Get your lawn under control with the $69 Orbit B-hyve smart sprinkler system

Don’t let grass ruin your life.

The Orbit B-hyve 6-station controller is down to $68.99 on Amazon. It normally sells around $100 and has never dropped this low before.

This Orbit B-hyve 12-station smart sprinkler system controller is also on sale for $82.99. It regularly sells right around $120. It has seen a few drops close to $90 but those have all been temporary and never as low as this.

Compare this to the Rachio sprinkler controller, which is the market leader for these sort of devices. An 8-zone Rachio is $200. You’re getting a lot more value from the Orbit with 6 stations for $69. The Orbit has 4.5 stars based on 765 user reviews.

Features include:

• The B-hyve app is fully functional for Android, iOS or web devices, and gives you control wherever you need it. Program your timer on the app, at the timer, or let the weather-based software create a program for you
• WeatherSense technology provides watering based on site conditions such as slope, soil type, sun/shade, forecast ET and live weather feeds. It automatically adjusts your controller to deliver the right amount of water to your plants
• Utilize the swing panel for easy access to the angled wiring terminals, which makes wiring simple and convenient, and the plug-and-go line cord that can also be cut for hardwired applications
• Made in the USA with global materials, the B-hyve comes in a weather-resistant case, allowing you to mount your timer indoors or outdoors without risk of weather damage, and a locking cabinet that keeps your timer safe from harm
• Integrate your B-hyve timer with use of Catch Cups (Orbit 26250) to save up to 50% more water than with traditional controllers. Optimize the way you water, so you can keep everything lush and green without breaking the bank

Get an Alexa device like the Amazon Echo Dot to enable voice control.

See at Amazon

Essential Phone review re-do: What a difference a discount makes

I normally like to wait at least a year to give a smartphone the old “review re-do” treatment, but this is a special case. The Andy Rubin-led startup Essential changed everything when it slashed the price of its namesake flagship by $200 barely two months after launch, instantly instantly transforming the Essential Phone from an overpriced mobile with a terrible camera into a genuine bargain … with a terrible camera.

Yes, the Essential Phone’s optics remain a sore spot even after a 28% price cut – but as with most things Android, there’s a hack for that. Let’s find out if the deep discount combined with a little sideloading can make the PH-1 a late-season winner! Revisit Android Central’s Essential Phone review for some context, then hit up MrMobile’s Essential Phone Review Re-Do for the November update!

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Pixel 2 Driving Do Not Disturb API coming to developers in 2018

Helping to silence notifications while on the road.

It can sometimes be quite easy to get lost in our phones, and while there’s nothing inherently wrong with this, there are times and places where our devices need to put away. Picking up your phone to check notifications or browse through Twitter while driving can be awfully tempting at times, and Google is rolling out a new API next year to help cut back on these potential distractions.

One of the new features of the Pixel 2/2 XL is Driving Do Not Disturb. This is a setting that allows you to automatically turn on Do Not Disturb when your Pixel 2 detects that you’re behind the wheel, and the phone does this through the use of low power signals, Bluetooth, and Wi-Fi connections.

The feature is currently limited to Do Not Disturb settings for the Pixel 2, but beginning in early 2018, Google says that it’ll be releasing the API for this tech so that developers can integrate it into their own applications.

Google is calling it the Activity Recognition Transition API, and we’ll likely see it adopted into navigation and safe driving apps that are meant to safely be used when on the road. A more specific ETA for the API’s release has yet to be announced, but we can probably expect to see apps add the tech within Q1 of 2018.

Google Pixel 2 will automatically enable Do Not Disturb if you’re driving

Google Pixel 2 and Pixel 2 XL

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These are the 8 thinnest cases we could find for the Galaxy Note 8

What’s the thinnest case for Galaxy Note 8?

It’s a good idea to protect your big ol’ Note 8 with a case, but you don’t have to give up the slim form factor in the process. These are the absolute thinnest cases we could find for Galaxy Note 8!

It’s almost like your phone is… Naked?

Bare cases are super thin, and make your phone feel like it’s wearing nothing at all… nothing at all… nothing at all. $30!

In the vacuum of space, no one can see how thin your case is

But you can, down here with the Jupiter Lights case for $10.

Spigen Thin Fit — that pretty much says it right there

Blue, gold, black, or gray, and yours for $10.

A case like skin for your phone — that’s Peel

Black, gray, or silver. Match it and grab it for $25.

