Towards Artificial Understanding


In our journey through the technological renaissance, we’ve witnessed the rise and evolution of artificial intelligence (AI). However, a new concept is emerging, poised to redefine our understanding of machine intelligence: and I call it Artificial Understanding (AU). This groundbreaking idea extends the capabilities of AI, pushing it beyond mere data processing and content generation. Today, we explore what Artificial Understanding entails and how it can reshape the landscape of AI.

AU drawn by DALL-E

The Essence of Artificial Understanding

Artificial Understanding represents a significant leap in the field of AI. It’s about endowing machines with the ability to not just process but to interpret and comprehend information in a contextually meaningful way. AU takes AI a step further from performing predefined tasks to possessing a level of understanding akin to basic human cognition. This advancement is pivotal as it transforms AI from a tool that follows instructions to an entity that can grasp context, nuances, and the subtleties of the data it processes.

Point 1: Transformers - The Building Blocks of Understanding

Understanding Sequences of Text

Central to the concept of AU are transformer models. Unlike conventional algorithms that treat data as isolated points, transformers understand sequences of text, grasping the context and relationships within. This capability is akin to a rudimentary form of understanding, where the model discerns patterns and meanings beyond mere words.

Case Studies

GPT (Generative Pre-trained Transformer) and BERT (Bidirectional Encoder Representations from Transformers) serve as prime examples of this technology. They demonstrate how transformers can not only generate coherent and contextually relevant text but also interpret and respond to human language with a surprising degree of understanding.

Point 2: Efficient Compression of Knowledge

Beyond Generative Capabilities

The true marvel of transformer models, such as GPT or Gemini, lies in their remarkable efficiency in knowledge compression. This efficiency is a critical aspect of Artificial Understanding. These models manage to compress vast amounts of text – potentially equivalent to petabytes of data – into a model of just a few billion (or hundred-million) parameters, which occupies only gigabytes (or less) of space. This is not merely a triumph in data storage but a reflection of how these models distill and encapsulate vast information, enabling them to ‘understand’ and apply this knowledge effectively.

Implications and Future Prospects

The implications of Artificial Understanding are vast and far-reaching. In the realm of customer service, it could lead to chatbots that not only answer queries but understand the emotions and intents behind them. In data analysis, AU could enable systems to not just process numbers but to interpret trends and insights in a contextually rich manner. The potential applications are limitless, stretching across various domains from healthcare, where understanding patient history and symptoms could revolutionize diagnoses, to finance, where understanding market nuances is key.


Artificial Understanding is more than just an incremental addition to the AI landscape; it is a paradigm shift. It challenges our preconceptions of machine capabilities, opening doors to possibilities where machines don’t just calculate but comprehend. As we stand at the precipice of this new era in AI, the promise and potential of Artificial Understanding beckon a future where the lines between human and machine understanding become increasingly blurred, leading to innovations we can only begin to imagine.

[This post was created with the assistance of LLMs]