AI First

AI First

Focus on the right stuff

For a long time ‘mobile first’ was our software developers paradigm. Every new application should not only take the mobile user into account but also focus on mobile use as the primary device. Nowadays, Artificial Intelligence, (AI) is also a subject matter. But what does it mean for developers, and what happened to mobile?

AI first, mobile second?

Mobile is not forgotten. The ‘mobile first’ paradigm was necessary to make the step from desktop to mobile and adapt features of mobile devices. Since mobile devices were put in first place it’s common to design and build from a mobile perspective. Mobile is the new normal, just like desktop was in the 90’s and web (still on desktop btw) in the zero’s.

Artificial intelligence (AI) is not new. This in contrast to mobile. Twenty years ago we could only dream about the mobile revolution. What we find normal these days most visionaries didn’t predict a decade ago. But AI is something from the mid 50’s. And from far before that. It’s been in your minds for generations, machines behaving like humans.

Artificial General intelligence (AGI)

There are roughly two categories of AI: applied or general. The last one, Artificial General Intelligence, is used for the general purpose systems that more or less behave like the human brain. These systems can be even better (whatever that might be) than the Human General Intelligence. This is what we think about when we talk about AI. But it’s elusive.
Have you seen the movie Her? About a man falling love with his Operating System. This is what we think AI is or should be. Thinking like a human, but without the disadvantages of the human brain (the need for sleep for instance). But it is also the image that scares us most, isn’t it. Computers and robots taking over and making us, humans, superfluous.

Sometimes it/IT looks like magic. Do you remember this magician David Copperfield making the Statue of Liberty disappear? We all knew it was an illusion (although we didn’t know how he did it). In 18’s century an automated chess player was invented. A machine that could play chess. Turns out, there was a tiny chess player inside this machine.
In the 90’s the development of neural networks where very popular. Computers programmed to behave and learn like the human brain. Finally we had real AI! Very promising, but about a decade later we learned about big data. Why predict the future if you can calculate it?! Google could predict the flu based on the search result. That’s what going on these days. And that’s what makes IT an interesting playground for Uniface.

Artificial Applied Intelligence (AAI)

Systems that replicate specific human behavior or intelligence. It varies from old fashioned fuzzy logic (like the controller of your central heating system and the PLC’s the control the traffic lights in your city) to Machine Learning (you wished the traffic lights were controlled by). It all might look like it’s a kind of intelligence, but most of the time it’s something the developer more or less created. But the reaction started by a certain action is depending on previous results: “Last time you, the user, where satisfied when I did this after you did that, so I am going to do exactly the same.” There is nothing magical about that. That’s combining data, computing power and a bit commonsense.

An example where I wish the developer did use AAI. On my phone I have a public transport app. When I type the name of the street where I want to travel to, it shows me all the cities with this street. Of course I can start typing the name of the city, but I want the system to know where I want to go. Since I always use this system within my own city. I expect the system to learn how I use it and show the street in the city where I am on top of the list.

Big data and sensors

Tesla knows how to use AAI. Their autopilot it mostly depending on AAI. Every time something unexpectedly happens the car communicates this to a centralized system. The system learns by comparing the specific situation, the performed actions and the results of these actions.
In fact, it’s relatively easy. Only thing the system has to do is decide if the current course is safe. Constantly monitoring all real-time input. On the internet you can find videos about Tesla’s predicting a collision and taking proper actions to prevent it. The autopilot stopped the car, as it should. From a human perspective not a big deal, this is what our brain is doing constantly while we are awake (and even in our sleep). And that is exactly what AAI is all about, replicate a specific part of the human intelligence.

Modern times

Most ‘old fashioned’ developers and probably even the organizations they work for, still want build software that is hardcoded van A to Z. That makes the development process manageable and testable. Software that is supporting the business processes of yesterday.
Nowadays users expect software to think with them. Software that supports their wishes and demands of tomorrow. Within a few years they expect their systems to think for them! In modern software development AI must kept in mind. Not every situation can be programmed nor tested. It is not a developer thinking about every possible situation. Software is more then a long list of ‘if then’ statements; it’s less.

All it takes, is a database with all possible situations and actions. Every new situation is added as soon as it occurs, updating this system on every possible occasion. The heart of the system consists of algorithms that determine which action is the best option given a certain situation.

This is how a chess playing computer beats a human grandmaster without cheating like the machine mentioned above: by playing (and winning and loosing) over and over again and learning from it.

And this is how a robot learns itself to walk: by walking, and falling and standing up over and over again.

Another example where I want to have more intelligence is my calendar. When I have an appointment I want my calendar software to tell me when I should leave to be on time. Based on my current location, the means of transportation, the traffic, my behavior (I walk fast, but leave always just too late), etc. And I want the software to warn me when a new appointment endangers my schedule for that day.


What makes a programming language suitable for AAI purposes?

• AAI is about data. Some of the data is static and stored in a database. With Uniface we can build data intensive applications. That’s where Uniface’s is designed for. It’s technology independent, scalable and very stable.The Uniface programming language is optimized for reading data from and storing it into every common database system.

• AAI is about using sensors. Not all data is (relatively speaking) static, some is realtime from sensors or user input. The Progressive Web Apps built with Uniface can use every hardware feature on mobile devices. And Uniface can even be installed on devices like Raspberry Pi and use every sensor attached to the system.

• AAI is also about user input. Uniface supports a wide range of user devices. From the old fashioned desktops to mobile apps on a smartphone.

• AAI is about computing power. Applications build in Uniface can be deployed on every mainstream OS. The coding is interpreted efficiently.

• AAI is about building clever algorithms. Developers don’t have to worry about OS and database specifics. So they can focus on writing clever software. Building algorithms is something every developer loves!

That sounds ideal for Uniface. And it is! I am very curious about your first AAI applications!

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