The BBC is developing a voice assistant, code named ‘Beeb’ – gpgmail


The BBC — aka, the British Broadcasting Corporation, aka the Beeb, aka Auntie — is getting into the voice assistant game.

The Guardian reports the plan to launch an Alexa rival, which has been given the working title ‘Beeb’, and will apparently be light on features given the Corp’s relatively slender developer resources vs major global tech giants.

The BBC’s own news site says the digital voice assistant will launch next year without any proprietary hardware to house it. Instead the corporation is designing the software to work on “all smart speakers, TVs and mobiles”.

Why is a publicly funded broadcaster ploughing money into developing an AI when the market is replete with commercial offerings — from Amazon’s Alexa to Google’s Assistant, Apple’s Siri and Samsung’s Bixby to name a few? The intent is to “experiment with new programmes, features and experiences without someone else’s permission to build it in a certain way”, a BBC spokesperson told BBC news.

The corporation is apparently asking its own staff to contribute voice data to help train the AI to understand the country’s smorgasbord of regional accents.

“Much like we did with BBC iPlayer, we want to make sure everyone can benefit from this new technology, and bring people exciting new content, programmes and services — in a trusted, easy-to-use way,” the spokesperson added. “This marks another step in ensuring public service values can be protected in a voice-enabled future.”

While at first glance the move looks reactionary and defensive, set against the years of dev already ploughed into cutting edge commercial voice AIs, the BBC has something those tech giant rivals lack: Not just regional British accents on tap — but easy access to a massive news and entertainment archive to draw on to design voice assistants that could serve up beloved personalities as a service.

Imagine being able to summon the voice of Tom Baker, aka Doctor Who, to tell you what the (cosmic) weather’s like — or have the Dad’s Army cast of characters chip in to read out your to-do list. Or get a summary of the last episode of The Archers from a familiar Ambridge resident.

Or what about being able to instruct ‘Beeb’ to play some suitably soothing or dramatic sound effects to entertain your kids?

On one level a voice AI is just a novel delivery mechanism. The BBC looks to have spotted that — and certainly does not lack for rich audio content that could be repackaged to reach its audience on verbal command and extend its power to entertain and delight.

When it comes to rich content, the same cannot be said of the tech giants who have pioneered voice AIs.

There have been some attempts to force humor (AIs that crack bad jokes) and/or shoehorn in character — largely flat-footed. As well as some ethically dubious attempts to pass off robot voices as real. All of which is to be expected, given they’re tech companies not entertainers. Dev not media is their DNA.

The BBC is coming at the voice assistant concept from the other way round: Viewing it as a modern mouthpiece for piping out more of its programming.

So while Beeb can’t hope to compete at the same technology feature level as Alexa and all the rest, the BBC could nonetheless show the tech giants a trick or two about how to win friends and influence people.

At the very least it should give their robotic voices some much needed creative competition.

It’s just a shame the Beeb didn’t tickle us further by christening its proto AI ‘Auntie’. A crisper two syllable trigger word would be hard to utter…




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Microsoft tweaks privacy policy to admit humans can listen to Skype Translator and Cortana audio – gpgmail


Microsoft is the latest tech giant to amend its privacy policy after media reports revealed it uses human contractors to review audio recordings of Skype and Cortana users.

A section in the policy on how the company uses personal data now reads (emphasis ours):

Our processing of personal data for these purposes includes both automated and manual (human) methods of processing. Our automated methods often are related to and supported by our manual methods. For example, our automated methods include artificial intelligence (AI), which we think of as a set of technologies that enable computers to perceive, learn, reason, and assist in decision-making to solve problems in ways that are similar to what people do. To build, train, and improve the accuracy of our automated methods of processing (including AI), we manually review some of the predictions and inferences produced by the automated methods against the underlying data from which the predictions and inferences were made. For example, we manually review short snippets of a small sampling of voice data we have taken steps to de-identify to improve our speech services, such as recognition and translation.

The tweaks to the privacy policy of Microsoft’s Skype VoIP software and its Cortana voice AI were spotted by Motherboard — which was also first to report that contractors working for Microsoft are listening to personal conversations of Skype users conducted through the app’s translation service, and to audio snippets captured by the Cortana voice assistant.

