Facebook contest reveals deepfake detection is still an ‘unsolved problem’

Facebook has announced the results of its first Deepfake Detection Challenge, an open competition to find algorithms that can spot AI-manipulated videos. The results, while promising, show there’s still lots of work to be done before automated systems can reliably spot deepfake content, with researchers describing the issue as an “unsolved problem.”

Facebook says the winning algorithm in the contest was able to spot “challenging real world examples” of deepfakes with an average accuracy of 65.18 percent. That’s not bad, but it’s not the sort of hit-rate you would want for any automated system.

Deepfakes have proven to be something of an exaggerated menace for social media. Although the technology prompted much handwringing about the erosion of reliable video evidence, the political effects of deepfakes have so far been minimal. Instead, the more immediate harm has been the creation of nonconsensual pornography, a category of content that’s easier for social media platforms to identify and remove.

Mike Schroepfer, Facebook’s chief technology officer, told journalists in a press call that he was pleased by the results of the challenge, which he said would create a benchmark for researchers and guide their work in the future. “Honestly the contest has been more of a success than I could have ever hoped for,” he said.

Examples of clips used in the challenge. Can you spot the deepfake?
Video by Facebook

Some 2,114 participants submitted more than 35,000 detection algorithms to the competition. They were tested on their ability to identify deepfake videos from a dataset of around 100,000 short clips. Facebook hired more than 3,000 actors to create these clips, who were recorded holding conversations in naturalistic environments. Some clips were altered using AI by having other actors’ faces pasted on to their videos.

Researchers were given access to this data to train their algorithms, and when tested on this material, they produced accuracy rates as high as 82.56 percent. However, when the same algorithms were tested against a “black box” dataset consisting of unseen footage, they performed much worse, with the best-scoring model achieving an accuracy rate of 65.18 percent. This shows detecting deepfakes in the wild is a very challenging problem.

Schroepfer said Facebook is currently developing its own deepfake detection technology separate from this competition. “We have deepfake detection technology in production and we will be improving it based on this context,” he said. The company announced it was banning deepfakes earlier this year, but critics pointed out that the far greater threat to disinformation was from so-called “shallowfakes” — videos edited using traditional means.

The winning algorithms from this challenge will be released as open-source code to help other researchers, but Facebook said it would be keeping its own detection technology secret to prevent it from being reverse-engineered.

Schroepfer added that while deepfakes were “currently not a big issue” for Facebook, the company wanted to have the tools ready to detect this content in the future — just in case. Some experts have said the upcoming 2020 election could be a prime moment for deepfakes to be used for serious political influence.

“The lesson I learned the hard way over the last couple of years, is I want to be prepared in advance and not be caught flat footed,” said Schroepfer. “I want to be really prepared for a lot of bad stuff that never happens rather than the other way around.”

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The best alternatives to Amazon for buying tech right now

While Amazon has traditionally been a one-stop shop for tech gear, its mid-April delays are making other retailers viable options for getting what you want with a shorter lead time. Online stores like Newegg and B&H are stepping up, alongside (gasp!) actual brick-and-mortar stores like Best Buy and Office Depot.

Alas, the old way of buying tech—looking for the absolute lowest price—is being set aside, as consumers are willing to pay a bit more for any products actually in stock. While we can’t guarantee a particular retailer will have the specific product you’re looking for, all of the ones we checked appeared to have a decent assortment of items in stock that we haven’t been able to get through Amazon.

Why you can’t find anything on Amazon

The watchword right now isn’t so much price as availability. According to retail analyst firm NPD, productivity hardware saw “historic” sales increases over the first two weeks of March, as more regions or entire states started issuing shelter-in-place orders. NPD vice president and technology analyst Stephen Baker noted that sales of computer monitors to consumers almost doubled during the first two weeks of March, versus a year ago. Mice, keyboards, and notebook PC sales increased by 10 percent.  Businesses are buying, too: Corporate notebook sales were up 30 percent in the last week of February, and then 50 percent for the first two weeks of March.

