Everything A CEO Needs To Know About Machine Learning
In this short article, you will learn everything a CEO needs to know about machine learning. How Machine Learning works. After that what you can do with it. In conclusion how to get started.
It is all about connecting A to B
What a CEO needs to know about machine learning? How Machine Learning works. After that what you can do with it. In conclusion how to get started. Therefore, information on ML is often confusing and sometimes downright misleading (I’m looking at you, Microsoft). But ML for business is simple:
“99% of the economic value created by AI today is through one type of AI, which is learning A to B or input to output mappings.” Andrew Ng
Almost every business application of ML today is about learning to produce certain outputs from certain inputs:
How does ML learn to predict the correct output?
- You collect examples of input –> output pairs (the more the better).
- The ML algorithm learns the connection between input and output (that’s the magic).
- You apply the trained algorithm (the “model”) to new input data to predict the correct output.
In other words, almost everyone who uses ML to make $$ does it exactly like this.
Use ML when you have a lot of data
ML needs data
Therefore, ML is powerful because it turns data into insights. But it is less efficient at learning than people are (yes, way less efficient), so it needs a lot of data in order to learn. If you have lots of data, you should think about it!
Data is a competitive advantage, not Algorithms.
However, that’s why Google and Facebook have no problem open sourcing their algorithms. But they definitely don’t open source their data. If you have a lot of data no one else has, then that’s the perfect opportunity to build a unique ML system.
The 3 simplest ways to find your ML use cases
For instance, you have a lot of data. Now, what do you do? Here are the 3 best ways I know to discover ML use cases:
1. Improve automated decision-making
If you are wondering what the CEO needs to know about machine learning. Where do you have software that automates rule-based decisions?
For example:
- Call Routing
- Credit Scoring
- Image Classification
- Marketing Segmentation
- Product Classification
- Document screening
There is a good chance ML can improve the accuracy of these decisions because ML models can capture more of the underlying complexity that connects A to B. By comparison when you write rules into software manually (the traditional way), you can only encode rudimentary dependencies.
2. Things people can do in < 1- second
Another great heuristic I first heard from Andrew Ng is:
“Pretty much anything that a normal person can do in <1 sec, we can now automate with AI.” Twitter
So what are some things humans can decide in < 1 sec?
- Who’s in that picture?
- Do I have a good feeling about this potential customer?
- Does this look like a problematic CT Scan?
Many jobs are a sequence of < 1-sec decisions. Like driving:
- Is that person going to cross the street?
- Am I too close to the sidewalk?
- Should I slow down?
- … and many, many more.
Anything you can do in less than 1 second, ML can most likely do too (or it will be able to soon).
3. Get inspired by Kaggle competitions
Large corporations like Zillow, Avito, Home Depot, Santander, Allstate, and Expedia are running data science competitions on Kaggle. These are challenges they want outside data scientists to solve. So these competitions give you an idea of what types of AI solutions they are working on. It’s really a great resource.
Have a look at the competitions and get inspired.
Finding ML Use Cases:
- Upgrade decision-making that’s already automated
- Automate things people do in < 1 sec
- Get inspired by Kaggle competitions
Don’t wait until you have a Data Science Team
Building a good data science team is super hard (and expensive!)
Many companies struggle (and ultimately fail) to build an efficient data science team. Why is it so hard?
- Misinformation about who to hire
- Tough competition for talent
- Few managers who can lead data science teams effectively
In addition…
You don’t know yet whether you need a data science team
You might have a lot of data and a lot of ideas, but that doesn’t mean you need your own data science team. Only after you’ve built your first AI systems will you really know how much manpower you’ll need in the long run?
Above all build something that works — fast
Your first goal should be to pick the lowest hanging AI fruit and finish it quickly. This gets you miles ahead:
- You’ll achieve the tangible success that gets investors, the board, and your team excited;
- Get to know the taste of ML and get real-life experience of what works and what doesn’t;
- Have better ideas about where to use AI next.
With that first finished system under your belt, you are in a much better position to hire and train a data science team. Or maybe you find that you don’t want (or need) to hire a whole team because that low-hanging fruit already accounted for 80% of what you can feasibly do with AI.
ML teams should work across the whole company
However, If you do build an AI team, you should build it for the whole company, not just one department. A horizontal AI team.
In other words, don’t think in departments
AI experience is very transferable: To a data scientist, your CRM data looks almost the same as your inventory data. And to an algorithm, they look even more similar. It makes a lot of sense to build one AI task force for the whole firm.
Side note: For that to work, you should also have your data in one place!
More life-saving tips
In other words, don’t listen to people selling a “better algorithm”
Either they have no experience, or they’re trying to sell you open source for a markup. In AI especially, everyone is cooking with water, meaning they’re using publically available algorithms.
Above all focus on business experience & engineering quality
Also, Work with someone who takes the time to really understand the business problem you’re trying to solve and has high standards when it comes to engineering quality. However, If you want lots of results with fewer headaches, then the plumbing is more important than the cute algorithm that flows through it.
Magic Triangle meetings 😋
In other words, the best ideas develop when you put three types of people in a room:
- A person who’s in touch with the current business priorities (you?),
- A person knows the data you have (your database engineer), and
- Someone who has lots of practical experience building ML systems.
For instance, together, these three people can make realistic plans, really fast.
In conclusion:
- AI is about connecting A to B.
- Look into processes that involve a lot of data and < 1-sec decisions.
- Get the first win before you plan big.
In conclusion, you learned everything a CEO Needs To Know About Machine Learning. In other words, If you need help along the way, drop me a line. I’m always happy to hear about exciting challenges: manja.bogicevic at kageera.ai