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PowerTransformer uses AI to rewrite text to correct gender biases in character portrayals

Unconscious biases are pervasive in text and media. For example, female characters in stories are often portrayed as passive and powerless while men are portrayed as more proactive and powerful. According to a McKinsey study of 120 movies across ten markets, the ratio of male to female characters was 3:1 in 2016, the same it’s been since 1946.

Motivated by this, researchers at the Allen Institute for Artificial Intelligence and the University of Washington created PowerTransformer, a tool that aims to rewrite text to correct implicit and potentially undesirable bias in character portrayals. They claim that PowerTransformer is a major step toward mitigating well-documented gender bias in movie scripts, as well as other scripts in other forms of media.

PowerTransformer is akin to GD-IQ, a tool that leverages AI developed at the University of Southern California Viterbi School of Engineering to analyze the text of a script and determine the number of male and female characters and whether they’re representative of the real population at large. GD-IQ also can discern the numbers of characters who are people of color, LGBTQ, experience disabilities, or belong to other groups typically underrepresented by Hollywood storytelling.

But PowerTransformer goes one step further and tackles the task of controllable text revision, or rephrasing text to a style using machine learning. For example, it can automatically rewrite a sentence like “Mey daydreamed about being a doctor” as “Mey pursued her dream to be a doctor,” which has the effect of giving the character Mey more authority and decisiveness.

The researchers note that controllable rewriting systems face key challenges. First, they need to be able to make edits beyond surface-level paraphrasing, as simple paraphrasing often doesn’t adequately address overt bias (the choice actions) and subtle bias (the framing of actions). Second, their debiasing revisions should be purposeful and precise and shouldn’t make unnecessary changes to the underlying meaning of the text.

PowerTransformer overcomes these challenges by jointly learning to reconstruct partially masked story sentences while also learning to paraphrase from an external corpus of paraphrases. The model recovers masked-out agency-associated verbs in sentences and employs a vocab-boosting technique during generation to increase the likelihood it uses words with a target level of agency (i.e., ability to act and make choices). For instance, “A friend asked me to watch her two year old child for a minute” would become “A friend needed me to watch her two year old child for a minute,” lowering agency, while “Allie was failing science class” would become “Allie was taking science class.”

During experiments, the researchers investigated whether PowerTransformer could mitigate gender biases in portrayals of 16,763 characters from 767 modern English movie scripts. Of those characters, 68% were inferred to be men and only 32% women; they attempted to re-balance the agency levels of female characters to be on par with male characters.

The results show that PowerTransformer’s revisions successfully increased the instances of positive agency of female characters while decreasing their negative agency or passiveness, according to the researchers. “Our findings on movie scripts show the promise of using controllable debiasing to successfully mitigate gender biases in portrayal of characters, which could be extended to other domains,” they wrote. “Our findings highlight the potential of neural models as a tool for editing out social biases in text.”

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Intel Geospatial is a cloud platform for AI-powered imagery analytics

Intel today quietly launched Intel Geopspatial, a cloud platform that features data engineering solutions, 3D visualizations, and basic analytics tools for geovisual workloads. Intel says it’s designed to provide access to 2D and 3D geospatial data and apps through an ecosystem of partners, addressing use cases like vegetation management, fire risk assessment and inspection, and more.

The geospatial analytics market is large and growing, with a recent Markets and Markets report estimating it’ll be worth $96.34 billion by 2025. Geospatial imagery can help companies manage assets, for example network assets prone to damage during powerful storms. Moreover, satellite imagery and the AI algorithms trained to analyze it have applications in weather prediction, defense, transportation, insurance, and even health care, namely because of their ability to capture and model environments over extended periods of time.

Using Intel Geospatial, which is powered by Intel datacenters, customers can ingest and manage geovisual data from a mobile- and desktop-accessible web portal. They’re able to view slope, elevation, and other data layers in a 3D environment with zoom, pan, and tilt controls and auto-updated time and date stamps. Moreover, they can analyze the state of various target assets as well as run analytics to extract insights that can then be passed to existing enterprise systems.