The buying spree has put a severe and unexpected crimp in the tech supply chain. Notebooks and monitors are still shipping from Amazon, though the company has said it’s prioritizing household goods. A survey of Amazon products PCWorld recently conducted showed a wide swath of tech products delayed until late April, and that’s still the case, generally. We are seeing more short-term availability of tech goods than before, though not on a par with Amazon’s typical performance. 

amazon usb c hub edited Mark Hachman / IDG

Right now, even products listed as available from Amazon Prime are typically a week or so out in terms of availability.

Newegg: A good place to start

Newegg is making an aggressive pitch to replace Amazon as the one-stop shop for tech gear during the work-from-home mandates. “Newegg is very much open for business with little to no interruption,” said Anthony Chow, the global chief executive of Newegg, when asked for comment. “Sales across the board—especially tech products—are up substantially in recent weeks. In light of that, we’ve been working to secure even more inventory to keep up with the demand, much of which stems from those who need hardware and other products to work from home.”

newegg deals page Mark Hachman / IDG

Newegg caters more to the hardcore enthusiast than the work-from-home crowd, but there’s still deals to be had.

Like Amazon, Newegg is a storefront for many sellers, and that’s likely why Newegg’s inventory is broader and more available right now across a variety of categories than it is at other retailers. Newegg has built in quite a bit of wiggle room, though.

Unlike Amazon, Newegg doesn’t guarantee its shipping dates. It offers only a window within which “most customers” will likely receive their products. In the example below, for instance, Newegg merely says that “most customers” will receive their webcam shipment within 6 to 16 days.

newegg logitech webcam 2 Mark Hachman / IDG

Newegg seems to have good availability and reasonable prices, but this seems excessively wishy-washy, “Most” customers, in 6 to 16 days?

Once you start reading the fine print and realize the product’s shipping from Australia, the elasticity of the ship date starts to make a lot more sense. It was a similar deal with the Razer Kiyo 1080p camera with a ring light: At press time, Amazon was entirely sold out, but via Newegg, Razer promised delivery—from Hong Kong—in as little as 4 days…or as many as 17. 

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Tech News

Old Google Pay in the US will become useless in April

It isn’t exactly out of the ordinary that Google retires an app or service in favor of a new one. Often, but not always, it waits for the new version to at least be ready for the all old users to switch to. That didn’t actually happen smoothly in the transition to YouTube Music but hopefully, Google Pay will be a different story. No, users are just being pushed to a new version of the Google Pay app and the company will be forcing users’ hands by making the old one practically useless in April.

The new Google Pay announced late last year is both a redesign of the old app as well as a consolidation of its confusing “G Pay” brand of the past. It wasn’t a one-for-one replacement of the old Google Pay, with some of its features moved to other parts of the Google Play services framework. That said, it also added more features that the old app didn’t have and will never have since it has been deprecated.

Not all users may have moved over to the new Google Pay app by now for one reason or another but they may have no other choice soon. Come April 5, the old Google pay app will lose its ability to send or receive money, view past transactions, or even see your remaining balance. In other words, you’ll still be able to open the app but can’t do anything else unless you move to the new app.

This change, however, applies only to the US, according to Google’s confirmation to Android Police. The new Google Pay app isn’t available in other markets yet so those will keep the status quo, at least for now. Curiously, there is no mention of other markets that do have the new Google Pay app already.

The new Google Pay app is apparently still marked as Early Access, suggesting it’s still in pre-release development. That could change before April 5, however, and there’s still plenty of time to polish up the app before that deadline.

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Pokemon GO March updates include Beldum and Gible, for starters

Pokemon GO has a collection of updates and releases ready to roll for the month of March, 2021. In this month of chills, users will find a Research Breakthrough connected to Gible. If you’re looking to make use of this tiny baby monster, you’ll need to start this Research Breakthrough quest and follow through on seven individual days – it’s not going to be easy.

