I can clearly remember my Computer Science classes as an undergraduate. Back then, it was all about translating. The goal was to translate paper and Excel artifacts into software-driven processes. The massive amounts of paper processes took a while to undergo the online transformation. It took time to simplify and transform old processes so people could do on a computer the same things they used to do manually. Nowadays, this is no longer the case. Sure, there is the occasional person/team that still uses Excel or paper as their main tools, but the majority relies on online-first methodologies. Everything is done online — storage, backup, searchability, etc. This makes it easier for everyone to work out of one central “location”, accessible from anywhere in the world. Back then, in my early college days, AI was still in its infancy, and neural network libraries were not robust enough to be used to solve real-world problems.

In contrast, nowadays, it’s all about process automation, the same way that back then it was all about process translation. The process automation phase will take much longer than translating paper artifacts. This is natural. During the translation period, the goals were well defined. Sure, there were some improvements introduced along the way. Most of the time, however, the requirements were clear — make the computer allow us to do what we can do on paper. With automation, this is not the case. There are no clear requirements. Automation at its core is about allowing machines to essentially replace humans in repetitive or mundane tasks, improving a process whenever possible. The problem is that no matter how repetitive and well defined a task is, there are always deviations. There’s always some path that requires real-time decision making.

Artificial Intelligence is the natural next step in this process. It will allow us to complete our technological journey: from translation to automation to AI. What comes after AI is still to be seen.

Some say that the next big thing is AI and human integration. Projects like Neuralink (also see Elon Musk & team YouTube Nuralink presentation for more information) are gaining traction with recent amazing results. I believe that Neuralink is not the next progression after AI. On the technology evolution timeline, I would place it somewhere in between automation and AI so that the progression would look more like this: translation to automation to Neuralink to AI. Only time will tell if this would be the case.

Current technology is very limiting. It’s not fit to build fully functional intelligent machines. There may be some industry niches where they are using AI to complete tasks, but this is limited to specific use cases (self-driving cars, widget-building robots, speech recognition, etc.). Although still in their infancy, these projects seem to be plagued by problems, requiring constant human intervention and tweaking. AI, the way the general public sees it, is not only non-existent, but I would venture to say not possible with current technologies. We will need paradigm-shifting progress — both in software and hardware — to even consider building such machines. For those thinking that quantum computing is the answer, I don’t agree, especially with already known limitations of such computers. But I do hope that I’m wrong.

So please stop using buzz words like AI to describe anything seemingly “intelligent”. It demeans the field and gives a false impression of having achieved AI already. There’s a lot more research that needs to be done in this field before we can start considering AI for real-world use.