Slow-Reading is The New Deep Learning

Slow-Reading is The New Deep Learning

In today's fast-paced world, speed is often valued over depth. We skim through articles, scroll through social media feeds, and consume information in bite-sized pieces. But what if I told you that slow-reading is the new deep learning? That taking the time to truly engage with a text can lead to a deeper understanding and connection with the content?

It's no secret that speed-reading has gained popularity in recent years. The idea of being able to read faster and absorb more information sounds enticing. However, research has shown that as reading speed increases, comprehension decreases. In our quest to read more, we often sacrifice understanding.

When it comes to acquiring knowledge, slow-reading is the way to go. By taking the time to engage with the content, we can associate new concepts with our existing knowledge. This process allows us to form connections and truly understand the material. So instead of rushing through a text, take the time to read slowly and focus on understanding.

But slow-reading isn't just about comprehension. It's also about expanding our knowledge base. As we encounter experiences in the world, we store information in our sensory memory. This overwhelming firehose of data is quickly filtered and passed on to our short-term memory. However, it's through slow-reading that we can transfer this information to our long-term memory, expanding our knowledge base.

In a world where lists and searchable databases dominate, the idea of curation may seem outdated. However, Benedict Evans argues that all curation grows until it requires search, and all search grows until it requires curation. The key is finding the right balance between the two.

Lists, or curated collections of information, have their advantages. They provide a sense of constraint and allow us to navigate through a curated selection of content. Whether it's unbundling Craigslist or creating a fashion catalog, lists can help us find what we're looking for in a more efficient way.

On the other hand, searchable databases offer the convenience of finding exactly what we want. The problem, however, lies in scale. As the number of entries grows, it becomes increasingly difficult to browse through a list. At a certain point, search becomes the more practical option.

So how can we apply these insights to our own learning and knowledge acquisition? Here are three actionable pieces of advice: