Web search – Chatologica http://chatologica.com/ Fri, 17 Sep 2021 20:46:00 +0000 en-US hourly 1 https://wordpress.org/?v=5.8 https://chatologica.com/wp-content/uploads/2021/08/cropped-icon-32x32.png Web search – Chatologica http://chatologica.com/ 32 32 Xayn is privacy-safe, personalized mobile web search powered by on-device AIs – TechCrunch https://chatologica.com/xayn-is-privacy-safe-personalized-mobile-web-search-powered-by-on-device-ais-techcrunch-2/ https://chatologica.com/xayn-is-privacy-safe-personalized-mobile-web-search-powered-by-on-device-ais-techcrunch-2/#respond Sat, 04 Sep 2021 16:17:18 +0000 https://chatologica.com/?p=495 As TC readers know, the tricky trade-off of the modern web is privacy for convenience. Online tracking is how this ‘great intimacy robbery’ is pulled off. Mass surveillance of what Internet users are looking at underpins Google’s dominant search engine and Facebook’s social empire, to name two of the highest profile ad-funded business models. TechCrunch’s […]]]>

As TC readers know, the tricky trade-off of the modern web is privacy for convenience. Online tracking is how this ‘great intimacy robbery’ is pulled off. Mass surveillance of what Internet users are looking at underpins Google’s dominant search engine and Facebook’s social empire, to name two of the highest profile ad-funded business models.

TechCrunch’s own corporate overlord, Verizon, also gathers data from a variety of end points — mobile devices, media properties like this one — to power its own ad targeting business.

Countless others rely on obtaining user data to extract some perceived value. Few if any of these businesses are wholly transparent about how much and what sort of private intelligence they’re amassing — or, indeed, exactly what they’re doing with it. But what if the web didn’t have to be like that?

Berlin-based Xayn wants to change this dynamic — starting with personalized but privacy-safe web search on smartphones.

Today it’s launching a search engine app (on Android and iOS) that offers the convenience of personalized results but without the ‘usual’ shoulder surfing. This is possible because the app runs on-device AI models that learn locally. The promise is no data is ever uploaded (though trained AI models themselves can be).

The team behind the app, which is comprised of 30% PhDs, has been working on the core privacy vs convenience problem for some six years (though the company was only founded in 2017); initially as an academic research project — going on to offer an open source framework for masked federated learning, called XayNet. The Xayn app is based on that framework.

They’ve raised some €9.5 million in early stage funding to date — with investment coming from European VC firm Earlybird; Dominik Schiener (Iota co-founder); and the Swedish authentication and payment services company, Thales AB.

Now they’re moving to commercialize their XayNet technology by applying it within a user-facing search app — aiming for what CEO and co-founder, Dr Leif-Nissen Lundbæk bills as a “Zoom”-style business model, in reference to the ubiquitous videoconferencing tool which has both free and paid users.

This means Xayn’s search is not ad-supported. That’s right; you get zero ads in search results.

Instead, the idea is for the consumer app to act as a showcase for a b2b product powered by the same core AI tech. The pitch to business/public sector customers is speedier corporate/internal search without compromising commercial data privacy.

Lundbæk argues businesses are sorely in need of better search tools to (safely) apply to their own data, saying studies have shown that search in general costs around 18% of working time globally. He also cites a study by one city authority that found staff spent 37% of their time at work searching for documents or other digital content.

“It’s a business model that Google has tried but failed to succeed,” he argues, adding: “We are solving not only a problem that normal people have but also that companies have… For them privacy is not a nice to have; it needs to be there otherwise there is no chance of using anything.”

On the consumer side there will also be some premium add-ons headed for the app — so the plan is for it to be a freemium download.

Swipe to nudge the algorithm

One key thing to note is Xayn’s newly launched web search app gives users a say in whether the content they’re seeing is useful to them (or not).

It does this via a Tinder-style swipe right (or left) mechanic that lets users nudge its personalization algorithm in the right direction — starting with a home screen populated with news content (localized by country) but also extending to the search result pages.

The news-focused homescreen is another notable feature. And it sounds like different types of homescreen feeds may be on the premium cards in future.

Another key feature of the app is the ability to toggle personalized search results on or off entirely — just tap the brain icon at the top right to switch the AI off (or back on). Results without the AI running can’t be swiped, except for bookmarking/sharing.

Elsewhere, the app includes a history page which lists searches from the past seven days (by default). The other options offered are: Today, 30 days, or all history (and a bin button to purge searches).

There’s also a ‘Collections’ feature that lets you create and access folders for bookmarks.

As you scroll through search results you can add an item to a Collection by swiping right and selecting the bookmark icon — which then opens a prompt to choose which one to add it to.

The swipe-y interface feels familiar and intuitive, if slightly laggy to load content in the TestFlight beta version TechCrunch checked out ahead of launch.

Swiping left on a piece of content opens a bright pink color-block stamped with a warning ‘x’. Keep going and you’ll send the item vanishing into the ether, presumably seeing fewer like it in future.

Whereas a swipe right affirms a piece of content is useful. This means it stays in the feed, outlined in Xayn green. (Swiping right also reveals the bookmark option and a share button.)

While there are pro-privacy/non-tracking search engines on the market already — such as US-based DuckDuckGo or France’s Qwant — Xayn argues the user experience of such rivals tends to fall short of what you get with a tracking search engine like Google, i.e. in terms of the relevance of search results and thus time spent searching.

Simply put: You probably have to spend more time ‘DDGing’ or ‘Qwanting’ to get the specific answers you need vs Googling — hence the ‘convenience cost’ associated with safeguarding your privacy when web searching.

Xayn’s contention is there’s a third, smarter way of getting to keep your ‘virtual clothes’ on when searching online. This involves implementing AI models that learn on-device and can be combined in a privacy-safe way so that results can be personalized without putting people’s data at risk.

“Privacy is the very fundament… It means that quite like other privacy solutions we track nothing. Nothing is sent to our servers; we don’t store anything of course; we don’t track anything at all. And of course we make sure that any connection that is there is basically secured and doesn’t allow for any tracking at all,” says Lundbæk, explaining the team’s AI-fuelled, decentralized/edge-computing approach.

On-device reranking

Xayn is drawing on a number of search index sources, including (but not solely) Microsoft’s Bing, per Lundbæk, who described this bit of what it’s doing as “relatively similar” to DuckDuckGo (which has its own web crawling bots).

The big difference is that it’s also applying its own reranking algorithms in order generate privacy-safe personalized search results (whereas DDG uses a contextual ads-based business model — looking at simple signals like location and keyword search to target ads without needing to profile users).

The downside to this sort of approach, according to Lundbæk, is users can get flooded with ads — as a consequence of the simpler targeting meaning the business serves more ads to try to increase chances of a click. And loads of ads in search results obviously doesn’t make for a great search experience.

“We get a lot of results on device level and we do some ad hoc indexing — so we build on the device level and on index — and with this ad hoc index we apply our search algorithms in order to filter them, and only present you what is more relevant and filter out everything else,” says Lundbæk, sketching how Xayn works. “Or basically downgrade it a bit… but we also try to keep it fresh and explore and also bump up things where they might not be super relevant for you but it gives you some guarantees that you won’t end up in some kind of bubble.”

Some of what Xayn’s doing is in the arena of federated learning (FL) — a technology Google has been dabbling in in recent years, including pushing a ‘privacy-safe’ proposal for replacing third party tracking cookies. But Xayn argues the tech giant’s interests, as a data business, simply aren’t aligned with cutting off its own access to the user data pipe (even if it were to switch to applying FL to search).

Whereas its interests — as a small, pro-privacy German startup — are markedly different. Ergo, the privacy-preserving technology it’s spent years building has a credible interest in safeguarding people’s data, is the claim.

“At Google there’s actually [fewer] people working on federate learning than in our team,” notes Lundbæk, adding: “We’ve been criticizing TFF [Google-designed TensorFlow Federated] at lot. It is federated learning but it’s not actually doing any encryption at all — and Google has a lot of backdoors in there.

