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A foundation of the folklore of the American right is the story of how National Reviews William F. Buckley, in the early- to mid-1960s, cast the John Birch Societyand by extension the entire kooky, conspiracist wing of the rightout of the conservative movement.

This was part of a larger struggle for the soul of the right. Older conservative publications such as the American Mercury, which had once been the home of such luminaries as H.L. Mencken and Henry Hazlitt, had turned into a forum for antisemitic conspiracy theoriesbefore eventually being taken over outright by neo-Nazis. The response was an effort by Buckley and other conservative thinkers, with the help of political frontmen Barry Goldwater and Ronald Reagan, to create a conservative movement with more ideological and philosophical substanceone based not on conspiracy theories or mere reactionary emotions but on ideas. (Too bad he also tried to get rid of Ayn Rand.)

Looking at American politics today, it sure looks like this seminal conservative achievement is unraveling. The Birchers are back. And theyre winning.

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The John Birch Society, to refresh your memory, was started in 1958 by a conservative businessman who thought President Eisenhower was secretly a Soviet agent. It had a certain kind of cracked appeal as an easy explanation for various setbacks in the early years of the Cold War. The Soviet domination of Eastern Europe, the Communist takeover of China, the Soviet development of nuclear weaponsthese werent the results of Western mistakes, or large and difficult-to-control social forces, or just the fortunes of war. No, it was all a secret plot, and THEY were lying to you.

This worldview was tremendously popular, more popular than todays conservatives would probably like to admit. In 1962, Barry Goldwater complained, Every other person in Phoenix is a member of the John Birch Society. Im not talking about commie-haunted apple pickers or cactus drunks. Im talking about the highest cast of men of affairs.

The Birchers had such a big following on the right that Buckley, Goldwater, and Reagan hemmed and hawed for years before breaking with them. Even then, it took repeated denunciations, combined with the Birchers increasing notoriety as a national laughingstock, to eventually reduce their appeal and relegate them to the crazy fringes.

Consider the elements of this history:

We have a conspiracy theory that explains everything conservatives think has gone wrong in the world by positing the machinations of a secret cabal that controls everything from the intelligence agencies to the schools.

We have the rapid spread of these crackpot theories to otherwise normal and respectable people in the rank and file of the movement.

We have an attempt to make the conspiracists into the ultimate representatives of opposition to totalitarian communism, and a corresponding attempt to dismiss any conservative critics of the conspiracists as weak-kneed appeasers handing over the country to its enemies.

We have the uneasy balancing act of conservatives in the media and in politics who dont want to denounce the crackpots for fear of angering their partys base.

Isnt this also precisely the state of conservatism today?

We tend to think that our culture war is something new, rising out of the unique challenges of our own era. But youd be surprised how much of it is just the same old culture war being endlessly rehashed.

Todays equivalent of the John Birch Society is the QAnon conspiracy theory, an online grift that got out of hand and became a worldview. It posits its own spectacularly implausible conspiracy theory: That there is a global network of pedophiles who secretly run the world and control our politics so that they can abuse children. This conspiracy theory has in turn spawned other conspiracy theories which claim that the 2020 election was stolen from Donald Trump. It is currently being mainstreamed in attacks on Disney as a corporation bent on grooming children to prepare them for exploitation by pedophiles.

And where are todays conservative leaders, the intellectuals and politicians, the Buckleys and Reagans, who have the authority to shut this down?

Well, Ben Sasse wrote a piece once. But most of todays conservative and Republican leaders are actually trying to hitch themselves to the new John Birchers.

Donald Trump famously refused to denounce the QAnon crazies, describing them only as people who are against pedophiliathe most flattering possible description of the group. Its like saying that the John Birchers were against communism. In both cases, the actual salient characteristic of these groups is their wild, paranoid, evidence-free conspiracy theories.

Trumps sympathy for QAnon helped ease it into the conservative mainstream, and we can see the results in two recent incidents.

