Don’t browse the internet without these ad blockers for Chrome – gHacks Tech News

I personally don’t use Chrome because Google and their snooping is getting ridiculous these days… Vivaldi, Edge, & Opera are good Chromium based alternative browsers

Best Ad Blockers for Chrome

Ads are one of the biggest annoyances that you will come across while surfing the internet. Websites place ad banners, and when visitors view the ads or interact with them, it earns the site revenue.  Nearly every website has ads, even big ones like Facebook, Twitter, Reddit, YouTube, etc.  Why? Because it costs money to operate website.

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New MosaicLoader malware targets software pirates via online ads

New MosaicLoader malware targets software pirates via online ads

You gotta be a newbie to fall for this BS…. but it is a lot of dumb lazy asses in the world these days…. smh


An ongoing worldwide campaign is pushing new malware dubbed MosaicLoader camouflaged as cracked software via search engine advertising to infect wannabe software pirates’ systems.

MosaicLoader is a malware downloader designed by its creators to deploy more second-stage payloads on infected systems, as Bitdefender researchers revealed in a report published today and shared with BleepingComputer last week.

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Anonymous Claims to Leak Data on the Texas GOP

Members of the hacker collective Anonymous claim to have leaked a large cache of data belonging to the Republican Party of Texas. The leak, the size of which is unclear, is said to include the “private documents” of the organization.

According to the hackers, those documents are part of the trove of data that was recently stolen from Epik, the controversial web registrar that has often been criticized for its hosting of far-right groups and individuals.

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FBI Had REvil’s Kaseya Ransomware Decryption Key for Weeks: Report

Feds just businesses floating in the wind…. is anyone surprised???

The Kaseya ransomware attack, which occurred in July and affected as many as 1,500 companies worldwide, was a big, destructive mess—one of the largest and most unwieldy of its kind in recent memory. But new information shows the FBI could have lightened the blow victims suffered but chose not to.

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How Your Ad Blocker Can Track You Across the Web

Big business just don’t GAF about privacy or user rights….. hell did they ever?


Ah, ad blockers. Even if you aren’t among the growing number of people downloading one of these extensions, chances are you’ve heard people sing their praises for all sorts of reasons. They make the web a less cluttered, less laggy, less invasive place to be. So naturally, the money-hungry tech upstarts have found a way to ruin these tools for their own gain.

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Boston Dynamics’ robots can parkour better than you | Engadget


Don’t expect an easy getaway if one of Boston Dynamics’ Atlas robots ever chases you down. The Hyundai-owned firm has shared a video (below) of the humanoid bots successfully completing a parkour routine in an obstacle course for the first time. The pair of Atlas machines leapt gaps, vaulted beams and even coordinated a backflip, all without missing a beat — they might be more graceful than you are.

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What Is AI Bias and How Can Developers Avoid It?


Artificial intelligence capabilities are expanding exponentially, with AI now being utilized in industries from advertising to medical research. The use of AI in more sensitive areas such as facial recognition software, hiring algorithms, and healthcare provision, have precipitated debate about bias and fairness.

Bias is a well-researched facet of human psychology. Research regularly exposes our unconscious preferences and prejudices, and now we see AI reflect some of these biases in their algorithms.

So, how does artificial intelligence become biased? And why does this matter?


How Does AI Become Biased?

Finger pointing with neural network

For the sake of simplicity, in this article, we’ll refer to machine learning and deep learning algorithms as AI algorithms or systems.

Researchers and developers can introduce bias into AI systems in two ways.

Firstly, the cognitive biases of researchers can be embedded into machine learning algorithms accidentally. Cognitive biases are unconscious human perceptions that can affect how people make decisions. This becomes a significant issue when the biases are regarding people or groups of people and can harm those people.


These biases can be introduced directly but accidentally, or researchers might train the AI on datasets that were themselves affected by bias. For instance, a facial recognition AI could be trained using a dataset that only includes light-skinned faces. In this case, the AI will perform better when dealing with light-skinned faces than dark. This form of AI bias is known as a negative legacy.

Secondly, biases can arise when the AI is trained on incomplete datasets. For instance, if an AI is trained on a dataset that only includes computer scientists, it will not represent the entire population. This leads to algorithms that fail to provide accurate predictions.


Examples of Real World AI Bias

Woman in front of computer interface

There have been multiple recent, well-reported examples of AI bias that illustrate the danger of allowing these biases to creep in.