Sexy and sophisticated slenderness

At least that’s what I’m guessing based on the hot dude in the promo image. Either way it’s $10 on Amazon.

Love thin

By Love Ying. Clear and oh so skinny minnie. $8 on Amazon.

Go the the source

Not the store, but like the source of the phone. Like Samsung. Samsung makes this case. $19 on Amazon.

Finding a thin case is a mSnap

Black, turquoise, and rose gold because why not. This slim-hipped little minx is $10 on Amazon.

Got a great thin case of your own?

Sound off in the comments below!

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Samsung announces Exynos 9810 processor capable of gigabit LTE speeds

All new tech for 2018.

Although Samsung phones here in the U.S. are powered by Qualcomm Snapdragon processors, those same devices in other parts of the world feature Samsung’s own Exynos silicon. The company recently published a press release announcing the CES 2018 Innovation Awards that it’s already won, and in doing so, quietly announced the all-new Exynos 9810 processor.

There aren’t many technical details present here, but what we do know is that is that it features an upgraded CPU and GPU with support for gigabit LTE speeds.

The full description of the processor is as follows –

The Exynos 9 Series 9810 is Samsung’s latest flagship processor, with 3rd-generation custom CPU cores, upgraded GPU, and gigabit LTE modem with industry-first 6CA support. It is built on 2nd generation 10nm process technology.

Gigabit LTE is being pushed more and more heavily these days by carriers and processor manufacturers alike, and the 6CA technology in the 9810 will allow for downlink speeds up to a whopping 1.2Gbps. Carriers certainly need to adapt and enhance their networks to allow for such increased data, but knowing that Samsung is already playing its cards right in these regards is great to see.

Exact specifications likely won’t be unveiled for the Exynos 9810 until CES 2018 this January, but when they are, we’ll be sure to outline all of the important details to see just how it compares to Qualcomm’s imminent Snapdragon 845.

Samsung Galaxy Note 8 India review: Two months later

Counterfeiters are using AI and machine learning to make better fakes

It’s terrifyingly easy to just make stuff up online these days, such is life in the post-truth era. But recent advancements in machine learning (ML) and artificial intelligence (AI) have compounded the issue exponentially. It’s not just the news that’s fake anymore but all sorts of media and consumer goods can now be knocked off thanks to AI. From audio tracks and video clips to financial transactions and counterfeit products — even your own handwriting can be mimicked with startling levels of accuracy. But what if we could leverage the same computer systems that created these fakes to reveal them just as easily?

People have been falling for trickery and hoaxes since forever. Human history is filled with false prophets, demagogues, snake-oil peddlers, grifters and con men. The problem is that these days, any two-bit huckster with a conspiracy theory and a supplement brand can hop on YouTube and instantly reach a global audience. And while the definition of “facts” now depends on who you’re talking to, one thing that most people agreed to prior to January 20th this year is the veracity of hard evidence. Video and audio recordings have long been considered reliable sources of evidence but that’s changing thanks to recent advances in AI.

In July 2016, researchers at the University of Washington developed a machine learning system that not only accurately synthesizes a person’s voice and vocal mannerisms but lip syncs their words onto a video. Essentially, you can fake anybody’s voice and create a video of them saying whatever you want. Take the team’s demo video, for example. They trained the ML system using footage of President Obama’s weekly address. The recurrent neural network learned to associate various audio features with their respective mouth shapes. From there, the team generated CGI mouth movements, and with the help of 3D pose matching, ported the animated lips onto a separate video of the president. Basically, they’re able to generate a photorealistic video using only its associated audio track.

While the team took an outsized amount of blowback over the potential misuses of such technology, they had far more mundane uses for it in mind. “The ability to generate high-quality video from audio could signicantly reduce the amount of bandwidth needed in video coding/transmission (which makes up a large percentage of current internet bandwidth),” they suggested in their study, Synthesizing Obama: Learning Lip Sync from Audio. “For hearing-impaired people, video synthesis could enable lip-reading from over-the-phone audio. And digital humans are central to entertainment applications like film special effects and games.”

UW isn’t the only facility looking into this sort of technology. Last year, a team from Stanford debuted the Face2Face system. Unlike UW’s technology, which generates video from audio, Face2Face generates video from other video. It uses a regular webcam to capture the user’s facial expressions and mouth shapes, then uses that information to deform the target YouTube video to best match the user’s expressions and speech — all in real time.