Florida Governor Ron DeSantis is the leading candidate to become the sane Trumpa Republican who can harness Trumps populist appeal, but in a disciplined and calculating way. But after DeSantiss defenders rushed out to assure everyone that his bill targeting teachers was not a Dont Say Gay Law and was not animated by anti-homosexual bias, his press secretary Christina Pushaw declared that the bill would be more accurately described as an Anti-Grooming Bill, adding, If youre against the Anti-Grooming Bill, you are probably a groomer. A groomer, for those who are fortunate enough not to know, is a child predator who manipulates his victims to prepare them to accept abuse.

So much for being the sane Trump.

The idea that gay teachers are predators preparing to groom children is an old trope with a history in Florida. You may recall that previous iterations of the culture war attempted to ban homosexuals from teaching jobs. But more significant is the way this claim taps into the QAnon conspiracy theory. The whole base of QAnon is the dangerous delusion that their enemies are all secret pedophiles. This is the line that has been taken up by conservatives and endlessly repeated, including in a conservative campaign to boycott the Walt Disney Company (and also to subject it to land-use and antitrust regulations) as a political reprisal for opposing the Florida law. And why not if, as authoritarian conservative Rod Dreher puts it, Disney has gone groomer?

Taking a bill with many serious problemsa vaguely worded restriction and an enforcement mechanism designed to facilitate legal harassmentand characterizing any criticism of it as grooming and as support for pedophiles and predators has created an atmosphere of constant, vicious defamation aimed at any and all opponents. This is being egged on, of course, by the usual unscrupulous carnival barkers.

This mode of conspiracy thinking was also reflected in the scurrilous conduct of the Senate hearings for Ketanji Brown Jackson, when Senator Josh Hawley pandered to the QAnon vote by trying to portray the judges past sentencing work as soft on pedophiles. Many people, including conservative authors such as National Reviews Andrew McCarthy, have debunked the smear, showing that Judge Jacksons sentences were in line with the consensus view of other judges.

But once given this talking point, the crazies will chant it forever as if it is the gospel truth. Except that practically everyone is one of the crazies now. Hence the spectacle of Mollie Hemingway, of the Federalist and Fox News, trying her hardest to imply that Mitt Romney is a secret pedophile.

Which makes as much sense as Eisenhower being a secret communist.

From the top down, the Birchers have won. They now own the conservative movement and the Republican party.

Conspiracy theories have consequences. If you have been arguing these issues on social media, you will find that in among the groomer smears lobbed around carelessly there is an undertone of menace, with reminders that we know what to do with pedophiles. Before this is all over, someone is going to take this groomer and pedo talk literally. There will be blood.

We should also remember what conservatives accomplished by purging their crazies the last time around: By basing the movement on substantive ideas and having the courage and self-discipline to purge the kooks who claimed to be on our side, we achieved a few little things like pulling the U.S. out of the national malaise of the 1970s and winning the Cold War, followed by a period of peace, prosperity, and the spread of free societies across the globe. It wasnt just good for the movement, it was good for the country and the world.

If we want to experience anything like those triumphs again, we need build new institutions defined by pro-liberty ideasand we need to push the conspiracy theorists back to the fringes.

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Did the John Birch Society Win in the End? - The Bulwark

It was first mentioned in 1111.

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This article was prepared for an edition of the Spectacular Slovakia travel guide and was published in the travel guide Slovakia.

Draovce, a village at the foothill of Zobor Hill, has been part of Nitra since 1975.

Its first mention comes from the Zobor Charter of 1111. The village represents an important archaeological site where the Great Moravian fortification and graves from the 11th century were revealed.

It became popular for the romanesque Church of St Michael the Archangel from the 11th century. The church stands on a hill overlooking Draovce, offering beautiful views of the surrounding area. Experts have identified 10 building phases, six of which were done in the romanesque period. Legend has it that the Virgin Mary once appeared before a shepherd and pointed to the decaying church.

Residents suddenly realised the church was in poor condition and began to reconstruct it. Thanks to their huge efforts, it has survived to date.