There’ll be some Legendary Shadow Pokemon action in the month of March, starting with Shadow Articuno. It’s POSSIBLE we’ll see the rest of the Legendary bird Pokemon collection available in a similar manner throughout the year – but Niantic’s currently tight-lipped on the matter. For now, the first of these will appear with Giovanni in a battle that ALSO, you guessed it: won’t be easy.

Throughout the month of March, 2021, there’ll be several events. The one we’re most looking forward to will be “Weather Week”, that’ll take place from Wednesday, March 24, 2021, all the way to March 29, 2021. That event will deliver weather-themed Pokemon aplenty! This means we’re seeing Tonadus, Thundurus, and Landorous, all of which will be joined by new avatar items for great justice!

On Sunday, March 14, 2021, there’ll be an Incense Day. That Incense Day will allow said Incense to attract more Psychic and Steel-type Pokemon than usual. According to Niantic, this specifically includes Beldum. If you’ve ever wanted a Metagross, now’s the time to go at it full force!

This is only part of the list of March bits and pieces for Pokemon GO. We’re expecting a whole lot more specific details on the event series that’ll effectively be the most action-packed March in the history of Pokemon GO – stay tuned as we learn more!

UPDATE: Niantic suggests they’ll be delivering a free one-time bundle every single Monday in March, 2021. These bundles will include 10 Ultra Balls, 10 Razz Berries, and a Remote Raid Pass. You’ll need to head to the in-game shop and tap said bundle to access for free – easy!

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Tech News

Starlink speeds could be boosted by ongoing system upgrades

Almost all of Elon Musk’s endeavors and dreams have proven to be controversial but most of them prove themselves in the long run. While The Boring Company has still to bear any usable fruit and Neuralink is still too early in its infancy, SpaceX’s Starlink Internet satellite constellation continues to split people up. It still has to reach its full potential, both in number of satellites and promised speeds, but Elon Musk says that there might be a speed bump on the way.

Just a few days ago, Musk boasted that Starlink’s speeds will eventually reach 300 MB/s with latencies as low as around 20ms. Those are definitely ambitious goals compared to the range that SpaceX advertises. Just to set expectations correctly, it says that speeds could go from 50 to 150 MB/s, depending on certain conditions, and latency can be as high as 40 ms.

Those numbers might still be higher than what most beta testers experience on average but figures are really not that consistent. Such is really the case when using satellites that have to move at certain positions in low earth orbits which, in turn, also determines the speeds that users get depending on their location on the Earth.

That said, Elon Musk is saying that users might experience higher downloads at times while their testing system upgrades. That said, those upgrades can also cause performance to fluctuate during that period.

Users are reporting exactly that, with some getting 20 to 180 MB/s, at certain times. The irregularity is to be expected during this system upgrade testing phase but some beta testers are hoping that Starlink would be able to deliver on the advertised 150 MB/s promise first before promising double that speed sometime later this year.

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Why IT needs to lead the next phase of data science

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Most companies today have invested in data science to some degree. In the majority of cases, data science projects have tended to spring up team by team inside an organization, resulting in a disjointed approach that isn’t scalable or cost-efficient.

Think of how data science is typically introduced into a company today: Usually, a line-of-business organization that wants to make more data-driven decisions hires a data scientist to create models for its specific needs. Seeing that group’s performance improvement, another business unit decides to hire a data scientist to create its own R or Python applications. Rinse and repeat, until every functional entity within the corporation has its own siloed data scientist or data science team.

What’s more, it’s very likely that no two data scientists or teams are using the same tools. Right now, the vast majority of data science tools and packages are open source, downloadable from forums and websites. And because innovation in the data science space is moving at light speed, even a new version of the same package can cause a previously high-performing model to suddenly — and without warning — make bad predictions.

The result is a virtual “Wild West” of multiple, disconnected data science projects across the corporation into which the IT organization has no visibility.

To fix this problem, companies need to put IT in charge of creating scalable, reusable data science environments.

