Artificial Intelligence — Simply Explained

What is the difference between AI and ML?

Here we give you a non-technical explanation -- and a little peek into the crystal ball. First, let's clear up the terms a bit. In conversations about artificial intelligence, you have probably seen the abbreviation “AI/ML”, which means l'intelligenza artificiale and machine-learning. But can these be used synonymously?

Well. AI is the overarching branch of computer science that deals with how we can use data to teach algorithms to solve tasks. Machine learning (ML) is a large subset of this one. So you can say that AI is more than ML, but today ML accounts for almost all available technology that is relevant for us consumers to discuss.

Not wholly mind reading

Contrary to what some might think, there is basically nothing “mysterious” about machine learning. In practice, only statistics are used. You feed data into a system, and the more data you put in, the better the system gets at solving what it's supposed to solve.

The principle is not very different from traditional research, where we look at data sets, do analyses and draw conclusions. That's exactly what machine learning does too, and many of the algorithms are taken straight from classical statistics. It is not, therefore, that an ML model “thinks for itself”, explains the developer.

For example, imagine that you are going to create a search engine for your online store. Then you want it to find as much as possible your customer indeed scavenging after, not necessarily what they keyed in.

Here you can use your search data and run it into an ML model. By analyzing all the data points, such as what customers search for and what they end up buying, the algorithm can get better at “figuring out” what the customer wants.

Democratized technology

The power of this kind of data analysis increases in tandem with the amount of data we sit on. And just that volume is bigger than ever: If you have a website, you know click rates, churn rates, user patterns and everything in between. Do you also have a webshop, you're sitting on huge amounts of data about your customers and their relationship with your store.

Many are sitting on a mountain of data, but it has no value in itself. The point is to utilize the data of anything, and it's about to get easier than ever.

That's why AI/ML is relevant to you as well. Because where you used to need your own teams of data analysts, there are now ML models that are in practice “plug and play”.

It is only now that this technology is becoming available, even to people who are not necessarily experts. Costs have decreased and opportunities have increased. There are already out-of-the-box solutions that can be customized for each business, and today even small online stores can take advantage of machine learning.

Never get tired

The uses, of course, are many. If you work in eCommerce, you can use it to create better customer journeys. If you have enough data, you can categorize your users by patterns of action — and give them the right offer at the right time.

Today we spend a lot of manual thinking power on this kind of marketing and user analysis. But algorithms never forget, and they never get tired.

ML algorithms will also be able to help you further develop goods and services based on data about what customers want — and what sells.

If you have enough data, you can theoretically design the perfect product. In addition, you will be able to discover completely new contexts that you may not have thought of before. Algorithms are good at finding signals in the noise.

Us, only better

Another important area of application is the streamlining of existing processes and systems. For example, if you are going to tweak the flow in a value chain, you should know that indeed There is an optimal way to do it.

Traditional optimization tries to do this, but it is incredibly heavy and demanding to do it manually. Most likely, you will never find “facit”. If you have enough data, machine learning will be able to find the best solution with fairly high accuracy.

If you combine this with automation, you get a technology that is going to turn upside-down in large parts of working life.

Self-driving cars, which many people talk about, are only a small part of it. We are already seeing an introduction of AI/ML in medicine and law, among others. It is not for nothing that artificial intelligence will eventually be able to do most of what we do, only faster and better.

Be curious

But until the machines squeeze us completely out of our jobs, they remain useful tools. No matter if you are a multinational corporation or a shop on the corner.

This is something everyone sooner or later is going to have to have in their toolbox. You don't have to learn you AI, just as little as you need to teach you accounting or coding to order a solution.

But the developer advises you to be curious and forward. With increasing globalisation comes increased competition from those who are already the best at using data.

Today, one engineer with the right tools can do more than the world's top 100 data analysts could 20 years ago. It's not a development any business can afford to ignore for very long.