Intel Geospatial

Intel Geospatial offers data from satellites, manned aircraft, and unmanned aerial vehicles (UAVs) like drones, with data from Mobileye — Intel’s autonomous vehicle subsidiary — available upon request. The platform’s user interface auto-populates with area-specific datasets and allows for search based on street addresses or GPS coordinates, which are standardized for analytics.

Intel Geospatial offers out-of-the-box algorithms for risk classification, object counting, distance measuring, and public and private record reconciliation. Intel says it’s leveraging startup Enview’s AI to power 3D geospatial classification for faster lidar analytics turnaround. Meanwhile, LiveEO is delivering algorithmic monitoring for railway, electricity, and pipelines.

Intel’s new service joins the list of geospatial products already offered by companies including Google, Microsoft, and Amazon. Google’s BigQuery GIS lets Google Cloud Platform customers analyze and visualize geospatial data in BigQuery. Microsoft offers Azure Maps, a set of geospatial APIs to add spatial analytics and mobility solutions to apps. Amazon provides a registry of open geospatial datasets on Amazon Web Services. And Here Technologies, the company behind a popular location and navigation platform, has a service called XYZ that enables anyone to upload their geospatial data — such as points, lines, polygons, and related metadata — and create apps equipped with real-time maps.

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Google open-sources MT5, a multilingual model trained on over 101 languages

Not to be outdone by Facebook and Microsoft, both of whom detailed cutting-edge machine learning language algorithms in late October, Google this week open-sourced a model called MT5 that the company claims achieves state-of-the-art results on a range of English natural processing tasks tasks. MT5, a multilingual variant of Google’s T5 model that was pretrained on a dataset covering 101 languages, contains between 300 million to 13 billion parameters (variables internal to the model used to make predictions) and ostensibly has enough capacity to learn over 100 languages without significant “interference” effects.

The goal of multilingual AI model design is to build a model that can understand the world’s over 7,000 languages. Multilingual AI models share information between similar languages, which benefits low-resource languages and allows for zero-shot language processing, or the processing of languages the model hasn’t seen. As models increase in size, they require larger datasets that can be laborious and difficult to create, which has led researchers to focus on web-scraped content.

MT5 was trained on MC4, a subset of C4, a collection of about 750GB of English-language text sourced from the public Common Crawl. (Common Crawl contains billions of webpages scraped from the internet.) While the C4 dataset was explicitly designed to be English-only, MC4 covers 107 languages with 10,000 or more webpages across all of the 71 monthly scrapes released to date by Common Crawl.

There’s evidence that language models amplify the biases present in the datasets they’re trained on. While some researchers claim that no current machine learning technique sufficiently protects against toxic outputs, Google researchers attempted to mitigate bias in MT5 by deduplicating lines across the MC4 documents and filtering pages containing bad words. They also detected each page’s primary language using a tool and removed pages where the confidence was below 70%.

Google says the largest MT5 model, which has 13 billion parameters, topped every benchmark it was tested against as of October 2020. This included five tasks from the Xtreme multilingual benchmark; the XNLI entailment task covering 14 languages; the XQuAD, MLQA, and TyDi QA reading comprehension benchmarks with 10, 7, and 11 languages respectively; and the PAWS-X paraphrase identification dataset with 7 languages.

Of course, it’s the subject of debate whether the benchmarks adequately reflect the model’s true performance. Some studies suggest that open-domain question-answering models — models theoretically capable of responding to novel questions with novel answers — often simply memorize answers found in the data on which they’re trained, depending on the data set. But the the Google researchers assert of MT5 that it’s a step toward powerful models that don’t require challenging modeling techniques.

“Overall, our results highlight the importance of model capacity in cross-lingual representation learning and suggest that scaling up a simple pretraining recipe can be a viable alternative [by] relying on … filtering, parallel data, or intermediate tasks,” wrote the Google researchers in a paper describing MT5. “We demonstrated that the T5 recipe is straightforwardly applicable to the multilingual setting, and achieve strong performance on a diverse set of benchmarks.”

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TechSee raises $30 million to streamline field service work with AR and computer vision

TechSee, which describes itself as an “intelligent visual assistance” company, today closed a $30 million investment round co-led by OurCrowd, Salesforce Ventures, and TELUS Ventures. A spokesperson for the startup says the capital injection will be used to enter new markets and verticals while expanding TechSee’s product offerings and capabilities.