“You have to understand what does Google actually want to do with that? Google wants to replace [tracking] cookies — but especially they want to replace this kind of bumpy thing of asking for user consent. But of course they still want your data. They don’t want to give you any more privacy here; they want to actually — at the end — get your data even easier. And with purely federated learning you actually don’t have a privacy solution.

“You have to do a lot in order to make it privacy preserving. And pure TFF is certainly not that privacy-preserving. So therefore they will use this kind of tech for all the things that are basically in the way of user experience — which is, for example, cookies but I would be extremely surprised if they used it for search directly. And even if they would do that there is a lot of backdoors in their system so it’s pretty easy to actually acquire the data using TFF. So I would say it’s just a nice workaround for them.”

“Data is basically the fundamental business model of Google,” he adds. “So I’m sure that whatever they do is of course a nice step in the right direction… but I think Google is playing a clever role here of kind of moving a bit but not too much.”

So how, then, does Xayn’s reranking algorithm work?

The app runs four AI models per device, combining encrypted AI models of respective devices asynchronously — with homomorphic encryption — into a collective model. A second step entails this collective model being fed back to individual devices to personalize served content, it says. 

The four AI models running on the device are one for natural language processing; one for grouping interests; one for analyzing domain preferences; and one for computing context.

“The knowledge is kept but the data is basically always staying on your device level,” is how Lundbæk puts it.

“We can simply train a lot of different AI models on your phone and decide whether we, for example, combine some of this knowledge or whether it also stays on your device.”

“We have developed a quite complex solution of four different AI models that work in composition with each other,” he goes on, noting that they work to build up “centers of interest and centers of dislikes” per user — again, based on those swipes — which he says “have to be extremely efficient — they have to be moving, basically, also over time and with your interests”.

The more the user interacts with Xayn, the more precise its personalization engine gets as a result of on-device learning — plus the added layer of users being able to get actively involved by swiping to give like/dislike feedback.

The level of personalization is very individually focused — Lundbæk calls it “hyper personalization” — more so than a tracking search engine like Google, which he notes also compares cross-user patterns to determine which results to serve — something he says Xayn absolutely does not do.

Small data, not big data

“We have to focus entirely on one user so we have a ‘small data’ problem, rather than a big data problem,” says Lundbæk. “So we have to learn extremely fast — only from eight to 20 interactions we have to already understand a lot from you. And the crucial thing is of course if you do such a rapid learning then you have to take even more care about filter bubbles — or what is called filter bubbles. We have to prevent the engine going into some kind of biased direction.”

To avoid this echo chamber/filter bubble type effect, the Xayn team has designed the engine to function in two distinct phases which it switches between: Called ‘exploration’ and (more unfortunately) ‘exploitation’ (i.e. just in the sense that it already knows something about the user so can be pretty certain what it serves will be relevant).

“We have to keep fresh and we have to keep exploring things,” he notes — saying that’s why it developed one of the four AIs (a dynamic contextual multi-armed bandit reinforcement learning algorithm for computing context).

Aside from this app infrastructure being designed natively to protect user privacy, Xayn argues there are a bunch of other advantages — such as being able to derive potentially very clear interests signs from individuals; and avoiding the chilling effect that can result from tracking services creeping users out (to the point people they avoid making certain searches in order to prevent them from influencing future results).

“You as the user can decide whether you want the algorithm to learn — whether you want it to show more of this or less of this — by just simply swiping. So it’s extremely easy, so you can train your system very easily,” he argues.

There is potentially a slight downside to this approach, too, though — assuming the algorithm (when on) does some learning by default (i.e in the absence of any life/dislike signals from the user).

This is because it puts the burden on the user to interact (by swiping their feedback) in order to get the best search results out of Xayn. So that’s an active requirement on users, rather than the typical passive background data mining and profiling web users are used to from tech giants like Google (which is, however, horrible for their privacy).

It means there’s an ‘ongoing’ interaction cost to using the app — or at least getting the most relevant results out of it. You might not, for instance, be advised to let a bunch of organic results just scroll past if they’re really not useful but rather actively signal disinterest on each.

For the app to be the most useful it may ultimately pay to carefully weight each item and provide the AI with a utility verdict. (And in a competitive battle for online convenience every little bit of digital friction isn’t going to help.)

Asked about this specifically, Lundbæk told us: “Without swiping the AI only learns from very weak likes but not from dislikes. So the learning takes place (if you turn the AI on) but it’s very slight and does not have a big effect. These conditions are quite dynamic, so from the experience of liking something after having visited a website, patterns are learned. Also, only 1 of the 4 AI models (the domain learning one) learns from pure clicks; the others don’t.”

Xayn does seem alive to the risk of the swiping mechanic resulting in the app feeling arduous. Lundbæk says the team is looking to add “some kind of gamification aspect” in the future — to flip the mechanism from pure friction to “something fun to do”. Though it remains to be seen what they come up with on that front.

There is also inevitably a bit of lag involved in using Xayn vs Google — by merit of the former having to run on-device AI training (whereas Google merely hoovers your data into its cloud where it’s able to process it at super-speeds using dedicated compute hardware, including bespoke chipsets).

“We have been working for over a year on this and the core focus point was bringing it on the street, showing that it works — and of course it is slower than Google,” Lundbæk concedes.

“Google doesn’t need to do any of these [on-device] processes and Google has developed even its own hardware; they developed TPUs exactly for processing this kind of model,” he goes on. “If you compare this kind of hardware it’s pretty impressive that we were even able to bring [Xayn’s on-device AI processing] even on the phone. However of course it’s slower than Google.”

Lundbæk says the team is working on increasing the speed of Xayn. And anticipates further gains as it focuses more on that type of optimization — trailing a version that’s 40x faster than the current iteration.

“It won’t at the end be 40x faster because we will use this also to analyze even more content — to give you can even broader view — but it will be faster over time,” he adds.

On the accuracy of search results vs Google, he argues the latter’s ‘network effect’ competitive advantage — whereby its search reranking benefits from Google having more users — is not unassailable because of what edge AI can achieve working smartly atop ‘small data’.

Though, again, for now Google remains the search standard to beat.

“Right now we compare ourselves, mostly against Bing and DuckDuckGo and so on. Obviously there we get much better results [than compared to Google] but of course Google is the market leader and is using quite some heavy personalization,” he says, when we ask about benchmarking results vs other search engines.

“But the interesting thing is so far Google is not only using personalization but they also use kind of a network effect. PageRank is very much a network effect where the most users they have the better the results get, because they track how often people click on something and bump this also up.

“The interesting effect there is that right now, through AI technology — like for example what we use — the network effect becomes less and less important. So actually I would say that there isn’t really any network effect anymore if you really want to compete with pure AI technology. So therefore we can get almost as relevant results as Google right now and we surely can also, over time, get even better results or competing results. But we are different.”

In our (brief) tests of the beta app Xayn’s search results didn’t obviously disappoint for simple searches (and would presumably improve with use). Though, again, the slight load lag adds a modicum of friction which was instantly obvious compared to the usual search competition.

Not a deal breaker — just a reminder that performance expectations in search are no cake walk (even if you can promise a cookie-free experience).

An opportunity for competition?

“So far Google has so far had the advantage of a network effect — but this network effect gets less and less dominant and you see already more and more alternatives to Google popping up,” Lundbæk argues, suggesting privacy concerns are creating an opportunity for increased competition in the search space.

“It’s not anymore like Facebook or so where there’s one network where everyone has to be. And I think this is actually a nice situation because competition is always good for technical innovations and for also satisfying different customer needs.”

Of course the biggest challenge for any would-be competitor to Google search — which carves itself a marketshare in Europe in excess of 90% — is how to poach (some of) its users.

Lundbæk says the startup has no plans to splash millions on marketing at this point. Indeed, he says they want to grow usage sustainably, with the aim of evolving the product “step by step” with a “tight community” of early adopters — relying on cross-promotion from others in the pro-privacy tech space, as well as reaching out to relevant influencers.