In the current reality, each individual data science team pulls the data they need or want from the company’s data warehouse and then replicates and manipulates it for their own purposes. To support their compute needs, they create their own “shadow” IT infrastructure that’s completely separate from the corporate IT organization. Unfortunately, these shadow IT environments place critical artifacts — including deployed models — in local environments, shared servers, or in the public cloud, which can expose your company to significant risks, including lost work when key employees leave and an inability to reproduce work to meet audit or compliance requirements.

Let’s move on from the data itself to the tools data scientists use to cleanse and manipulate data and create these powerful predictive models. Data scientists have a wide range of mostly open source tools from which to choose, and they tend to do so freely. Every data scientist or group has their favorite language, tool, and process, and each data science group creates different models. It might seem inconsequential, but this lack of standardization means there is no repeatable path to production. When a data science team engages with the IT department to put its model/s into production, the IT folks must reinvent the wheel every time.

The model I’ve just described is neither tenable nor sustainable. Most of all, it’s not scalable, something that’s of tantamount importance over the next decade, when organizations will have hundreds of data scientists and thousands of models that are constantly learning and improving.

IT has the opportunity to assume an important leadership role in creating a data science function that can scale. By leading the charge to make data science a corporate function rather than a departmental skill, the CIO can tame the “Wild West” and provide strong governance, standards guidance, repeatable processes, and reproducibility — all things at which IT is experienced.

When IT leads the charge, data scientists gain the freedom to experiment with new tools or algorithms but in a fully governed way, so their work can be raised to the level required across the organization. A smart centralization approach based on Kubernetes, Docker, and modern microservices, for example, not only brings significant savings to IT but also opens the floodgates on the value the data science teams can bring to bear. The magic of containers allows data scientists to work with their favorite tools and experiment without fear of breaking shared systems. IT can provide data scientists the flexibility they need while standardizing a few golden containers for use across a wider audience. This golden set can include GPUs and other specialized configurations that today’s data science teams crave.

A centrally managed, collaborative framework enables data scientists to work in a consistent, containerized manner so that models and their associated data can be tracked throughout their lifecycle, supporting compliance and audit requirements. Tracking data science assets, such as the underlying data, discussion threads, hardware tiers, software package versions, parameters, results, and the like helps reduce onboarding time for new data science team members. Tracking is also critical because, if or when a data scientist leaves the organization, the institutional knowledge often leaves with them. Bringing data science under the purview of IT provides the governance required to stave off this “brain drain” and make any model reproducible by anyone, at any time in the future.

What’s more, IT can actually help accelerate data science research by standing up systems that enable data scientists to self-serve their own needs. While data scientists get easy access to the data and compute power they need, IT retains control and is able to track usage and allocate resources to the teams and projects that need it most. It’s really a win-win.

But first CIOs must take action.  Right now, the impact of our COVID-era economy is necessitating the creation of new models to confront quickly changing operating realities. So the time is right for IT to take the helm and bring some order to such a volatile environment.

Nick Elprin is CEO of Domino Data Lab.


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PlayStation owners grow frustrated as PSN outage spans weekend

A number of PlayStation owners reported issues playing certain games starting on Friday. Soon after the reports started rolling in, Sony updated its PlayStation Network status to indicate that it is experiencing issues in its ‘games and social’ category — an issue that has persisted through Saturday and into Sunday with no clear relief in sight.

Sony acknowledged that its PSN is having problems, but it has since remained quiet about the issue. It’s unclear when the issue is expected to be fixed and what is behind the troubles. The problem revolves around network gameplay, making it difficult to get into online matches in some games like Fortnite.

Down Detector continues to show issues with the PSN, with reports remaining steady through around midnight Eastern Time into Sunday. The majority of reports from users cite issues with playing games, though a significant portion also states they’re having problems signing into their PlayStation accounts.

Likewise, some PlayStation owners are also reporting issues with the platform’s social features, which include things like messaging and parties. Sony notes on its PSN status website that this issue is impacting the PlayStation 4 and PlayStation 5, as well as the older PS Vita handheld console and the PS3.

