Why Multimodal Language Models Can't Read the Room

In the grand circus of AI achievements, Multimodal Large Language Models (MLLMs) have been touted as the ringmasters, pulling off remarkable feats in interpreting the natural world. Yet, dealing with discrete symbols, those pesky building blocks of human thought, these models appear more clown than conductor.

The Symbolic Struggle

The problem with symbols is that they’re not just lines and loops, but the essence of everything from math to chemistry. Unlike visual data that flows continuously, symbols demand precision and deeper understanding. Unfortunately, our shiny MLLMs seem to miss this memo. A newly introduced benchmark puts these models through the wringer across language, culture, mathematics, physics, and chemistry. The results? A startling revelation that these AI wunderkinds flub basic symbol recognition.

: If a model can juggle complex reasoning tasks but drops the ball on something as simple as recognizing a symbol, can we truly call it intelligent? It’s like applauding a child prodigy who can play Beethoven but can’t read sheet music.

The Cognitive Mismatch

Here’s the rub. The investigation revealed that MLLMs often rely on linguistic probability more than genuine visual perception. In simpler terms, they’re guessing based on context rather than understanding. This cognitive mismatch is akin to a tourist trying to navigate Tokyo with a Paris map. It highlights a glaring gap in AI’s current capabilities: the struggle to genuinely perceive and grasp the symbolic languages important for scientific breakthroughs and abstract thinking.

Naturally, this isn’t just an academic exercise. The press release said innovation. The 10-K said losses. If AI is to be more than a parlor trick, it needs to bridge this symbolic chasm.

Charting a New Course

The researchers have inadvertently handed us a roadmap for future development. If MLLMs are to align with human cognition, they need to learn the language of symbols as a toddler does letters. Without this, we’re merely fancying up calculators with poetic flair. It’s absurd to think we’re anywhere near true AI when our models can’t tell an integral from an ink blot.

So, where do we go from here? Spare me the roadmap. The AI apparatus needs to get its act together. This isn’t a call to toss out what’s been done, but an urgent plea to address the elephant in the room: AI without symbol recognition is like a writer who can’t spell. Futuristic, impressive, but ultimately incomplete.