Wisdom in fragments

We have a significant ability to change our cognitive trajectory. But first, we need to believe it's possible. And second, we need to engage in the things that allow it to happen.

What they found was that the majority of people — above 50% — maintained cognitive function from their 50s into their 80s. We knew this more than fifty years ago.

If dementia is preventable, it means we're changing the trajectory of cognitive decline — which means that trajectory isn't inevitable.

When those inputs are removed, it starts to prune function and structure, because those functions aren't needed anymore. It's still that same process — we just call it ageing. And it happens because we've removed physical activity, cognitive stimulus, and social connection. The brain responds accordingly.

Think about a rabbit sitting in a field. If that rabbit saw a hawk circling above and decided to wait for the back-propagation step before responding, it would be dead. The better you model the world, and the faster you can act on that model, the more likely your genes are to survive.

You cannot expend more energy than you can consume. That's a fundamental law of physics. If you do, you starve, you die, and you remove yourself from the gene pool. Biological systems have therefore been under enormous selective pressure to develop highly efficient intelligence.

The things that are trivially easy for humans turn out to be extraordinarily difficult for machines, and vice versa. I cannot do the square root of a large number in my head, but my pocket calculator can do that instantly. But my pocket calculator still cannot make me a cup of coffee.

The brain is definitely not doing computation in the purest sense. We are not crunching numbers in binary ones and zeros in our heads. A more important question is: what are your inputs, what output do you want, and how intelligently can the system get from one to the other?

I think intelligence is best understood as an entity that has the ability to improve a metric through repeated exposure over time. By that definition, machine learning algorithms are learning systems — they get better with more data and more exposures.