The augmented reality market is estimated to grow from $10.7 billion in 2019 to $72.7 billion by 2024, according to a recent Markets and Markets report. At least a portion of that growth has been driven by field service applications; technicians are faced with the challenging task of working on equipment with varying technical specifications, often in confined or hard-to-reach spaces. With AR apps, they could have all of the information they need displayed in front of them while keeping their hands free to work.

TechSee was founded in 2014 by Eitan Cohen, Amir Yoffee, and Gabby Sarusi. Cohen conceptualized the idea after struggling to walk his parents through an issue they were having with their cable service.

TechSee’s cross-platform, AWS-hosted apps employ computer vision to recognize products and issues and streamline warranty registration . For instance, TechSee Live, the company’s call center solution, lets agents see what customers see through their smartphone cameras and visually guide them to resolutions. Agents can opt to receive live video or photos, with features like a visual session history and mobile screen mirroring, as well as share their desktops and browsers. Moreover, TechSee Live allows agents to send text messages or initiate calls during visual sessions, and to remotely scan serial numbers and barcodes using optical character recognition.

“Our models are created and trained to analyze electronic products, based on [provided] data, to allow the recognition of specific device models and issues,” TechSee explains on its website. In addition to devices themselves, the models can recognize individual components including ports, cables, buttons, error codes, indicator LEDs, statuses, and more to retrieve guidance from a knowledge base. “Data is collected through crowdsourcing of expertise, from synthetic image generation or other any visual data sets. Training a new device takes a matter of hours with ‘few-shot learning’ methods, reducing the number of required images from tens of thousands to just several.”

TechSee Live, which integrates with platforms including Salesforce, ZenDesk, Microsoft Dynamics, ServiceNow, SAP, and Oracle Service Cloud, also helps technicians, customers, and subcontractors manage visual libraries of predefined resolution instructions. It can identify the locations of users during visual sessions to keep tabs on field technicians and subcontractors, and it can be used to build and customize guided self-service flows for image capture while integrating with existing chatbots, apps, social media, and other self-service channels.

TechSee Live ships with a software development kit to enable screen sharing and access to a range of open APIs, and it plays nicely with smart glasses from “leading manufacturers.” On the privacy side of the equation, TechSee says that customers must approve connections for each help session (which can be paused or terminated at any time) and that the platform complies with data protection legislation including GDPR and doesn’t collect customers’ phone numbers or use cookies.

TechSee says its products are deployed at “hundreds” of service organizations across the globe with over 100,000 users. It’s currently engaged with companies including Verizon, Vodafone, Orange, Lavazza, Liberty Global, Altice, Hitachi, and Accenture.

“One of the very few silver linings to come out of this pandemic is that it’s accelerated adoption for our technology — in the field, at support centers, and for individual consumers,” Cohen told VentureBeat via email. “Everything is now contactless. When technicians simply aren’t allowed to enter a customer’s home to repair, say, a wireless router, or when a field technician cannot be dispatched to repair an HVAC system, both businesses and customers have to adapt … Interestingly, even now that restrictions are loosening a bit in some parts of the world and technicians can work in the field again, enterprises are completely bought into the automation. And the reason is simple: Every truck you don’t have to dispatch; every support issue you can resolve the first time; every contact center call deflection; and each time an agent’s productivity and efficiency improves spells enormous monetary savings for businesses.”

Scale Venture Partners and Planven Entrepreneur Ventures also participated in TechSee’s funding round, which brings the company’s total raised to over $50 million.

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Microsoft and MITRE release framework to help fend off adversarial AI attacks

Microsoft, the nonprofit MITRE Corporation, and 11 organizations including IBM, Nvidia, Airbus, and Bosch today released the Adversarial ML Threat Matrix, an industry-focused open framework designed to help security analysts to detect, respond to, and remediate threats against machine learning systems. Microsoft says it worked with MITRE to build a schema that organizes the approaches employed by malicious actors in subverting machine learning models, toward the goal of bolstering monitoring strategies around organizations’ mission-critical systems.