He also reckons there’s enough mainstream media interest in the privacy topic to generate some uplift.

“I think we have such a relevant topic — especially now,” he says. “Because we want to show also not only for ourselves that you can do this for search but we think we show a real nice example that you can do this for any kind of case.

“You don’t always need the so-called ‘best’ big players from the US which are of course getting all of your data, building up profiles. And then you have these small, cute privacy-preserving solutions which don’t use any of this but then offer a bad user experience. So we want to show that this shouldn’t be the status quo anymore — and you should start to build alternatives that are really build on European values.”

And it’s certainly true EU lawmakers are big on tech sovereignty talk these days, even though European consumers mostly continue to embrace big (US) tech.

Perhaps more pertinently, regional data protection requirements are making it increasing challenging to rely on US-based services for processing data. Compliance with the GDPR data protection framework is another factor businesses need to consider. All of which is driving attention onto ‘privacy-preserving’ technologies.

 

Xayn’s team is hoping to be able spread its privacy-preserving gospel to general users by growing the b2b side of the business, according to Lundbæk — so it’s hoping some home use will follow once employees get used to convenient private search via their workplaces, in a small-scale reverse of the business consumerization trend that was powered by modern smartphones (and people bringing their own device to work).

“We these kind of strategies I think we can step by step build up in our communities and spread the word — so we think we don’t even need to really spend millions of euros in marketing campaigns to get more and more users,” he adds.

While Xayn’s initial go-to-market push has been focused on getting the mobile apps out, a desktop version is also planned for Q1 next year.

The challenge there is getting the app to work as a browser extension as the team obviously doesn’t want to build its own browser to house Xayn. tl;dr: Competing with Google search is mountain enough to climb, without trying to go after Chrome (and Firefox, and so on).

“We developed our entire AI in Rust which is a safe language. We are very much driven by security here and safety. The nice thing is it can work everywhere — from embedded systems towards mobile systems, and we can compile into web assembly so it runs also as a browser extension in any kind of browser,” he adds. “Except for Internet Explorer of course.”


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Huawei offers personalized web search ranking by subject, incorporating user interests and semantic match https://chatologica.com/huawei-offers-personalized-web-search-ranking-by-subject-incorporating-user-interests-and-semantic-match/ https://chatologica.com/huawei-offers-personalized-web-search-ranking-by-subject-incorporating-user-interests-and-semantic-match/#respond Tue, 24 Aug 2021 07:00:00 +0000 https://chatologica.com/huawei-offers-personalized-web-search-ranking-by-subject-incorporating-user-interests-and-semantic-match/ Although most web search engines don’t publish user statistics, the software marketing company Hub Spot estimates that industry leader Google now performs 5.6 billion web searches per day. The task of providing web search results is largely handled by ranking systems based on pre-trained language models that learn the semantic correspondence between query and document […]]]>

Although most web search engines don’t publish user statistics, the software marketing company Hub Spot estimates that industry leader Google now performs 5.6 billion web searches per day. The task of providing web search results is largely handled by ranking systems based on pre-trained language models that learn the semantic correspondence between query and document terms – an approach that ignores the advanced personalization signals such as user clicks.

In the new journal TPRM: A Custom Subject-Based Ranking Model for Web Search, a research team from Huawei Technologies’ Artificial Intelligence Applications Research Center is broadening the reach of ranking systems, delivering a custom subject-based ranking model (TPRM) that incorporates pre-trained terminology representations of language models with user profiles built by a subject model to fit a more relevant output ranking list.

The team summarizes its main contributions as follows:

  1. Integrate a subject model-based user profile with a pre-trained language model to produce a new custom ranking system, outperforming industry-leading ad hoc ranking models and custom ranking models on an AOL dataset real.
  2. Present the interpretability of thematic user profiles by providing a way to view user preferences when selecting documents under the given query.
  3. Disclose the effects of user interests and semantic correspondence learned from queries and documents, revealing their positive contributions to TPRM performance.

The proposed TPRM model architecture includes four modules: (1) User Interest Modeling, which uses a topic model based on documents clicked in the search history to model user interest; (2) Matching User-Doc interests via a kernel pooling approach; (3) the Query-Doc semantic match, which uses the large BERT language model to calculate the semantic match of a query and candidate documents; and (4) a custom ranking, which uses user-doc and query-doc match vectors to calculate a custom relevance score.

For their empirical study, the team compared TPRM with the BM25 algorithm and advanced ad hoc ranking models such as KNRM, Conv-KNRM, CEDR-KNRM, P-Click, SLTB, etc. Experiments were conducted on the real-world AOL research log and used the mean mean precision (MAP), mean reciprocal rank (MRR), P @ 1 (first position accuracy) and A.Clk (click position) average) as metrics to assess the quality of the generated ranking lists.

The results show that most custom ranking models will outperform ad-hoc ranking models, indicating the effectiveness of user profiles in improving the performance of ranking systems. In the experiments, TPRM significantly outperformed the TPRM-semantic model, verifying the benefits of user profiles constructed via the proposed thematic model approach.

The paper TPRM: A Custom Subject-Based Ranking Model for Web Search is on arXiv.


Author: Hecate Il | Editor: Michael Sarazen, Zhang Channel


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Customer reviews are not a signal for web search https://chatologica.com/customer-reviews-are-not-a-signal-for-web-search/ https://chatologica.com/customer-reviews-are-not-a-signal-for-web-search/#respond Sun, 08 Aug 2021 07:00:00 +0000 https://chatologica.com/customer-reviews-are-not-a-signal-for-web-search/ Google’s John Mueller says customer reviews are still not reflected in web search results. This topic is discussed during the Google Search Central SEO Hangout recorded on August 6, 2021. A question is submitted to Mueller which reads as follows: “Does Google look at the number of customers and / or reviews a website has […]]]>

Google’s John Mueller says customer reviews are still not reflected in web search results.

This topic is discussed during the Google Search Central SEO Hangout recorded on August 6, 2021.

A question is submitted to Mueller which reads as follows:

“Does Google look at the number of customers and / or reviews a website has to rank it higher in search results?” “

Mueller responds by saying that customer reviews aren’t a factor for web search, but they’re not totally irrelevant to gaining visibility on Google.

Read his full answer in the section below.

Google’s John Mueller on Customer Reviews and SEO

Although Google displays information about customer reviews in search results, such as aggregate star ratings, it is not a factor in determining the ranking of web content.

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Mueller says:

“As far as I know, we don’t use customer count or reviews when it comes to web search, in terms of ranking. Sometimes we extract this information and we can show it as some kind of rich result in search results.

The rich results of Google customer reviews may give the impression that they are being used as a ranking signal, but they are not.

However, as Mueller alludes to in his response, customer reviews are used elsewhere in Google search:

“It may be that for the Google My Business side of things, that may be taken into account more. I don’t have a lot of insight into this. But when it comes to normal web search, we don’t take that into account.

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To be fair, Google My Business is not Mueller’s department, but it is absoutely the case where customer reviews are taken into account.

In a local search, the business listing pack displayed at the top of Google is ranked using its own set of factors.

Google is transparent about what these factors are and presents them in a help guide under a title titled: “How Google Determines Local Rankings”.

The section reads as follows:

“Local results are mainly based on relevance, distance and importance. A combination of these factors helps us find the best match for your search.

The importance the ranking factor refers to the reputation of a company. One of the ways that Google measures brand awareness is by examining customer reviews.

The Google My Business help guide confirms that the number of reviews and the average score are taken into account when ranking local results:

“The number of Google reviews and the local search ranking review score factor. More reviews and positive ratings can improve your business’s local ranking.

Customer reviews won’t help (or harm) a business’s web search results, but they do. will impact the ranking of its Google My Business listing.

This is in line with what was previously known about customer reviews and their impact on Google search. It’s a common misconception among business owners that customer reviews influence search rankings, making it a topic worth revisiting every now and then.