US-Based Healthcare Prioritization

In 2019, a machine learning algorithm was designed to help hospitals and insurance companies determine which patients would benefit most from certain healthcare programs. Based on a database of around 200 million people, the algorithm favored white patients over black patients.

It was determined that this was because of a faulty assumption in the algorithm regarding varying healthcare costs between black and white people, and the bias was eventually reduced by 80%.


The Correctional Offender Management Profiling for Alternative Sanctions, or COMPAS, was an AI algorithm designed to predict whether particular people would re-offend. The algorithm produced double the false positives for black offenders compared with white offenders. In this case, both the dataset and model were flawed, introducing heavy bias.



The hiring algorithm that Amazon uses to determine the suitability of applicants was found in 2015 to favor men over women heavily. This was because the dataset almost exclusively contained men and their resumes since most Amazon employees are male.


How to Stop AI Bias

AI is already revolutionizing the way we work across every industry. Having biased systems controlling sensitive decision-making processes is less than desirable. At best, it reduces the quality of AI-based research. At worst, it actively damages minority groups.

There are examples of AI algorithms already being used to aid human decision-making by reducing the impact of human cognitive biases. Because of how machine learning algorithms are trained, they can be more accurate and less biased than humans in the same position, resulting in fairer decision-making.


But, as we’ve shown, the opposite is also true. The risks of allowing human biases to be cooked into and amplified by AI may outweigh some of the possible benefits.

At the end of the day, AI is only as good as the data that it’s trained with. Developing unbiased algorithms requires extensive and thorough pre-analysis of datasets, ensuring that data is free from implicit biases. This is harder than it sounds because so many of our biases are unconscious and often hard to identify.


Challenges in Preventing AI Bias

In developing AI systems, every step must be assessed for its potential to embed bias into the algorithm. One of the major factors in preventing bias is ensuring that fairness, rather than bias, gets “cooked into” the algorithm.

Defining Fairness

Fairness is a concept that’s relatively difficult to define. In fact, it’s a debate that’s never reached a consensus. To make things even more difficult, when developing AI systems, the concept of fairness has to be defined mathematically.


For instance, in terms of the Amazon hiring algorithm, would fairness look like a perfect 50/50 split of male to female workers? Or a different proportion?

Determining the Function

The first step in AI development is to determine exactly what it is going to achieve. If using the COMPAS example, the algorithm would predict the likelihood of criminals reoffending. Then, clear data inputs need to be determined to enable the algorithm to work. This may require defining important variables, such as the number of previous offenses or the type of offenses committed.

Defining these variables properly is a difficult but important step in ensuring the fairness of the algorithm.

Making the Dataset

As we’ve covered, a major cause of AI bias is incomplete, non-representative, or biased data. Like the case of facial recognition AI, the input data needs to be thoroughly checked for biases, appropriateness, and completeness before the machine learning process.


Choosing Attributes

In the algorithms, certain attributes can be considered or not. Attributes can include gender, race, or education—basically anything that may be important to the algorithm’s task. Depending on which attributes are chosen, the predictive accuracy and bias of the algorithm can be severely impacted. The problem is that it’s very difficult to measure how biased an algorithm is.

AI Bias Isn’t Here to Stay

AI bias occurs when algorithms make biased or inaccurate predictions because of biased inputs. It occurs when biased or incomplete data is reflected or amplified during the development and training of the algorithm. The good news is that with funding for AI research multiplying, we’re likely to see new methods of reducing and even eliminating AI bias.

What Is AI Bias and How Can Developers Avoid It?

Nigerian Woman Builds Software for Teaching African Languages | EYEGAMBIA

In what many refer to as an excellent initiative to sustain African languages in the modern era, a young Nigerian-American woman known as Omolabake Adenle has developed a software which teaches about five local African languages with expectations to expand the scope in the near future.

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What Is Chia Cryptocurrency and Why Is It About to Cause an SSD Shortage?

chia cryptocurrency network feature

A new cryptocurrency known as Chia could soon be responsible for a hike in hardware prices. The reason for this may surprise you, as the price increases are influenced by the validation mechanism used to produce blocks on the Chia blockchain.

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How to Run a Safety Check on Google Chrome

Google Chrome security

This is a good idea to do from time to time…

We’re used to running antivirus scans on our computers, but that doesn’t cover your online safety. For that, Google Chrome offers a tool that lets you perform a similar checkup to secure your web browsing. Here’s how to run a safety check on Chrome.

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