AI-based audio-video transcription is a two-way street. Just as UW’s system managed to generate video from an audio feed, a team from MIT’s CSAIL figured out how to create audio from a silent video reel. And do it well enough to fool human audiences.

“When you run your finger across a wine glass, the sound it makes reflects how much liquid is in it,” Andrew Owens, the paper’s lead author told MIT News. “An algorithm that simulates such sounds can reveal key information about objects’ shapes and material types, as well as the force and motion of their interactions with the world.”

The MIT’s deep learning system was trained over the course of a few months using 1,000 videos containing some 46,000 sounds resulting from different objects being poked, struck or scraped with a drumstick. Like the UW algorithm, MIT’s learned to associate different audio properties with specific onscreen actions and synthesize those sounds as the video played. When tested online against a video with authentic sound, people actually chose the fake audio over the real twice as often as the baseline algorithm.

The MIT team figures that they can leverage this technology to help give robots better situational awareness. “A robot could look at a sidewalk and instinctively know that the cement is hard and the grass is soft, and therefore know what would happen if they stepped on either of them,” Owens said. “Being able to predict sound is an important first step toward being able to predict the consequences of physical interactions with the world.”

Research into audio synthesization isn’t limited to universities; a number of major corporations are investigating the technology as well. Google, for example, has developed Wavenet, a “deep generative model of raw audio waveforms.” Among the first iterations of computer-generated text-to-speech (TTS) systems is “concatenative” TTS. That’s where a single person records a variety speech fragments, those are fed into a database and then reconstructed by a computer to form words and sentences. The problem is that the output sounds more like the MovieFone guy (ask your parents) than a real person.

Waveform, on the other hand, is trained on waveforms of people speaking. The system samples those recordings for data points up to 16,000 times per second. To output sound, Waveform uses a model to predict what the next sound will be based on the sounds that came before it. The process is computationally expensive but does produce superior audio quality compared to the conventional TTS methods.

In the future, robots could potentially forge your signature on official documents, if this AI-based handwriting mimic developed at the University College London is ever misused. Dubbed the “My Text in Your Handwriting” program, this system can accurately recreate a subject’s handwriting with as little as a paragraph’s input. The program is based on “glyphs,” essentially the unique traits of each person’s handwriting. By measuring various aspects like horizontal and vertical spacing, connectors between letters and writing texture, the program can readily copy the style.

“Our software has lots of valuable applications. Stroke victims, for example, may be able to formulate letters without the concern of illegibility, or someone sending flowers as a gift could include a handwritten note without even going into the florist,” Dr. Tom Haines, UCL Computer Science and lead author of the study, told UCL News. “It could also be used in comic books where a piece of handwritten text can be translated into different languages without losing the author’s original style.”

And while this technology could be used to create forgeries, it can just as easily be leveraged to spot them as well. “Forgery and forensic handwriting analysis are still almost entirely manual processes,” Dr. Gabriel Brostow, of the UCL computer science department, said. “But by taking the novel approach of viewing handwriting as texture-synthesis we can use our software to characterise handwriting to quantify the odds that something was forged.”

Forgeries and faked products don’t stop at the the bounds of the internet. Recent estimates by the Organisation for Economic Co-operation and Development put the global market for counterfeit goods at around $460 billion annually. And that’s where the Entrupy authentication system comes in.

“In an ideal world, we shouldn’t exist,” Entrupy CEO Vidyuth Srinivasan lamented. “The more we can instill trustworthiness in the market, the better it will be for commerce in general.”

The company first imaged a wide variety of luxury goods and uses that database to help its customers — generally those in secondary retail markets like vintage clothing stores or eBay sellers — authenticate products with around 98.5 percent accuracy. Customers receive a handheld microscope and take various images of the product in question, such as the exterior, logo or interior lining. These photos are then fed into a mobile app and transmitted to the company’s servers where a classification algorithm goes to work, differentiating between legitimate goods and counterfeits. If the product is real, the Entrupy will provide a certificate of authenticity.

Although the company’s product database is varied, there are limits to the system’s current capabilities. Because it’s optical, reflective or transparent items are no good, nor is anything without surface texture. Some things that it does not work on include porcelain, diamonds and glass, pure plastic and bare metal.