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Listen to Mueller’s full response in the video below:


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Xayn is privacy-safe, personalized mobile web search powered by on-device AIs – TechCrunch https://chatologica.com/xayn-is-privacy-safe-personalized-mobile-web-search-powered-by-on-device-ais-techcrunch/ https://chatologica.com/xayn-is-privacy-safe-personalized-mobile-web-search-powered-by-on-device-ais-techcrunch/#respond Wed, 04 Aug 2021 10:53:35 +0000 https://chatologica.com/?p=147 As TC readers know, the tricky trade-off of the modern web is privacy for convenience. Online tracking is how this ‘great intimacy robbery’ is pulled off. Mass surveillance of what Internet users are looking at underpins Google’s dominant search engine and Facebook’s social empire, to name two of the highest profile ad-funded business models. TechCrunch’s […]]]>

As TC readers know, the tricky trade-off of the modern web is privacy for convenience. Online tracking is how this ‘great intimacy robbery’ is pulled off. Mass surveillance of what Internet users are looking at underpins Google’s dominant search engine and Facebook’s social empire, to name two of the highest profile ad-funded business models.

TechCrunch’s own corporate overlord, Verizon, also gathers data from a variety of end points — mobile devices, media properties like this one — to power its own ad targeting business.

Countless others rely on obtaining user data to extract some perceived value. Few if any of these businesses are wholly transparent about how much and what sort of private intelligence they’re amassing — or, indeed, exactly what they’re doing with it. But what if the web didn’t have to be like that?

Berlin-based Xayn wants to change this dynamic — starting with personalized but privacy-safe web search on smartphones.

Today it’s launching a search engine app (on Android and iOS) that offers the convenience of personalized results but without the ‘usual’ shoulder surfing. This is possible because the app runs on-device AI models that learn locally. The promise is no data is ever uploaded (though trained AI models themselves can be).

The team behind the app, which is comprised of 30% PhDs, has been working on the core privacy vs convenience problem for some six years (though the company was only founded in 2017); initially as an academic research project — going on to offer an open source framework for masked federated learning, called XayNet. The Xayn app is based on that framework.

They’ve raised some €9.5 million in early stage funding to date — with investment coming from European VC firm Earlybird; Dominik Schiener (Iota co-founder); and the Swedish authentication and payment services company, Thales AB.

Now they’re moving to commercialize their XayNet technology by applying it within a user-facing search app — aiming for what CEO and co-founder, Dr Leif-Nissen Lundbæk bills as a “Zoom”-style business model, in reference to the ubiquitous videoconferencing tool which has both free and paid users.

This means Xayn’s search is not ad-supported. That’s right; you get zero ads in search results.

Instead, the idea is for the consumer app to act as a showcase for a b2b product powered by the same core AI tech. The pitch to business/public sector customers is speedier corporate/internal search without compromising commercial data privacy.

Lundbæk argues businesses are sorely in need of better search tools to (safely) apply to their own data, saying studies have shown that search in general costs around 18% of working time globally. He also cites a study by one city authority that found staff spent 37% of their time at work searching for documents or other digital content.

“It’s a business model that Google has tried but failed to succeed,” he argues, adding: “We are solving not only a problem that normal people have but also that companies have… For them privacy is not a nice to have; it needs to be there otherwise there is no chance of using anything.”

On the consumer side there will also be some premium add-ons headed for the app — so the plan is for it to be a freemium download.

Swipe to nudge the algorithm

One key thing to note is Xayn’s newly launched web search app gives users a say in whether the content they’re seeing is useful to them (or not).

It does this via a Tinder-style swipe right (or left) mechanic that lets users nudge its personalization algorithm in the right direction — starting with a home screen populated with news content (localized by country) but also extending to the search result pages.

The news-focused homescreen is another notable feature. And it sounds like different types of homescreen feeds may be on the premium cards in future.

Another key feature of the app is the ability to toggle personalized search results on or off entirely — just tap the brain icon at the top right to switch the AI off (or back on). Results without the AI running can’t be swiped, except for bookmarking/sharing.

Elsewhere, the app includes a history page which lists searches from the past seven days (by default). The other options offered are: Today, 30 days, or all history (and a bin button to purge searches).

There’s also a ‘Collections’ feature that lets you create and access folders for bookmarks.

As you scroll through search results you can add an item to a Collection by swiping right and selecting the bookmark icon — which then opens a prompt to choose which one to add it to.

The swipe-y interface feels familiar and intuitive, if slightly laggy to load content in the TestFlight beta version TechCrunch checked out ahead of launch.

Swiping left on a piece of content opens a bright pink color-block stamped with a warning ‘x’. Keep going and you’ll send the item vanishing into the ether, presumably seeing fewer like it in future.

Whereas a swipe right affirms a piece of content is useful. This means it stays in the feed, outlined in Xayn green. (Swiping right also reveals the bookmark option and a share button.)

While there are pro-privacy/non-tracking search engines on the market already — such as US-based DuckDuckGo or France’s Qwant — Xayn argues the user experience of such rivals tends to fall short of what you get with a tracking search engine like Google, i.e. in terms of the relevance of search results and thus time spent searching.

Simply put: You probably have to spend more time ‘DDGing’ or ‘Qwanting’ to get the specific answers you need vs Googling — hence the ‘convenience cost’ associated with safeguarding your privacy when web searching.

Xayn’s contention is there’s a third, smarter way of getting to keep your ‘virtual clothes’ on when searching online. This involves implementing AI models that learn on-device and can be combined in a privacy-safe way so that results can be personalized without putting people’s data at risk.

“Privacy is the very fundament… It means that quite like other privacy solutions we track nothing. Nothing is sent to our servers; we don’t store anything of course; we don’t track anything at all. And of course we make sure that any connection that is there is basically secured and doesn’t allow for any tracking at all,” says Lundbæk, explaining the team’s AI-fuelled, decentralized/edge-computing approach.

On-device reranking

Xayn is drawing on a number of search index sources, including (but not solely) Microsoft’s Bing, per Lundbæk, who described this bit of what it’s doing as “relatively similar” to DuckDuckGo (which has its own web crawling bots).

The big difference is that it’s also applying its own reranking algorithms in order generate privacy-safe personalized search results (whereas DDG uses a contextual ads-based business model — looking at simple signals like location and keyword search to target ads without needing to profile users).

The downside to this sort of approach, according to Lundbæk, is users can get flooded with ads — as a consequence of the simpler targeting meaning the business serves more ads to try to increase chances of a click. And loads of ads in search results obviously doesn’t make for a great search experience.

“We get a lot of results on device level and we do some ad hoc indexing — so we build on the device level and on index — and with this ad hoc index we apply our search algorithms in order to filter them, and only present you what is more relevant and filter out everything else,” says Lundbæk, sketching how Xayn works. “Or basically downgrade it a bit… but we also try to keep it fresh and explore and also bump up things where they might not be super relevant for you but it gives you some guarantees that you won’t end up in some kind of bubble.”

Some of what Xayn’s doing is in the arena of federated learning (FL) — a technology Google has been dabbling in in recent years, including pushing a ‘privacy-safe’ proposal for replacing third party tracking cookies. But Xayn argues the tech giant’s interests, as a data business, simply aren’t aligned with cutting off its own access to the user data pipe (even if it were to switch to applying FL to search).

Whereas its interests — as a small, pro-privacy German startup — are markedly different. Ergo, the privacy-preserving technology it’s spent years building has a credible interest in safeguarding people’s data, is the claim.

“At Google there’s actually [fewer] people working on federate learning than in our team,” notes Lundbæk, adding: “We’ve been criticizing TFF [Google-designed TensorFlow Federated] at lot. It is federated learning but it’s not actually doing any encryption at all — and Google has a lot of backdoors in there.

“You have to understand what does Google actually want to do with that? Google wants to replace [tracking] cookies — but especially they want to replace this kind of bumpy thing of asking for user consent. But of course they still want your data. They don’t want to give you any more privacy here; they want to actually — at the end — get your data even easier. And with purely federated learning you actually don’t have a privacy solution.

