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. 

AI and the Lack of Privacy

As humans, we have an innate ability to know what is private and what is not. We use our judgment to determine whether or not certain pieces of information should be utilized. However, if the database is mishandled, AI systems can inadvertently breach unauthorized information, disclosing personal data or misleading details as the system goes rogue. This is exactly what happened at the Def Con conference in Las Vegas, where AI systems were manipulated into going rogue. 

This type of circumstance is more common than we think. AI systems are trained to dig through vast data volumes to drive their insights, making no difference between what is allowed and what is not when disclosing an action. Without humans implementing strong access controls and data encryption protocols, AI systems can endanger an organization’s security. 

Why Human Intelligence Is Essential to Prevent Bias 

The cybersecurity landscape is quite widespread, with AI systems consistently used to defend against malware, phishing attacks, and nationwide threats from organized crime groups. Each type of threat has its own nuance and complexities, requiring personalized approaches for detection, mitigation, and overall prevention. 

The problem is that the nuanced world of cybersecurity could also lead to false positives and negatives, along with cyberattacks being misread. Without careful monitoring, AI systems could unintentionally discriminate or incorrectly categorize an attack, leading to delays and potential breaches in security. By incorporating human intelligence into the equation, the threats could be detected and mitigated early on before they escalate. 

This can be rather difficult to obtain, considering that there is still a global shortage of AI experts. To prevent this, we need heavy research and development, as well as investments in comprehensive training programs. By nurturing the talent pool to recognize unhealthy AI behavior, defenses may be bolstered. When different situations are put through vulnerability tests, we can prevent missteps caused by AI bias.

The Bottom Line

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.

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Introduction

1. The Role of AI in Creating and Executing Test Cases

2. AI as a Job Transformer for QA Professionals

3. The Impact of AI on the Future of QA Engineering

3.1. AI-Driven Test Automation

3.2. Intelligent Test Environments and Data Management

3.3. AI-Driven Test Execution and Analysis

Conclusion

Introduction

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.

1. 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.

2. 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.

AI-driven tools have the potential to alert users to errors. However, the decision-making power remains in the hands of the user, providing complete control of the process. In this particular case, it allows users with a tester to change their approach from doing the work themselves to overseeing the work done by AI.

3. The Impact of AI on the Future of QA Engineering

The rise of AI has opened new doors in software testing of products in different situations and environments, enabling software testers to identify issues and make quick decisions to solve them. This significant change in software testing has played a transforming role in creating a brighter future in the field of QA engineering. Let’s understand the good parts of AI and automation in software testing:

3.1. AI-Driven Test Automation

AI will revolutionize the process of testing by enabling automation that eventually creates smarter and self-healing test scripts. Further, the AI algorithms can analyze application changes, make quick decisions to augment the test scripts, and predict potential failure points.

3.2. Intelligent Test Environments and Data Management

AI will be optimized in test environments by analyzing usage patterns and dynamically provisioning resources as per the QA engineer’s needs. It will assist the QA team in generating and managing test data to ensure that the test environments are replicas of real-world scenarios.

3.3. AI-Driven Test Execution and Analysis

Soon, AI can aid in the execution and analysis of tests by detecting abnormalities, patterns, and correlations within the results, speeding up the process of identifying the root bugs and providing deeper insight to solve the bug issues.

Conclusion 

It is probably too soon to conclude the usability of AI for QA engineers, as the implementation is still in progress.

Until today, automation and AI have certainly revolutionized QA engineering, but human expertise remains an essential element in ensuring quality software. As outlined above, AI technologies can open up software testing challenges and streamline the entire process in multiple situations.

And with time, we can expect to see some developments in AI in quality assurance that can accelerate the process of testing at a faster pace.

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AI has emerged as a powerful force of transformation, revolutionizing numerous sectors, and breathing new life into traditional processes. From healthcare and finance to transportation and manufacturing, the influence of AI is undeniable. But what about the creative industry? How can this revolutionary technology, with its computational prowess and ability to mimic human intelligence, contribute to and enhance the world of art and innovation?

Before diving into AI’s potential in the creative realm, let’s take a step back and understand the creative industry’s traditional processes. For centuries, artists and innovators have relied on their inherent abilities, honed skills, and manual techniques to craft their masterpieces. From painters meticulously blending colors on a canvas to musicians pouring their souls into compositions, the creative process has long been rooted in human intuition and expertise.

However, the rise of AI has introduced a paradigm shift, challenging conventional notions of creativity. With its ability to analyze vast amounts of data, recognize patterns, and generate new ideas, AI holds immense potential to revolutionize creative practices. While prominent artists have voiced strong criticisms regarding the extent of technology’s influence on the creative process, the undeniable truth remains—humans will stay central to all creative breakthroughs. Technology, however, will serve as a mere catalyst, accelerating the realization of ideas.