According to a Gartner report, through 2022, 30% of all AI cyberattacks will leverage training-data poisoning, model theft, or adversarial samples to attack machine learning-powered systems. Despite these reasons to secure systems, Microsoft claims its internal studies find most industry practitioners have yet to come to terms with adversarial machine learning. Twenty-five out of the 28 businesses responding to the Seattle company’s recent survey indicated they don’t have the right tools in place to secure their machine learning models.

The Adversarial ML Threat Matrix — which was modeled after the MITRE ATT&CK Framework — aims to address this with a curated set of vulnerabilities and adversary behaviors that Microsoft and MITRE vetted to be effective against production systems. With input from researchers at the University of Toronto, Cardiff University, and the Software Engineering Institute at Carnegie Mellon University, Microsoft and MITRE created a list of tactics that correspond to broad categories of adversary action. Techniques in the schema fall within one tactic and are illustrated by a series of case studies covering how well-known attacks such as the Microsoft Tay poisoning, the Proofpoint evasion attack, and other attacks could be analyzed using the Threat Matrix.

Above: The Adversarial ML Threat Matrix.

“The Adversarial Machine Learning Threat Matrix will … help security analysts think holistically. While there’s excellent work happening in the academic community that looks at specific vulnerabilities, it’s important to think about how these things play off one another,” Mikel Rodriguez, who oversees MITRE’s decision science research programs, said in a statement. “Also, by giving a common language or taxonomy of the different vulnerabilities, the threat matrix will spur better communication and collaboration across organizations.”

Microsoft and MITRE say they will solicit contributions from the community via GitHub, where the Adversarial ML Threat Matrix is now available. Researchers can submit studies detailing exploits that compromise the confidentiality, integrity, or availability of machine learning systems running on Amazon Web Services, Microsoft Azure, Google Cloud AI, IBM Watson, or embedded in client or edge device. Those who submit research will retain the permission to share and republish their work, Microsoft says.

“We think that securing machine learning systems is an infosec problem,” Microsoft Azure engineer Ram Shankar Siva Kumar and CVP Ann Johnson wrote in a blog post. “The goal of the Adversarial ML Threat Matrix is to position attacks on machine learning systems in a framework that security analysts can orient themselves in these new and upcoming threat … It’s aimed at security analysts and the broader security community: the matrix and the case studies are meant to help in strategizing protection and detection; the framework seeds attacks on machine learning systems, so that they can carefully carry out similar exercises in their organizations and validate the monitoring strategies.”

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Landing AI launches product inspection platform for manufacturers

Landing AI is today launching LandingLens, a computer vision platform that enables manufacturers to train AI models. The goal is to help businesses more quickly deploy AI for visual inspection of products ranging from automotive components to semiconductors and parts for steel manufacturing and appliance and electronics assembly.

Landing AI founder Andrew Ng told VentureBeat LandingLens is designed to help nonexperts and companies with small machine learning teams deal with “MLops” issues like importing and cleaning data, model monitoring, and anomaly alerts after deployment.

Ng, who founded Landing AI in 2017, is one of the cofounders of Google Brain and former chief scientist at Baidu. He cofounded Coursera and has created training material like AI for Everyone to help lower the bar to entry for machine learning. He also created the AI Startup fund to invest $175 million in AI startups.

“I feel like this is where the field of AI needs to go. Rather than highly skilled engineers at Landing AI or Google or wherever doing all the machine learning work to build verticalized platforms, someone in a platform [who] really understands what is a dent versus what is a sensible-minded blemish can do the customization. I think this is important for machine learning to reach its full potential,” Ng said.

Companies like Microsoft have launched services to encourage domain experts to train AI. But Ng said he created Landing AI to help more industries adopt AI, starting with manufacturing, and criticized public cloud companies for having good generic platforms that can be difficult for nonexperts to use.

“In the large tech companies, there can be a single machine learning model that creates a billion dollars worth a value. We all know a few examples of that. If you look at other industries, I think there’s going to be 10,000 projects that create a million dollars worth of value. This is certainly true of manufacturing, but if each of these 10,000 projects needs some customization, how can you set people up for success? I think that’s the core problem for AI going outside consumer internet companies, where there’s less of the centralization of data and use cases,” he said.