“You have to do a lot in order to make it privacy preserving. And pure TFF is certainly not that privacy-preserving. So therefore they will use this kind of tech for all the things that are basically in the way of user experience — which is, for example, cookies but I would be extremely surprised if they used it for search directly. And even if they would do that there is a lot of backdoors in their system so it’s pretty easy to actually acquire the data using TFF. So I would say it’s just a nice workaround for them.”

“Data is basically the fundamental business model of Google,” he adds. “So I’m sure that whatever they do is of course a nice step in the right direction… but I think Google is playing a clever role here of kind of moving a bit but not too much.”

So how, then, does Xayn’s reranking algorithm work?

The app runs four AI models per device, combining encrypted AI models of respective devices asynchronously — with homomorphic encryption — into a collective model. A second step entails this collective model being fed back to individual devices to personalize served content, it says. 

The four AI models running on the device are one for natural language processing; one for grouping interests; one for analyzing domain preferences; and one for computing context.

“The knowledge is kept but the data is basically always staying on your device level,” is how Lundbæk puts it.

“We can simply train a lot of different AI models on your phone and decide whether we, for example, combine some of this knowledge or whether it also stays on your device.”

“We have developed a quite complex solution of four different AI models that work in composition with each other,” he goes on, noting that they work to build up “centers of interest and centers of dislikes” per user — again, based on those swipes — which he says “have to be extremely efficient — they have to be moving, basically, also over time and with your interests”.

The more the user interacts with Xayn, the more precise its personalization engine gets as a result of on-device learning — plus the added layer of users being able to get actively involved by swiping to give like/dislike feedback.

The level of personalization is very individually focused — Lundbæk calls it “hyper personalization” — more so than a tracking search engine like Google, which he notes also compares cross-user patterns to determine which results to serve — something he says Xayn absolutely does not do.

Small data, not big data

“We have to focus entirely on one user so we have a ‘small data’ problem, rather than a big data problem,” says Lundbæk. “So we have to learn extremely fast — only from eight to 20 interactions we have to already understand a lot from you. And the crucial thing is of course if you do such a rapid learning then you have to take even more care about filter bubbles — or what is called filter bubbles. We have to prevent the engine going into some kind of biased direction.”

To avoid this echo chamber/filter bubble type effect, the Xayn team has designed the engine to function in two distinct phases which it switches between: Called ‘exploration’ and (more unfortunately) ‘exploitation’ (i.e. just in the sense that it already knows something about the user so can be pretty certain what it serves will be relevant).

“We have to keep fresh and we have to keep exploring things,” he notes — saying that’s why it developed one of the four AIs (a dynamic contextual multi-armed bandit reinforcement learning algorithm for computing context).

Aside from this app infrastructure being designed natively to protect user privacy, Xayn argues there are a bunch of other advantages — such as being able to derive potentially very clear interests signs from individuals; and avoiding the chilling effect that can result from tracking services creeping users out (to the point people they avoid making certain searches in order to prevent them from influencing future results).

“You as the user can decide whether you want the algorithm to learn — whether you want it to show more of this or less of this — by just simply swiping. So it’s extremely easy, so you can train your system very easily,” he argues.

There is potentially a slight downside to this approach, too, though — assuming the algorithm (when on) does some learning by default (i.e in the absence of any life/dislike signals from the user).

This is because it puts the burden on the user to interact (by swiping their feedback) in order to get the best search results out of Xayn. So that’s an active requirement on users, rather than the typical passive background data mining and profiling web users are used to from tech giants like Google (which is, however, horrible for their privacy).

It means there’s an ‘ongoing’ interaction cost to using the app — or at least getting the most relevant results out of it. You might not, for instance, be advised to let a bunch of organic results just scroll past if they’re really not useful but rather actively signal disinterest on each.

For the app to be the most useful it may ultimately pay to carefully weight each item and provide the AI with a utility verdict. (And in a competitive battle for online convenience every little bit of digital friction isn’t going to help.)

Asked about this specifically, Lundbæk told us: “Without swiping the AI only learns from very weak likes but not from dislikes. So the learning takes place (if you turn the AI on) but it’s very slight and does not have a big effect. These conditions are quite dynamic, so from the experience of liking something after having visited a website, patterns are learned. Also, only 1 of the 4 AI models (the domain learning one) learns from pure clicks; the others don’t.”

Xayn does seem alive to the risk of the swiping mechanic resulting in the app feeling arduous. Lundbæk says the team is looking to add “some kind of gamification aspect” in the future — to flip the mechanism from pure friction to “something fun to do”. Though it remains to be seen what they come up with on that front.

There is also inevitably a bit of lag involved in using Xayn vs Google — by merit of the former having to run on-device AI training (whereas Google merely hoovers your data into its cloud where it’s able to process it at super-speeds using dedicated compute hardware, including bespoke chipsets).

“We have been working for over a year on this and the core focus point was bringing it on the street, showing that it works — and of course it is slower than Google,” Lundbæk concedes.

“Google doesn’t need to do any of these [on-device] processes and Google has developed even its own hardware; they developed TPUs exactly for processing this kind of model,” he goes on. “If you compare this kind of hardware it’s pretty impressive that we were even able to bring [Xayn’s on-device AI processing] even on the phone. However of course it’s slower than Google.”

Lundbæk says the team is working on increasing the speed of Xayn. And anticipates further gains as it focuses more on that type of optimization — trailing a version that’s 40x faster than the current iteration.

“It won’t at the end be 40x faster because we will use this also to analyze even more content — to give you can even broader view — but it will be faster over time,” he adds.

On the accuracy of search results vs Google, he argues the latter’s ‘network effect’ competitive advantage — whereby its search reranking benefits from Google having more users — is not unassailable because of what edge AI can achieve working smartly atop ‘small data’.

Though, again, for now Google remains the search standard to beat.

“Right now we compare ourselves, mostly against Bing and DuckDuckGo and so on. Obviously there we get much better results [than compared to Google] but of course Google is the market leader and is using quite some heavy personalization,” he says, when we ask about benchmarking results vs other search engines.

“But the interesting thing is so far Google is not only using personalization but they also use kind of a network effect. PageRank is very much a network effect where the most users they have the better the results get, because they track how often people click on something and bump this also up.

“The interesting effect there is that right now, through AI technology — like for example what we use — the network effect becomes less and less important. So actually I would say that there isn’t really any network effect anymore if you really want to compete with pure AI technology. So therefore we can get almost as relevant results as Google right now and we surely can also, over time, get even better results or competing results. But we are different.”

In our (brief) tests of the beta app Xayn’s search results didn’t obviously disappoint for simple searches (and would presumably improve with use). Though, again, the slight load lag adds a modicum of friction which was instantly obvious compared to the usual search competition.

Not a deal breaker — just a reminder that performance expectations in search are no cake walk (even if you can promise a cookie-free experience).

An opportunity for competition?

“So far Google has so far had the advantage of a network effect — but this network effect gets less and less dominant and you see already more and more alternatives to Google popping up,” Lundbæk argues, suggesting privacy concerns are creating an opportunity for increased competition in the search space.

“It’s not anymore like Facebook or so where there’s one network where everyone has to be. And I think this is actually a nice situation because competition is always good for technical innovations and for also satisfying different customer needs.”

Of course the biggest challenge for any would-be competitor to Google search — which carves itself a marketshare in Europe in excess of 90% — is how to poach (some of) its users.

Lundbæk says the startup has no plans to splash millions on marketing at this point. Indeed, he says they want to grow usage sustainably, with the aim of evolving the product “step by step” with a “tight community” of early adopters — relying on cross-promotion from others in the pro-privacy tech space, as well as reaching out to relevant influencers.

He also reckons there’s enough mainstream media interest in the privacy topic to generate some uplift.

“I think we have such a relevant topic — especially now,” he says. “Because we want to show also not only for ourselves that you can do this for search but we think we show a real nice example that you can do this for any kind of case.

“You don’t always need the so-called ‘best’ big players from the US which are of course getting all of your data, building up profiles. And then you have these small, cute privacy-preserving solutions which don’t use any of this but then offer a bad user experience. So we want to show that this shouldn’t be the status quo anymore — and you should start to build alternatives that are really build on European values.”

