The Supremacy of Silicon Valley

by TR News
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A contribution from Markos.

Introduction

Many of you will know how precious freedom of speech is, be it offline or online. The election and eventual win of Republican nominee Donald Trump heavily relied on social media. Left-wing dominance has forced its way into three of the world’s biggest companies; they are Facebook, Twitter and YouTube. If by some miracle you aren’t aware of bias, then I’m afraid that you’re blind. Luckily, free-market capitalism offers us an array of many growing alternatives. Trust me! You’ll never run out, ever! The huge problem lurking in the ether of the internet is the power, control, influence and monopoly of the largest social media companies. Facebook, Google and Twitter have the means at their disposal to engineer elections towards an outcome they desire rather than leaving it entirely to the will of the people. So how Is Silicon Valley Vying for the Supremacy of the Internet?

Algorithms

Regarding the outcome of an election, Google is also playing a big role in online censorship. Google has changed its algorithm for its search results. For those who don’t know, an algorithm is an ordered set of executable steps, which defines a terminating process. There are numerous examples of different algorithms used in different walks of life. First, it is worth knowing about algorithms in general, because the technical side of technology can be difficult to understand. The tech giants are bound to know this one. A good algorithm should be finite, which means that an algorithm will never end in trying to solve a problem. A good algorithm should have well-defined instructions, which means that each step of the algorithm should be clear so that instructions can be carried out as intended by the user of a computer. A good algorithm should be effective, which means being able to give the correct result or solution to any given problem. There is a concern that Google happens to be working for and with the Chinese government after they shunned the Pentagon when it comes to developing its artificial intelligence (AI) systems. We will get into the basics of AI.

Artificial Intelligence

Tech giants like Facebook, Twitter, YouTube and Google are also getting involved in artificial intelligence (AI). For those who don’t know, AI is the field of computer science that seeks to build autonomous machines that can carry out complex tasks without the need for intervention from humans. In other words, AI seeks to make computers and other devices do the sorts of things that minds can do already. The goal of AI requires machines to be able to perceive and reason, it is these capabilities, which fall within the category of common sense activities, that are proving difficult for computers. The field of AI is enormous and merges with other subjects such as neurology, mathematics and linguistics. AI is being used by Facebook and Google but in different ways. There are so many branches of AI like robotics, neural networks and machine learning; there are too many to name them all. I will go into these aspects of AI because high-ranking employees in the field of tech have in passing, mentioned neural networks and machine learning.

Machine Learning

Machine Learning is a set of tools that can be used to teach a computer to do a task without explicitly telling the computer how to do it. Machine learning performs tasks by providing examples of how they should be done.
From the AI point of view, learning is central to human knowledge and intelligence, and, likewise, it is also essential for building intelligent machines. Years of effort in AI has so far shown that trying to build intelligent computers by programming all the rules can’t be done. For example, automatic learning is crucial. Humans aren’t born with the ability to understand language, so it makes sense to try and have computers learn language, rather than trying to program it all.

Machine learning is a core subarea of AI. It is very unlikely that systems capable of any intelligence in the field of language or vision can be built without using learning to get there. These tasks are simply too difficult or impossible to solve. The problem here is that a system wouldn’t be truly intelligent if it wasn’t capable of learning. Learning is, after all, at the very core of intelligence. Although a subarea of AI, machine learning intersects broadly with other fields especially so with statistics, mathematics, physics, theoretical computer science and more.

One example of a machine learning application is that of an Optical Character Recognition (OCR). For those who don’t know, Optical Character Recognition translates a scanned electronic image of a typed or handwritten text into an appropriate encoded form. This form is usually an ASCII or UNICODE that can be read by a word processor. This is used to input data into a computer system by recognising marks on a paper document.

Neural Networks

A neural network is a computer system that is modelled on a human being and its nervous system, so in essence, it is networking using neurons. Neural networks are typically organised into layers.
Layers are made up of many interconnected ‘nodes’, and they contain an ‘activation function’. Patterns are presented to the network via the ‘input layer’, which communicates to one or more ‘hidden layers’ where the actual processing takes place via a system of weighted ‘connections’.

A neural network is used to make programs. One of the common uses of neural networks is Image Processing. Image processing is an ever-growing field with widespread applications from facial recognition in social media, cancer detention in medicine to satellite imagery processing for agricultural and defence usage.

Are Big Tech Allowed to Do This?

Silicon Valley has taken a step too far; it must be stopped in its tracks. The United States Congress could easily propose, and pass legalisation specifically aimed at putting an end to this. The left argues that these companies are practically private entities and can do whatever they want. I say no! Tech firms pay taxes in the country of location and offer government services to users. For example, Donald Trump’s Facebook, Instagram and Twitter pages include updates such as hurricane warnings, notice of trade deals, or simply hitting back at his political opponents. A secret memo that was obtained by Breitbart revealed social networking site Facebook label its users as agents of hate if they associate with blacklisted figures. Figures such as Tommy Robinson and Paul Joseph Watson are an example. Those who follow the “hate agent” has his or her behaviour monitored offline, which is a total invasion of privacy.