Asked about the privacy policy changes, Microsoft told Motherboard: “We realized, based on questions raised recently, that we could do a better job specifying that humans sometimes review this content.”

Multiple tech giants’ use of human workers to review users’ audio across a number of products involving AI has grabbed headlines in recent weeks after journalists exposed a practice that had not been clearly conveyed to users in terms and conditions — despite European privacy law requiring clarity about how people’s data is used.

Apple, Amazon, Facebook, Google and Microsoft have all been called out for failing to make it clear that a portion of audio recordings will be accessed by human contractors.

Such workers are typically employed to improve the performance of AI systems by verifying translations and speech in different accents. But, again, this human review component within AI systems has generally been buried rather than transparently disclosed.

Earlier this month a German privacy watchdog told Google it intended to use EU privacy law to order it to halt human reviews of audio captured by its Google Assistant AI in Europe — after press had obtained leaked audio snippets and being able to re-identify some of the people in the recordings.

On learning of the regulator’s planned intervention Google suspended reviews.

Apple also announced it was suspending human reviews of Siri snippets globally, again after a newspaper reported that its contractors could access audio and routinely heard sensitive stuff.

Facebook also said it was pausing human reviews of a speech-to-text AI feature offered in its Messenger app — again after concerns had been raised by journalists.

So far Apple, Google and Facebook have suspended or partially suspended human reviews in response to media disclosures and/or regulatory attention.

While the lead privacy regulator for all three, Ireland’s DPC, has started asking questions.

In response to the rising privacy scrutiny of what tech giants nonetheless claim is a widespread industry practice, Amazon also recently amended the Alexa privacy policy to disclose that it employs humans to review some audio. It also quietly added an option for uses to opt-out of the possibility of someone listening to their Alexa recordings. Amazon’s lead EU privacy regulator is also now seeking answers.

Microsoft told Motherboard it is not suspending human reviews at this stage.

Users of Microsoft’s voice assistant can delete recordings — but such deletions require action from the user and would be required on a rolling basis as long as the product continues being use. So it’s not the same as having a full and blanket opt out.

We’ve asked Microsoft whether it intends to offer Skype or Cortana users an opt out of their recordings being reviewed by humans.

The company told Motherboard it will “continue to examine further steps we might be able to take”.


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Amazon quietly adds ‘no human review’ option to Alexa settings as voice AIs face privacy scrutiny – gpgmail


Amazon has tweaked the settings for its Alexa voice AI to allow users to opt out of their voice recordings being manually reviewed by the company’s human workers.

The policy shift took effect Friday, according to Bloomberg, which reports that Alexa users will now find an option in the settings menu of the Alexa smartphone app to disable human review of their clips.

The Alexa T&C did not previously inform users of the possibility that audio recordings captured by the service might be manually reviewed by actual humans. (Amazon still doesn’t appear to provide this disclosure on its main website either.)

But the Alexa app now includes a disclaimer in the settings menu that flags the fact human ears may in fact be listening, per the report.

This disclosure appears only to surface if users go digging into the settings menu.

Bloomberg says users must tap ‘Settings’ > ‘Alexa Privacy’ > ‘Manage How Your Data Improves Alexa’ before they see the following text: “With this setting on, your voice recordings may be used to develop new features and manually reviewed to help improve our services. Only an extremely small fraction of voice recordings are manually reviewed.”

The policy tweak comes as regulators are dialling up attention on the privacy risks posed by voice AI technologies.

This week it emerged that Google was ordered by a German data protection watchdog to halt manual reviews of audio snippets generated by its voice AI, after thousands of recordings were leaked to the Belgian media last month which was able to identify some of the people in the clips.

Google has suspended reviews across the whole of Europe while it liaises with EU privacy regulators.

In a statement on its website the Hamburg privacy watchdog raised concerns about other operators of voice AIs, urging EU regulators to make checks on providers such as Amazon and Apple — and “implement appropriate measures”.

Coincidentally (or not) Apple also suspended human reviews of Siri snippets this week — globally, in its case — following privacy concerns raised by a recent UK media report. The Guardian newspaper quoted a whistleblower claiming contractors regularly hearing confidential personal data captured by Siri.

While Google and Apple have entirely suspended human reviews of audio snippets (at least temporarily), Amazon has not gone so far.