Sadly, this isn’t the first PlayStation Network outage that has persisted over multiple days; it’s hard to guess when the solution will finally arrive. The outage hit only a day after Microsoft experienced similar issues with its Xbox services, but that problem was quickly resolved.

You can monitor the outage on Sony’s PlayStation Network status website.

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Lenovo Yoga C940 15 review: Doing what the MacBook Pro doesn’t

Plenty of Windows laptops try to position themselves as MacBook Pro alternatives, combining slick designs and vibrant displays with powerful PC performance, but the Lenovo Yoga C940 15 is no mere copycat.

While the Yoga looks sharp and has a bright display, it also leans into its differences as a Windows PC. It has a touchscreen that flips around into tablet mode, a built-in stylus for writing or sketching, and—thank heavens—a full-sized USB-A port to complement its two USB-C connections. It even fits in a number pad without cramping its excellent keyboard.

This review is part of our ongoing roundup of the best laptops. Go there for information on competing products and how we tested them.

That’s not to say the Lenovo Yoga C940 15 ticks every imaginable box. Screen backlighting is a bit uneven, audio quality could be better, and limited configuration options will prevent you from turning this into a beastly desktop replacement. Also, if we’re comparing to Apple’s MacBooks, Lenovo’s laptop doesn’t include all the same niceties, such as a slightly larger screen and jumbo-sized trackpad.

Still, the Lenovo Yoga C940 15 is a decent choice for those who want a luxurious workhorse PC without giving up what Windows does best.

Tech specs

Our Lenovo Yoga C940 15 review unit has a list price of $1,700 at Best Buy and includes the following specs:

  • 15.6-inch display with 1920 x 1080 resolution
  • 9th-generation Intel Core i7-9750H processor
  • Nvidia GeForce GTX 1650 Max-Q graphics
  • 512GB SSD
  • 16GB DDR4-2666 RAM
  • Left side: Two USB-C 3.1 ports (with Thunderbolt 3), proprietary charger, headphone jack
  • Right side: USB-A 3.0 port
  • Wi-Fi 6 Support
  • Stylus with 4,096 pressure sensitivity levels
  • Fingerprint reader
  • 720p webcam with privacy shutter
  • Windows 10 Home
  • Dimensions: 14 x 9.4 x 0.8 inches
  • Weight: 4.41 pounds (5.68 pounds with charger)
yogac94015left Jared Newman / IDG

The Lenovo Yoga C940 15 has two USB-C ports and a proprietary charging port on the left side, plus a USB-A port on the right.

Best Buy also offers a 4K display version with the same other specs as above for $1,899. Lenovo’s website offers several other configurations: On the low end, you can drop to 12GB of RAM and 256GB of storage (currently $1,460), while on the high end you can upgrade to an Intel Core-i9-9880H processor, 4K display, and 2TB SSD ($2,440 as of this writing). In all cases, the Nvidia GeForce GTX 1650 is the only option for graphics, and the non-upgradeable RAM tops out at 16GB.

While it’s nice that Lenovo included a USB-A port on this laptop, its use of a proprietary charger—similar to the one Lenovo uses for its ThinkPads—is a drag, as is the lack of a MicroSD card slot and additional USB ports on the right side of the laptop.

Design and display

The Lenovo Yoga C940 15 comes in either a darker “Iron Gray” or a lighter shade that Lenovo refers to as “Mica.” Its all-aluminum body, along with its sharp edges and flat sides, only serve to underscore the laptop’s heft, which is unavoidable given the discrete GPU inside. But for a workhorse laptop that folds around into a tablet, the C940’s size and weight are reasonable.

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Tech News

Most ads you see are chosen by a reinforcement learning model — here’s how it works

Every day, digital advertisement agencies serve billions of ads on news websites, search engines, social media networks, video streaming websites, and other platforms. And they all want to answer the same question: Which of the many ads they have in their catalog is more likely to appeal to a certain viewer? Finding the right answer to this question can have a huge impact on revenue when you are dealing with hundreds of websites, thousands of ads, and millions of visitors.