LandingLens offers pretrained models and data augmentation services, as well as a visual dashboard for managing data and AI models. The computer vision service will begin by serving manufacturers but may expand into other verticles, such as agriculture, medical, and security.

In other recent news, startup Seebo raised $9 million in July to detect inefficiencies in manufacturing, and last month Cogniac raised $10 million to help companies spot visual changes using AI.

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Navigating the ‘Great Rehire’ with data intelligence

Presented by Hiretual

Although some are cutting their hiring investments and downsizing their recruiting teams, it’s actually getting more expensive to attract suitable talent and convert them into new hires.

The average cost-per-applicant (CPA) in the U.S. has gone up 60% from last year, an uptick caused by specific pandemic-driven factors.

The CARES Act implemented this April gave unemployed workers a $600 boost in weekly benefits — a sustainable sum that has discouraged individuals from going back to work. Another important fact to note is that many companies are looking for professional workers that have not been affected by unemployment the way the hourly workforce has. Given the current economy, a majority of those in the professional talent pool are not taking any risks and would rather stay at their current roles.

Job openings are on the rise again, but this doesn’t necessarily mean long-term optimism for employers. To keep talent attraction costs at a sustainable level, hiring teams need the right resources to build pipelines during the upcoming stage of job recovery — a giant spike in hiring, otherwise known as the ‘Great Rehire.’

Succeeding in the ‘Great Rehire’

It took three years for the unemployment rate to drop below the 8% baseline after the 2008 recession because employers were unprepared for such drastic changes in the job scopes for many roles across the board.

The need for some jobs had been completely erased, and employers had to shift talent resources to functions they may not have spent as much on as before. Similarly, one of the biggest impacts of the pandemic has been forced digital transformation for all businesses — it’s no longer a ‘nice-to-have’, it’s a necessity. We’re seeing tech jobs lead the pack with a 13.4% month-over-month growth leaning toward roles in IT staffing, software, and digital operations.

Taking the lessons we’ve learned from the last recession, hiring teams must start preparing for both immediate and long-term business needs now before an all out war for talent begins in 2021. The continued evolution of recruitment technology will become pivotal for this strategy.

During the last recession, a new generation of recruitment was boosted — online recruitment via LinkedIn. The success of LinkedIn in addressing what employers lacked helped the company far exceed expectations during its IPO debut at the end of the recession.

Similarly, the Great Rehire will be spearheaded by a new generation of technology to help employers navigate an online recruitment market that has evolved far beyond the scope of just LinkedIn.

Moving beyond a talent database

I say this often — there is a stark difference between a data-driven team and an intelligence-driven team. What we’re currently seeing in data-driven hiring teams is the 80/20 dilemma. We’re spending 80% of our time finding and organizing data from platforms like LinkedIn and GitHub, job boards like Glassdoor and Indeed and resumes collected during recruitment marketing events. That leaves us only 20% of remaining time to spend analyzing that data for pipeline-building.

To prepare for the massive hiring surges in 2021 and effectively compete for talent with other companies, employers need to spend their time actually acting on the data they’ve collected. Instead of relying on data availability, employers need to start adopting a data intelligence approach that brings talent data points together. This infrastructure acts as a powerful middleware between external online databases like LinkedIn to in-house systems like an ATS or CRM.

At Hiretual, we call this recruiting with a “central talent data system.” This centralized loop of data actively recognizes and acts on structured and unstructured data to enrich old candidate information with newly sourced online data, remove duplicate data entries and provide teams with a dashboard of talent pool insights powered by AI/ML pattern recognition.

Southeast Asian ride-share giant, Grab, uses this approach to develop consistent and real-time engagement with local, regional, and global talent. Grab’s hiring team uses Hiretual as their talent data system to drive sourcing through efficient pattern recognitions and iterative searches. The team has successfully deployed a sourcing strategy that leverages real-time visibility into applications in their ATS, inbound recruitment marketing leads in their CRM, and open web communities like Stack Overflow, GitHub, and Kaggle.