In 1803, when a new church was built up in the village, the church on the hill stopped serving religious purposes. Under communism, its existence was in jeopardy as the nearby rocky terrain had been turned into a quarry. That is why the church now stands on the edge of a steep cliff.

The church opens on the Day of St Michael, but masses and weddings sometimes take place there. A visit to the church is possible on other days but visitors must talk to the local superintendent first.

The church has a rectangular ground plan with a semi-circular apse in the east and a brick pyramid tower in the west. Its invaluable architectural details include the remains of a former romanesque portal and two copies of stone tablets, which are linked to the overhauls of 1780-1829. Inside are restored remnants of romanesque plasters and the late gothic template painting in gray-black and red colours with a rhombus motif.

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Draovce: Terrain near the romanesque church turned into a quarry - The Slovak Spectator

Health systems and payers eager to trim costs think the answer lies in a small group of patients who account for more spending than anyone else.

If they can catch these patients typically termed high utilizers or high cost, high need before their conditions worsen, providers and insurers can refer them to primary care or social programs like food services that could keep them out of the emergency department. A growing number also want to identify the patients at highest risk of being readmitted to the hospital, which can rack up more big bills. To find them, theyre whipping up their own algorithms that draw on previous claims information, prescription drug history, and demographic factors like age and gender.

A growing number of the providers he works with globally are piloting and using predictive technology for prevention, said Mutaz Shegewi, research director of market research firm IDCs global provider IT practice.

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Crafted precisely and accurately, these models could significantly reduce costs and also keep patients healthier, said Nigam Shah, a biomedical informatics professor at Stanford. We can use algorithms to do good, to find people who are likely to be expensive, and then subsequently identify those for whom we may be able to do something, he said.

But that requires a level of coordination and reliability that so far remains rare in the use of health care algorithms. Theres no guarantee that these models, often homegrown by insurers and health systems, work as theyre intended to. If they rely only on past spending as a predictor of future spending and medical need, they risk skipping over sick patients who havent historically had access to health care at all. And the predictions wont help at all if providers, payers, and social services arent actually adjusting their workflow to get those patients into preventive programs, experts warn.

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Theres very little organization, Shah said. Theres definitely a need for industry standardization both in terms of how you do it and what you do with the information.

The first issue, experts said, is that theres not an agreed-upon definition of what constitutes high utilization. As health systems and insurers develop new models, Shah said they will need to be very precise and transparent about whether their algorithms to identify potentially expensive patients are measuring medical spending, volume of visits compared to a baseline, or medical need based on clinical data.

Some models use cost as a proxy measure for medical need, but they often cant account for disparities in a persons ability to actually get care. In a widely cited 2019 paper examining an algorithm used by Optum, researchers concluded that the tool which used prior spending to predict patient need referred white patients for follow-up care more frequently than Black patients who were equally sick.

Predicting future high-cost patients can differ from predicting patients with high medical need because of confounding factors like insurance status, said Irene Chen, an MIT computer science researcher who co-authored a Health Affairs piecedescribing potential bias in health algorithms.

If a high-cost algorithm isnt accurate, or is exacerbating biases, it could be difficult to catch especially when models are developed by and implemented in individual health systems, with no outside oversight or auditing by government or industry. A group of Democratic lawmakers has floated a bill requiring organizations using AI to make decisions to assess them for bias and creating a public repository of these systems at the Federal Trade Commission, though its not yet clear if it will progress.

That puts the onus, for the time being, on health systems and insurers to ensure that their models are fair, accurate, and beneficial to all patients. Shah suggested that the developers of any cost prediction model especially payers outside the clinical system cross-check the data with providers to ensure that the targeted patients do also have the highest medical needs.

If were able to know who is going to get into trouble, medical trouble, fully understanding that cost is a proxy for thatwe can then engage human processes to attempt to prevent that, he said.

Another key question about the use of algorithms to identify high-cost patients is what, exactly, health systems and payers should do with that information.

