Most of the times when you read about AI you find lots of technical explanations, so that finding a non-technical AI explanation in simple words is rare.

End of last month I presented for Nautilus Cyberneering, at a webinar where we got invited to collaborate. I talked about AI. The other presenter talked about the Meta-verse. Both presentations aimed to clarify terms and concepts for non-technical business people. The goal was to create a good base understanding, simplify these topics, give examples, and share some advice on where to start.

This is a link to the webinar in case that you are interested. In case that you want to get a copy of the slides here they are.

In this post I intend to do the same but I will also give the example of how we are approaching the development of our AI. This insight will give you a better understanding of what you need to consider.

So, if you are looking for technical knowledge this article is not for you. Nevertheless, if you want to learn the basics and get a non technical explanation to know what is possible and where to start, keep reading.

How did I end up knowing a little about AI?

To start, I am not technical expert but I am interested in technology. I have worked in different roles in business for the last 20 years and have often used technology to overcome challenges wherever I worked.

As for AI, I started dabbling with it about 2 years ago. My first contact was a course by Udacity on AI product management. This course taught me its potential, its training methods, challenges, costs, etc. as well as how to look at it from a business point of view. It was a good entry point.

Hereafter, I continued reading on the topic, and by coincidence I ended up in a team developing an AI product, where I am now working as a business developer.

It is an ambitious Open-Source project, where we create the necessary base product, infrastructure and community around a concept of a simple and expandable command line AI assistant. Evidently, you can imagine that AI is a constant discussion topic and I am learning something new every day.

What we do in this project explained visually is this:

AI in our daily lives

AI is already everywhere in our daily life. It has become a seamless integration in your day-to-day when you use your desktop computer, tablet, or mobile. You may be using it when you take a picture, search online searching, buy at your favorite online store, watch a movie or use your bank account.

Companies are using it all over the place. Netflix, Amazon, Google, your bank, etc. are just a few of them.

AI necessary basic understanding, conceptual differentiation, and types

To understand how you can use and how AI works, you first have to have a basic understanding of what AI is. It is also important to understand the difference between AI and the human mind.

What is AI? – In simple words

AI is a computer system that can learn and work on its own. It does this by using artificial intelligence algorithms, optimized or “taught”, so to say, to the machine.

However, what is an algorithm? An algorithm in the case of AI is a set of rules that have been created to solve a problem or accomplish some end. In the context of AI, these algorithm sets are what enable the computer system to learn and work on its own, be it for example image recognition, or any other task.

These algorithms are what make AI special and different from traditional computer systems. For example, the tasks an AI algorithm can perform are many, though it is often specialized to accomplish these in specific contexts or domains.

Conceptual AI – Strong vs Weak

There are two different types of AI: Strong AI and Weak AI.

Strong AI, named also general intelligence (AGI). This type of AI can solve any problem that a human mind can solve. It can think creatively, reason, and learn on its own. Strong AI does not exist yet, but scientists are working on it.

Weak AI, on the other hand, also called artificial narrow intelligence (ANI). An AI focused on solving specific tasks. Weak AI is the kind of AI that we have today. Most of the AI applications that we use today fall into this category. They describe it as “weak” but it can be very powerful.

Machine learning versus Deep learning – The differences

Now that we have a basic understanding of AI, let’s take a closer look at the two most popular types of AI: machine learning and deep learning.

As mentioned before, both deep learning and machine learning are sub-fields of artificial intelligence. And as you might have guessed, deep learning is a sub-field of machine learning.

So, what is the difference between machine learning and deep learning?

The main differences between machine learning and deep learning can be simplified as two:

1. Machine Learning is more dependent on human supervision to learn through prepared structured data, and Deep Learning is capable of doing this unsupervised and even with unstructured data.
2. The complexity of the layers of interrelated algorithms known as nodes, usually more than three layers of hidden nodes.

Here is an example setup of the layers in a Deep Learning Neural Network Algorithm:

Rather complicated, if you know that there are hundreds and thousands or even millions of such layers.

Difference between the Human Mind and AI

Now, if we consider the previous discussion we can say that AI is good at processing data from specific preset contexts, it runs data through its algorithms which it adjusts to improve its accuracy and outcome.

The human mind, on the other hand, though not as fast in processing data, can learn and adapt itself to new contexts, for which it uses abstraction and creativity. Thanks to this ability we have survived over the millennia and evolved till today.

To better understand this difference, let’s take a look at an example.