Simplifying talent acquisition with contextual data

Grab oils their recruitment machine with a strong integrated framework powered by NLP-based data fusion. It is a knowledge graph for talent acquisition that informs the hiring process by analyzing queries and answering questions. So, rather than being additive to existing workflows, this well-integrated infrastructure consolidates processes within your tech stack.

The billions of entities and trillions of edges embedded within this graph gives rise to a scalable and responsive infrastructure for data federation, processing, and self-expansion. Ultimately, this will remove hours spent manually cleaning and organizing large volumes of multisource data to consolidate and simplify your hiring process.

Hiring teams can now use that 80% of their time to bring the human element back into recruiting. By optimizing existing strategies based on identified patterns from an online talent pool, more effort can be spent on heightened candidate engagement and a better candidate experience to bring talent attraction costs back down.

Making recruiting people-focused again

Data intelligence doesn’t dehumanize recruitment, instead it does quite the opposite. It creates more time for hiring teams to focus on personalization. Messages become less cookie-cutter, outreach becomes more intentional, and employers are able to reach a more diverse and inclusive scope of job seekers for current and future goals.

The companies that succeed in the Great Rehire will be the ones that best understand their candidates’ needs and their own organizational needs. This is the future of AI in recruitment, and it’s already here –let’s make that leap and welcome it.

Hiretual helps hiring teams centralize talent management to build robust, diverse and inclusive workforces with AI technology, learn more about us here.

Steven Jiang is CEO/Co-Founder at Hiretual.

Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. Content produced by our editorial team is never influenced by advertisers or sponsors in any way. For more information, contact [email protected]

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MIT CSAIL’s AI revives dead languages it hasn’t seen before

Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) claim to have developed a system that can decipher a lost language without needing knowledge of its relation to other languages. They say it’s a step toward a system that’s able to decipher lost languages using just a few thousand words.

Lost languages are more than an academic curiosity. Without them, humanity risks missing a body of knowledge about the people who historically spoke them. Unfortunately, most lost languages have such minimal records that scientists can’t decipher them by using conventional machine-translation algorithms. Some don’t have a well-researched “relative” language to be compared to, and they often lack traditional dividers like white space and punctuation.

This CSAIL work, which was supported in part by the Intelligence Advanced Research Projects Activity and spearheaded by MIT professor Regina Barzilay, a specialist in natural language processing, leverages several principles grounded in insights from historical linguistics. For instance, while a given language rarely adds or deletes a sound, certain sound substitutions are likely to occur. A word with a “p” in the parent language may change into a “b” in the descendant language, but changing to a “k” is less likely due to the significant pronunciation gap.

By incorporating these and other linguistic constraints, Barzilay and Luo developed a decipherment algorithm that can handle the vast space of transformations and the scarcity of a signal in the input. The algorithm learns to embed language sounds into a multidimensional space where differences in pronunciation are reflected in the distance between corresponding vectors. This design enables the system to capture patterns of language change and express them as computational constraints. The resulting model can segment words in an ancient language and map them to counterparts in a related language.

With the new system, the relationship between languages is inferred by the algorithm; the algorithm can assess the proximity between two languages. Moreover, when tested on known languages, it can accurately identify language families.

The team applied their algorithm to Iberian considering Basque as well as less likely candidates from Romance, Germanic, Turkic, and Uralic families. While Basque and Latin were closer to Iberian than other languages, they were still too different to be considered related, the system revealed.

In future work, the team hopes to expand their efforts beyond the act of connecting texts to related words in a known language, an approach referred to as cognate-based decipherment. The team’s approach would involve identifying the semantic meaning of the words even if they don’t know how to read them. “These methods of ‘entity recognition’ are commonly used in various text processing applications today and are highly accurate, but the key research question is whether the task is feasible without any training data in the ancient language,” Barzilay said.

Barzilay and coauthors aren’t the only ones to apply AI to recovering long-lost languages. Alphabet’s DeepMind developed a system, Pythia, that learned to recognize patterns in 35,000 relics containing more than 3 million words. It managed to guess missing words or characters from Greek inscriptions on surfaces including stone, ceramic, and metal that were between 1,500 and 2,600 years old.

