more. Quality of life and work/life balance being two key ones.
The Easterlin Paradox states that while individuals in higher GDP countries were more likely to report happiness, it doesn't hold at a national level, creating the paradox.
The existence of a hedonic treadmill - or set point of happiness - may be up to debate, but this hits on an important distinction. Most people start yearning for money as a means of protection and care for themselves and their families, but don't change their goals once they've accomplished that.
If obtaining more money comes at the cost of your happiness is it worth it?
Finding the right balance can be hard. With most high-achievers, there's always a best next step, a new mountain to climb, a tougher challenge to surmount ... and nothing is ever enough.
While achievement drives me as well, I think the story below is worth considering below.
The Story of The Mexican Fisherman:
An American investment banker was at the pier of a small coastal Mexican village when a small boat with just one fisherman docked. Inside the small boat were several large yellowfin tuna. The American complimented the Mexican on the quality of his fish and asked how long it took to catch them.
The Mexican replied, “only a little while. The American then asked why didn’t he stay out longer and catch more fish? The Mexican said he had enough to support his family’s immediate needs. The American then asked, “but what do you do with the rest of your time?”
The Mexican fisherman said, “I sleep late, fish a little, play with my children, take siestas with my wife, Maria, stroll into the village each evening where I sip wine, and play guitar with my amigos. I have a full and busy life.” The American scoffed, “I am a Harvard MBA and could help you. You should spend more time fishing and with the proceeds, buy a bigger boat. With the proceeds from the bigger boat, you could buy several boats, eventually you would have a fleet of fishing boats. Instead of selling your catch to a middleman you would sell directly to the processor, eventually opening your own cannery. You would control the product, processing, and distribution. You would need to leave this small coastal fishing village and move to Mexico City, then LA and eventually New York City, where you will run your expanding enterprise.”
The Mexican fisherman asked, “But, how long will this all take?”
To which the American replied, “15 – 20 years.”
“But what then?” Asked the Mexican.
The American laughed and said, “That’s the best part. When the time is right you would announce an IPO and sell your company stock to the public and become very rich, you would make millions!”
“Millions – then what?”
The American said, “Then you would retire. Move to a small coastal fishing village where you would sleep late, fish a little, play with your kids, take siestas with your wife, stroll to the village in the evenings where you could sip wine and play your guitar with your amigos.”
Finding your own personal brand of happiness is vital ... to your success, your companies success, and the success of your relationships, and I was reminded of a video my dad shot on zero-based thinking.
To be happy, or successful, are there things you need to stop doing? Something you want to start doing? Or things you need to do more of? Food for thought to pair with your leftovers from Thanksgiving.
Today's investors have access to data and information that would have been unheard of 10 years ago ... and unfathomable 20 years ago. In the past, investors relied on information and experience from their real lives, from counterparties, and from fastidious attention to CNBC and stock tickers.
While the games, the rules, and the players have all changed, the goal hasn't ... more alpha ... more money ... more reliably.
What's Changed?
Algorithmic trading isn't new, but there is a shift in who's making the algorithms. For example, you can crowdsource development through Quantopian ... or let machines do the heavy lifting through A.I.-based firms like Sentient.
But I'd argue that's only true if you look at the same data, the same way.
The Future of Trading
One of the reasons A.I. is a great option for trading is that it takes away the human element of fear, greed, and discretionary mistakes.
Sentient's founder says:
"For me, it's scarier to be relying on those human-based intuitions and justifications than relying on purely what the data and statistics are telling you." - Babak Hodjat
In addition, people tend to get similar results because they do things similarly. As A.I. matures (and more researchers become better versed in what's possible) solutions will evolve.
It won't be a Ph.d. writing an algorithm ... it will be machines and code trying unthinkable combinations and finding edges that otherwise would remain invisible and unused.
Currently, most people train their algorithms on markets, or with human intervention, but there are more data sets that can be used to build more robust models.
Alternative Data
Alternative data, to most, means tracking Twitter and Facebook sentiment, but confining your definition to that limits potential alpha.
New sources of data are being mined everywhere, and are letting investors understand trends "before they happen".
For example, mobile devices, low-cost sensors, and a host of new technologies have led to an explosion of new potential data sources to use directly for predictive insight or indirectly to help improve models.
In addition, private company performance, logistics data, and satellite imagery are becoming popular data sets in a data scientist's alpha creation toolbox.
There are often concerns about the cost and completeness of these datasets, but as we get better at creating and using them, both will improve.
Finding more ways to train algorithms on new data can help traders once again find an edge on their competition.
The thing about "sustainable alpha" is that while one might be able to achieve it, you can't expect to have it doing the same thing everyone else, or that you've always done.
Markets change, and what worked yesterday won't necessarily work today or tomorrow. Trading is a zero-sum game, and as we move toward the future, this only gets more apparent.
Behavioral Game Theory shows that human choices don't necessarily reflect the benefits they expect to receive. That's no longer the case with algorithms.
For more on Big Data and its potential, here's access to the full panel discussion I participated in recently at The Trading Show in New York.
But, what it means is up for interpretation. For example, one of the top digital marketers remains largely unfazed. Below is a video I did with Ryan Deiss, who has a different perspective on AI than I do.
Check it out:
Ryan understands that marketing relies heavily on data-analytics and automation ... but he believes that it is also reliant on the personal touch.
I agree that people are still a vital aspect of many businesses, and can't be fully replaced. However, I am dramatically more bullish on AI and its future and impact.
In many instances, today, what passes as AI is just an elegant use of brute force.
Nonetheless, AI is great at solving problems ... and is becoming increasingly able to digest and perform complex tasks (including tasks formerly thought of as done exclusively by humans).
Ryan believes that the best AI makes a conversation more human (in regards to selling and retail) and allows humans to be more human. In other words, as technology frees people up – they are free to spend their time on more valuable tasks and processes.
This has happened many times in society. Fewer people work in farming or manufacturing ... and yet there are more people doing more jobs.
So, obviously, in the same way that mechanization freed up workforces for better jobs, AI can do the same.
Realize, however, that human perception is linear ... while technological growth is exponential. Consequently, we probably do not know what AI will give (or take from) humans.
Only time will tell.
Meanwhile, some of the biggest companies are making big bets on R&D.
Here Are Some Links For Your Weekly Reading - December 22nd, 2017
Here are some of the posts that caught my eye recently. Hope you find something interesting.
Lighter Links:
Trading Links:
Posted at 03:41 PM in Business, Current Affairs, Healthy Lifestyle, Ideas, Just for Fun, Market Commentary, Personal Development, Religion, Science, Trading, Trading Tools, Web/Tech | Permalink | Comments (0)
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