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Brazil is a market with very significant opportunities for Huawei and we have a competitive portfolio to satisfy consumers’ expectations,”

Huawei Technologies is making a second attempt at the Brazilian smartphone market

China’s Huawei Technologies Co Ltd is making a second attempt at cracking the Brazilian smartphone market, the fourth-largest in the world, with the launch of two high-end handsets this month, after its cheaper offerings failed to catch on earlier in the decade.

The move will take Huawei beyond its current role in Brazil as a supplier of cellular network equipment to challenge Samsung Electronics Co and Lenovo Group’s Motorola brand which dominate the local smartphone market.
“Brazil is a market with very significant opportunities for Huawei and we have a competitive portfolio to satisfy consumers’ expectations,” said Ketrina Dunagan, Huawei’s vice president of marketing for the Americas, in a statement emailed to Reuters.

Huawei’s plans for Brazil underscore the rise of Chinese companies expanding in Latin America’s technology and consumer sectors, moving beyond a traditional focus on commodities and infrastructure.
Brazil is a rare Latin American market where Huawei’s phones are still absent from store shelves. The company currently sells handsets in more than a dozen countries in the region, often with a double-digit market share.
Huawei, the world’s third largest smartphone manufacturer, first launched a smartphone in Brazil in 2014, but the Ascend P7 handset met with weak demand and the project was discontinued.

Now, Huawei is planning to import two premium devices from the new P30 Series equipped with high-resolution cameras, the company said, withholding details ahead of the April 30 launch.

“The commercial strategy is completely different this time because the brand is still not well-known by Brazilians,” said a person familiar with the matter, requesting anonymity to speak openly about plans that have not been made public. 

Whereas five years ago, Huawei set up as a supplier to mobile carriers, which sell just a tenth of new smartphones in Brazil, this time the company is looking to partner with retail chains that sell more than two-thirds of handsets, the source said.

More cutting-edge models should also help Huawei lure the attention of increasingly sophisticated buyers.

“The Brazilian market has reached a maturity level and manufacturers must bring novelties to convince consumers to replace their smartphones for new ones,” said Renato Meireles, a research analyst at IDC Brasil.
Smartphone sales in Brazil are expected to fall 4.3 % this year, Meireles added, after a 6.8 % drop in 2018.

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For this situation, a group of mathematicians structured a machine-learning issue called "evaluating the greatest" or "EMX."

Austrian-born mathematician Kurt Godel at the Institute of Advanced Study.
Mathematicians have found an issue they can't fathom. It isn't so much that they're not keen enough; there essentially is no answer.

The issue has to do with machine learning — the sort of computerized reasoning models a few PCs use to "realize" how to complete an explicit assignment.

Whenever Facebook or Google perceives a photograph of you and proposes that you label yourself, it's utilizing machine learning. At the point when a self-driving vehicle explores a bustling crossing point, that is machine learning in real life. Neuroscientists use machine figuring out how to "read" somebody's musings. The thing about machine learning is that it depends on math. What's more, accordingly, mathematicians can ponder it and comprehend it on a hypothetical dimension. They can compose proofs about how machine learning functions that are supreme and apply them for each situation.
For this situation, a group of mathematicians structured a machine-learning issue called "evaluating the greatest" or "EMX."

To see how EMX functions, envision this: You need to put promotions on a site and boost what number of watchers will be focused by these advertisements. You have promotions pitching to sports fans, feline darlings, vehicle devotees and exercise buffs, and so forth. Be that as it may, you don't know ahead of time who will visit the site. How would you pick a choice of advertisements that will augment what number of watchers you target? EMX needs to make sense of the appropriate response with only a little measure of information on who visits the site.

In other machine-learning issues, mathematicians can normally say if the learning issue can be tackled in a given case dependent on the informational index they have. Could the fundamental technique Google uses to perceive your face be connected to anticipating securities exchange patterns? I don't have the foggiest idea, however somebody may.

The inconvenience is, math is kind of broken. It's been broken since 1931, when the scholar Kurt Gödel distributed his renowned deficiency hypotheses. They demonstrated that in any numerical framework, there are sure inquiries that can't be replied. They're not by any stretch of the imagination troublesome — they're mysterious. Mathematicians discovered that their capacity to comprehend the universe was generally constrained. Gödel and another mathematician named Paul Cohen found a model: the continuum speculation.

The continuum speculation goes this way: Mathematicians definitely realize that there are boundless qualities of various sizes. For example, there are endlessly numerous whole (numbers like 1, 2, 3, 4, 5, etc); and there are vastly numerous genuine numbers (which incorporate numbers like 1, 2, 3, etc, however they likewise incorporate numbers like 1.8 and 5,222.7 and pi).

Be that as it may, despite the fact that there are boundlessly numerous whole numbers and vastly numerous genuine numbers, there are plainly more genuine numbers than there are numbers. Which brings up the issue, are there any vast qualities bigger than the arrangement of whole numbers yet littler than the arrangement of genuine numbers? The continuum theory says, indeed, there are.

Gödel and Cohen demonstrated that it's difficult to demonstrate that the continuum theory is correct, yet in addition it's difficult to refute that it's. "Is the continuum speculation genuine?" is an inquiry without an answer.

In a paper distributed Monday, Jan. 7, in the diary Nature Machine Intelligence, the analysts demonstrated that EMX is inseparably connected to the continuum speculation.
Things being what they are, EMX can take care of an issue just if the continuum speculation is valid. In any case, if it's not valid, EMX can't.. That implies that the inquiry, "Can EMX figure out how to fathom this problem?"has an answer as mysterious as the continuum speculation itself.

Fortunately the answer for the continuum theory isn't vital to the greater part of arithmetic. Furthermore, correspondingly, this changeless puzzle probably won't make a noteworthy deterrent to machine learning.

"Since EMX is another model in machine learning, we don't yet know its helpfulness for growing genuine calculations," Lev Reyzin, an educator of arithmetic at the University of Illinois in Chicago, who did not take a shot at the paper, wrote in a going with Nature News and Views article. "So these outcomes probably won't end up having useful significance," Reyzin composed.

Running up against an unsolvable issue, Reyzin composed, is a kind of plume in the top of machine-learning specialists.

It's proof that machine learning has "developed as a numerical order," Reyzin composed.

Machine adapting "now joins the numerous subfields of science that bargain with the weight of unprovability and the unease that accompanies it," Reyzin composed. Maybe results, for example, this one will convey to the field of machine taking in a sound portion of quietude, even as machine-learning calculations keep on altering our general surroundings. "

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