Nor does it automatically opt users out. The policy change just lets users disable reviews — which requires consumers to both understand the risk and act to safeguard their privacy.

Amazon’s disclosure of the existence of human reviews is also currently buried deep in the settings, rather than being actively conveyed to users.

It’s not clear whether any of this will wash with regulators in Europe. 

Bloomberg reports that Amazon declined to comment on whether it had been contacted by regulators about the Alexa recordings review program, saying only: “We take customer privacy seriously and continuously review our practices and procedures We’ll also be updating information we provide to customers to make our practices more clear.”

We reached out to Amazon with questions but at the time of writing a spokesperson was not available.


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Dasha AI is calling so you don’t have to – gpgmail


While you’d be hard pressed to find any startup not brimming with confidence over the disruptive idea they’re chasing, it’s not often you come across a young company as calmly convinced it’s engineering the future as Dasha AI.

The team is building a platform for designing human-like voice interactions to automate business processes. Put simply, it’s using AI to make machine voices a whole lot less robotic.

“What we definitely know is this will definitely happen,” says CEO and co-founder Vladislav Chernyshov. “Sooner or later the conversational AI/voice AI will replace people everywhere where the technology will allow. And it’s better for us to be the first mover than the last in this field.”

“In 2018 in the US alone there were 30 million people doing some kind of repetitive tasks over the phone. We can automate these jobs now or we are going to be able to automate it in two years,” he goes on. “If you multiple it with Europe and the massive call centers in India, Pakistan and the Philippines you will probably have something like close to 120M people worldwide… and they are all subject for disruption, potentially.”

The New York based startup has been operating in relative stealth up to now. But it’s breaking cover to talk to gpgmail — announcing a $2M seed round, led by RTP Ventures and RTP Global: An early stage investor that’s backed the likes of Datadog and RingCentral. RTP’s venture arm, also based in NY, writes on its website that it prefers engineer-founded companies — that “solve big problems with technology”. “We like technology, not gimmicks,” the fund warns with added emphasis.

Dasha’s core tech right now includes what Chernyshov describes as “a human-level, voice-first conversation modelling engine”; a hybrid text-to-speech engine which he says enables it to model speech disfluencies (aka, the ums and ahs, pitch changes etc that characterize human chatter); plus “a fast and accurate” real-time voice activity detection algorithm which detects speech in under 100 milliseconds, meaning the AI can turn-take and handle interruptions in the conversation flow. The platform can also detect a caller’s gender — a feature that can be useful for healthcare use-cases, for example.

Another component Chernyshov flags is “an end-to-end pipeline for semi-supervised learning” — so it can retrain the models in real time “and fix mistakes as they go” — until Dasha hits the claimed “human-level” conversational capability for each business process niche. (To be clear, the AI cannot adapt its speech to an interlocutor in real-time — as human speakers naturally shift their accents closer to bridge any dialect gap — but Chernyshov suggests it’s on the roadmap.)

“For instance, we can start with 70% correct conversations and then gradually improve the model up to say 95% of correct conversations,” he says of the learning element, though he admits there are a lot of variables that can impact error rates — not least the call environment itself. Even cutting edge AI is going to struggle with a bad line.

The platform also has an open API so customers can plug the conversation AI into their existing systems — be it telephony, Salesforce software or a developer environment, such as Microsoft Visual Studio.

Currently they’re focused on English, though Chernyshov says the architecture is “basically language agnostic” — but does requires “a big amount of data”.

The next step will be to open up the dev platform to enterprise customers, beyond the initial 20 beta testers, which include companies in the banking, healthcare and insurance sectors — with a release slated for later this year or Q1 2020.

Test use-cases so far include banks using the conversation engine for brand loyalty management to run customer satisfaction surveys that can turnaround negative feedback by fast-tracking a response to a bad rating — by providing (human) customer support agents with an automated categorization of the complaint so they can follow up more quickly. “This usually leads to a wow effect,” says Chernyshov.

Ultimately, he believes there will be two or three major AI platforms globally providing businesses with an automated, customizable conversational layer — sweeping away the patchwork of chatbots currently filling in the gap. And of course Dasha intends their ‘Digital Assistant Super Human Alike’ to be one of those few.

