Class Action Fraud Suit Multi Level Marketing And Donald Trump PdfBy Evrard P. In and pdf 03.06.2021 at 08:08 6 min read
File Name: class action fraud suit multi level marketing and donald trump .zip
Published on October 30th,
- News Coverage
- Trump’s MLM Pitch Misled Consumers, Lawsuit Says
- Lawsuit Against Pacemaker Company
- Oh no, there's been an error
Caitlin Ruiz, a year-old resident of Tucson, Arizona, first got involved in multilevel marketing companies in her early 20s. Ruiz was attending school and working full-time, and like many somethings, searching for a fulfilling career. Ruiz was promised flexibility, the ability to stay home with her future children and the opportunity to build a business that would eventually allow her to quit working completely. MLMs hook people with the promise of becoming independent business owners with unlimited earning potential.
Quantum computers offer great promise for cryptography and optimization problems. ZDNet explores what quantum computers will and won't be able to do, and the challenges we still face. Read More. In , some 2. By a margin of 57 to 43, those readers reported they favored the Republican governor of Kansas, Alf Landon, over the incumbent Democrat, Franklin D. The week after the election, the magazine's cover announced in bold, black letters the message, "Is Our Face Red! Also: Could quantum computers fix political polls?
The following January, Oxford University's Public Opinion Quarterly published an essay that examined how a seemingly much smaller survey of only 50, participants, conducted by a fellow named George Gallup, yielded a far more accurate result than did Literary Digest. Gallup's poll was "scientific," and Oxford wanted to explain what that meant, and why opinion polling deserved that lofty moniker.
For the first time, the Oxford publication explained a concept called selection bias. Specifically, if you don't ask people for enough facts about themselves, you never attain the information you need to estimate whether the people around them think and act in similar ways. The 2.
Their likelihood of owning telephones, it turned out, was much greater than for the general population -- for , a surprisingly narrow subset.
Before long, the post mortem showed, it'd be a safe bet they were more likely to vote Republican, and proudly. What's more, the participant count represented less than a quarter of the magazine's mailing list, meaning about 7. One could hypothesize that these non-respondents might have more likely to vote Democratic than the survey group but were less likely to admit as much to a literary publisher.
This was a prime example of what, for the first time, was called non-response bias. By contrast, Gallup compartmentalized his poll's participants into groups, whose classification structure would later be dubbed demographics. He would then use mathematical weights coefficients as a means of balancing one group's participation in the total poll sample, against that group's representation in the voting population at large.
With each group balanced out, he could assemble a snapshot of the entire nation based on small subsets. Gallup's great contribution to the science of understanding the behavior of large subsets of people, was weighting.
And let's be honest about this: Today's neural networks and deep learning experiments like to play like they're all about neurons and axons and deeply esoteric concepts called "perceptrons," as though they were introduced in Star Trek along with Klingons, but they're actually almost entirely about weighting.
They're Gallup polls on steroids. When a neural net model is trained, the values it stores trigger the adjustment of coefficient weights, which influence the degrees to which that model "perceives" successive training elements.
The only significant difference is that Gallup had certain categories already in mind; by comparison, neural nets start blank, compartmentalizing along the way. At one level, weighting is a means of neutralizing bias by making adjustments in the opposite direction. At another, it's the injection of bias into a pattern being learned, so that a network can trigger a reaction when it recognizes the pattern elsewhere.
Here, bias is your friend. In Part 1 of this voyage of Scale , we referenced three major political elections -- two in the US, one in the UK -- where the offset between the predicted result and the final one was attributed by experts to bias or at least the wrong kind of bias. Statistical adjustments were being made, and history seemed to indicate such adjustments should have been the proper ones.
They were just yielding disastrous results, as though the same assumptions one could make about society in no longer applied to , , or In this second and final part, we examine two questions: Why shouldn't a neural network model be considered a natural progression of the evolution of public opinion science?
Secondly, why shouldn't a quantum computer QC be the best-suited platform upon which to stage such an evolved model? Mainly: If you know little or nothing about the people you're surveying, then you're likely to collect information only from a subgroup with particular tendencies or preferences that will likely skew your results. The negative of this function is also true: If you know everything you need to know about the subgroup of people to whom you're speaking, then any information you choose to give that subgroup that you've packaged as "news" or "truth" is more likely to be believed by them.
Representative polling gave public opinion professionals the tools they needed to account for the disparities between the people they surveyed, and the representation of those people's demographic groups in districts, counties, and states.
And since , it appeared to be working. The general trend of predictions was toward greater and greater accuracy. Then that trend hit a wall, very loudly. As you saw in Part 1, suddenly there were several major national polls whose results differed wildly from one another, but whose aggregate failed to represent the final general sentiment of voters by any reasonable or useful amount. Researchers identified two potential causes for this phenomenon, both of which may be applicable simultaneously:.