Unlike the other AI-based systems discussed here, there’s little chance of the Entrupy system being corrupted or gamed. “We have had [counterfeiters] pose as real customers and legitimate businesses to try and buy [the system] and we’re fine with it,” Srinivasan explained. That’s because the system doesn’t actually tell the user which of the images they’re taking are actually being used to verify the product’s authenticity. “We ask our customers to take images of different parts of the item because it’s not just pure material [being used for verification]…,” he continued. “It’s a holistic view of the different aspects of the item — from the workmanship to the material used to the wear” as well as a number of other contextual bits of metadata.

What’s more, the system is continually updated with new data, not just from the company’s internal efforts of posing as secret buyers to acquire counterfeit goods, but also from the users themselves. Images taken during the authentication process — whether the item turns out to be real or not — are incorporated into the company’s database, further improving the system’s accuracy.

“In the near to medium future, I think that AI and ML will, as a counterfeiting solution, will definitely raise the bar,” he concluded. “It’s a spy versus spy game, cat versus mouse.”

Increasing our ability to spot fakes will force counterfeiters to up their game and start using better quality materials and better workmanship. That, however, will increase the production cost of these products, hopefully to a price that is no longer economically viable. “The MO of any counterfeiter is to make something that they can sell a lot of, that can be easily produced and that does not cost a lot to produce a fake of,” Srinivasan sid. “Otherwise there’s no profitability.”

Similar measures have been adopted by Paypal, one of the the internet’s top financial service providers, for cases of account fraud. “Say my account was accessed today from San Francisco, tomorrow from NYC, and some other IP the day after,” Hui Wang, Paypal’s senior director of global risk sciences, told Engadget. This sort of activity is indicative of some kind of account takeover. “In order to detect these kinds of fraud,” she explained, “we track the IP we track the machine and we track the network.”

The company created an algorithm that looks at both the IP and the geolocation of that IP, then compares them to your account history to see if this matches up with previous actions. Paypal developed a proprietary technology that compares this IP location patten with other users, to see if there is a larger effect at work or there’s a reasonable explanation for the movement — i.e., perhaps you’re flying through New York on business and buy a souvenir at the airport gift shop before continuing on the trip.

The company’s AI system also attempts to identify each previous IP, whether it’s a hotel’s secured ethernet connection or the public WiFi at the airport. “[The algorithm] is retrieving tons of data from your account history and going beyond your account to look at the traffic on your network, like the other people using the same IP,” Wang said. From this raw information, the algorithm selects specific data points and uses those to estimate whether the transaction is legitimate.

Most of these actions and their subsequent decisions — such as verifying or denying a payment — are performed autonomously. However, if the algorithm’s confidence value is too low, human investigators from the operations center will investigate the transaction manually.

“We are also in the process of ensuring that human intelligence can be fed back into the automated system,” she continued, so that the ML system continually learns, improves and increases its accuracy.

These sorts of systems, both those designed to generate fakes and those trained to uncover them, are still in their infancy. But in the coming decades, artificial intelligence and machine learning techniques will continue to improve, often in ways that we have yet to envision. There is a very real danger in technologies that can create uncannily convincing lies, hoaxes and fakes — in front of our very eyes, no less. But, like movable type, radio and internet that came before it, AI systems like these, ones capable of generating photorealistic content, will only be as dangerous as the intentions of the people using it. And that’s a terrifying thought.

StubHub and Viagogo raided in UK ticket touting probe

Scalpers aren’t unique to the UK, but the government has made stamping out ticket touts one of its top priorities. Ministers have already set out legislation as part of the Digital Economy Act that will make the use of ticket-buying bots illegal, but regulators are also putting the squeeze on secondary ticket sellers. The Guardian reports that the Competition and Markets Authority (CMA) raided the offices of secondary ticket marketplaces StubHub and Viagogo as part of a probe into “suspected breaches of consumer law.”

According to sources, CMA officials raided the offices in August and seized information about the companies’ dealings with popular ticket touts, who buy up tickets for popular events with the intention of selling them on with a high mark-up. The watchdog had earlier issued four ticket resale companies — including GetMeIn and Seatwave — with an “information notice” asking for information on their relationships with major resellers and the money they’ve earnt from ticket sales.

GetMeIn and Seatwave handed over what was asked of them, but eBay-owned StubHub and Swiss-based Viagogo did not comply.

It’s believed that officials targeted data relating to StubHub’s “top seller” programme, which provides its biggest resellers — who sell more than $250,000 worth of tickets a year — with exclusive discounts on fees and a dedicated platform that allows them to better manage their listings.

