Beware of the AI tag
Recognize the hype in disguise.
Category: Data Science
Author: Petar Papikj

Beware of the AI tag
I would go ahead and say that I’m no expert in artificial intelligence but I do engage in conversations related with AI quite often. While having breakfast with my friend and founder of Pendulibrium, Ilija Lalkovski we ended up talking about the apparent omnipresence of the two most common letters put together in the tech world in the past period, AI. We concluded that this abbreviation is slowly taking sweep and it’s automatically incorporated into every web site or digital marketing effort, without any justification. Since Ilija is fully dedicated to AI, aware of most of the challenges, yet not all, we continued discussing about this shameless approach and whether this AI spread is possible at this point.
All of a sudden we’re seeing this everywhere around us. Out of nowhere, every service company has its own AI enhanced solution that is probably used for amping up the price of their service or to provoke enough curiosity just to get you knocking on the door.

There are many writings about the hype in AI but I’d like to give fresh perspective and understanding to people, such as myself, who are not technically knowledgeable of what this AI tag entails.
If you’re mildly trying to educate yourself about AI and how you can get somewhere with it, you should probably continue reading so to understand, if nothing else, that the road before you is long and full of unknowns.
I’d go on to highlight several points in a broad manner which I believe everyone should take into consideration when looking for a company providing AI services or providing any service with sprinkles of AI on it.
1. Data analysis is not AI. But it’s an essential part of it and you must have it. 
Yes, data analysis is a necessity when talking about AI and it’s one of the most excruciating parts of the job. This step helps you to understand what your data can offer, whether it’s any good i.e. relevant and useful. Many companies that are already doing this are definitely one step ahead of the rest. If you see a company offering AI services ask how they are analyzing the data.
2. Domain Knowledge.
In order to create a solution, you must understand the field that’s surrounding the problem. You cannot go and build an AI solution that can forecast cashflow without at least understanding basic finance. Bradford Cross, the CEO of Merlon Intelligence explains how both tech expertise and domain knowledge are interdependent and necessary for one such product to succeed:
Building full-stack products requires deep subject matter expertise. Selling these products requires trust, respect, and relationships within the industry. Teams that manage to combine the subject matter and technical expertise are able to model the domain richly and drive innovation that comes from thinking outside the box by understanding what the box is. Teams that come with a domain-first approach tend to get stuck inside the box, and teams that come with a tech-first tend to get stuck out in left field.
This shouldn’t scare you since most of the companies, I believe, that are offering their services have the domain knowledge from their respective fields. However, the question here is whether they have the tech expertise that’s extremely hard to get these days due to the scarcity, which brings me to the next point.
3. Scarcity.
What has to be understood is the fact that the market is in deficit when it comes to bright minds in AI. And it’ll probably stay like that for some time. Companies like Google and Facebook are relentlessly trying to snatch every single person they can find that has background in AI. We’re constantly reading of collaborations between prestigious universities with companies (mostly tech behemoths) in a way that enables both parties to grow and prosper. Courses are emerging behind every corner. This just indicates that the force is not yet here. The force is being built. Any company that’s offering AI services has to have AI people on board. Be sure to check that box as well.
4. Actual data that’s real and relevant.
Data is bigger than model. Since we all have some sense as to what data is, let’s focus on the model. Without going in too much technicalities, the model can be understood as something that trains continuously on the data through a process of iteration which provides a better insight. This is why the data has to be real and has to relate with the things you do. You cannot play with any data just because you have it in abundance. I mean, okay, you can, if you have spare time but this is not how things should work. Quite often you’ll find that not all data is useful, as we do in real life, and very quickly you’ll become aware that what matters is the quality of the data. 
Why real and relevant? Well, you have to compare it with something, you have to validate your results against results from the real world in order to figure out whether you’re on the right track. Knowing this, don’t forget to ask what kind of data is being used and how.