“There is clearly no platform [yet],” he says. “Five years from now this will sound very weird that all companies now are trying to build something. Because in five years it will be obvious — why do you need all this stuff? Just take Dasha and build what you want.”

“This reminds me of the situation in the 1980s when it was obvious that the personal computers are here to stay because they give you an unfair competitive advantage,” he continues. “All large enterprise customers all over the world… were building their own operating systems, they were writing software from scratch, constantly reinventing the wheel just in order to be able to create this spreadsheet for their accountants.

“And then Microsoft with MS-DOS came in… and everything else is history.”

That’s not all they’re building, either. Dasha’s seed financing will be put towards launching a consumer-facing product atop its b2b platform to automate the screening of recorded message robocalls. So, basically, they’re building a robot assistant that can talk to — and put off — other machines on humans’ behalf.

Which does kind of suggest the AI-fuelled future will entail an awful lot of robots talking to each other… 🤖🤖🤖

Chernyshov says this b2c call screening app will most likely be free. But then if your core tech looks set to massively accelerate a non-human caller phenomenon that many consumers already see as a terrible plague on their time and mind then providing free relief — in the form of a counter AI — seems the very least you should do.

Not that Dasha can be accused of causing the robocaller plague, of course. Recorded messages hooked up to call systems have been spamming people with unsolicited calls for far longer than the startup has existed.

Dasha’s PR notes Americans were hit with 26.3BN robocalls in 2018 alone — up “a whopping” 46% on 2017.

Its conversation engine, meanwhile, has only made some 3M calls to date, clocking its first call with a human in January 2017. But the goal from here on in is to scale fast. “We plan to aggressively grow the company and the technology so we can continue to provide the best voice conversational AI to a market which we estimate to exceed $30BN worldwide,” runs a line from its PR.

After the developer platform launch, Chernyshov says the next step will be to open up access to business process owners by letting them automate existing call workflows without needing to be able to code (they’ll just need an analytic grasp of the process, he says).

Later — pegged for 2022 on the current roadmap — will be the launch of “the platform with zero learning curve”, as he puts it. “You will teach Dasha new models just like typing in a natural language and teaching it like you can teach any new team member on your team,” he explains. “Adding a new case will actually look like a word editor — when you’re just describing how you want this AI to work.”

His prediction is that a majority — circa 60% — of all major cases that business face — “like dispatching, like probably upsales, cross sales, some kind of support etc, all those cases” — will be able to be automated “just like typing in a natural language”.

So if Dasha’s AI-fuelled vision of voice-based business process automation come to fruition then humans getting orders of magnitude more calls from machines looks inevitable — as machine learning supercharges artificial speech by making it sound slicker, act smarter and seem, well, almost human.

But perhaps a savvier generation of voice AIs will also help manage the ‘robocaller’ plague by offering advanced call screening? And as non-human voice tech marches on from dumb recorded messages to chatbot-style AIs running on scripted rails to — as Dasha pitches it — fully responsive, emoting, even emotion-sensitive conversation engines that can slip right under the human radar maybe the robocaller problem will eat itself? I mean, if you didn’t even realize you were talking to a robot how are you going to get annoyed about it?

Dasha claims 96.3% of the people who talk to its AI “think it’s human”, though it’s not clear what sample size the claim is based on. (To my ear there are definite ‘tells’ in the current demos on its website. But in a cold-call scenario it’s not hard to imagine the AI passing, if someone’s not paying much attention.)

The alternative scenario, in a future infested with unsolicited machine calls, is that all smartphone OSes add kill switches, such as the one in iOS 13 — which lets people silence calls from unknown numbers.

And/or more humans simply never pick up phone calls unless they know who’s on the end of the line.

So it’s really doubly savvy of Dasha to create an AI capable of managing robot calls — meaning it’s building its own fallback — a piece of software willing to chat to its AI in future, even if actual humans refuse.

Dasha’s robocall screener app, which is slated for release in early 2020, will also be spammer-agnostic — in that it’ll be able to handle and divert human salespeople too, as well as robots. After all, a spammer is a spammer.

“Probably it is the time for somebody to step in and ‘don’t be evil’,” says Chernyshov, echoing Google’s old motto, albeit perhaps not entirely reassuringly given the phrase’s lapsed history — as we talk about the team’s approach to ecosystem development and how machine-to-machine chat might overtake human voice calls.

