Tag: Human Intelligence

AI bias has the potential to cause significant damage to cybersecurity, especially when it is not controlled effectively. It is important to incorporate human intelligence alongside digital technologies to protect digital infrastructures from causing severe issues.

AI technology has significantly evolved over the past few years, showing a relatively nuanced nature within cybersecurity. By tapping into vast amounts of information, artificial intelligence can quickly retrieve details and make decisions based on the data it was trained to use. The data can be received and used within a matter of minutes, which is something that human intelligence might not be able to do.

With that said, the vast databases of AI technologies can also lead the systems to make ethically incorrect or biased decisions. For this reason, human intelligence is essential in controlling potential ethical errors of AI and preventing the systems from going rogue. This article will discuss why AI technology cannot fully replace humans and why artificial intelligence and human intelligence should be used side-by-side in security systems.

Inherent Limitations of AI

AI technology has significantly improved throughout the years, especially regarding facial recognition and other security measures. That said, while its recognition abilities have become superior, it is still lacking when it comes to mimicking human judgment.

Human intelligence is influenced by factors like intuition, experience, context, and values. This allows humans to make decisions while considering different perspectives, which may or may not be present in a data pool. As AI systems are still far from being perfectly trained with all the information in the world, they can present errors in judgment that could have otherwise not happened with human intelligence.

AI data pools also draw information from “majorities,” registering through information that was published decades ago. Unless effectively trained and updated, it may be influenced by information that is now irrelevant. For instance, AI could unfairly target specific groups subjected to stereotypes in the past, and the lack of moral compass could create injustice in the results.

One significant problem of using AI as the sole system for data gathering is that it can have substantial limitations in fact-checking. Data pools are updated day by day, which can be problematic as AI systems can take years to train fully. AI can wrongfully assume that a piece of information is false, even though the data is correct. Without human intelligence to fact-check the details, the risk of using incorrect data might cause someone to misinterpret crucial information.

Unfortunately, AI bias can cause significant disruptions within an algorithm, making it pull inaccurate or potentially harmful information from its data pool. Without human intelligence to control it, not only can it lead to misinformation, but it could also inflict severe privacy and security breaches. Hybrid systems could be the answer to this because they are better at detecting ethical issues or errors.

To Know More, Read Full Article @ https://ai-techpark.com/human-role-in-ai-security/ 

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Future of QA Engineering

Trending Category - Threat Intelligence & Incident Response

We believe that the journey of developing software is as tough as quality assurance (QA) engineers want to release high-quality software products that meet customer expectations and run smoothly when implemented into their systems. Thus, in such cases, quality assurance (QA) and software testing are a must, as they play a crucial role in developing good software.

Manual testing has limitations and many repetitive tasks that cannot be automated because they require human intelligence, judgment, and supervision.

As a result, QA engineers have always been inclined toward using automation tools to help them with testing. These AI tools can help them understand problems such as finding bugs faster, and more consistently, improving testing quality, and saving time by automating routine tasks.

This article discusses the role of AI in the future of QA engineering. It also discusses the role of AI in creating and executing test cases, why QA engineers should trust AI, and how AI can be used as a job transformer.

The Role of AI in Creating and Executing Test Cases

Before the introduction of AI (artificial intelligence), automation testing and quality assurance were slow processes with a mix of manual and automatic processes.

Earlier software was tested using a collection of manual methodologies, and the QA team tested the software repetitively until and unless they achieved consistency, making the whole method time-consuming and expensive.

As software becomes more complex, the number of tests is naturally growing, making it more and more difficult to maintain the test suite and ensure sufficient code coverage.

AI has revolutionized QA testing by automating repetitive tasks such as test case generation, test data management, and defect detection, which increases accuracy, efficiency, and test coverage.

Apart from finding bugs quickly, the QA engineers use AI by using machine learning (ML) models to identify problems with the tested software. The ML models can analyze the data from past tests to understand and identify the patterns of the programs so that the software can be easily used in the real world.

AI as a Job Transformer for QA Professionals

Even though we are aware that AI has the potential to replace human roles, industrialists have emphasized that AI will bring revolutionary changes and transform the roles of QA testers and quality engineers.

Preliminary and heavy tasks like gathering initial ideas, research, and analysis can be handled by AI. AI assistance can be helpful in the formulation of strategies and the execution of these strategies by constructing a proper foundation.

The emergence of AI has brought speed to the process of software testing, which traditionally would take hours to complete. AI goes beyond saving mere minutes; it can also identify and manage risks based on set definitions and prior information.

To Know More, Read Full Article @ https://ai-techpark.com/ai-in-software-testing/

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