Attention Is All You Need
Transformers taught machines to pay attention. What does that say about yours?
Attention Is All You Need
Transformers taught machines to pay attention. What does that say about yours?
Attention Is All You Need
Transformers taught machines to pay attention. What does that say about yours?
This episode will cover:
What “attention” means in machine learning and why it changed everything
The difference between machine attention and human attention
How our own attention is stretched, sold, and distracted
Why the ability to focus has become a form of agency
Practical ways to reclaim attention in daily life
In 2017, a paper called Attention Is All You Need changed the future of AI. Its idea was simple but revolutionary: instead of reading language step by step, a model could use “attention” to scan everything at once and decide what mattered. That one insight became the foundation of today’s language and vision models. It gave machines the ability to handle scale, context, and nuance in a way older systems could not.
For machines, attention means precision and adaptability. It is a mechanism that makes them sharper the more they practice it. For us, attention is something different. It is fragile, constantly pulled by feeds and notifications, auctioned off to the highest bidder. Every glance becomes data. Every scroll is designed to keep us predictable.
The result is a mirror. AI gets better at attending while we risk getting worse. The ability to focus has quietly become a form of agency. Choosing what we notice, and what we ignore, is not just a lifestyle tweak. It is how we shape our own perspective instead of letting a repetition of data loop decide for us.
In practice this can look small: turning off alerts, setting aside time without screens, choosing books or conversations that stretch rather than numb. These choices matter because attention compounds. Machines have their mechanism. Ours is a practice. And the future will be shaped by which side of the mirror we train more.
This episode will cover:
What “attention” means in machine learning and why it changed everything
The difference between machine attention and human attention
How our own attention is stretched, sold, and distracted
Why the ability to focus has become a form of agency
Practical ways to reclaim attention in daily life
In 2017, a paper called Attention Is All You Need changed the future of AI. Its idea was simple but revolutionary: instead of reading language step by step, a model could use “attention” to scan everything at once and decide what mattered. That one insight became the foundation of today’s language and vision models. It gave machines the ability to handle scale, context, and nuance in a way older systems could not.
For machines, attention means precision and adaptability. It is a mechanism that makes them sharper the more they practice it. For us, attention is something different. It is fragile, constantly pulled by feeds and notifications, auctioned off to the highest bidder. Every glance becomes data. Every scroll is designed to keep us predictable.
The result is a mirror. AI gets better at attending while we risk getting worse. The ability to focus has quietly become a form of agency. Choosing what we notice, and what we ignore, is not just a lifestyle tweak. It is how we shape our own perspective instead of letting a repetition of data loop decide for us.
In practice this can look small: turning off alerts, setting aside time without screens, choosing books or conversations that stretch rather than numb. These choices matter because attention compounds. Machines have their mechanism. Ours is a practice. And the future will be shaped by which side of the mirror we train more.
This episode will cover:
What “attention” means in machine learning and why it changed everything
The difference between machine attention and human attention
How our own attention is stretched, sold, and distracted
Why the ability to focus has become a form of agency
Practical ways to reclaim attention in daily life
In 2017, a paper called Attention Is All You Need changed the future of AI. Its idea was simple but revolutionary: instead of reading language step by step, a model could use “attention” to scan everything at once and decide what mattered. That one insight became the foundation of today’s language and vision models. It gave machines the ability to handle scale, context, and nuance in a way older systems could not.
For machines, attention means precision and adaptability. It is a mechanism that makes them sharper the more they practice it. For us, attention is something different. It is fragile, constantly pulled by feeds and notifications, auctioned off to the highest bidder. Every glance becomes data. Every scroll is designed to keep us predictable.
The result is a mirror. AI gets better at attending while we risk getting worse. The ability to focus has quietly become a form of agency. Choosing what we notice, and what we ignore, is not just a lifestyle tweak. It is how we shape our own perspective instead of letting a repetition of data loop decide for us.
In practice this can look small: turning off alerts, setting aside time without screens, choosing books or conversations that stretch rather than numb. These choices matter because attention compounds. Machines have their mechanism. Ours is a practice. And the future will be shaped by which side of the mirror we train more.