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When we talk about Machine Learning (ML), it is present everywhere from Facebook’s face recognition to self-driving cars.
Machine learning is a metric in computer science, where the computer learns automatically without the need for any specific program. ML is a study of algorithms, statistical models and patterns used by computer systems for fulfilling a particular function.
Ideally, you can call machine learning the subset of artificial intelligence.
Read More: Artificial Intelligence: Future of Outsourcing for call centres in UK
The algorithm of machine learning looks after certain available data and performs a particular task on the same. Machine learning works on two phases driven by mathematical methods:
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Training
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Prediction
Further, depending on how the machine learning algorithms “learn”, they are divided into two categories:
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Supervised learning
Under this category, the algorithm is given the data with the answer.
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Unsupervised learning
In this, the ML algorithm is given the data. However, the answer to the same is unavailable. The algorithm finds the structure in the data and tries to bring a relevant answer thereafter. Market segment analysis generally uses unsupervised learning.
Again, based on the outputs of these learning methods, the algorithms are divided into two categories further:
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Classification
The outputs under this algorithm are based on data.
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Regression
These outputs here are continuous-valued. The results under regression do not categorize data, rather they are based on the association amid variables. E.g. - Liner regression.
Machine learning aims to identify and analyze trends, further bringing a relatable solution. To get value-driven results, pairing machine learning algorithms with the correct processes and tools are obligatory.
Moving further, when we talk about machine learning spam, the ML model used by Google results in several spam possibilities, although the shortcomings are being checked.
With the threat of fraud increasing rapidly, I have a query!
What should an organization focus to create effective machine learning systems? Let’s understand through the diagram below:
Recently, you must have come across the news saying spammers leveraging Google’s algorithms to bring their content on the top.
Well, you must be curious to know how it is possible!
It is possible for spammers as they use machine learning to create video content. This content is created through video content from webpages. The same ML technology is used to convert text content from podcasts.
Source- searchenginejournal.com
Well, the machine learning spam isn’t just philosophy, but it is live on Google! The question here is can Google fight this fraud threat!
Here is a brief on different machine learning frauds detected, check out:
The Text to Video Spam
This spam is live on Google and I noticed the text to video dispute after searching a news headline online!
We all very well know that Google ranks videos that are informative and can drive the audience’s interest. When a topic hits the trending list, Google automatically promotes videos related to the same content.
Well, here is where scammers get the opportunity to exploit Google’s loopholes.
Since the algorithms do not rank the content checking whether they are duplicate of the audio or text content. Thus, scammers try to fool the algorithms in ranking their videos based on the trending keywords.
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How do scammers create video content?
The fake video content is created by downloading the text news format from an RSS feed and then converting the same into the audio converter.
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Is Google cloud by any means helping spammers?
It sounds weird and un-trustable, but somewhere it is correct!
Google’s cloud has a machine-learning product, which helps to transcribe 4 million texts to voice characters for free.
After 4 million characters, Google charges a certain amount based on characters. If you use non-google services, you have to spend a certain amount on an hourly basis.
With the facility to transcribe, the spammers get a chance here to download the feature image from the article and use the same as their image for the video.
The fake downloaded image also includes a screen displaying “Presented By” wherein a voice reads out the article.
Thus, the loopholes of Google lead to spam disputes.
The Podcast to Text Spam
The podcast to text spam is another spam technique where the downloaded audio is run over audio to text software.
You must be puzzled as to how it can be converted!
Well, it is surprising that among the several possible ways, it is Google’s Gboard app for Android, which helps to convert the video to text.
Gboard is a free Andriod application of keyboard having transcription function. Scammers use the note application, where they click the microphone option as the podcast runs to get recorded. As a result, you get the content free!
Not only Gboard, however, other apps also help scammers with audio to text conversion.
Amid all these spam fears, does Google has any clue what is going on!
Well, Google does know about the dispute, as Gary Illyes from Google, replied on the tweet. Check out:
Source- searchenginejournal.com
Everyone knows about the spam technique, which is why publishers need to be aware of the same.
Since the content marketing campaigns are said to boost more by 2020, marketers need to make sure that no one copies their video or text content in an unauthorized manner.
With the increasing significance of audio content, 90% of marketers feel they will soon shift to audio/podcasts. Marketers are also signifying the use of augmented reality.
Here is a report by eMarketer:
Well, all these highlights the increasing importance of content marketing for ranking pages, which displays that Google needs to take an essential step to check fraud possibilities so that content uniqueness and credibility is not at risk.
Wrap Up:
The freedom to use machine-learning technologies for business success has resulted in several disputes.
Google generally creates search results based on different algorithms where the trending news videos differ from the core algorithm, which brings regular search outputs.
Read More: Google EAT Update: What does it mean to the Market!
Consequently, it is due to the lacking integration of search display algorithms and other components that are causing spam issues. There needs to be a filter for the same to check any possible spam report.
Thanks for reading!
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