Fortunately (for the ad agencies, at least), reinforcement learning, the branch of artificial intelligence that has become renowned for mastering board and video games, provides a solution. Reinforcement learning models seek to maximize rewards. In the case of online ads, the RL model will try to find the ad that users are more likely to click on.

The digital ad industry generates hundreds of billions of dollars every year and provides an interesting case study of the powers of reinforcement learning.

Naïve A/B/n testing

To better understand how reinforcement learning optimizes ads, consider a very simple scenario: You’re the owner of a news website. To pay for the costs of hosting and staff, you have entered a contract with a company to run their ads on your website. The company has provided you with five different ads and will pay you one dollar every time a visitor clicks on one of the ads.

Your first goal is to find the ad that generates the most clicks. In advertising lingo, you will want to maximize your click-trhough rate (CTR). The CTR is ratio of clicks over number of ads displayed, also called impressions. For instance, if 1,000 ad impressions earn you three clicks, your CTR will be 3 / 1000 = 0.003 or 0.3%.

Before we solve the problem with reinforcement learning, let’s discuss A/B testing, the standard technique for comparing the performance of two competing solutions (A and B) such as different webpage layouts, product recommendations, or ads. When you’re dealing with more than two alternatives, it is called A/B/n testing.

[Read: How do you build a pet-friendly gadget? We asked experts and animal owners]

In A/B/n testing, the experiment’s subjects are randomly divided into separate groups and each is provided with one of the available solutions. In our case, this means that we will randomly show one of the five ads to each new visitor of our website and evaluate the results.

Say we run our A/B/n test for 100,000 iterations, roughly 20,000 impressions per ad. Here are the clicks-over-impression ratio of our ads:

Ad 1: 80/20,000 = 0.40% CTR

Ad 2: 70/20,000 = 0.35% CTR

Ad 3: 90/20,000 = 0.45% CTR

Ad 4: 62/20,000 = 0.31% CTR

Ad 5: 50/20,000 = 0.25% CTR

Our 100,000 ad impressions generated $352 in revenue with an average CTR of 0.35%. More importantly, we found out that ad number 3 performs better than the others, and we will continue to use that one for the rest of our viewers. With the worst performing ad (ad number 2), our revenue would have been $250. With the best performing ad (ad number 3), our revenue would have been $450. So, our A/B/n test provided us with the average of the minimum and maximum revenue and yielded the very valuable knowledge of the CTR rates we sought.

Digital ads have very low conversion rates. In our example, there’s a subtle 0.2% difference between our best- and worst-performing ads. But this difference can have a significant impact on scale. At 1,000 impressions, ad number 3 will generate an extra $2 in comparison to ad number 5. At a million impressions, this difference will become $2,000. When you’re running billions of ads, a subtle 0.2% can have a huge impact on revenue.

Therefore, finding these subtle differences is very important in ad optimization. The problem with A/B/n testing is that it is not very efficient at finding these differences. It treats all ads equally and you need to run each ad tens of thousands of times until you discover their differences at a reliable confidence level. This can result in lost revenue, especially when you have a larger catalog of ads.

Another problem with classic A/B/n testing is that it is static. Once you find the optimal ad, you will have to stick to it. If the environment changes due to a new factor (seasonality, news trends, etc.) and causes one of the other ads to have a potentially higher CTR, you won’t find out unless you run the A/B/n test all over again.

What if we could change A/B/n testing to make it more efficient and dynamic?

This is where reinforcement learning comes into play. A reinforcement learning agent starts by knowing nothing about its environment’s actions, rewards, and penalties. The agent must find a way to maximize its rewards.

In our case, the RL agent’s actions are one of five ads to display. The RL agent will receive a reward point every time a user clicks on an ad. It must find a way to maximize ad clicks.

The multi-armed bandit

multi-armed bandit