Say you want to buy a new car. You would go online, research the different models, their prices, features, etc. This is all data processable by an AI system.

Now, let’s say you want to buy a new house. The research part would be the same, but you would also need to consider the location, the schools in the area, commute, etc. You possibly would imagine life in that area, how it would be, how convenient, etc. this is where abstraction and creativity come into play.

The human mind can take all this data and more, process it, and come up with a decision. The AI system, on the other hand, would need to be “taught” how to do this.

So, while AI is good at processing lots of data, the human mind is better at making decisions based on available data, and possibly better with little data, if it is in a completely new setting or context. And this is just one example of the difference between the human mind and AI.

Business use cases for AI in different industries

There are many different real-world applications of artificial intelligence today as I already mentioned before. The most common also in business are:

• Fraud detection, discovering unusual bank transactions, etc.
• Predicting consumer behavior, based on historic consumption and personal information
• Speech recognition is used in many different industries such as healthcare, law, and customer service.
• Image recognition is used in business for tasks such as product identification, facial recognition, security, document automation, etc.
• Customer service is an industry that is using AI more and more to provide better service to customers.

This is just a small list of examples, but there are many other possible applications for AI in business today, and in the future, there will be even more.

AI is rapidly evolving and the potential uses for it are endless. I am convinced that shortly we will see more and more businesses using AI to automate tasks, improve efficiency, and make better decisions.

How much can it cost and how complex is it

Well according to an article which I read, it can cost as little as 0 € to about 300 k € to implement AI in your company.

The most complex is usually creating a custom AI model. For instance, GPT-3 a Natural Language Processing model which is currently “on fire” is said to have cost almost 5 million USD $. Do you need to do this though? NO!! There are cheaper and even free Models out there.

Now, here is a little overview of a cost chart.

In the image above you may have wondered about Open – Source. Open-Source stands for publicly available software, data sets, models which are frequently used in developments since they are either free or cost much less than so-called closed source equivalents. Also, for your information very often closed source software makes heavy use of open-source components for their development.

Recommendations on how to start using AI in your business or organization

Now when talking about the companies which are already using it heavily they have some common characteristics. They usually:

• Produce lots of data
• Have complex processes
• Operate in variable settings or industries
• Are large companies

So, if you are a small business or startup it is likely harder for you to start using AI. But that doesn’t mean it’s impossible.

There are many different ways to start using AI in your business today. My advice here is to be practical and go for an incremental approach, not running off to look for “experts” right away. I would not do so now but later.

First, I would recommend the following to do in-house:

1. Assign a person or a team to the task of getting familiar with the AI.
2. Invest into training them on the basics
3. Have them look at what data you are producing.
4. Review your internal processes in search of complexity and large required time efforts.
5. Have them research simple AI-empowered tools in the different areas.
6. Check to which processes you can apply such tools.
7. Implement these tools.

Second, now that you have an internal person or team with some practice, knowledge, and understanding of what can be achieved I would suggest to:

1. Review the data and processes which require a custom approach due to the nature of your business.
2. Contact external AI consultants and have them analyze your case and make a proposal and cost estimate of the required investment.

Important for this second phase is that when you approach them you have them consider Open-Source and not only consider large AI models, since according to several benchmarks smaller specialized models beat larger general models in predetermined contexts.

Conclusion

So there you have it. I hope that I could explain AI to you in simple words and give you some insight into the use cases. If so, you are now a little more knowledgeable about AI, how you can start using it in your business, and the ways it is impacting our lives.

To me it’s important to understand the basics so that one can conceptualize what is happening around us and differentiate between hype and potential. With the topic of AI there is always going to be people who over-hype things and make claims that AI will take over the world or do away with certain jobs.

I am sure that there are changes coming, but remember, as humans, we have an incredible ability to learn and adapt – something that artificial intelligence still has a lot of catching up to do o, but it is evident from the examples that AI used as a complementary tool lets people be much more productive.

Now, with all of this in mind, go forth and experiment with some AI tools! There are many great ones out there waiting to help you do your work. I leave you a list here of some that I am trying out right now.

Also if you want to read a little more on AI these are some links to articles that I wrote or re-shared:

Natural Language Processing AI Technology a Quick and Brief Intro with Examples

The company that I currently work for, Nautilus Cyberneering, has a 5 year project for which the so called Natural Language Processing AI is key. We essentially want to create a virtual artificial intelligence assistant that you can run from your own local computer and communicate with you through a command line interface. This assistant […]