How to watch Brazil vs Costa Rica: live stream World Cup football free from anywhere

With the group stages of the World Cup 2018 reaching the halfway stage, we’re beginning to get a clearer idea of who will make the all-important knock-out stages at Russia 2018. And we know that people will be live streaming Brazil vs Costa Rica in their droves – so this page will tell you how.

Brazil will once again be hot favourites in this Group E clash, with a talent-rich squad expected to put on a show against Costa Rica. Hoping to pass a late fitness test,, Neymar will surely be influential in orchestrating Brazil’s assault on the Costa Rican goal, but if the Paris St-Germain star is not firing (or not available) there are plenty of dangerous forwards to pick up the slack. 

You wouldn’t envy Costa Rica’s rearguard going into this one, yet hearteningly for the Los Ticos, much of their strength lies in their organisation and defensive qualities at this World Cup. Giancarlo González of Bologna and Oscar Duarte of Espanol form a solid presence in the middle of the defence, while star of the squad, Real Madrid’s Keylor Navas, will be hard to beat behind them. 

Could we have another upset on the cards? Or is this Brazil side the real deal, and the Switzerland draw was a mere blip? You can find out with a free live stream of Brazil vs Costa Rica via one of the options outlined below. Don’t forget you can stay across all live streams at the 2018 World Cup wherever you are in the world, courtesy of our World Cup watching guide

Use a VPN to watch the World Cup 2018 from anywhere for FREE

You don’t have to miss a single minute of World Cup soccer – even if the country where you are isn’t broadcasting certain games. Because every second of action is being shown somewhere (the UK, for example, is televising every game for free – see below), you can simply use a VPN to login to a region that is broadcasting the game. And it’s really easy to do:

How to stream Brazil vs Costa Rica live in the UK 

How to watch Brazil vs Costa Rica: US live stream 

How to watch Costa Rica vs Brazil: Canada live stream 

How to watch Brazil vs Costa Rica: Australia live stream 

How to watch Costa Rica vs Brazil: New Zealand live stream 

Exclusive World Cup competition with VyprVPN

Amazon Fire TV Cube release date, news, and rumors

The Fire TV Cube is officially here. The handy device combines the best of the traditional Amazon Fire TV streaming platform with that of Amazon’s Echo family of speakers and the result is a cord-cutting, smart-home lover’s dream come true.

You can snag one for yourself starting at $119.99 here in the US starting on June 21, with other regions and availability still to be announced by Amazon.

We’ve reached out to Amazon for additional details, and should hopefully have a review of the Amazon Fire TV Cube live on the website. 

Until then, what follows is everything we know about Amazon’s amalgamation of the smart home and streaming business it’s worked the last decade to build.

Cut to the chase

  • What is it? A Fire TV streaming player / Echo speaker hybrid
  • When is it out? Set for a June 21 release in the US, with no fixed date for other regions
  • What will it cost? $119.99, with no UK price confirmed

Fire TV Cube: official announcements

We now have official word from Amazon’s website, which has replaced its cryptic ‘What is Fire TV Cube?’ teaser with a full product listing and specifications, and official June 21 release date for the US.

The Cube offers a general technical upgrade on 2017’s Fire TV model, which also offered 4k Ultra HD support and Dolby Atmos audio across compatible streaming services linked to the device: Hulu, HBO, Amazon Prime Video, Netflix, and the like.

The upcoming iteration will also have its own Ethernet port, a larger 16GB of storage, and a built-in speaker – circumventing the need to use a TV’s audio output. With all those extra components, will come in at a heftier 465g.

Fire TV Cube: specifications

Voice search and control

Every function of the device is intended to be voice activated, combining capability from the Amazon Echo so that you won’t need an accompanying remote for streaming and playback (though one is included in the box). 

You’ll be able to use voice commands to search and browse content, as well as pause and resume shows and films. Amazon are making much of the fact that this is their first ‘hands-free’ 4K streaming device.

It will naturally be powered by Amazon’s widespread voice assistant, Alexa, and be compatible with a variety of other smart home devices, whether that’s additional speakers, soundbars, thermostats, or cameras around your home.

Pricing options

The Fire TV Cube is retailing on Amazon for $119.99, with the option of including a Cloud Cam night-vision camera for a total of $199.98. Amazon hasn’t announced prices for other regions yet, but we’d expect the basic Cube to come in at around £120 in the UK.

Good sound quality

As a speaker specifically designed for television enthusiasts, we were hoping that the sound quality would be good enough that it can act as a television speaker, rather than a glorified television remote.