And it’s certainly true EU lawmakers are big on tech sovereignty talk these days, even though European consumers mostly continue to embrace big (US) tech.

Perhaps more pertinently, regional data protection requirements are making it increasing challenging to rely on US-based services for processing data. Compliance with the GDPR data protection framework is another factor businesses need to consider. All of which is driving attention onto ‘privacy-preserving’ technologies.

 

Xayn’s team is hoping to be able spread its privacy-preserving gospel to general users by growing the b2b side of the business, according to Lundbæk — so it’s hoping some home use will follow once employees get used to convenient private search via their workplaces, in a small-scale reverse of the business consumerization trend that was powered by modern smartphones (and people bringing their own device to work).

“We these kind of strategies I think we can step by step build up in our communities and spread the word — so we think we don’t even need to really spend millions of euros in marketing campaigns to get more and more users,” he adds.

While Xayn’s initial go-to-market push has been focused on getting the mobile apps out, a desktop version is also planned for Q1 next year.

The challenge there is getting the app to work as a browser extension as the team obviously doesn’t want to build its own browser to house Xayn. tl;dr: Competing with Google search is mountain enough to climb, without trying to go after Chrome (and Firefox, and so on).

“We developed our entire AI in Rust which is a safe language. We are very much driven by security here and safety. The nice thing is it can work everywhere — from embedded systems towards mobile systems, and we can compile into web assembly so it runs also as a browser extension in any kind of browser,” he adds. “Except for Internet Explorer of course.”


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The Last 5 Dark Web Search Engines for 2021 https://chatologica.com/the-last-5-dark-web-search-engines-for-2021/ https://chatologica.com/the-last-5-dark-web-search-engines-for-2021/#respond Wed, 28 Jul 2021 13:06:50 +0000 https://chatologica.com/the-last-5-dark-web-search-engines-for-2021/ For those new to the dark web, it is almost impossible to find a website on the Tor browser or how it works and this is where the dark web search engines help. For some, the dark web remains an important source of information as some people may feel much more comfortable posting there due […]]]>

For those new to the dark web, it is almost impossible to find a website on the Tor browser or how it works and this is where the dark web search engines help.

For some, the dark web remains an important source of information as some people may feel much more comfortable posting there due to the anonymity it offers. However, to access the layers of data hidden within it, a search engine is a necessity.

Now we have done publish an article earlier in February this year for the best dark web search engines. But, as it is well known, most dark websites do not last long and can go offline forever. Therefore, we’re back with a new article detailing the top 5 dark web search engines currently active.

  1. Ahmia.fi

Home page of the Ahmia search engine on the dark web (Image: Hackread.com)

Also on our previous list, Ahmia.fi remains reliable and also has a policy against any ‘material abuse’, something different from many other dark web search engines that also index websites with content. child sexual abuse.

Ahmia is also available on the surface canvas and also supports i2p network searches. You can visit the .Onion domain of Ahmia here.

The hidden wiki

The Last 5 Dark Web Search Engines for 2021

Hidden Wiki’s dark web search engine homepage (Image: Hackread.com)

Okay, it’s not a literal search engine but it’s very useful. You see, every time you do a search query on a dark web search engine you are bound to see 20 spam links, then maybe 1 legitimate. This can make the research process tedious.

However, this Wiki helps solve this problem by providing a list of website directories on the dark web for you to access easily. A surface web version is also available.

Other websites that offer similar content include TorLinks and Onion Links. You can visit The Hidden Wiki by following its link .Onion here.

  1. Haystak

The Last 5 Dark Web Search Engines for 2021

Haystak dark web search engine homepage (Image: Hackread.com)

Claiming to have indexed over 1.5 billion pages, including over 260,000 websites, Haystak would indeed stand out as an ingenious engine among the list.

It also has a paid version which offers a number of additional features such as searching using regular expressions, browsing to now-defunct Onion sites, and accessing their API.

You can’t bet they’ve got your best interests right at their fingertips, even if one of these features gives you access to a database of credit card information and email addresses as well. stolen.

You can visit Haystak by following his link .Onion here.

  1. Torch

The Last 5 Dark Web Search Engines for 2021

Torch dark web search engine homepage (Image: Hackread.com)

This too is one of those search engines that last quite a long time (since 1996) but the search results are not impressive. In the above query, I wanted to know the URL of the Facebook onion, a very simple piece of information. Still, he reported everything except that, showing how far these search engines need to go to improve.

On the other hand, it is fast and can still be useful. Nonetheless, you can visit Torch by following its link .Onion here.

  1. DuckDuckGo

The Last 5 Dark Web Search Engines for 2021

DuckDuckGo dark web search engine homepage (Image: Hackread.com)

A longtime enemy of Google, you are bound to find it on almost any dark web search engine listing. Still, it brings in more surface web search results than the dark web, and so the only good point we have left is the anonymity it offers. I actually couldn’t find 1 search result on the dark web thanks to a few random queries.

You can visit DuckDuckGo by visiting its .Onion link here.

To conclude, we hope this list will help you use TOR browser more efficiently. Just be careful to avoid browsing illegal websites as this can get you in trouble with moral choice of course.

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Google web search and local search ranking algorithm updates over the weekend https://chatologica.com/google-web-search-and-local-search-ranking-algorithm-updates-over-the-weekend/ https://chatologica.com/google-web-search-and-local-search-ranking-algorithm-updates-over-the-weekend/#respond Mon, 26 Apr 2021 07:00:00 +0000 https://chatologica.com/google-web-search-and-local-search-ranking-algorithm-updates-over-the-weekend/ As of around Friday, April 23, there appears to have been an unconfirmed Google search ranking algorithm on both the web search results side and the local search results side. There is chatter in the SEO forums coupled with the automated tracking tools detecting big changes, it looks like we’ve had some sort of unconfirmed […]]]>

As of around Friday, April 23, there appears to have been an unconfirmed Google search ranking algorithm on both the web search results side and the local search results side. There is chatter in the SEO forums coupled with the automated tracking tools detecting big changes, it looks like we’ve had some sort of unconfirmed update.

To be clear, the product reviews update ended on Thursday, April 22. Could it be related to that? I do not think so. Also, there was a lot more gossip with this unconfirmed update than the product review update in general – so I don’t think the two are related.

I have to say that there has also been some significant discussion that Google is not indexing content recently. Maybe, just maybe, this is why Gary Illyes spoke out on passing the quality checks for indexing – maybe this update had an impact on what Google will index and what has the quality threshold been changed here? I just guess, I don’t know.

Here are some of the gossip in the WebmasterWorld forums; note, I have cited below include the son and articles on Google’s indexing issues, but more focused on ranking fluctuations – but they can be related:

Big drop this afternoon. Has anyone noticed any fluctuations?

Anyone else noticed that Google favors freshness right now?

You have a competitor who just repostes the same type of post every few days with a different title and redefines the content and skyrockets in the rankings and does well on Google Discover. The Fluff article just republished every few days.

My traffic in the US is down 24% today … the drop started at 2pm. Traffic from all other countries on average or increasing. I’m terribly fed up with my traffic in the United States being strangled for half the day …

Exactly the same. At 2 p.m. the fall began. It is becoming commonplace every day. Yesterday was more important.

I saw a big drop yesterday too. Still very low today …

I was up over 30% yesterday from the same day last week. Last Saturday was lower than usual but not significantly. It is up 20% from the previous Saturday. The good fortunes continue today as traffic continues to trend.

I ended the day yesterday with -18% on USA traffic. Meanwhile, Google sent much higher than normal traffic levels from Germany at + 100% yesterday and + 300% today … it’s now going on for about ten days. The United Kingdom and Canada also made strong progress. American traffic continues to limp and has never recovered from the decline of 2/13 and the even larger drop of 3/15. The drop also seems to target my most important and profitable landing pages and keywords, like Google is crawling your most important content and cutting traffic … it’s amazing how accurate they are.