Big Tech – Individual Companies

Facebook

Facebook is scarily becoming more monopolised and powerful as its popularity grows. What is quite telling is that former Liberal Democrat leader, Nick Clegg, is now their vice president of global affairs. I think if tech censorship isn’t dealt with, we’ll have to rely on mailing lists and alternative options to stay alive in the digital sphere. Even so, this alternative does not match (at this moment in time), the reach of mainstream media.

Google

Another Big Tech behemoth is Google. Google is the closest thing to an all-powerful information monopoly the world has ever seen. It accounts for 90% of Internet searches in most countries and runs on the Android operating system on 80% of the world’s smartphones and tablets. Google owns YouTube. If you go back to 2012, Eric Schmidt, who was the CEO of Google, supported Barack Hussein Obama on election night in 2012. He helped to recruit people, choose technology and coached Obama’s campaign manager, David Axelrod. This was the admittance of Obama operative David Plouff. In 2016, he campaigned for Hillary Clinton. Multinational company Google is full of people who are anti-Trump (which explains the political leanings of Google). During the 2016 Presidential Election, Google was found to have manipulated the search algorithms to make it favour Hilary Clinton. It is also worth noting that the visitor logs during the Obama administration show that Google’s lobbyists visited the White House 128 times between January 2009 and October of 2015. The number of visitations by Google was higher than that of any other tech giant such as Facebook, Verizon, Comcast and Amazon put together. In 2015, Google spent $16 million on lobbying, which was more than any other tech giant.

Google CEO Eric Schmidt wanted to be ‘head outside adviser’ to the Clinton campaign. We know this because Wikileaks published hacked emails from Hillary Clintons campaign manager John Podesta. According to Podesta’s email, Eric Schmidt expressed this wish as early as 2014. Schmidt set up a grassroots organisation to put Hillary Clinton in the White House. Ahead of the 2016 campaign, he set up a shadowy grassroots organisation called ‘The Groundwork.’ Little was known about its operations and composition, but its stated aim was to harness the expertise of Silicon Valley to put Hillary Clinton in the White House.

After Donald Trump won the 2016 presidential election, Google engineers discussed changing algorithms to disfavour conservative publications. The Daily Caller, a conservative website, claimed both they and Breitbart were singled out as sites to “bury” after some Google employees suggested they did not deserve a prime spot on the search engine’s result. This was considered even though both media outlets can be considered more popular than mainstream media websites. On the 9th November, liberal employees shared emails that conspired to reduce the search result visibility even if articles contained useful information that would usually be featured prominently. Google engineers allegedly discussed changing the algorithm of search results in a reaction to Donald Trump winning the 2016 presidential election against Hillary Clinton.

How a Google Search Works

It is worth getting into how a Google search and ranking Google searches actually work. Crawling and Indexing is the first step in a Google search whereby trillions of documents are found and indexed. Google does this by a piece of software called Web Crawlers, which discovers publicly available webpages. The most common crawler is called Googlebot. Crawlers look at webpages and follow links on those pages and go from link to link and bring data about those webpages back to Google’s servers. Google essentially gathers pages during the crawl process and then creates an index much like the index in the back of books. The Google index includes information about words and their locations. When people search, at the most basic level, Google algorithms lookup an individual’s search terms in the index to find the appropriate pages.

Algorithms are another part of the search in play. Algorithms take queries and solve them from thousands of webpages with helpful information. As we will get into later on, Google uses an algorithm called PageRank. Google’s algorithms rely on more than 200 unique signals. They include things such as the terms on websites, the freshness of content and the region that it came from. These things make it possible for an algorithm to make a calculated guess on what people could be trying to find out. Fighting Spam is another area when searching on Google. Spam sites try to be on the top of search results through techniques like repeating keywords over and over. Spam sites are also known for buying links that pass PageRank or put invisible text on the screen. This is bad for searching because relevant websites get buried. It is also bad for legitimate website owners because their sites become harder to find. The good news is that Google’s algorithms can detect the vast majority of spam and demote it automatically.

Alternative Platforms

Gab, is notorious for having a userbase of around 20,000 Neo-Nazis; however, it is dedicated to defending your right to speak freely regardless of a users choice of speech. It was founded in 2016 by CEO Andrew Torba. Next, there is Bitchute, which is effectively a substitute for YouTube. One problem was that PayPal stopped Bitchute from using its services as it aimed at depriving YouTube a monopoly in video-sharing. Launched in January 2017 by Ray Vahey, it operates as a peer-2-peer Web Torrent.

There is also Minds, which is another version of Facebook, founded in August 2017 by Bill Ottman. Then there is an application named Telegram, which was created in August 2013 by brothers Nikolai and Pavel Durov. This application allows you to set up public channels and broadcast to massive audiences. Telegram is widely hated in Totalitarian countries such as China and Russia, where the government simply IP blocks it or so they think. The best part about it is that it’s super resistant to censorship and is a hybrid of WhatsApp, Twitter and Facebook crammed together.

Another video-sharing alternative to YouTube is Brighteon. Like Bitchute, Brighteon aims to defend and also dares to respect free speech. Despite the extreme censorship, they remain resilient as they work diligently to insulate themselves from the increasing scourge of censorship by infrastructure providers and ISPs.

The capitalist free-market does offer alternatives to “the big three” so why not give them a chance?

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