How AI Learned the Meaning Behind Words
In an earlier post, we explored how machines learned to read language – cleaning, counting, and classifying words.
But reading is not understanding.
For humans and machines alike, meaning is everything. Without context, communication collapses.
If you run a marketing agency, that’s an important point. The future of AI is not about tools that write more words – it is about systems that understand them.
This week, Callum Healey takes us deeper into how AI bridges what researchers call the semantic gap – the space between recognising words and truly grasping what they mean.
From Reading Words to Understanding Meaning
AI can now clean and count words, but until recently, it had no idea what those words meant.
Take the sentence: The bank is closed.
Is it talking about a riverbank or a financial bank?
Earlier models like Bag-of-Words and TF-IDF could not tell. They could spot frequency, not context.
That’s the semantic gap – and bridging it is where AI begins to feel almost human.
The breakthrough came with a model called Word2Vec.
Word2Vec: Teaching Machines Context
Word2Vec is the tool that lets machines capture meaning.
It trains a neural network to predict a missing word based on the words around it.
If the model reads throne, crown, castle, kingdom, it might predict queen.
Through repetition, it learns that queen, king, and monarch appear in similar contexts — so their meanings are related.
This is what we call semantic similarity.
Machines measure this numerically as cosine similarity – words with similar meanings sit close together on a graph, while unrelated words (like banana) sit far apart.
In short, Word2Vec taught AI something powerful: context defines meaning.
The King, the Queen, and the Banana Test
Callum explains this visually. Imagine plotting king and queen in a digital space.
They’re close together because their meanings overlap.
Now add banana – and suddenly the distance widens.
This “angle” between word vectors is how AI measures meaning.
It’s how tools like ChatGPT and search algorithms know that “creative strategy” is closer to “brand positioning” than it is to “fruit salad.”
For agency owners, that means AI can now understand your messaging, audience tone, and brand personality – not just repeat your words.
A Little Bit of Fun: The Football Test
In the video, Callum uses football to prove how Word2Vec holds meaning.
Start with the vector for Manchester City, subtract success, and add nostalgia – the result is close to Manchester United.
It’s tongue-in-cheek, but it shows how AI captures relationships between ideas, not just literal definitions.
It can recognise difference in sentiment, not just difference in spelling.
That’s a big leap forward for language models – and a useful lesson for agencies: tone and context matter just as much as content.
From Words to Sentences: The Bigger Picture
Once machines can represent individual words as vectors, they can go further – to entire sentences.
Using a technique called Average Word2Vec, AI can capture the overall meaning of a sentence by averaging the meaning of its words.
So when it reads The bank is closed, it uses surrounding words like river or money to decide which meaning applies.
That’s how AI begins to truly understand nuance – something agencies rely on every day in branding, messaging, and creative strategy.
Why This Matters for Agencies
Understanding how AI learns meaning helps you use it more intelligently.
If you lead an agency, here’s why this matters:
Sharper prompts: Knowing how AI reads context lets you write more effective instructions.
Smarter insights: You can analyse tone, feedback, and audience language with more nuance.
Better positioning: AI can now detect what makes your brand voice distinct – and replicate it.
More efficiency: As machines grasp meaning, they need less hand-holding and more strategic direction.
The agencies that thrive will not just use AI – they’ll understand how it thinks.
Agents of Change: Turning Understanding into Advantage
At Agents of Change, we help agency owners combine strategic clarity with intelligent technology.
We bridge the gap between business ambition and AI implementation, so you can grow faster, run leaner, and remove chaos – not creativity.
This video is just a glimpse into that process: understanding the building blocks before you apply them at scale. Watch the video and get in touch if you’d like to explore how AI could unlock new performance levels inside your agency.
Callum Healey, AI Integration Lead, Agents of Change
Callum helps marketing agencies understand and integrate artificial intelligence to work smarter, faster, and more creatively. With a background in psychology and a growing specialism in AI systems, he bridges the gap between human behaviour and machine intelligence.
At Agents of Change, Callum focuses on helping agencies use AI to streamline operations, boost efficiency, and enhance client delivery - making technology practical, not theoretical.