Pokemon GO maker responds to furious gamers, but it’s not what they want to hear

Earlier this month, Niantic reverted the changes it made to Pokemon GO at the outset of the COVID-19 pandemic. This included bringing the PokeStop and Pokemon Gym interaction distances back to their normal ranges, decreasing the number of gifts Buddy Pokemon grant, and reducing the effectiveness of incense when players are standing still. Niantic received a lot of pushback from the Pokemon GO community for this decision, primarily because of the changes to Gym and PokeStop interaction ranges.

Now Niantic has responded to those upset players, but unfortunately, it looks like the company isn’t interested in budging on this matter – at least not for now. Niantic’s statement gets into the nitty-gritty after explaining that it has heard the feedback and giving us background on the changes and their reversions in the US and New Zealand.

“We have heard your feedback about one change in particular – that of the PokéStop and Gym interaction distance,” Niantic’s statement reads. “We reverted the interaction distance from 80 meters back to the original 40 meters starting in the U.S. and New Zealand because we want people to connect to real places in the real world, and to visit places that are worth exploring.”

“However, we have heard your input loud and clear and so to address the concerns you have raised, we are taking the following actions: We are assembling an internal cross-functional team to develop proposals designed to preserve our mission of inspiring people to explore the world together, while also addressing specific concerns that have been raised regarding interaction distance,” the company continued. “We will share the findings of this task force by the next in game season change (September 1). As part of this process, we will also be reaching out to community leaders in the coming days to join us in this dialogue.”

So, for now, at least, the PokeStop and Pokemon Gym interaction ranges are going back to their original distances of 40 meters in the United States and New Zealand, and it looks like that will be the case for at least the next month. Of course, it’s impossible to know what this so-called task force will decide, but nothing is changing for now, and that’s probably not going to sit well with Pokemon GO players who wanted to see these interaction range increases stick around.

Of course, Niantic always intended these changes to be temporary, so we knew a day would come when they would be reverted. However, in the time that they were live, it seems many players ended up preferring the changes. The Pokemon GO community made some compelling arguments for keeping the distance changes yesterday, but it’s clear with Niantic’s statement that the message the community sent did not have the effect players were hoping for. We’ll let you know when Niantic shares more about this matter, so stay tuned.

Repost: Original Source and Author Link


Fast and Furious is returning to ‘Rocket League’

Ahead of the release of F9, a trio of cars from the Fast and Furious franchise are barreling on to Rocket League. The vehicle bundle includes the return of two iconic franchise faves that have been absent since the game went free to play last summer, along with the debut of a pivotal custom car from the upcoming sequel. Fans will once again be able to take Dominic Toretto’s Dodge Charger and his dearly departed bro Brian O’Conner’s Nissan Skyline for a spin. Alongside the American muscle and heritage Japanese street racer will be F9‘s rocket-strapped Pontiac Fiero. 

Rocket League


You’ll be able to buy the three-vehicle bundle, complete with a bunch of decals, for 2400 Credits. Individually, they’ll cost 1000 credits or 300 credits for those that already own the Charger or Skyline. Of course, it wouldn’t be a Fast and Furious celebration without some Reggaeton. So, Rocket League is adding two player anthems, including a new song by Anitta called Furiosa and an additional anthem that will be revealed when it goes live. 

All the goodies will be available in the item shop from June 17th to June 30th. As a freebie, the “Tuna, No Crust” title — a nod to an exchange between love birds O’Conner and Mia Toretto — will be available alongside the new car packs. F9, meanwhile, will roar on to US theaters on June 25th.

All products recommended by Engadget are selected by our editorial team, independent of our parent company. Some of our stories include affiliate links. If you buy something through one of these links, we may earn an affiliate commission.

Repost: Original Source and Author Link

Tech News

Furious AI researcher creates a list of non-reproducible machine learning papers

On February 14, a researcher who was frustrated with reproducing the results of a machine learning research paper opened up a Reddit account under the username ContributionSecure14 and posted the r/MachineLearning subreddit: “I just spent a week implementing a paper as a baseline and failed to reproduce the results. I realized today after googling for a bit that a few others were also unable to reproduce the results. Is there a list of such papers? It will save people a lot of time and effort.”

The post struck a nerve with other users on r/MachineLearning, which is the largest Reddit community for machine learning.

“Easier to compile a list of reproducible ones…,” one user responded.

“Probably 50%-75% of all papers are unreproducible. It’s sad, but it’s true,” another user wrote. “Think about it, most papers are ‘optimized’ to get into a conference. More often than not the authors know that a paper they’re trying to get into a conference isn’t very good! So they don’t have to worry about reproducibility because nobody will try to reproduce them.”

A few other users posted links to machine learning papers they had failed to implement and voiced their frustration with code implementation not being a requirement in ML conferences.

The next day, ContributionSecure14 created “Papers Without Code,” a website that aims to create a centralized list of machine learning papers that are not implementable.

“I’m not sure if this is the best or worst idea ever but I figured it would be useful to collect a list of papers which people have tried to reproduce and failed,” ContributionSecure14 wrote on r/MachineLearning. “This will give the authors a chance to either release their code, provide pointers or rescind the paper. My hope is that this incentivizes a healthier ML research culture around not publishing unreproducible work.”

Reproducing the results of machine learning papers

Machine learning researchers regularly publish papers on online platforms such as arXiv and OpenReview. These papers describe concepts and techniques that highlight new challenges in machine learning systems or introduce new ways to solve known problems. Many of these papers find their way into mainstream artificial intelligence conferences such as NeurIPS, ICML, ICLR, and CVPR.

Having source code to go along with a research paper helps a lot in verifying the validity of a machine learning technique and building on top of it. But this is not a requirement for machine learning conferences. As a result, many students and researchers who read these papers struggle with reproducing their results.

“Unreproducible work wastes the time and effort of well-meaning researchers, and authors should strive to ensure at least one public implementation of their work exists,” ContributionSecure14, who preferred to remain anonymous, told TechTalks in written comments. “Publishing a paper with empirical results in the public domain is pointless if others cannot build off of the paper or use it as a baseline.”

But ContributionSecure14 also acknowledges that there are sometimes legitimate reasons for machine learning researchers not to release their code. For example, some authors may train their models on internal infrastructure or use large internal datasets for pretraining. In such cases, the researchers are not at liberty to publish the code or data along with their paper because of company policy.

“If the authors publish a paper without code due to such circumstances, I personally believe that they have the academic responsibility to work closely with other researchers trying to reproduce their paper,” ContributionSecure14 says. “There is no point in publishing the paper in the public domain if others cannot build off of it. There should be at least one publicly available reference implementation for others to build off of or use as a baseline.”

In some cases, even if the authors release both the source code and data to their paper, other machine learning researchers still struggle to reproduce the results. This can be due to various reasons. For instance, the authors might cherry-pick the best results from several experiments and present them as state-of-the-art achievements. In other cases, the researchers might have used tricks such as tuning the parameters of their machine learning model to the test data set to boost the results. In such cases, even if the results are reproducible, they are not relevant, because the machine learning model has been overfitted to specific conditions and won’t perform well on previously unseen data.

“I think it is necessary to have reproducible code as a prerequisite in order to independently verify the validity of the results claimed in the paper, but [code alone is] not sufficient,” ContributionSecure14 said.

Efforts for machine learning reproducibility