Quirky Ways AI Researchers Gather Data to Feed their Algorithms model



Google wants to turn your prose up. To this end, the researchers created the largest set of data(Algorithms model) that were broken by equivalent meanings with lower meanings.

Where can you find massive amounts of data? Wikipedia, of course.

In the richness of the history of Wikipedia editing, the research team released instances that feature long sentences. Result:

60 sentences split 60 times more than one example and words that speak 90 times more than the previous dataset. Datasets also reach different languages.

In the new data for the machine learning model, (Algorithms model)91% achieved accuracy. (Here, after reviewing the percent,

it reflects the sentences that maintain meaning and grammatical correctness proportionally).

Search: Quantum Supremacy within Months

(Algorithms model)

A trained model with previous data got 32% accuracy. When combining data sets and preparing another model, it achieved 95% accuracy.

Researchers have concluded that future improvements will help to find more sources of data.


Studies have shown that the language generated can be a great prediction of our race, gender and age, even if this information is explicitly stated.(Algorithms model)

To this end, researchers from Bar-Ilan University tried to use the text to divert AI from Israel and the Allen Institute of Artificial Intelligence to remove these inserted indicators

To get enough data that could have a language model of different demographics, they went to Twitter. They collected a series of Tweets evenly distributed among black non-Hispanic and non-black people; between men and women; between the ages of 18-34 and over 35.

Then, two neural network networks used an interdisciplinary approach to eliminate demographic indicators within tweets.

A neural net wanted to predict demography, while attempting to be completely neutral, to reduce the accuracy of the predictive model prediction (50%). This approach, in short, includes a rational race, (Algorithms model)gender, and age, with remarkable and incomplete indicators.




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