Meet the ‘neutral fermion’.
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Two gorillas at San Diego Zoo have tested positive.
Cuando se practica ejercicio intenso al aire libre, incluso en la escarcha o la nieve, los depósitos de grasa se disuelven más rápido que en el gimnasio.
English Wikipedia articles are slightly more politically biased than Encyclopedia Britannica articles, and most of this bias is in favor of Democratic views. That is, in almost all cases, Wikipedia leans more to the left than Britannica.
The bias disappears as more edits are made. Something to keep in mind after having attended that kind of granguiñolesca performance what was he storming of the capitol.
Objective/subjective information
One of the biggest problems of encyclopedias is to objectively present the knowledge they contain. involves subjective, unverifiable or controversial information. Even today's issues like immigration, gun control, abortion, and foreign policy are open to fervent debate depending on who is giving an opinion.
Using data from Encyclopædia Britannica, written by experts, and Wikipedia, an encyclopedia produced by an online community, in A study compared the bias and bias of pairs of articles on identical topics in American politics.
The lean measure is less (more) than zero when an article leans toward Democratic (Republican) views, while the bias is the absolute value of the lean.
What was found is that Wikipedia articles lean more toward Democratic views than Britannica articles, and they are also more biased. All in all, perhaps the most interesting finding of the research is that the more times an article is reviewed on Wikipedia, the less bias it is likely to show.
However, the number of revisions needed to start showing this effect is quite large (at least 2000 issues) and the articles most read by users are not necessarily the most reviewed by editors.
These results highlight the pros and cons of each knowledge production model, help identify the extent of empirical generalizability of previous studies comparing the information quality of the two production models, and offer implications for organizations managing knowledge. crowd-based knowledge production.
If Wikipedia wants to improve its objectivity, the study's authors note, is recommended to encourage editors to review the most read stories first, as well as encourage people with different political leanings to edit the same article. Only in this way, perhaps, will we blur a little the increasingly opaque border between Democrats and Republicans:
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The news
Wikipedia is a little more politically biased than Encyclopedia Britannica (in favor of Democrats)
was originally published in
Xataka Science
by
Sergio Parra
.
According to A study conducted by researchers at the Max Planck Institute of Psychiatry, led by Nikolaos Koutsouleris, a combination of human and artificial intelligence optimizes prediction of mental health problems, including psychosis.
The study combined psychiatric evaluations with machine learning models that analyze clinical and biological data.
Predictions of the course of the disease
Although psychiatrists can make very accurate predictions about the positive outcomes of mental illness, they may underestimate the frequency of adverse cases that lead to relapses.
Therefore, algorithmic pattern recognition helps doctors to better predict the course of the disease.
The results of the study show that it is the combination of artificial and human intelligence that optimizes the prediction of mental illnesses, and not just one or the other. With the algorithm, then, doctors can identify at an early stage those patients who need therapeutic intervention and those who do not. As Koutsouleris explains:
This algorithm allows us to improve the prevention of psychosis, especially in young patients at high risk or with emerging depression, and to intervene in a more specific and timely manner.
Therefore, the algorithm does not replace treatment by medical professionals; rather, it aids decision making and provides recommendations on whether further testing should be performed on an individual basis.
The results of our study could help drive a reciprocal and interactive process of clinical validation and improve prognostic tools in real-world screening services.
Norman Bates I would celebrate it.
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The news
This algorithm is used to predict if you are going to suffer psychosis (if mixed with human intelligence)
was originally published in
Xataka Science
by
Sergio Parra
.