!FULL! Download 1 Million Words

I will say, once you've written a million words, you can write just about anything you set your mind to. You might not be writing well, but then that's one of the things I learned over the last three years; if you want every word out of your pen to be perfect then you will not write.

A million words sounds like an awful lot, but it's really just a matter of consistency rather than bursts of inspiration. I write barely a thousand words a day. I'm capable of around eight thousand a day (I've pounded out entire short stories in a matter of hours), but I restrict myself to that thousand most of the time.

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Badges & Certificates - Students will be rewarded with badges as they hit words-read milestones, and will receive a certificate to honor their Millionaire status. Bragging rights included.

Three years ago, I shared 10 lessons from working 4 years remotely, and today I passed another milestone while working at a remote company. After almost 7 years of working at Automattic, today I published my millionth word.

Logan and her colleagues randomly selected 30 books from both lists and counted how many words were in each book. They found that board books contained an average of 140 words, while picture books contained an average of 228 words.

With that information, the researchers calculated how many words a child would hear from birth through his or her 5th birthday at different levels of reading. They assumed that kids would be read board books through their 3rd birthday and picture books the next two years, and that every reading session (except for one category) would include one book.

A controversial 1992 study suggested that children growing up in poverty hear about 30 million fewer words in conversation by age 3 than those from more privileged backgrounds. Other studies since then suggest this 30 million word gap may be much smaller or even non-existent, Logan said.

When dealing with complex problems such as image or speech recognition, traditional computers are limited in terms of computational efficiency and energy consumption. Computers today use an architecture where memory is physically separated from the processing unit, and instructions are executed step by step. Processing temporal information as the brain does, however, mixes memory and processing to achieve higher computational efficiency and flexibility. Implementing a brain-inspired computer in photonic hardware, which processes information via light, can lead to further improvements by making use of low-power optical components and providing high speed through the use of broadband telecom devices. We demonstrate the capabilities of such a device, based on a brain-inspired paradigm known as reservoir computing. Our approach exhibits state-of-the-art speed on speech recognition tasks, identifying up to one million words per second with very low error rates.

Our design uses off-the-shelf components to implement a reservoir computing architecture that relies on electro-optical phase delay dynamics, which encodes information in the phase of light waves, as opposed to their intensity, to provide more accurate and faster processing. We demonstrate speech recognition using a standard database of recorded words, which can be processed and identified by our system after a learning procedure. In addition to our speed performance, we find improvements in computing efficiency compared to other recent implementations of photonic reservoir computing.

For those who have come here to read Bakuman, well, sorry, first of all. That will resume shortly. I had to put it on pause for NaNoWriMo (national novel writing month), and, more specifically, my word goal.

One million words in 30 Days. The 10th Part of the rough draft of my novel series.

I wrote the entirety of this Million words on my Freewrite Version 2 (not sponsored, by the by) which I have used borderline religiously since I purchased one in 2017. The device itself tracks words (essential) and has a dropbox but also, aside from the aesthetic and practical appeals of it, I have developed a habit of unloading my emotional self onto it. Which is kind of the point.

Immediately my heart palpitates. I type faster, trying to outrun the clock. I know that the train home is going to eat a solid chunk of my time. My brain goes into overdrive. I pick up my things and, to cut through the thicket of words, I write on the train, the train stop and at the bus stop in below-freezing weather. I am shaking and uncomfortable and my entire body is bathed in cold and soreness as I hit 21, 000 words right as the bus gets there.

But, in spite of that, I plugged away. Some days I wrote fewer words, some days I wrote more. I even managed to get in one solid break day. But at all times, the words were on my mind. Listening to music, reading, it all subordinated to the word count.

And this is why I cannot, in good faith, recommend that you write a million words in a month, my friend. I knew my limits and I did my best to respect them. Even so, I was still thoroughly exhausted and almost drifted into the realm of harm. And I cannot recommend anything that is potentially harmful.

I said I cannot recommend writing a million words in a month. Not knowing you, dear parasocially related reader, I cannot guarantee that you are in the same emotional, physical, or mental state I was to do so. I cannot recommend that you endanger yourself because there is a risk of harm.

The 30 million-word gap was originally developed by researchers Betty Hart and Todd Risley and suggests that children up to age 4 from a lower socioeconomic status heard 30 million fewer words than children from a higher socioeconomic status. That gap is often cited by politicians, educators and those who work with children to indicate the need for more support for early childhood learning.

This "million word gap" could be one key in explaining differences in vocabulary and reading development, said Jessica Logan, lead author of the study and assistant professor of educational studies at The Ohio State University.

"Kids who hear more vocabulary words are going to be better prepared to see those words in print when they enter school," said Logan, a member of Ohio State's Crane Center for Early Childhood Research and Policy.

Based on these calculations, here's how many words kids would have heard by the time they were 5 years old: Never read to, 4,662 words; 1-2 times per week, 63,570 words; 3-5 times per week, 169,520 words; daily, 296,660 words; and five books a day, 1,483,300 words.

"This isn't about everyday communication. The words kids hear in books are going to be much more complex, difficult words than they hear just talking to their parents and others in the home," she said.

Logan said the million word gap found in this study is likely to be conservative. Parents will often talk about the book they're reading with their children or add elements if they have read the story many times.

Scholars argue about whether age stereotypes (beliefs about old people) are becoming more negative or positive over time. No previous study has systematically tested the trend of age stereotypes over more than 20 years, due to lack of suitable data. Our aim was to fill this gap by investigating whether age stereotypes have changed over the last two centuries and, if so, what may be associated with this change. We hypothesized that age stereotypes have increased in negativity due, in part, to the increasing medicalization of aging. This study applied computational linguistics to the recently compiled Corpus of Historical American English (COHA), a database of 400 million words that includes a range of printed sources from 1810 to 2009. After generating a comprehensive list of synonyms for the term elderly for these years from two historical thesauri, we identified 100 collocates (words that co-occurred most frequently with these synonyms) for each of the 20 decades. Inclusion criteria for the collocates were: (1) appeared within four words of the elderly synonym, (2) referred to an old person, and (3) had a stronger association with the elderly synonym than other words appearing in the database for that decade. This yielded 13,100 collocates that were rated for negativity and medicalization. We found that age stereotypes have become more negative in a linear way over 200 years. In 1880, age stereotypes switched from being positive to being negative. In addition, support was found for two potential explanations. Medicalization of aging and the growing proportion of the population over the age of 65 were both significantly associated with the increase in negative age stereotypes. The upward trajectory of age-stereotype negativity makes a case for remedial action on a societal level.

In the current study, we applied computational linguistics in a novel way to the Corpus of Historical American English (COHA), a recently compiled historical database of approximately 400 million words [7], which allowed us to study trends in age stereotypes across two centuries. The database, that includes all the words of approximately 100,000 texts published from 1810 to 2009, is 100 times larger than any other structured corpus of historical English, and draws equally from popular magazines, newspapers, fiction and non-fiction books across time [7]. According to Cultivation Theory, different forms of media provide valuable resources to study because they reflect the culture, as well as present images that can impact how society and its members think about themselves [8].

Finding similar results in the trend toward more negative age stereotypes over 200 years in the two ways of sampling elderly synonyms increased our confidence in the pattern of findings. The sub-sample of three elderly synonyms had the advantage of including the same three words across all 20 decades. The full sample of 11 elderly-synonyms, which was a comprehensive set of all words that appeared in the historical thesaurus that met the study criteria, had the advantage of reflecting the range of ways an old person was referred to over the 200 years [21]. ff782bc1db