StubHub said in a statement: “We understand the CMA investigation is ongoing and therefore await the outcome of this.”

Source: The Guardian

Facebook relaunches Events app with a Yelp-like focus

Last year, Facebook launched Events as a standalone app to help you find something to do. It wasn’t exactly a runaway hit; you’d be forgiven if you’d never even heard of it. That’s why it’s not surprising that Facebook has relaunched the app as “Facebook Local,” with an emphasis on finding restaurants, bars and local businesses. It appears to be a direct competitor for Yelp. You can download it now for iOS and Android.

While plenty of people use Facebook’s integrated local features, including business reviews, it’s not clear why it needs to be spun out into a separate app. Facebook Local Product Manager Aditya Koolwal told TechCrunch that the goal of the product is to basically make it easier to quickly look up information when making plans with friends.

However, the information is still available within Facebook itself — the “Nearby” tab is being rebranded as “Local.” It’s not clear what would attract people to Facebook Local from the apps they already use, such as Yelp, but having such a broad user base is a constant advantage for the social network. Time will tell how the company chooses to develop the service going forward.

Source: TechCrunch, Facebook

ESPN Plus standalone service will start streaming in spring 2018

After purchasing streaming company BamTech for $1.58 billion, Disney said it would offer content via its own on-demand service, rather than going through Netflix. That’s not just going to be Star Wars, Marvel and other movie content, but sports from ESPN, too. During its earnings call yesterday, Disney CEO Bob Iger revealed that ESPN’s streaming service would be called ESPN Plus (ESPN+) and launch in the spring of 2018.

“The product will be accessible through a new and fully redesigned ESPN app, which will allow users to access sports scores and highlights, stream our channels on an authenticated basis and subscribe to ESPN+ for additional sports coverage, including thousands of live sporting events,” Iger said. “This one app [will offer] sports fans far more than they can get on any other app, website or channel.”

It’s not clear whether the service will be offered by itself to cord-cutters or require a cable TV package. ESPN previously said that consumers would be able to purchase packages for individual sports, however. Iger didn’t reveal any pricing, but suggested that since it would have less volume that streaming services like Netflix or Hulu Plus, “the pricing will reflect that.”

Ironically, ESPN will lay off more than 100 staff after the US Thanksgiving, according to Sports Illustrated. That will reportedly include on-air talent, producers, executives and digital tech staff. In 2015, the company laid off around 300 employees, representing about five percent of its workers. Despite that, Iger said yesterday that ESPN never “lost confidence in ESPN,” and plans to launch a new morning program on the regular cable channel.

IBM’s processor pushes quantum computing closer to ‘supremacy’

IBM Q research has built and tested an operational 50 qubit prototype processor, a huge leap up from its previous record of 17 qubits. The company is also set to make a 20 qubit quantum system available online for clients to try, with an updated superconducting design, connectivity and packaging. That’ll let users run computations with a “field-leading” 90 microseconds of coherence, allowing “high-fidelity quantum operations,” IBM says.

Quantum computers work much differently than regular supercomputers, taking advantage of weird quantum physics principals like “superposition.” In theory, they can run specific programs, like encryption-cracking algorithms, many, many times faster than regular computers.

The 50 qubit system (shown below) is a significant leap toward practical quantum computers. In September, Harvard University researchers said they built a 51 qubit model, but it appears that IBM’s model held “coherence” longer, allowing more calculations to be done. “We are really proud of this, it’s a big frickin’ deal,” IBM AI and quantum computer director Dario Gil told MIT Technology Review. Other players in quantum computing including Google, Intel and Rigetti.

IBM’s 50 qubit computer is just a prototype, but it will soon have a working 20 qubit computer that users can try online by the end of 2017, with improvements planned throughout 2018. The company has already made lower-powered machines available for cloud use, and used a 7 qubit model to simulate a molecule, for example. IBM says around 60,000 users have run 1.7 million experiments, resulting in 35 research papers.

Quantum computers haven’t been able to run programs that a regular computer can’t, so the massive speed breakthrough many have hoped for has yet to arrive. Still, Google researchers said last month that a 50 qubit computer they’re working on could surpass current supercomputers, achieving an (excellently-named) milestone called Quantum Supremacy. The technology is tricky, though, so there’s good reason not to get too excited. But, there’s also a good chance that quantum computers will finally break that barrier sometime in the next year or two.

Source: IBM