“At some point in the future we will be talking to various robots much more than we probably talk to each other — because you will have some kind of human-like robots at your house,” he predicts. “Your doctor, gardener, warehouse worker, they all will be robots at some point.”

The logic at work here is that if resistance to an AI-powered Cambrian Explosion of machine speech is futile, it’s better to be at the cutting edge, building the most human-like robots — and making the robots at least sound like they care.

Dasha’s conversational quirks certainly can’t be called a gimmick. Even if the team’s close attention to mimicking the vocal flourishes of human speech — the disfluencies, the ums and ahs, the pitch and tonal changes for emphasis and emotion — might seem so at first airing.

In one of the demos on its website you can hear a clip of a very chipper-sounding male voice, who identifies himself as “John from Acme Dental”, taking an appointment call from a female (human), and smoothly dealing with multiple interruptions and time/date changes as she changes her mind. Before, finally, dealing with a flat cancelation.

A human receptionist might well have got mad that the caller essentially just wasted their time. Not John, though. Oh no. He ends the call as cheerily as he began, signing off with an emphatic: “Thank you! And have a really nice day. Bye!”

If the ultimate goal is Turing Test levels of realism in artificial speech — i.e. a conversation engine so human-like it can pass as human to a human ear — you do have to be able to reproduce, with precision timing, the verbal baggage that’s wrapped around everything humans say to each other.

This tonal layer does essential emotional labor in the business of communication, shading and highlighting words in a way that can adapt or even entirely transform their meaning. It’s an integral part of how we communicate. And thus a common stumbling block for robots.

So if the mission is to power a revolution in artificial speech that humans won’t hate and reject then engineering full spectrum nuance is just as important a piece of work as having an amazing speech recognition engine. A chatbot that can’t do all that is really the gimmick.

Chernyshov claims Dasha’s conversation engine is “at least several times better and more complex than [Google] Dialogflow, [Amazon] Lex, [Microsoft] Luis or [IBM] Watson”, dropping a laundry list of rival speech engines into the conversation.

He argues none are on a par with what Dasha is being designed to do.

The difference is the “voice-first modelling engine”. “All those [rival engines] were built from scratch with a focus on chatbots — on text,” he says, couching modelling voice conversation “on a human level” as much more complex than the more limited chatbot-approach — and hence what makes Dasha special and superior.

“Imagination is the limit. What we are trying to build is an ultimate voice conversation AI platform so you can model any kind of voice interaction between two or more human beings.”

Google did demo its own stuttering voice AI — Duplex — last year, when it also took flak for a public demo in which it appeared not to have told restaurant staff up front they were going to be talking to a robot.

Chernyshov isn’t worried about Duplex, though, saying it’s a product, not a platform.

“Google recently tried to headhunt one of our developers,” he adds, pausing for effect. “But they failed.”

He says Dasha’s engineering staff make up more than half (28) its total headcount (48), and include two doctorates of science; three PhDs; five PhD students; and ten masters of science in computer science.

It has an R&D office in Russian which Chernyshov says helps makes the funding go further.

“More than 16 people, including myself, are ACM ICPC finalists or semi finalists,” he adds — likening the competition to “an Olympic game but for programmers”. A recent hire — chief research scientist, Dr Alexander Dyakonov — is both a doctor of science professor and former Kaggle No.1 GrandMaster in machine learning. So with in-house AI talent like that you can see why Google, uh, came calling…

 

But why not have Dasha ID itself as a robot by default? On that Chernyshov says the platform is flexible — which means disclosure can be added. But in markets where it isn’t a legal requirement the door is being left open for ‘John’ to slip cheerily by. Bladerunner here we come.

The team’s driving conviction is that emphasis on modelling human-like speech will, down the line, allow their AI to deliver universally fluid and natural machine-human speech interactions which in turn open up all sorts of expansive and powerful possibilities for embeddable next-gen voice interfaces. Ones that are much more interesting than the current crop of gadget talkies.

