This is going to be a short, to-the-point article. First I’ll talk about the problems with AI right now, then the problems with understanding and applying AI in business scenarios, finally a first glance on the solutions I’m thinking.
Part I. The AI world

Since the beginning of humanity as a society, we’ve been trying to find a way to work better and smarter. Artificial Intelligence(AI) it’s one of those things we image will help us in many ways to improve our daily lives.
I’m going to give a very simple definition of AI. But first I need to talk about it’s parts = Artificial and Intelligence.
If we think of Intelligence as the ability to accomplish complex goals, something complex as something having many parts related to each other in ways that may be difficult to understand, and understanding as the ability to turn complex information into simple, useful information, then we can say:
Intelligence is the ability to accomplish complex goals by understanding the parts that form the main goal.
We humans do this by modeling. This is the process of "seeing" the "reality", the world around us, and then creating a higher level prototype that will describe the things we are seeing, hearing and feeling, but it’s a representative thing, not the "actual" or "real" thing.
So we humans (hopefully there are not bots reading this) model the world and use our intelligence to understand the way things work and use that understanding and knowledge to solve difficult and complex problems.
The artificial part here means that humans are not the ones (directly) using intelligence, but a machine, or software or algorithm. These algorithms are not programmed as usual, where you tell them exactly what to do. They are learning through data.

Part 2. AI in business

If we take the above diagram and we add a business flavor to it, then we are trying to apply AI for business.

Here the A-BSPF is the Agile Business Science Problem Framework. You can read more about it here:
Agile Framework For Creating An ROI-Driven Data Science Practice
Here we need to get the data from the world or "nature" and the business. Then we need data from the business itself, and define a problem within it. It’s very important that you read the above article to understand how to do this in a systematic and agile way.
For me the problems companies face when trying to use AI are not exactly in the AI or Model step, but in the first steps. Defining the business case, get and analyze the data they have and getting data from the outside world.
As I said before:
… but it all starts with the data. As you can image, data is an important asset (maybe the most important one) for companies right now. So before you can apply machine learning or deep learning [and AI], at all, you need to have it, know what you have, understand it, govern it, clean it, analyze it, standardize it (maybe more) and then you can think of using it.
That’s why I proposed a shift into a Data Fabric, then join that with end-to-end tools for AI. You can read more about it here:
Everyone wants to be disruptive, and use data for everything, before even knowing why. That’s just crazy. We need to get out of this space. I really recommend to read in this point to read this article
by Tyler Elliot Bettilyon and then go back. As he said:
We are drowning in technology that was created for its own sake, then aggressively marketed, lobbied, and otherwise pushed to reluctant consumers.
and:
The ideas of "creative destruction" and "disruption" are enshrined as de facto good among the investor and CEO class of Silicon Valley …
Before trying to be disruptive in every way, get things done! Solve your data problems, and really think on a way you can use it to answer them.
Part 3. The path I want to take with you

If you read my articles, you may figured out that they all have a purpose and they follow a path. That was me trying to get to this point. I work everyday trying to help people and companies success in their path to AI and Data Science. Now I’m taking that to the next level. So I have three announcements:
- Data Science for Business with Business Science: In the beginning of next year I’m launching a course with Business Science and Matthew Dancho about "Business Analysis with Python" (DS4B 101-P). As the first course for you to really understand how to use Python to solve complex business cases and prepare you for DS4B 201-P where I’ll take you to an end-to-end business solution with the A-BSPF using Python, Spark, H2O, Lime and much more. You can read more about it here:
- Deep Learning with Deep Cognition: I’ve been talking about Deep Learning and Deep Cognition for a while. And now with the help of the company and Akshay Bahadur, I’m going to launch a course next year too where you will learn how to apply Deep Learning using the Deep Learning Studio from Deep Cognition to solve business problems, with lots of examples, code and tutorials for you. I’ll announce that soon, so remember to visit their webpage to be updated:
- The Data Fabric and AutoX: My take on the future of Data Science and AI for business is the combination of a knowledge-base graph, data layers, semantics and ontologies with AutoX. So I’ll be talking a lot about that next year, and more surprises will come. If you want to know more about it read it here:
Soon I’ll talk about how to do ML and AI in general with this technologies.
Please if you have any suggestions let me know 🙂
If you have questions just follow me on Twitter:
and LinkedIn:
See you there 🙂