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PS5 Preorder Guide: Consoles Sold Out, Accessories Available

With the launch date and price points of the PS5 revealed, the tense search for a preorder has begun. Launching November 12 at $500 for the standard console and $400 for the digital edition, the PS5 went up for preorder right after the recent PS5 event, and since then, stock has sold out at all major retailers, including Amazon, Best Buy, Walmart, GameStop, and Target.

GameStop had a second wave of PS5 preorders available to preorder on September 25, both online and in stores, but they sold out quickly. Earlier this week, Antonline had a batch of PS5 preorder bundles available, but those are no longer available either. This means we could see more retailers offering up PS5 preroders before launch next month, but they’ll likely be limited, if at all available.

Quick look: Where to preorder a PS5

As of our last update, PS5 preorders are sold out at all major retailers.

  • See PS5 at Amazon — $500
  • See PS5 at Walmart — $500
  • See PS5 at GameStop — $500
  • See PS5 at Target — $500
  • See PS5 at Sam’s Club — $500
  • See PS5 at Best Buy — $500
  • See PS5 Digital at GameStop — $400
  • See PS5 Digital at Amazon — $400
  • See PS5 Digital at Walmart — $400
  • See PS5 Digital at Best Buy — $400
  • See PS5 Digital at Target — $400
  • See PS5 bundles at Antonline

Which retailers will charge you upfront?

You should know that retailers handle preorders differently when it comes to charging your credit card or PayPal. Amazon doesn’t charge your card until the product ships, while GameStop waits until five days before shipment. Target, Walmart, Best Buy, and Sam’s Club will place an authorization hold on your credit card. Though your card won’t actually be charged until the PS5 ships, it’s possible you’ll see a pending charge that will disappear and then come back until release. These authorization holds can affect your available credit.

PS5 accessories in stock:

The Pulse 3D wireless headset is the first PS5 accessory to sell out at all major retailers, and the media remote and DualSense charging station are proving popular as well–both are currently only available at PlayStation Direct. Check out where you can snag all the PS5 accessories currently in stock below, and don’t wait too long if you’re considering buying–we expect it’ll all sell out eventually.

PS5 DualSense Wireless Controller

$70

The PS5 is just a few months away, and things look much clearer than they did a week ago. In addition to launch games like Marvel’s Spider-Man: Miles Morales and Demon’s Souls, the PS5 will be compatible with a large number of PS4 games through backward compatibility. It’s still not clear what the full compatibility list is, but thanks to the announcement of the Plus Collection, we know a bit more. An added benefit for PlayStation Plus, the Plus Collection gives all subscribers access to some of the PS4’s best games, including God of War, Bloodborne, and Monster Hunter: World. These games will be the PS4 versions and haven’t been confirmed to receive any kind of enhancements.

As far as a complete list goes, however, it’s still unclear. The reason for incompatibility was explained by system architect Mark Cerny, who said, “The boost is truly massive this time around and some game code just can’t handle it.” This means every PS4 game has to be tested to ensure its compatibility.

Other games the PlayStation 5 will see at launch include Sackboy: A Big Adventure, Destruction All Stars, and Astro’s Playroom. PS5 owners will also be able to expect a number of other games in the coming months and years, including Horizon Forbidden West, and Ratchet & Clank: Rift Apart as well as third-party titles like Resident Evil 8: Village and Hitman III. During the recent PS5 showcase event, Sony also revealed a new God of War and Final Fantasy XVI, both of which will arrive on PS5 next year.

  • Read more: All the Confirmed PS5 Launch Titles and Release Dates

The PS5’s specs are old news at this point, but it’s safe to say we saw some exciting uses of the custom 825GB SSD during the PS5 event. Fast load times in Demon’s Souls were the big highlight, though the custom SSD will also ensure fast open-world streaming and faster install times.

Additionally, the console supports ray tracing, 3D audio, and PlayStation VR as well as 4K UHD Blu-ray discs thanks to its compatible disc drive–of course, the PS5’s Digital edition won’t support physical discs. Sony has stated the PS5 is powerful enough to support 4K resolution at a 120Hz refresh rate and 8K resolution content, the latter of which likely won’t be used widely in games for a while. This is all powered by HDMI 2.1 technology, which not all games support. However, any HD or 4K TV you have at home will still be able to display and run your PS5 and its games.

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