Put more simply, we don't live in anymore. George Gallup's breakthrough methods may not work the same way today. Thus, any more highly evolved method that's still rooted in the concepts Gallup introduced, may not necessarily be any better.
Or, as the quantum scientists told us, simply taking the same computer problem and relocating it to a bigger machine, doesn't resolve it. In August , a team of researchers from universities in New Mexico, Tennessee, and Hong Kong trained a deep neural network with survey data from local elections in five US states and Hong Kong, whose results had already been certified.
Their objective was to determine if they supplied the typical age group, household income, racial, and ideological demographics to a neural net classical, not quantum , and fed it the same responses that real survey participants provided to professional pollsters, would the net yield better predictions?
No, not even close. On one test, their system miscalled a Florida US House of Representatives race by about 15 points. But what their experiment may have proven is this: If our assumptions are wrong about demographic correlations, then any modern approach to applying weights to compensate for what you think is selection bias, will only amplify the disparities rather than minimize them. That theory had already been tested in , by researchers including Prof. Prior to the US national elections, they delivered a survey solely to individuals guaranteed to provide selection bias: Xbox gamers.
They tended to skew male, young, and pre-graduate, compared with the rest of the country. When they applied conventional bias compensation methods to the results they received, their sample overwhelmingly favored Republican Mitt Romney over Democrat Barack Obama. Then the Gelman team took the same responses, and applied an already well-used technique in the polling industry, but "turned it up to Already, MRP applies regression models to small groups, compensating for selection bias.
Their innovation was to make these groups much smaller. As the team explained:. Poststratification is a popular method for correcting for known differences between sample and target populations. The core idea is to partition the population into cells based on combinations of various demographic and political attributes, use the sample to estimate the response variable within each cell, and finally aggregate the cell-level estimates up to a population-level estimate by weighting each cell by its relative proportion in the population.
George Gallup would be smiling widely. At this level of application, it's almost a manually compiled neural network. Once the Gelman team applied their highly MRP-adjusted results to the Xbox gamers' responses, the results were. Applied to a geographic model of the country, where states' winners receive given numbers of electoral votes, the result was a model that gave Mr. But that's no better than conventional polls. This team hit the same wall. Subdividing into smaller and smaller cells buys you some accuracy, but only to a point.
In a report produced by Gelman with Columbia colleagues four years later [ PDF ], he drew a prescient conclusion: Polling models do not account for how people and societies change. It reads in part:. In particular, the method is not currently well suited for exploring temporal changes in opinion.
Existing MRP technology is best utilized for creating static measures of preferences -- that is, using national surveys conducted during time t with t representing a year or set of years to create a single opinion estimate for each geographic unit. Though such static measures have already proven invaluable, they do not go far enough. Policymakers, the media, and scholars want to understand how and why public opinion is changing. Researchers, across a range of disciplines, need dynamic measures of the public's preferences in order to better establish causal links between public opinion and outcomes.
Political scientists, for example, may want to see whether public policy changes in response to shifting public preferences, while psychologists may want to investigate whether the mental health of lesbian, gay, and bisexual populations improve in places where public where tolerance for homosexuality is rising. Someplace in the world, there should be a living, breathing record of people's individual sentiments -- not only their states but their changes over time, for any given time t that matters.
You'd think that would have been invented by now. If a mechanism existed that was capable of subdividing Mister-P cells down to the level of single individuals , where the computational cost switched from incalculable to trivial, could the polling bias problem be effectively resolved without having to reinvent an entire branch of statistical science? Speaking with ZDNet, Riedel continued:.
I believe there are definitely examples where a modest speedup is known to exist for sure, which is to say, a quadratic speedup.
So if it took n -squared steps on a classical computer, it would only take n steps on a quantum computer. There also exists at least a few proof-of-concept cases where quantum computers could get an exponential speedup.
However, my understanding of the field is that these cases suffer from a lot of problems. They've been specially constructed [ for ] quantum computers having a big advantage, so they may not actually be interesting computations that you'd want to do.
In Part 1, we heard from quantum scientists who argued that rephrasing public opinion polls as data for QC algorithms, may not be worth bothering with. If a problem is phrased in the form of calculus rather than algorithms, then perhaps the best "speedup" the scientific term for "acceleration" one could hope for is only quadratic.
And that's achievable with classical machines. The alternative is to phrase the problem as something that qualifies for exponential acceleration. Typically, political survey-based predictions are not considered algorithmic. There isn't a cyclical process that is iteratively repeated, refining the constituent factors until a result is obtained. Yet neural networks are algorithmic. What's more, they're already familiar with the native context of opinion polling science: literally speaking, weights and measures.
But what Dr. Riedel is suggesting is that, while quadratic speedup missions may be too banal and unworthy of mention, exponential ones -- the only alternative -- may be out of the question for the foreseeable future.
