Explore the evolution of machine intelligence, from coding to AI automation. Understand how this progression revolutionizes efficiency and opens up unprecedented opportunities.
What’s fascinating about the evolution of our human species is our capacity to create tools that can perform tasks better than we do.
This inherent trait comes from the way we use our hands, this small difference between us and other species led to massive distribution and technological advancements in the last 10 000 years.
We saw a faster and deeper advancement in the last 30 years with the development of computers and the Internet. We entered the world of machine intelligence:
Tools no longer need our active presence to run themselves and can now perform tasks in a quasi-automated way.
Let’s dig down on those revolutions and how the recent advancements in AI completely reshaped the landscape of machine intelligence with a new concept: AI automation.
Machine intelligence was born with coding, the first interaction between man and machine.
Computer programming languages such as Fortran and Lisp were key developments during this era. These languages were pivotal tools in the process of making machines execute complex calculations swiftly and accurately, an ability that was profoundly beyond human pace and precision.
Lisp, in particular, emerged as a consequential player in this nascent stage of machine intelligence.
It was created in the late 1950s by John McCarthy, Lisp was a high-level programming language specifically designed for processing symbolic information which is an important aspect in the field of Artificial Intelligence.
The brilliance of coding during this phase was that it enabled machines to perform tasks they were instructed to do with an unfailing level of accuracy and consistency. Tasks that would take humans hours, days, or even weeks, could be completed by machines in fractions of that time.
Coding can thus be viewed as the first instance of machine intelligence.
The code was the 'brain' of the machine, telling it what to do and how to do it. This form of intelligence was, of course, rudimentary and entirely dependent on human input.
Despite the limitation of machines only doing what they were programmed to do, the intelligence aspect lies in their ability to execute tasks without error, at speed and scale that humans could not match.
Coding started the evolution of machine intelligence, taking the first step towards AI automation.
The narrative of machine intelligence then shifted to low-code platforms. Coding was still a daunting process, you repeat many tasks that can be semi-automated.
By lowering the barrier to entry, these platforms allowed a broader audience to engage with software development. Platforms like OutSystems and Mendix showed the potential for rapid application development and deployment, saving time and resources.
But low code was just a stepping stone toward mass adoption of software. Many creatives are non-tech people, which leads to an ocean of good Ideas that never saw the light of day.
The real goal of low-code was not to faster creation, but to empower people that couldn’t build things without it.
Still, low code didn’t have the goal intended. The no-code revolution marked the next significant development, extending the capability of creating software for non-technical individuals. No-code tools like Bubble became the harbingers of this change.
An excellent example of this democratization is Tara Reed, a non-technical entrepreneur who used Bubble (a no-code platform to build apps) to develop Kollecto an art app.
This revolution significantly widened the pool of software developers and sparked a wave of innovative digital solutions. Machine intelligence became a commodity, as long as you knew how to use it properly.
Now that everyone can access No-code automation, the need shifted to scaling challenges. How to properly use different tools and to build a concrete working application.
Distribution was always a challenge, and without a functioning infrastructure in your app, your project is doomed to fail.
The era of automation signaled a shift from manual repetitive tasks to automated workflows, enhancing productivity across various sectors.
Automation tools like Zapier, Make, and Automate.io brought about substantial improvements in work efficiency.
Multinational companies like Netflix and Slack adopted automation tools to streamline workflows, saving countless work hours, reducing the possibility of human error, and ultimately increasing productivity.
This shift was indeed a leap in our approach to work, freeing humans from mundane tasks to focus more on strategic and creative aspects.
Automation also opened the door to deeper reflection on the role of machines in our daily lives, can intelligence create purely on text requests from us?
How down the rabbit hole of machine intelligence can we go?
Generative AI was the next milestone, providing a new dimension to creativity, creation, and automation.
Powerful AI models like GPT-4 from OpenAI show the capability of generating coherent and contextually relevant text, but not only, the power of these models expanded to image, then code, then sound, and then video.
All industries and fields are impacted from content creation to customer service. Generative AI opened the door to a new realm of possibilities: AI automations.
Where machine intelligence could not just interact with our requests and need our support, but come with specific actionable plans to execute based on a one-and-only request.
AI automation is the next big thing in machine intelligence, and that’s exactly what we’re currently building at Argil.
We currently entering a new era: the one of AI automation, where artificial intelligence meets no code and automation. This integration allows machines to learn, adapt, and make decisions, driving operational efficiencies at an unprecedented scale.
Platforms like UiPath have leveraged the power of Robotic Process Automation (RPA) and AI to automate complex tasks, saving substantial time and effort.
NASA, for instance, employed UiPath to automate the time-consuming process of data entry, thus improving accuracy and freeing up personnel for more strategic tasks.
AI automation has penetrated various sectors, from healthcare to finance to education. In healthcare, AI automation is being used for diagnostic purposes.
Google's DeepMind has developed an AI system capable of detecting over 50 eye diseases as accurately as expert doctors. Such advancements underline the immense potential of AI automation in augmenting human capabilities.
Still, something is troubling with all these examples:
- Why is it only accessible for massive companies, or scaling use cases?
- Why can’t ai automation be streamlined and used by end-application-users?
Argil is building this new form of ai automation.
You’re going through the same process daily? Losing time and energy on daunting and inefficient tasks?
Jump on Argil and check the ai automation available and those you can build in no code and with clicks only on the platform.