Even if you might be able to predict that a human being next year is going to cost a lot more because this year they have colon cancer stage 3, you cant wish away their cancer, so that cost is not preventable, Shah said.

For now, the hard work of figuring out what to make of the predictions produced by algorithms has been left in the hands of the health systems making their own models. So, too, is the data collection to understand whether those interventions make a difference in patient outcomes or costs.

At UTHealth Harris County Psychiatric Center, a safety net center catering primarily to low-income individuals in Houston, researchers are using machine learning to better understand which patients have the highest need and bolster resources for those populations. In one study, researchers found that certain factors like dropping out of high school or being diagnosed with schizophrenia were linked to frequent and expensive visits. Another analysis suggestedthat lack of income was strongly linked to homelessness, which in turn has been linked to costly psychiatric hospitalizations.

Some of those findings might seem obvious, but quantifying the strength of those links helps hospital decision makers with limited staff and resources decide what social determinants of health to address first, according to study author Jane Hamilton, an assistant professor of psychiatry and behavioral sciences at the University of Texas Health Science Center at Houstons Medical School.

The homelessness study, for instance, led to more local intermediate interventions like residential step-down programs for psychiatric patients. What youd have to do is get all the social workers to really sell it to the social work department and the medical department to focus on one particular finding, Hamilton said.

The predictive technology isnt directly embedded in the health record system yet, so its not yet a part of clinical decision support. Instead, social workers, doctors, nurses, and executives are informed separately about the factors the algorithm identifies for readmission risk, so they can refer certain patients for interventions like short-term acute visits, said Lokesh Shahani, the hospitals chief medical officer and associate professor at UTHealths Department of Psychiatry and Behavioral Sciences. We rely on the profile the algorithm identifies and then kind of pass that information to our clinicians, Shahani said.

Its a little bit harder to put a complicated algorithm in the hospital EHR and change the workflow, Hamilton said, though Shahani said the psychiatric hospital plans to link the two systems so that risk factors are flagged in individual records over the next few months.

Part of changing hospital operations is identifying which visits can actually be avoided, and which are part of the normal course of care. Were really looking for malleable factors, Hamilton said. What could we be doing differently?

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Health systems are using machine learning to predict high-cost care. Will it help patients? - STAT

BOSTON, April 13, 2022 (GLOBE NEWSWIRE) -- VMRay, a provider of automated malware analysis and detection solutions, today announced the release of new Machine Learning-based capabilities for its flagship VMRay Platform, helping enterprise security teams detect and neutralize novel malware and phishing threats. Recognized as the gold standard for advanced threat detection and analysis, the high-fidelity threat data used by VMRay to train and evaluate its Machine Learning system is both highly accurate and relevant, allowing customers to detect threats such as zero-day malware which were previously thought to be undetectable.

To get the best out of AI, you need a carefully arranged combination of Machine Learning and other cutting-edge technologies. Because the value and efficacy of each ML utilization is dependent on how you train and evaluate the model: namely, the quality of the inputs and the expertise of the team, said Carsten Willems, co-founder and CEO of VMRay. The data that you use to train the model and evaluate the accuracy of its predictions must be accurate, noise-free, and relevant to the task at hand. This is why Machine Learning can only add value when its based on an already advanced technology platform with outstanding detection capabilities. Our approach is to use ML together with our best-of-breed technologies to enhance detection capabilities to perfection, by combining the best of two worlds.

Todays threat landscape is a dynamic one, evolving by the day with attacks growing in complexity, scale and stealth. Since late detection and response is among the most important problems that cause huge costs, its more critical than ever that security teams can rapidly identify and stop these threats at the initial point of entry, before a minor incident cascades into a full-blown data breach. Whereas conventional signature and rule-based heuristics are unable to detect unknown or sophisticated threats that use advanced evasive techniques, the VMRay Platform is able to detonate a malicious file or URL in a safe environment, observe and document the genuine behavior of the threat as the threat is unaware that its being observed.

