This is hard to accept in the age of the Internet. It has been very hard for me to explain that the more data you get, the less you know what’s going on, and the more iatrogenics you will cause. People are still under the illusion that “science” means more data.

Too much information creates the same result as too little:

Today’s companies have an insatiable appetite for data, mistakenly believing that more data always creates more value. But big data is usually dumb data.

We live in a world saturated with information. We have virtually unlimited amounts of data at our fingertips at all times, and we’re well versed in the arguments about the dangers of not knowing enough and not doing our homework. But what I have sensed is an enormous frustration with the unexpected costs of knowing too much, of being inundated with information. We have come to confuse information with understanding.

Too much information creates the same result as too little: you don't have what you need, when and in the way you need it.

The more you know, the more you know you don't know.

In deep learning, there’s no data like more data. The more examples of a given phenomenon a network is exposed to, the more accurately it can pick out patterns and identify things in the real world.

A very great deal more truth can become known than can be proven.

The more you ask certain questions, the more dangerous they become.

We have come to confuse information with understanding.

With the overwhelming amount of information available to us, we are more reliant than ever on categorization, labeling, and shortcuts.

When a man’s knowledge is not in order, the more of it he has, the greater will be his confusion.

Increasingly, data can only truly deliver via negativa–style knowledge — it can be effectively used to debunk, not confirm.

The longer the explanation, the bigger the lie.... Always remember that.