The rest of the machine necessary to pull off such a mission, evidently has not been invented yet. And while his own research, introduced in Part 1, suggests the next major milestone for QC production will come in , as Prof.
Lieven Vandersypen of Dutch quantum research association QuTech pointed out, not even a working QC can pinpoint how many qubits would be required to exponentially speed up neural net-based opinion research enough to become -- to borrow Riedel's term for it -- interesting.
But it's not yet on very firm, scientific footing, to my knowledge. One way to visualize a typical optimization problem, he suggested, involves seeking the lowest point in a topological landscape.
Trump’s MLM Pitch Misled Consumers, Lawsuit Says
President Donald Trump and Russian President Vladimir Putin shake hands before attending a joint press conference after a meeting in Helsinki on July 16, At the heart of the inquiry into the alleged collusion between Trump and Russia is money. It provides concrete evidence of relationships, methods, and motives. It may be foreign nations who govern us, and not we, the people, who govern ourselves. The threat of foreign influence over our elections did not wane in the intervening years: Today, the United States has a president whose election was aided by the fraud and intrigue of a foreign nation.
Case cv Document 1 Filed 10/29/18 Page 1 of level marNeting company (“MLM”) that offers a business opportunity to individual participants. Protection Law; common law false advertising; common law fraud; and common law englishdistrictlifeline.org
Lawsuit Against Pacemaker Company
For more than 30 years, Donald Trump has been almost continuously in the public eye, portraying himself as the epitome of business success and shrewd dealmaking. He took a business founded by his father to build modest middle-class housing in the outer boroughs of New York City and transformed it into a high-profile operation focused on glitzy luxury condominiums, hotels, casinos and golf courses around the world. Operating through the Trump Organization, his family holding company, Trump also capitalized on the name recognition gained through years of reality-television appearances in a wide range of licensing deals. Trump's decision to enter the race for the Republican presidential nomination in brought a great deal of new attention to his wide range of business activities and the controversies associated with many of them. Those controversies -- involving issues such as alleged racial discrimination, lobbying violations, investor and consumer deception, tax abatements, workplace safety violations, union avoidance and environmental harm -- are summarized below.
Lawsuit Against Pacemaker Company. The lawsuit also alleges that, because Valve requires developers to. The lawsuit originated from civil.
Quantum computers offer great promise for cryptography and optimization problems. ZDNet explores what quantum computers will and won't be able to do, and the challenges we still face. Read More. In , some 2.
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Немец называл эту женщину… Беккер слегка потряс Клушара за плечи, стараясь не дать ему провалиться в забытье. Глаза канадца на мгновение блеснули. - Ее зовут… Не отключайся, дружище… - Роса… - Глаза Клушара снова закрылись. Приближающаяся медсестра прямо-таки кипела от возмущения. - Роса? - Беккер сжал руку Клушара. Старик застонал. - Он называл ее… - Речь его стала невнятной и едва слышной.
У нас есть время, но только если мы поспешим, - сказал Джабба. - Отключение вручную займет минут тридцать. Фонтейн по-прежнему смотрел на ВР, перебирая в уме остающиеся возможности. - Директор! - взорвался Джабба. - Когда эти стены рухнут, вся планета получит высший уровень допуска к нашим секретам.
Case Event History
Он застонал. - Джабба. Скорее вылезай. Он неохотно выполз из-под компьютера. - Побойся Бога, Мидж. Я же сказал тебе… - Но это была не Мидж.
Заставил меня сесть на мотоцикл. Смотрите сюда! - Он попытался поднять левую руку. - Кто теперь напишет материал для моей колонки. - Сэр, я… - За все сорок три года путешествий я никогда еще не оказывался в таком положении. Вы только посмотрите на эту палату. Мою колонку перепечатывают издания по всему миру.
ГЛАВА 7 Мозг Сьюзан лихорадочно работал: Энсей Танкадо написал программу, с помощью которой можно создавать шифры, не поддающиеся взлому. Она никак не могла свыкнуться с этой мыслью. - Цифровая крепость, - сказал Стратмор. - Так назвал ее Танкадо.
А теперь выходи.
Через неделю Сьюзан и еще шестерых пригласили. Сьюзан заколебалась, но все же поехала. По приезде группу сразу же разделили. Все они подверглись проверке на полиграф-машине, иными словами - на детекторе лжи: были тщательно проверены их родственники, изучены особенности почерка, и с каждым провели множество собеседований на всевозможные темы, включая сексуальную ориентацию и соответствующие предпочтения.
Сьюзан стояла прямо и неподвижно, как статуя. Глаза ее были полны слез.
Неужели так. - Утечка информации! - кричал кто-то. - Стремительная. Все люди на подиуме потянулись к терминалу в одно и то же мгновение, образовав единое сплетение вытянутых рук. Но Сьюзан, опередив всех, прикоснулась к клавиатуре и нажала цифру 3.