There’s no word on the audio output on the actual device, though we assume it will match the middling audio quality of the Echo or Echo Plus – making it perfectly acceptable for general use but not quite on the level of the Sonos One or HomePod.

Some users are likely to still want additional soundbars, speakers, or other hardware support, especially as the Cube will be able to play Dolby Atmos 7.1 surround sound content from compatible streaming services.

Stereo link-up

As a TV speaker, the Cube is thankfully able to link multiple devices together with its infrared sensor (or IR cable for devices out of sight) to make a tannoy or surround-sound system. 

This is a feature that Apple has been touting for the HomePod, but is yet to deliver. With Amazon’s existing network of Echo devices, and a growing list of third-party devices offering Alexa compatibility, we’re hoping this technology can be used to solve the problem that is still taxing Apple.

High frame rate

Like the 2017 Fire TV dongle, the Cube will be capable of streaming at frame rates as high as 60fps, but not at the 120fps some high-end television sets are coming out with. Maybe a Cube Plus model next year?

Fire TV Cube: leaks and rumors

We had a fairly solid idea of what the Fire TV Cube was going to look like (yes it’s a cube) thanks to a leak from AFTVNews. The leaked image included both the Cube and the 2017 Amazon Fire TV, which at that time hadn’t been revealed yet. 

In the leak, AFTVNews confirmed that the device would have a far-field microphone array and speaker built in, as well as infrared emitter that would allow it to control televisions and A/V equipment – all of which turned out to be reliable information.

Intel CEO Brian Krzanich resigns over relationship with employee

PayPal to buy Simility, a specialist in AI-based fraud and risk management, for $120M

Payment provider PayPal continues apace with its acquisitions streak to bring more modern tools into its platform to serve its 237 million customers. Today the company announced that it is buying Simility, a fraud prevention specialist, for $120 million in cash.

PayPal had been an investor in Simility (it owns three percent of the company, it says), along with Accel, Trinity Ventures and others. The startup had raised just under $25 million and was last valued at $52.75 million, according to figures from PitchBook, making this a decent return for its backers. The deal is expected to close in Q3.

Online fraud involving either buyers or sellers has been one of the biggest limiting factors to the growth of e-commerce, and it has only grown as e-commerce has become more mature and spread to more platforms.

Simility’s approach is to use a set of APIs and beacons that essentially monitor digital transactions and buying activity wherever they happen to take place: on mobile, web or in physical environments Augmenting these with machine learning and feeds from other data sources, it creates something it calls “adaptive” risk management: a changing approach and protection strategy based on what the threat of the moment might be.

Acquiring a company like this makes sense on two levels for PayPal: not just for its own systems, but for that of its customers, who make PayPal-powered transactions on the web, on mobile and at physical points of sales.

“Digital commerce has exploded, and fraudsters have taken note, adapting and developing new methods to carry out their crimes,” said Bill Ready, chief operating officer, PayPal, in a statement. “PayPal has been at the forefront of developing innovative fraud prevention and risk management solutions for nearly 20 years, but until now, merchants haven’t been able to configure those solutions to manage the unique complexities of their businesses. Together with Simility, we will be able to put more control in the hands of our merchants to fight fraud while helping make commerce experiences faster and more secure.”

Ready in a separate blog described the company’s strategy currently as an effort to create a one-stop shop for all things commerce, and simplification is also an aspect of this deal: Simility already has a number of customers that also work with PayPal such as PayPal’s former owner eBay/StubHub, OfferUp, Dicks Sporting Goods and Rebtel. The acquisition will mean a more integrated approach for them where their PayPal services have a stronger layer of fraud protection on them, and they also get used to help form a bigger picture about the overall state of fraud that the companies.

PayPal said that after the deal closes, it will also extend Simility’s tools to the rest of the merchants on its platform.

“Our vision for Simility was to create an adaptive risk management platform that empowers organizations operating in a digital world to manage an evolving fraud and risk landscape where data breaches are the new normal,” said Rahul Pangam, co-founder and CEO, Simility, in a statement. “We are excited to enter the next phase of our growth with PayPal and are thrilled to join them to help drive the next generation of payment and commerce solutions while scaling our business together.”

PayPal has made a number of acquisitions over the last few weeks, all pointing to adding in new technologies and tools to reflect our changing times and how that is playing out in the world of payments. They have included European mobile payments and financial services business iZettle, payments aggregator Hyperwallet and AI-based CRM specialist Jetlore.