5. Limited Possibilities (Desires vs. Reality).
Not everything can be done… yet. People are still in the dark, but there’s light. I think that in his interview, Facebook’s AI Director, Yann LeCun perfectly explained the actual difficulties AI people are facing each day:
AI researchers, down in the trenches, have to strike a delicate balance: be optimistic about what you can achieve, but don’t oversell what you can do. Point out how difficult your job is, but don’t make it sound hopeless. You need to be honest with your funders, sponsors, and employers, with your peers and colleagues, with the public, and with yourself. It is difficult when there is a lot of uncertainty about future progress, and when less honest or more self-deluded people make wild claims of future success. That’s why we don’t like hype: it is made by people who are either dishonest or self-deluded, and makes the life of serious and honest scientists considerably more difficult.This honest approach can help us understand that we’re far from having the AI implemented in almost everything, as we’re seeing now.
This honest approach can help us understand that we’re far from having the AI implemented in almost everything, although this hype and the information it feeds us tells otherwise. There are breakthroughs but all the points mentioned in this post are required in order to have the AI implemented with the actual benefits spurring from it. You shouldn’t expect anything tangible if you don’t have these things set in place.
In another post, I will go in depth about the effort we’re putting into a pilot project and how long it took us to create a prototype with data that’s already been processed and served on a platter.
To get back to the main advice, make sure the company you’re working with is honest with you about their AI enhanced services. The desire is different from the reality but this doesn’t stop companies in using this as their unique advantage.
6. Computational power and cost.
Someone needs to train the models using the data. And all this costs computational power and time, thus money. Understand that running a model and training it requires tons of tweaks in the data and the model itself. It’s also highly important to note that the type of data that’s being processed affects the cost big time. Working with data sets consisted of numbers is completely different from working with data sets of images, let alone videos. Just as an example, at last year’s Nvidia conference in Munich, Mercedes provided their behind the scenes numbers when trying to improve the driver’s experience about their next possible destination.
Mercedes-Benz’s presentation at Nvidia’s GTC conference in October, 2017
They exploited their GPUs for more than 18,000 hours just to find the right model for their data sets. And this is coming from a company that can safely afford this luxury. Although I must say that what Mercedes is pursuing is worthy for the whole AI community, I’d suggest trying to keep these numbers in mind next time you spot AI on some website offering design services, financial assistance, or what have you.
7. Primarily, AI should be beneficial for the business itself, not for its clients.
There’s no need to pump up prices by stating you have AI backing your solution, especially if you’re not in the AI business. It’s good, but this shouldn’t be the business’ main point. AI is just the buzz word and any business with lots of data and main focus on something completely different than AI shouldn’t be taken for granted. AI is more than some advanced algorithm. It should help us predict better with time, it should learn from itself, it should grow independently. The AI should solve day to day problems and make the businesses more efficient. It should transform the business in its core. So remember, businesses should cut their costs by having AI on board. You should still get the full service for what you paid for and not pay a penny more.
8. No silver bullets.
This is probably the most important thing. One solution cannot be applied to a general problem. At least not yet. There are just so many small things affecting the big picture in a way that cannot go unnoticed. General and small breakthroughs don’t work. If they do show promise, they are quickly accepted as a general and common truths, trivialities even, and they get quickly dispersed into the community which is, without question, something marvelous to see as a joint effort of contribution.
In his post about choosing the right vertical, Brendon also notes the following:
The reality is that these low level tasks are commoditized very quickly. Today’s novelty is tomorrow’s open source, and that’s happening faster and faster each year. Look at low level tasks as building blocks that you compose into higher level solutions rather than as the critical IP of your business.
I refrain from using the word tailored but it fits perfectly in this occasion. Understand that you cannot kill two birds with one stone. Every solution to its own problem.
I hope I shed some light to some of you and helped you recognize the hype in disguise. Remember that sticking these two letters, or their less attractive alternative (ML — machine learning) to a service company should always be questioned.
If you found this post useful I’d appreciate a clap and be sure to share your thoughts in the comments.