May 4, 2021

How Google fought search spam using AI in 2020

AI
Google
Spam
searchengine
Tilly Kenyon
2 min
Google says Artificial Intelligence (AI) offers ‘unprecedented potential to revolutionise’ spam fighting
Google has said that Artificial Intelligence (AI) offers ‘unprecedented potential to revolutionise’ spam fighting...

Last year Google was able to build their very own spam-fighting AI, that can catch both known and new spam trends. 

Hacked spam was still widespread in 2020 as the number of vulnerable websites remained quite large, although Google has said they have improved their detection capability by more than 50% and removed most of the hacked spam from search results. They have also reduced sites with auto-generated and scraped content by more than 80% compared to a couple of years ago.

What is search engine spam? 

Search engine spam refers to measures that try to influence the position a website has in search engines, often for pages that contain little or no relevant content.

How Google prevents spam from reaching you 

Before Google delivers a set of search results, there is a lot that happens. Every day they are discovering, crawling, and indexing billions of web pages of which they discover 40 billion spammy pages. 

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This diagram shows how Google defends against spam.

Firstly, they have systems that can detect spam when they crawl pages or other content. Crawling is when their automatic systems visit content and consider it for inclusion in the index they use to provide search results. 

These systems also work for the content they discover through sitemaps and Search Console. For example, Search Console has a 'request indexing' feature so creators can let Google know about new pages that should be added quickly. Google has previously observed spammers hacking into vulnerable sites, pretending to be the owners of these sites, verifying themselves in the Search Console, and using the tool to ask Google to crawl and index the many spammy pages they created. Using AI, Google was able to pinpoint suspicious verifications and prevented spam URLs from getting into the index this way.

Next, they have systems that analyse the content that is included in the index. When you issue a search, they work to double-check if the content that matches might be spam. If so, that content won’t appear in the top search results. 

The result is that very little spam actually makes it into the top results anyone sees for a search, thanks to the automated systems that are aided by AI. Google has estimated that these automated systems help keep more than 99% of visits from Search completely spam-free. As for the percentage left, their teams take manual action to further improve the automated systems.

(Image: Google)

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Jun 15, 2021

The advantages and disadvantages of AI in cloud computing

AI
CloudComputing
Data
ML
3 min
AI is being used in cloud computing, which works by allowing client devices to access data over the internet remotely, but are there pros and cons?

Cloud computing offers businesses more flexibility, agility, and cost savings by hosting data and applications in the cloud. AI capabilities are now combining with cloud computing and helping companies manage their data, look for patterns and insights in information, deliver customer experiences, and optimise workflows.

We take a look at some of the benefits and drawbacks of AI in cloud computing. 
 

The benefits of AI in cloud computing

 

Lower costs

A major advantage of cloud computing is that it eliminates costs related to on-site data centers, such as hardware and maintenance. Those upfront costs can be restrictive with AI projects, but with cloud enterprises you can access these tools for a monthly fee, making research and development related costs more manageable. AI tools can also gain insights from the data and analyse it without human intervention, reducing staff costs.

Deeper insights 

AI is able to identify patterns and trends in large data sets. Using historical data, AI compares it to the most recent data, which provides IT teams with well-informed, data-backed intelligence. AI tools can also perform data analysis fast so enterprises can rapidly and efficiently address customer queries and issues. The observations and valuable advice gained from AI capabilities result in quicker and more accurate results.

Improved data management

AI enables extensive data management, and cloud computing maximises information security, making it possible to deal with massive amounts of data in a programmed manner to analyse them properly, allowing the business to leverage information that has been “mined” and filtered to meet each need. AI can also be used to transfer data between on-premises and cloud environments. 
 

Intelligent automation 

Businesses use AI-driven cloud computing to be more efficient and insight-driven. AI can automate repetitive tasks to boost productivity, and also perform data analysis without any human intervention. IT teams can also use AI to manage and monitor core workflows. IT teams can focus more on strategic operations while AI performs the mundane tasks. 

Increased security 

With businesses deploying more applications in the cloud, security is crucial in order to keep data safe. IT teams can use different AI-powered network security tools which can track network traffic, they can flag issues, such as finding an anomaly. 
 

The drawbacks of AI in cloud computing

 

Data privacy 

 Enterprises need to create privacy policies and secure all data when using AI in cloud computing. AI applications require a large amount of data, which can include consumer and vendor information. While some data can be anonymous and can't be tied to personally identifiable information, knowing who the data belongs to makes it more valuable. When sensitive information is used, data protection and compliance is a major concern.

Connectivity concerns 

IT teams use the internet to send raw data to the cloud service and recover processed data. Poor internet access can hinder the advantages of cloud-based machine learning algorithms, as cloud-based machine learning systems need consistent internet connectivity. 

While processing data in the cloud is quicker than conventional computing, there is a time lag between transmitting data to the cloud and receiving responses. This is a significant issue when using machine learning algorithms for cloud servers, where prediction speed is one of the primary concerns.

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