This approach can be nerve-wracking. Yes, you are writing in the dark. Yes, you don’t know how it will turn out. Keep go

“It took time to learn that the hard thing about writing is to let the story write itself, while one sits at the typewriter and does as little thinking as possible. It happened over and over again, and the beginner learned — when you start puzzling over an idea, and slowing down on the keys, the writing gets worse and worse.” — Richard Bach

Here’s a fun tip for writing descriptions — use Google Maps street view. You can pull up virtually any street and walk down it as if you were there, looking at the trees, the storefronts, the architecture. It’s a great way to revisit a place you haven’t been to in a while, to jog your memory. Again, place your character’s house on a specific street, and if you can’t physically walk down that street to soak in the details, use Google Maps.

There’s a lot of debate about how you should structure and plan fiction. The “architects” advocate planning everything out in advance — writing proceeds by following a detailed outline. On the other hand, the “gardeners” start with the seed of an idea and then follow it where it leads. With no outline, the writing is done “blind.”

That night, I stopped at the grocery store on the way home to pick up some extra food. If cases keep rising, I know restaurants will likely start reverting to takeout orders only and closing earlier. My weekend appeared likely to be an isolated one.

When putting in setting descriptions, you don’t want to use too much detail, either. You want to let your reader fill in a lot of the color. You only need enough to get their imaginative juices flowing. You need only enough to convince them of the fictional dream.

Because the story opens with the intriguing description of “ninety-seven” advertising men holding up the phone lines and our character waiting two-and-a-half hours to place her call, we are invested. The call is important, and we want to know what it’s about. Who is it to? Before revealing those details, though, Salinger places us in the hotel room — describing the objects and how our character interacts with them. We receive a strong sense of place and character. If the first two sentences were removed from this story and it started with “The girl in 507 read an article in a women’s pocket-sized magazine,” we would not be as interested. We would drop the story by the time she started painting her nails.

You can think of setting descriptions like using bay leaves in cooking. If you’ve ever cooked a stew or a braise, you know the bay leaf goes into the pan during cooking so that the dish receives its distinctive flavor; however, you remove the bay leaf before serving the dish. You don’t leave it in whole. Writing a description is the same. On your first draft, lavish the prose with setting descriptions to give your story that strong flavor of place, but on the second draft, remove everything that isn’t necessary.

In this field, relative positional encodings have been found to work really well. They consist of learnable embeddings that learn to encode relative distances between features instead of encoding their global positions.

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A few weeks ago, a colleague came into my office and announced, “Damn, it looks like things are starting to get bad again.” He turned his phone toward me. It was opened to Worldometer’s Covid-19 tracking page. I scrolled down to select the country we live in: South Korea. “How many days has it been going up?” I asked. “It was 303 then 343 and 363 cases today. They’ve just increased the alert level to 1.5.”

When updating features with transformers, the order of the input sequence is lost. This order will be difficult or even impossible to learn by the Transformer itself, so what it is done is to aggregate a positional representation to the input embedding of the model. This positional encoding can be learned or it can be sampled from a fixed function, and the position where it is aggregated can vary , although it is usually done just at the input embeddings, right before being fed into the model.

In Computer Vision, these embeddings can represent either the position of a feature in a 1 dimensional flattened sequence or they can represent a the 2 dimensional position of a feature.

There are benefits and pitfalls to each writing approach, but I recommend the “gardener” approach for new fiction writers. You should write your stories “inside-out” rather than “top-down.” Decide your characters and your style (genre) first and go from there. A good story naturally emerges from good characters, not the other way around. You cannot reverse engineer character out of a plot.

At home, I turned on the news in America. I listened to an interview with a crying nurse. She was crying because the number of cases in America had risen exponentially, and, on her drive home after spending her day watching people die from the virus, she passed bar after bar packed with maskless people. She was talking about how helpless she felt, desperate for people to understand the severity and horror she was experiencing every day.

In this field, relative positional encodings have been found to work really well. They consist of learnable embeddings that learn to encode relative distances between features instead of encoding their global positions.