No code Machine Learning: Making AI Democratic

Posted by

Big Data and Machine Learning brougth Artificial Intelligence to the forefront in the 2000s, and as computing power became more affordable, so did AI models and their capabilities to mimic human cognitive processes. Year 2023, AI has taken the world by storm with the launch of generative AI and conversational models that become available to the general public. A year before that AI was still for the conneseurs, difficult to understand by the general public and with unfair and unrealistic media depictions.

It is important to look a the recent history of adopting AI. Up until some years ago AI was still in the hands of the few companies that could afford huge processing power to effectively run machine learning and deep learning task; we call this the “Age of Dictatorship” where AI was controlled by the few and consumed – mainly through search engines and social media apps – by many. Then hardware became affordable, the few shared their knowledge as open source and more AI experts – data scientists and machine learning engineers – coupled with free models and frameworks – and AI started to be implemented at enterprise leve: “the Age of Aristocracy“, as specialists were still scarce and expensive and AI was stuck in experiments and the “PoC graveyard.” Now, we’re rapidly heading towards an era of “AI Democracy”, where AI is not only becoming more affordable, but also easier to implement and with more control of the users over the output.

This trend is driven not just by open data, open source models and more AI specialists, but by principles that makes Artificial Intelligence closer to real business (and human societal) needs.

What is No Code AI?

Being able to quickly and easily build, deploy and run an AI agent without the need to code, by business users that have no in-depth understanding of AI but do understand the business needs; this is No-code AI, the last mile in making AI democratic.

The principles of No-Code AI are:

  • Anyone can build AI engines without writting code.
  • Secure deployments that cater to privacy and confidential data.
  • Rapid time to production, transforming models into engines with one click.
  • Integration into the Language Operations (LangOps) concept that not only puts AI at the forefront of language-specific processes, but holistically integrates all sources of knowledge into a single framework.

2 responses