Microsoft backpedals on VR promise

If you were still waiting patiently for the virtual reality features that Microsoft promised in 2016, then I have some bad news for you. During E3 last week, Microsoft’s chief marketing officer for gaming, Mike Nichols, told GamesIndustry.biz that the company had no plans to fulfill that promise.

“We don’t have any plans specific to Xbox consoles in virtual reality or mixed reality,” Nichols told GamesIndustry.biz.

This goes against a promise that Microsoft made two years ago when Xbox chief Phil Spencer told The Verge that the Xbox One X (then dramatically known as Xbox Scorpio) would support “[the kind of] high-end VR that you see happening in the PC space.”

The release of the Xbox One X came and went without any news of VR integration, but in the interim, Microsoft did make strides toward VR and mixed reality tech for PC gaming with the release of the Windows Mixed Reality headsets for Windows 10.

According to Nichols, it seems like Microsoft may be sticking to this PC gaming territory for awhile.

“PC is probably the best platform for more immersive VR and MR … but as it relates to Xbox, no,” he said.

Species-identifying AI gets a boost from images snapped by citizen naturalists

Someday we’ll have an app that you can point at a weird bug or unfamiliar fern and have it spit out the genus and species. But right now computer vision systems just aren’t up to the task. To help things along, researchers have assembled hundreds of thousands of images taken by regular folks of critters in real life situations — and by studying these, our AI helpers may be able to get a handle on biodiversity.

Many computer vision algorithms have been trained on one of several large sets of images, which may have everything from people to household objects to fruits and vegetables in them. That’s great for learning a little about a lot of things, but what if you want to go deep on a specific subject or type of image? You need a special set of lots of that kind of image.

For some specialties, we have that already: FaceNet, for instance, is the standard set for learning how to recognize or replicate faces. But while computers may have trouble recognizing faces, we rarely do — while on the other hand, I can never remember the name of the birds that land on my feeder in the spring.

Fortunately, I’m not the only one with this problem, and for years the community of the iNaturalist app has been collecting pictures of common and uncommon animals for identification. And it turns out that these images are the perfect way to teach a system how to recognize plants and animals in the wild.

Could you tell the difference?

You might think that a computer could learn all it needs to from biology textbooks, field guides and National Geographic. But when you or I take a picture of a sea lion, it looks a lot different from a professional shot: the background is different, the angle isn’t perfect, the focus is probably off and there may even be other animals in the shot. Even a good computer vision algorithm might not see much in common between the two.

The photos taken through the iNaturalist app, however, are all of the amateur type — yet they have also been validated and identified by professionals who, far better than any computer, can recognize a species even when it’s occluded, poorly lit or blurry.

The researchers, from Caltech, Google, Cornell and iNaturalist itself, put together a limited subset of the more than 1.6 million images in the app’s databases, presented this week at CVPR in Salt Lake City. They decided that in order for the set to be robust, it should have lots of different angles and situations, so they searched for species that have had at least 20 different people spot them.

The resulting set of images (PDF) still has more than 859,000 pictures of over 5,000 species. These they had people annotate by drawing boxes around the critter in the picture, so the computer would know what to pay attention to. A set of images was set aside for training the system, another set for testing it.

Examples of bounding boxes being put on images.

Ironically, they can tell it’s a good set because existing image recognition engines perform so poorly on it, not even reaching 70 percent first-guess accuracy. The very qualities that make the images themselves so amateurish and difficult to parse make them extremely valuable as raw data; these pictures haven’t been sanitized or set up to make it any easier for the algorithms to sort through.

Even the systems created by the researchers with the iNat2017 set didn’t fare so well. But that’s okay — finding where there’s room to improve is part of defining the problem space.

The set is expanding, as others like it do, and the researchers note that the number of species with 20 independent observations has more than doubled since they started working on the data set. That means iNat2018, already under development, will be much larger and will likely lead to more robust recognition systems.

The team says they’re working on adding more attributes to the set so that a system will be able to report not just species, but sex, life stage, habitat notes and other metadata. And if it fails to nail down the species, it could in the future at least make a guess at the genus or whatever taxonomic rank it’s confident about — e.g. it may not be able to tell if it’s anthopleura elegantissima or anthopleura xanthogrammica, but it’s definitely an anemone.

This is just one of many parallel efforts to improve the state of computer vision in natural environments; you can learn more about the ongoing collection and competition that leads to the iNat data sets here, and other more class-specific challenges are listed here.