Four of the top five global technology enterprises, three of the Big 4 accounting firms, and more than 50 government agencies across 17 countries today rely on VMRay to supplement their existing security solutions, automate security operations and thus, accelerate detection and response. Gartners Emerging Technologies: Tech Innovators in AI in Attack Detection report asserts that the critical requirements for an AI-based attack detection solution are improved attack detection and reduced false positives. This latest, ML-enhanced version of VMRay Platform addresses these two challenges with unmatched precision, delivering the following benefits to security teams and threat analysts:

Improved Threat Detection: Featuring a machine learning model that improves threat detection capabilities by recognizing additional patterns, the VMRay Platform brings advanced threat detection to customers existing security solutions and covers the blind spots. With this supplementary approach, VMRay minimizes security risks and maximizes the value that customers get from their security investment.

Reduced False Positives: False positives and alert fatigue continue to plague enterprise SOC teams, hampering their ability to quickly respond to genuine threats. VMRay Analyzer generates high-fidelity, noise-free reports that dramatically reduce false positives to keep teams efficient. Seamless integrations with all the major EDR, SIEM, SOAR, Email Security, and Threat Intelligence platforms enable full automation, empowering resource-strapped security teams to focus their energies on higher-value strategic initiatives.

To try VMRay Analyzer visit: https://www.vmray.com/try-vmray-products/

About VMRay

VMRay was founded with a mission to liberate the world from undetectable digital threats. Led by notable cyber security pioneers, VMRay develops best-of-breed technologies to detect unknown threats that others miss. Thus, we empower organizations to augment and automate security operations by providing the worlds best threat detection and analysis platform. We help organizations build and grow their products, services, operations, and relationships on secure ground that allows them to focus on what matters with ultimate peace of mind. This, for us, is the foundation stone of digital transformation.

Press ContactRobert NachbarKismet Communications206-427-0389rob@kismetcommunications.net

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VMRay Unveils Advanced Machine Learning Capabilities to Accelerate Threat Detection and Analysis - GlobeNewswire

In recent years, we have seen accelerated OLED materials development, aided by software tools based on machine learning and Artificial Intelligence. This is an excellent development which contributes to the continued improvement in OLED efficiency, brightness and lifetime.

Kyulux's Kyumatic AI material discover system

The promise of these new technologies is the ability to screen millions of possible molecules and systems quickly and efficiently. Materials scientists can then take the most promising candidates and perform real synthesis and experiments to confirm the operation in actual OLED devices.

The main drive behind the use of AI systems and mass simulations is to save the time that actual synthesis and testing of a single material can take - sometimes even months to complete the whole cycle. It is simply not viable to perform these experiments on a mass scale, even for large materials developers, let alone early stage startups.

In recent years we have seen several companies announcing that they have adopted such materials screening approaches. Cynora, for example, has an AI platform it calls GEM (Generative Exploration Model) which its materials experts use to develop new materials. Another company is US-based Kebotix, which has developed an AI-based molecular screening technology to identify novel blue OLED emitters, and it is now starting to test new emitters.

The first company to apply such an AI platform successfully was, to our knowledge, Japan-based Kyulux. Shortly after its establishment in 2015, the company licensed Harvard University's machine learning "Molecular Space Shuttle" system. The system has been assisting Kyulux's researchers to dramatically speed up their materials discovery process. The company reports that its development cycle has been reduced from many months to only 2 months, with higher process efficiencies as well.

Since 2016, Kyulux has been improving its AI platform, which is now called Kyumatic. Today, Kyumatic is a fully integrated materials informatics system that consists of a cloud-based quantum chemical calculation system, an AI-based prediction system, a device simulation system, and a data management system which includes experimental measurements and intellectual properties.

Kyulux is advancing fast with its TADF/HF material systems, and in October 2021 it announced that its green emitter system is getting close to commercialization and the company is now working closely with OLED makers, preparing for early adoption.

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How machine learning and AI help find next-generation OLED materials - OLED-Info