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. And even before that - they haven't even properly assessed whether AI is needed in their business. Remember, AI is a tool, not the goal.
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:

Monica Rogati via hackernoon
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.
Slower sometimes means faster.
Onwards.
A-To-Z of The Internet Minute in 2021
As I get older, time seems to move faster ... but it's also true that as I get older, more is accomplished every minute.
Technology is a powerful force function. In fact, the amount of data in the digital universe effectively doubles every two years.
Every couple of years, I revisit a chart about how much data is generated every minute on the internet.
In reverse chronological order, here's 2018, 2015, and 2011.
Here's an excerpt from 2015 for some perspective:
Throughout its (pretty short) history, the internet has been arguably the most important battlefield for relevancy and innovation.
So, what does the internet look like in 2021?
DOMO via visualcapitalist
Looking at the list, we see new editions like Clubhouse and Strava. Partially due to the quarantine, you're still seeing an increase in digital cash transfers with tools like Venmo, an increase in e-commerce shops like Shopify, and an increase in (you guessed it) collaboration tools like Zoom or Microsoft Teams.
Just to pick out some of the key figures in the chart this year.
Before 2020, I already thought that big tech had a massive influence on our lives. Yet, somehow this past year has pushed their impact even higher.
One other thing this chart also helps put into perspective is the rapid rate of adoption. As you look at different year's charts, you can see how quickly apps have become part of the cultural zeitgeist.
How do you think these numbers will grow or change in 2022?
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