I was 75% yesterday and generally expect 60-80% so okay but today with 5 hours of Googleday I’m 54% and it seems slow on all sites.

And as usual, huge drop in traffic after 2 p.m. G limited my traffic earlier today: – |

It seems like once you’re hit with a major update, every little update just makes it worse. When you get a major update, most smaller updates are beneficial.

There is more chatter on both the ranking fluctuation side and the indexing side.

Here are screenshots of the tracking tools; most show changes as early as the 22nd or 23rd.

Mozcast:

click for actual size

SERP metrics (watch this dive):

click for actual size

Algoroo:

click for actual size

Advanced web rankings:

click for actual size

Accuranker:

click for actual size

RangRanger:

click for actual size

Cognitive SEO:

click for actual size

Semrush:

click for actual size

And like I said, it’s also on the local side, see BrightLocalstool:

click for actual size

I have to note that this was the 9th anniversary of the Google Penguin update on Saturday the 24th – coincidence? 😛

In summary; I doubt this relates to updating product reviews, it could be a quality change made by Google that could impact indexing as well as local search. Google hasn’t confirmed anything yet.

Discussion forum at WebmasterWorld.


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How hospitals hide pricing data from web search results: WSJ https://chatologica.com/how-hospitals-hide-pricing-data-from-web-search-results-wsj/ https://chatologica.com/how-hospitals-hide-pricing-data-from-web-search-results-wsj/#respond Mon, 22 Mar 2021 07:00:00 +0000 https://chatologica.com/how-hospitals-hide-pricing-data-from-web-search-results-wsj/ HCA Healthcare and Universal Health Services are among hundreds of hospitals that have incorporated special coding into their websites to prevent previously confidential pricing information from appearing in web searches, according to a March 22 report. the Wall Street newspaper report. Seven things to know: 1. Pricing information for hospital services must be disclosed under […]]]>

HCA Healthcare and Universal Health Services are among hundreds of hospitals that have incorporated special coding into their websites to prevent previously confidential pricing information from appearing in web searches, according to a March 22 report. the Wall Street newspaper report.

Seven things to know:

1. Pricing information for hospital services must be disclosed under a new federal price transparency rule that came into effect on January 1, but hundreds of hospitals have embedded a code into their website that has blocked Google and other search engines display the pages with the price lists. , according to Newspaperexamining more than 3,100 sites.

2. The code prevents pages from appearing in searches, such as a hospital’s name and prices, computer experts told the. Newspaper. While the prices are still there, you have to click through multiple layers of pages to find them.

3. “It’s technically there, but good luck finding it,” said Chirag Shah, associate professor of computer science at the University of Washington. Newspaper. “It’s one thing not to optimize your site for search, it’s another to mark it as not searchable. It’s a clear indication of intentionality.”

4. Hospitals burying their pricing data include those owned by HCA Healthcare and Universal Health Services as well as the University of Pennsylvania Health System, NYU Langone Health, Beaumont Health and Novant Health, according to the Newspaper.

5. Penn Medicine, NYU Langone Health and Novant Health told the publication they used the block code to first direct patients to information they “found more useful than raw price data,” to which they included web links. UHS uses the block code to ensure consumers recognize a disclosure statement before viewing prices and makes no effort to withhold information, a hospital spokesperson told the Newspaper.

6. After the Newspaper contacted hospitals about its discovery, the search block code has been removed from sites such as HCA, Penn Medicine, Beaumont, Avera Health, Ballad Health and Northern Light Health.

7. A spokesperson for HCA told the post that the search blocker was “legacy code that we have removed”, and Avera, Ballad, Beaumont and Northern Light said the code was left on their websites. by mistake.


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What Travel Stocks To Consider After The Pandemic Based On Web Research Trends https://chatologica.com/what-travel-stocks-to-consider-after-the-pandemic-based-on-web-research-trends/ https://chatologica.com/what-travel-stocks-to-consider-after-the-pandemic-based-on-web-research-trends/#respond Tue, 02 Mar 2021 08:00:00 +0000 https://chatologica.com/what-travel-stocks-to-consider-after-the-pandemic-based-on-web-research-trends/ Americans tired by the pandemic are looking for places to escape on vacation as more people get vaccinated against COVID-19. “Yellowstone has exploded in a way it’s never seen before and you can kind of see it in the hottest destinations,” SimilarWeb’s Alisha Kapur told Yahoo Finance Live. Kapur is the leading travel industry consultant […]]]>

Americans tired by the pandemic are looking for places to escape on vacation as more people get vaccinated against COVID-19.

“Yellowstone has exploded in a way it’s never seen before and you can kind of see it in the hottest destinations,” SimilarWeb’s Alisha Kapur told Yahoo Finance Live. Kapur is the leading travel industry consultant at SimilarWeb, an analytics company that tracks millions of web clicks daily to determine potential investment trends for paying customers. “People are really looking for safe outdoor open spaces and Yellowstone and other national parks are offering this so you can see from consumer research trends that national parks are a priority for most consumers, especially that they are looking for safe, comfortable, remote places to get around. “

SimilarWeb tells customers that the top five getaway searches are Yellowstone National Park, Disney World, Disneyland, Universal Studios Florida, and the Grand Canyon.

Among the search winners in the post-pandemic travel reopening trade, Airbnb (ABNB), which recorded 49 million searches in January. That was 15 million more than the VRBO finalist who clicked 34 million. The only traditional accommodation “provider” in SimilarWeb data to do the top five searches was Marriott (MAR) at 17 million.

“I think alternative housing will sort of maintain this strong recovery that they have had over the next few months,” Kapur said. But she adds that traditional hotel chains like Hilton (HLT) and Hyatt (H) “are doing a lot to win back consumer trust, they have a lot of deals, they really, really try to make calls to action. their loyal consumer base. “

Getting there is half the fun

Passenger traffic through Transportation Security Administration (TSA) checkpoints at airports across the country continues to improve, according to Savanthi Syth, airline analyst Raymond James. She recently told her customers: “On Thursday, February 25, passenger numbers crossed one million, which had only been seen on weekends or holiday periods since the start of the pandemic.”

Syth said domestic demand started to recover two weeks ago, writing: “Delta has seen a significant increase in travel demand, linked to both short-term bookings and summer bookings (that’s that is, the reservation curve has started to lengthen). This is similar to Southwest’s comment. , as well as our recent weekly checks showing that domestic demand is improving even outside of peak holiday / weekend periods. “

“Southwestern airlines remain the favorites of the United States and have outperformed their peers due to their focus on domestic routes” which airlines call visiting friends and relatives, SimilarWeb said.

According to SimilarWeb, SouthWest (LUV) recorded 62% more web visits than United States (AAL) Delta (DAL) and United (UAL). Kapur said, “Southwest will continue to be the most successful airline in the short term, until more international destinations reopen to Americans.”

Lamar Villere, partner and portfolio manager at Villere and Co., said consumer behavior is returning faster than expected, especially in companies exposed to consumer discretionary spending.

“Maybe they book a few months, some book earlier, some book later, but people are booking things. People are going out of the house and looking for ways to spend their money right now,” said Villere.

The post-pandemic cruise still docked

Among major cruise lines, SimilarWeb said Carnival (CCL) performed 4 million searches in January, twice as many as rivals Royal Caribbean (RCL) and Norwegian Cruise Lines (NCLH) which had 2 million each. But SimilarWeb is telling customers that overall cruise search traffic remains depressed.

“We’re still seeing continued traffic to info pages, cancellation pages and really pages where consumers don’t necessarily book on site,” Kapur said. Carnival recently pushed back the suspension of operations until May 31, with other cruise lines following suit. Kapur predicts continued volatility in the cruise industry for the next few months with a possible recovery this summer.

