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:
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.
Global Chip Shortage and Automakers
In August, I wrote about the technologies I thought would impact the world most over the next 5-10 years.
In that article, I also briefly identified the global chip shortage as a supply chain issue impacting millions of businesses, which could also become a significant barrier to businesses adopting A.I. at scale.
Let's talk a little bit more about the scale of the shortage.
Chips (or semiconductors) are used in substantially all the world's electronic devices – and the more complex machines can require not only more chips ... but also more complex chips. For example, a modern car can have anywhere from 500-3000 chips in them.
When the pandemic hit, consumer demand shifts meant that semiconductor manufacturing had to slow down - and a foreseeable consequence of those actions presents us with the inconvenient truth that scaling back production can take up to a year-and-a-half. With demand increasing, the supply vs. demand ratio is getting more out of whack.
Luckily for you, semiconductor manufacturers prioritize the more lucrative goods (like smartphones and other consumer electronics), but that means that it will be harder for small businesses to get them - and it's also severely impacting the automotive industry.
You'll notice that the most affected brands have more production in North America. The reason for that discrepancy is that U.S. manufacturers depend heavily on chips from Asia. The Senate has recently approved $52 billion in subsidies for N.A. chip manufacturing, which hopefully will lessen that dependence over time.
If you were already worried about the skyrocketing prices of houses, you should expect to see a quick rise in the price of vehicles as well.
Buyer beware!
Posted at 07:23 PM in Business, Current Affairs, Ideas, Market Commentary, Science, Trading, Trading Tools, Web/Tech | Permalink | Comments (0)
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