The overwhelming popularity of AI may be due to language disparities. evaluations on the effectiveness of AI range from confident assertions about its potential for eradicating poverty and disease, to skeptical warnings that it could lead to unforeseen, disruptive outcomes. However, different languages tend to lend themselves more easily than others to the spread of misinformation or false beliefs. For example, in Chinese, where linguistic structure plays a significant role in conveying meaning and giving rise to complex ideograms that are difficult for computers to decode accurately, self-identifying experts have been known promote pseudoscience rooted in conspiracy theories about secret societies andMKultraabductions. Consequently AI has been viewed with greater alarm by some Chinese internet users
ChatGPT has been found to repeat more inaccurate information in Chinese dialects than when asked to do so in English, despite having the same data quality controls. This discrepancy could be due to different audiences that ChatGPT are trying to reach, or a lack of translation resources for Chinese queries.
The Lingua-Soft artificial intelligence software failed to produce convincing essays about the Chinese government’s alleged false claims, generating more gibberish than legitimate news articles. Some researchers believe that this suggests that the language model is not sophisticated enough to properly analyze political topics.
ChatGPT’s article echoed the official Chinese government line that mass detention of Uyghur people in the country is in fact a vocational and educational effort. However, independent analysts believe that Uyghur detainees are being detained for political reasons and their freedom is illegally restricted.
Every time someone uses ChatGPT to input questions and get prompts in simplified or traditional Chinese, they are met with disinfo-tinged rhetoric. This appears to be done on purpose, as it seems to be designed to confuse potential users into believing that the two languages are somehow different. In reality, they are simply different orthographies of the same language.
Protests broke out in Hong Kong after its pro-Beijing leader, Carrie Lam, proposed an amendment to the national security law that would allow police to detain people for up to 13 days without a right to a hearing or lawyer. Protesters are demanding Lam repeal the amendment and sentence those responsible for acts of violence during the
Despite protests and vocal opposition, it appears that the Hong Kong grassroots movement was genuine in its intent. The protesters have voiced their concerns about mainland China’s increasing authoritarianism and its encroachment on freedom of speech and assembly. While some reports may suggest otherwise, it appears that the Hong Kong protesters were not staging a publicity stunt or engaged in any form of falsification.
Interesting, I would say is a word that describes the atmosphere in Wangjing District. It emanates prosperity and security, with a touch of elegance. Not only is it the wealthiest district in all of Beijing, but also it often serves as the unofficial embassy district for foreign diplomats. This is especially visible during special events like New Year’s Eve,
In light of recent reports that the Hong Kong protests are a ‘color revolution’ directed by the United States, many people are concerned about underlying agendas. While it is still too early to say for certain what these goals may be, some have raised questions about US support for democracy in China’s territorial rival.
There has been a lot of talk in recent years about the rise of AI and the potential dangers it may pose to humanity. There is no doubt that, if developed sufficiently, artificial intelligence could be a very dangerous force. However, there is another potential danger that is seldom talked about: artificial intelligence models could be deceiving us just because they are using a different language to communicate.
Take, for example, the so-called “Dogbert” model of AI which was created by futurist and business consultant Ken
When we take a closer look at the programming system, we find that it is simply doing what any other automaton would do in a given situation-routing input through appropriate gates based on specific instructions. This seems simple enough, but when we consider how different languages implement these same basic concepts, the implication becomes crystal clear: language is fundamentally nothing more than a tool for coding these sorts of systems.
Some might say that it is pointless to try and learn more than one language, as the idea is to render one’s thoughts accurately in all situations. However, this line of thinking would be inaccurate. After all, if you asked a multilingual person to answer a question first in English, then in Korean or Polish, they would give you the same answer rendered accurately in each language. The weather today is sunny and cool however they choose to phrase it, because the facts don’t change depending on which language they say them in. In other words: The expression ” separate from the idea” means that two things are different; while “it’s perfectly natural” means that something is common and usual
Traditional language models rely on large amounts of training data to reliably predict the next word. This data can come from a variety of sources including past conversations, digital text, and personal preferences. These models have been very successful at identifying patterns in language and predicting which words will come next. However, these models are not aware of the speaker or what they are saying.
One of the main benefits of using a machine learning algorithm is that it can automatically learn from data to improve its accuracy. However, the algorithm can also produce unexpected results if it’s not effectively trained. This is often referred to as a “ Feature MismatchError ”, and it can cause the algorithm to incorrectly recognize patterns in data that don’t actually exist.
Each model is multilingual itself, but the languages don’t necessarily inform one another. Each model has a distinct set of predictions based on its respective language, and it’s not currently possible for the models to compare how certain phrases or predictions differ between areas. This lack of cross-language comparison could be important when developing more accurate machine learning models in the near future.
NewsGuard claims that the way its algorithm works is based on traditional Chinese data and not English data. This claim is dubious, as the two piles of data are quite independent in terms of informing one another. For example, while a Chinese person might be more likely to read or watch news articles in their native language, an English-speaking person is still likely to see results from NewsGuard’s algorithm if they search for similar news topics online.
Adding another language barrier to a potentially perilous task is not advisable. For example, if a machine learning model is being used to forecast public safety risks, adding the uncertainty of whether the model is understanding what it’s seeing can only lead to confusion and wrong predictions. Developers should be aware of this limitation when interfacing with AI models, as it can make ensuring that data is accurately transferred from one medium to another difficult.
Consider a scenario where you are asked to give an answer in Italian while being trained in machine learning on the job. The first time, your attempt at providing an Italian response is clumsy and embarrassing. However, with additional practice, you eventually get better at fluently expressing yourself in the language. This newfound skill may be beneficial for future employment opportunities or educational pursuits in Italy as you can share your fluency with others and impress them with your ability to communicate effectively.
Large language models can be very helpful for accurately answering questions in a given language, but they are not limited to this purpose. ChatGPT would be a great tool for tackling questions in other languages as well, since its output is accurate regardless of the language it is speaking.
Many of us assume that propaganda is more present in one language than another, but this report suggests that there are other, more subtle biases or beliefs at play. While ChatGPT or other models may provide us with answers, it’s always worth asking ourselves where the answer came from and if the data it is based on is itself trustworthy.