Adam Shapiro is co-presenter of Yahoo Finance Live from 3 p.m. to 5 p.m. Follow him on Twitter @Ajshaps

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8 best dark web search engines for 2021 https://chatologica.com/8-best-dark-web-search-engines-for-2021/ https://chatologica.com/8-best-dark-web-search-engines-for-2021/#respond Mon, 15 Feb 2021 08:00:00 +0000 https://chatologica.com/8-best-dark-web-search-engines-for-2021/ For a beginner, it is almost impossible to find a website on the Tor browser or how it works and this is where dark web search engines help. To the layman, there is only one type of internet – the one we use for normal everyday browsing. But, in reality, there are 3 main types […]]]>

For a beginner, it is almost impossible to find a website on the Tor browser or how it works and this is where dark web search engines help.

To the layman, there is only one type of internet – the one we use for normal everyday browsing. But, in reality, there are 3 main types of the Internet that are essential for understanding to get an accurate picture of how it works:

1: The Surface Web
2: The Deep Web
3: The Dark Web

The shallow web

The surface web is made up of all the pages that can be indexed by a normal search engine like Google and accessible to everyone.

The Deep Web

The deep web is made up of all those pages that are protected and therefore cannot be indexed by a search engine. This protection can take the form of several security measures such as passwords. An example is a private Instagram profile whose content cannot be displayed in Google search results.

The dark web

The dark web is made up of all websites that cannot be accessed using a normal browser and require a special type of network called The Onion Routing (TOR). All websites use a .onion appended to the end instead of top level domains such as “.com”.

Even though the first 2 are not consciously known by the vast majority of users to be distinct types, they are used daily by them. However, the real mystery lies in the third one, the dark web which is only a tiny fraction of the Internet containing around a little over 65,000 URLs.

Of these, too, only around 8000 are active and the majority of existing URLs do not work due to various issues. Yet this is only part of the problem.

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Dark web search engines

Another problem is the difficulty in finding dark websites. Unlike the normal surface web, site URLs do not have easily remembered names and therefore memorization is not an option in most cases. This naturally poses a question: What dark web search engines are available to Google? Turns out there are plenty, here are the 8 best dark web search engines:

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1. DuckDuckGo – 3g2upl4pq6kufc4m.onion

Built with the unique selling point of not following users, DuckDuckGo has long been used as a replacement for Google by privacy-conscious users. On the other hand, many also use it on the dark web for its anonymity features. Considering this is the default search engine for TOR Browser, that says a lot about its reputation as a good search engine in the community!

2. Torch – cnkj6nippubgycuj.onion

Also known as TorSearch, it claims to be the oldest search engine residing on the dark web and indexing over a billion pages, giving it considerable brownie points. Users are neither tracked nor censored to make full use of the information buried in the dark web.

3. Recon – reconponydonugup.onion

This particular search engine was built by Hugbunt3r, a prominent member of the popular Fright service on the dark web. It aims to serve as a database through which users can search for products from different vendors in different markets on the dark web.

Individual profile view options for vendors and markets are also available, including details like ratings, mirror links, number of listings, and percent uptime.

4. Ahmia.fi – msydqstlz2kzerdg.onion

An interesting part of Ahmia is that it allows you to browse dark web links using a normal browser like Google Chrome. That’s even if you would eventually need TOR to access those resulting links, but it at least lets you see them that way. On the other hand, it also has an onion url.

Usage statistics are also available on its site categorized by simple and unique search queries, and simple and unique search results on the TOR and I2P network. One notable feature of this search engine is that it appears to be simplistic while still being very functional.

In addition, it places great importance on the comfort of its users, an example being that with one click, it allows you to add your own hidden dark web service to its database.

5.notEvil – hss3uro2hsxfogfq.onion

Setting up an aura of simplicity, notEvil would have been modeled on Google. It is also reported to derive its name from Google’s era-era motto of “don’t be mean”. For search, users have several options to select their results among which titles, URLs or both combined.

6. Candle – gjobqjj7wyczbqie.onion

Built about 3 years ago where the design inspirations came for this site is obvious – Google. Trying to emulate the kind of simplicity that the tech giant has on the dark web has generated good traffic, with the number of indexed sites increasing every day.

7. Haystak – haystakvxad7wbk5.onion

Showing itself as having indexed over 1.5 billion pages, it certainly deserves a spot on the list. However, it should be noted that many of them may not work as only a small portion of sites created on the dark web stay online all the time, with most being deleted.

It also offers a premium version which can be ordered through a contact form.

8. Kilos – dnmugu4755642434.onion

Dark Web Kilos Search Engine Helps Users Find Hidden Markets

Kilos is one of the Dark Web search engines that was primarily designed for the Dark Web. It launched in November 2019 and provides cybercriminals with a platform to find answers to their dark queries, search the Dark Web for services, and find the right person to deal with for all the wrong tasks. For example, if someone wants to trade Bitcoin secretly, they just need to type in the relevant keywords and the deed will be done.

The only downside is that researchers who have investigated the use of Kilos believe the search engine helps cybercriminals more than someone keen to learn more about dark web markets.

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To conclude, you can also find links from other dark web search engines, but these are the ones that stand out the most. Also, as mentioned earlier, there are many sites that do not survive the stain of time in this strange land, so some of them might not exist tomorrow.

To see: Top The Pirate Bay Alternatives – Best Torrent Download Sites

To stay safe, avoid search results that can lead you to illegal sites such as those with child pornography, illegal drugs, or guns, as some of these search engines do not censor these results.

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Xayn introduces user-friendly and privacy-protecting web search https://chatologica.com/xayn-introduces-user-friendly-and-privacy-protecting-web-search/ https://chatologica.com/xayn-introduces-user-friendly-and-privacy-protecting-web-search/#respond Thu, 14 Jan 2021 08:00:00 +0000 https://chatologica.com/xayn-introduces-user-friendly-and-privacy-protecting-web-search/ I like the idea that there are a number of ways that people can take back control of their data, and I really like that my web search results aren’t used to direct ultra-targeted ads to me. Xayn I have been using DuckDuckGo for a while now, I use Presearch when using Chrome as my […]]]>

I like the idea that there are a number of ways that people can take back control of their data, and I really like that my web search results aren’t used to direct ultra-targeted ads to me.

Xayn introduces zdnet privacy-friendly and user-friendly web search

Xayn

I have been using DuckDuckGo for a while now, I use Presearch when using Chrome as my browser and Startpage is my search tab on my Edge browser.

Recently I took a look at The app of German tech start-up Xayn for my android device.

It is based on AI research protecting privacy and is synonymous with transparency and ethical AI made in Europe.

The app allows you to control its search algorithms.

By swiping left or right on the results, you can influence the results displayed and teach algorithms which results you want to see more of in the future.

Its AI model is a tiny multilingual quantified-BERT sentence optimized for mobile to understand the natural query language of the words you used in your query as well as in the results.

Then it uses an unsupervised clustering model to group these points into different clusters of interest, for example, sports or the arts.

It then calculates the distance to the clusters to reduce the computational cost in the future.

A third model called ListNet analyzes the history of search interactions to understand what types of domains you like – for example, Wikipedia instead of Instagram.

Research companies want to know as much as possible about you so that they can control the results that are delivered to you.

The problem is, in order to get the most accurate search results, users have to compromise their privacy. Xayn retains all of its data with the user.

With Xayn, users can customize features like enabling AI to provide unique search results and can turn it off if they don’t want the feature.

Xayn introduces zdnet privacy-friendly and user-friendly web search

Xayn

The app gives you one-handed control and no-click search to make it easy to use. Collect, store and sort your favorite web content so that you don’t lose any information.

Your Home screen displays your own personal web feed, determined by your search history.

Leif Nissen Lundbæk, co-founder and CEO of Xayn, said:

“I’ve always hated having to choose between privacy and convenience when searching online.

I also found it scary not to know why certain results were shown to me by the algorithms. Despite all of this, I was still using established search giants because I wasted too much time finding what I was looking for with privacy alternatives. “

The only challenge I see with Xayn is that users can sweep away any news articles that don’t match their core beliefs.

There is a risk that the app will show more and more right or left content over time. Users prefer to see the types of articles they like and form a “belief bubble” to validate their choice.

Truly smart AI will be able to fix this one day – but, for now, this neat, customizable app will deliver exactly the results you want to see with the privacy you need.


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