Large Concept Models: The Next Revolutionary Frontier In AI

Ramanakar Danda is an IT Architect at CNH Industrial, a leader in manufacturing machinery for agriculture, construction and other sectors.

Large language models (LLMs) form the most interesting component in the backend of AI. These are complex algorithms and engines behind the wheels of various AI systems, chatbots and virtual assistants we use today. They enable them to process and generate human-like text by recognizing and analyzing patterns in huge amounts of data comprising words, sentences and phrases.

Notwithstanding their powers, LLMs cannot "understand" material the way people do. Instead, they are good at applying the patterns they have seen during training to predict the next word in a sequence.

Thinking Process Of Humans And AI

Let us dig deeper into how an LLM could interpret a simple Python code. If you type:

def calculate_total_amount(...)

An LLM, knowing very well the common code patterns of Python, would predict the next part to probably be something like:

def calculate_total_amount(price, quantity):

It doesn't understand the context in which it is trying to write this code; instead, it has used patterns of the large dataset of Python code that it was trained on to predict what the most likely continuation could be. For example, here the LLM might guess that the method name could relate to the calculation of a total amount and suggest "price" and "quantity" based on the common patterns seen in programming.

Even though they're amazing, the LLMs are bounded by mere text handling; they cannot think of deeper ideas or any visual images.

What An LCM Could Do

This contrast points out the very critical limitations of LLMs and opens the door for the emerging frontier of large concept models (LCMs). Unlike LLMs, an LCM goes deeper, attempting to emulate human cognitive processes by constructing its frameworks from the very building blocks of human thought.

Let's take an example to see how LCM works: If one asks a human to describe the word "Java," then most of us flash up a picture or an idea depending on what exactly happened with them when they experienced this language. Humans are likely to associate Java with a piece of code, an IDE like IntelliJ IDEA or Eclipse, or the developer who wrote or executed that code. You could be thinking of abstract concepts such as object-oriented programming, class, inheritance or data types.

The descriptions from a human may be like this:

"Java is a powerful, object-oriented programming language used for building cross-platform applications. It is known for its syntax, strong memory management and features like classes, objects and the Java Virtual Machine (JVM) that allow programs to run on any device with a JVM."

For the human brain, Java means something far more than just words or code—it encompasses functionality, features and usage. Our understanding of it may include experiences such as writing or running Java code, exploring its ecosystem and appreciating the practical applications of this language.

Here, an LLM might be guessing the next word or would end up giving a very general response, whereas an LCM may strive for a more human-like experience in describing and explaining Java.

This paradigm shift from LLMs to LCMs is quite essential and can help solve some major challenges of AI. The concept of LCMs is being researched by several groups, too. According to the article "From Words to Concepts: Ushering in the Next Era of AI with LCM," "LCMs could create stories, art, or ideas by combining concepts in new and meaningful ways, rather than just generating text based on probability." LCMs could bring about the next wave of change in AI growth and development.

Trends And Potential Applications In The Healthcare Industry

Leading the way in research on LCMs, institutions like Meta have progressed from text processing to concept management. They’ve developed AI that not only understands vocabulary but also processes ideas, images and connections—mirroring human thought processes. This movement points to changes that would be radical across industries.

In this advancing AI landscape, some platforms, such as Snowflake, have already revolutionized data management in the healthcare sector. Their capabilities—such as flexible data warehousing and secure data sharing—enable organizations to manage vast volumes of data effectively, supporting informed decision-making and enhancing patient care.

With tools like Astrato adding native writeback functionality to Snowflake's analytics, healthcare providers can leverage data more effectively to enhance precision and efficiency in patient care. As AI evolves, so too will the collaborative tools that empower us, transforming the way we approach, interpret and utilize data.

Possible Challenges For LCMs In Healthcare

LCMs in healthcare are a door of opportunity, but the path forward isn't free of barriers. Imagine being in a position where you have to acquire personal data from patients. I was often part of discussions about the protection and confidentiality of patient data when working with healthcare providers. We had to make sure every piece of information conformed to rigid standards such as HIPAA for the data to remain confidential.

We were also continuously plagued with integration and data quality issues. You can imagine how scattered healthcare data sits in different systems: EHRs, wearables and medical imaging platforms, wherein each holds just a clue. My friends and I would constantly joke that we were time-share data detectives patching scattered bits of information to make out a picture.

The most advanced AI models get stuck if data is not seamlessly integrated. When we stitched these pieces of data into continuous streams, it was like compiling an album out of scattered hits. It worked, and thus it proved that good, available data matters.

The Future Of AI In Healthcare

I believe LCMs are going to be AI’s next frontier in revolutionizing quite a few aspects, ranging from healthcare personalization to drug discovery and clinical decision-making, among several others. Integration of a number of data types—clinical, genetic, imaging and many others—within an LCM will go a long way in developing adaptive, multi-optional and efficient healthcare systems.

Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?