This is where you could raid sci-fi/pop culture for inspiration. Such as Kitt, the dryly witty talking car from the 1980s TV series Knight Rider. Or, to throw in a British TV reference, Holly the self-depreciating yet sardonic human-faced computer in Red Dwarf. (Or indeed Kryten the guilt-ridden android butler.) Chernyshov’s suggestion is to imagine Dasha embedded in a Boston Dynamics robot. But surely no one wants to hear those crawling nightmares scream…

Dasha’s five-year+ roadmap includes the eyebrow-raising ambition to evolve the technology to achieve “a general conversational AI”. “This is a science fiction at this point. It’s a general conversational AI, and only at this point you will be able to pass the whole Turing Test,” he says of that aim.

“Because we have a human level speech recognition, we have human level speech synthesis, we have generative non-rule based behavior, and this is all the parts of this general conversational AI. And I think that we can we can — and scientific society — we can achieve this together in like 2024 or something like that.

“Then the next step, in 2025, this is like autonomous AI — embeddable in any device or a robot. And hopefully by 2025 these devices will be available on the market.”

Of course the team is still dreaming distance away from that AI wonderland/dystopia (depending on your perspective) — even if it’s date-stamped on the roadmap.

But if a conversational engine ends up in command of the full range of human speech — quirks, quibbles and all — then designing a voice AI may come to be thought of as akin to designing a TV character or cartoon personality. So very far from what we currently associate with the word ‘robotic’. (And wouldn’t it be funny if the term ‘robotic’ came to mean ‘hyper entertaining’ or even ‘especially empathetic’ thanks to advances in AI.)

Let’s not get carried away though.

In the meanwhile, there are ‘uncanny valley’ pitfalls of speech disconnect to navigate if the tone being (artificially) struck hits a false note. (And, on that front, if you didn’t know ‘John from Acme Dental’ was a robot you’d be forgiven for misreading his chipper sign off to a total time waster as pure sarcasm. But an AI can’t appreciate irony. Not yet anyway.)

Nor can robots appreciate the difference between ethical and unethical verbal communication they’re being instructed to carry out. Sales calls can easily cross the line into spam. And what about even more dystopic uses for a conversation engine that’s so slick it can convince the vast majority of people it’s human — like fraud, identity theft, even election interference… the potential misuses could be terrible and scale endlessly.

Although if you straight out ask Dasha whether it’s a robot Chernyshov says it has been programmed to confess to being artificial. So it won’t tell you a barefaced lie.

Dasha

How will the team prevent problematic uses of such a powerful technology?

“We have an ethics framework and when we will be releasing the platform we will implement a real-time monitoring system that will monitor potential abuse or scams, and also it will ensure people are not being called too often,” he says. “This is very important. That we understand that this kind of technology can be potentially probably dangerous.”

“At the first stage we are not going to release it to all the public. We are going to release it in a closed alpha or beta. And we will be curating the companies that are going in to explore all the possible problems and prevent them from being massive problems,” he adds. “Our machine learning team are developing those algorithms for detecting abuse, spam and other use cases that we would like to prevent.”

There’s also the issue of verbal ‘deepfakes’ to consider. Especially as Chernyshov suggests the platform will, in time, support cloning a voiceprint for use in the conversation — opening the door to making fake calls in someone else’s voice. Which sounds like a dream come true for scammers of all stripes. Or a way to really supercharge your top performing salesperson.

Safe to say, the counter technologies — and thoughtful regulation — are going to be very important.

There’s little doubt that AI will be regulated. In Europe policymakers have tasked themselves with coming up with a framework for ethical AI. And in the coming years policymakers in many countries will be trying to figure out how to put guardrails on a technology class that, in the consumer sphere, has already demonstrated its wrecking-ball potential — with the automated acceleration of spam, misinformation and political disinformation on social media platforms.

“We have to understand that at some point this kind of technologies will be definitely regulated by the state all over the world. And we as a platform we must comply with all of these requirements,” agrees Chernyshov, suggesting machine learning will also be able to identify whether a speaker is human or not — and that an official caller status could be baked into a telephony protocol so people aren’t left in the dark on the ‘bot or not’ question. 

“It should be human-friendly. Don’t be evil, right?”

Asked whether he considers what will happen to the people working in call centers whose jobs will be disrupted by AI, Chernyshov is quick with the stock answer — that new technologies create jobs too, saying that’s been true right throughout human history. Though he concedes there may be a lag — while the old world catches up to the new.

Time and tide wait for no human, even when the change sounds increasingly like we do.


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