Join us as we embark on this captivating journey, where the synergy between AI and creativity has the potential to reshape industries, redefine artistic boundaries, and empower the visionaries of tomorrow. 

Table of Contents

1. AI in Music Composition
2. AI in Visual Arts
3. AI in Content Generation
4. Augmenting Human Creativity with AI
5. Transformative Potential of AI in the Creative Industry
6. Future Trends and Possibilities in AI-Driven Creativity

1. AI in Music Composition

• The Evolution of AI-Generated Music: The realm of music composition has witnessed a fascinating evolution with the advent of AI. Initially, AI algorithms were programmed to analyze existing musical compositions and generate new pieces based on established patterns. However, as AI technology advanced, it began to learn and mimic the nuances of various musical styles, enabling the creation of original compositions that captured the essence of human creativity.

• Examples of Successful AI-Generated Compositions: Several notable examples demonstrate the success of AI-generated music. From symphonies to pop songs, AI has showcased its ability to compose captivating pieces. For instance, in 2016, a composition named “Daddy’s Car,” generated by AI developed by Sony CSL Research Laboratory, emulated the style of The Beatles. The composition was so convincing that it successfully blended into a playlist of Beatles songs without raising suspicion.

Another remarkable example is the AI-generated album “I AM AI” by Taryn Southern. The entire album was composed with the assistance of AI algorithms, covering a wide range of genres and demonstrating the potential for AI to create diverse and compelling musical experiences.

• Collaboration Between AI and Human Musicians: AI’s impact on music composition extends beyond independent creation. It has fostered collaboration between AI systems and human musicians, leading to exciting and innovative collaborations. AI can analyze a musician’s style, preferences, and existing work to generate suggestions or even assist in the composition process. This collaboration opens doors to new sonic landscapes, where AI acts as a creative partner, expanding the boundaries of human imagination.

• Benefits and Challenges of AI in Music Composition: The integration of AI into music composition brings forth a multitude of benefits. First and foremost, it enables musicians to explore uncharted territories, unburdened by traditional constraints. AI-generated compositions can inspire fresh ideas, challenge conventional structures, and push the boundaries of musical expression.

AI also offers valuable time-saving capabilities. Composing music is often a labor-intensive process, but AI algorithms can swiftly generate melodies, harmonies, and even entire arrangements, freeing up musicians’ time to focus on refining and adding their artistic touch.

However, the introduction of AI in music composition also poses certain challenges. Critics argue that AI-generated music lacks the emotional depth and human touch that comes from the personal experiences and vulnerabilities of human composers. There is a concern that relying heavily on AI could lead to a homogenization of musical styles, diminishing the uniqueness and individuality of artistic expression.

Furthermore, ethical considerations arise when it comes to copyright and ownership. As AI algorithms learn from vast musical datasets, the line between inspiration and plagiarism becomes blurred. Determining the ownership and originality of AI-generated compositions raises complex legal and ethical questions that require careful consideration.

2. AI in Visual Arts

• Applications of AI in Visual Arts, Such as Image Generation and Style Transfer: AI’s impact on visual arts extends across various applications. One prominent example is AI-powered image generation, where algorithms can create entirely new and visually stunning images from scratch. These AI-generated artworks range from abstract compositions to realistic renderings, showcasing the potential of AI to produce visually captivating and thought-provoking pieces.

Another notable application is style transfer, where AI algorithms can extract the unique style elements of one image and apply them to another, resulting in striking and unexpected combinations. This technique allows artists to explore artistic fusion, reimagine established styles, and create visually compelling artworks that merge diverse influences.

• The Intersection of AI and Traditional Artistic Techniques: The intersection of AI and traditional artistic techniques is an intriguing aspect of AI’s impact on visual arts. Rather than replacing traditional methods, AI serves as a complementary tool that expands artistic possibilities. Artists can incorporate AI algorithms into their creative processes, using them as a source of inspiration, experimentation, and refinement. This collaboration between AI and traditional techniques unlocks new avenues for artistic expression, where human intuition and expertise merge with AI’s computational capabilities.

For example, an artist may use AI to generate a base structure or form, which they can then refine and add their personal touch to, creating a harmonious blend of human artistry and AI-generated foundations.

• Ethical Considerations and Debates Surrounding AI-Generated Art: The rise of AI-generated art has sparked ethical considerations and debates within the art community and society at large. One prominent concern is the attribution of authorship and the blurring of lines between human and AI contributions. Questions arise regarding the extent to which AI-generated artworks should be credited to the artist, the AI system, or both. Additionally, issues surrounding the originality and uniqueness of AI-generated art emerge, as AI algorithms often learn from existing visual datasets, potentially leading to derivative or unoriginal works.

Furthermore, the commercialization and market value of AI-generated art raise questions about the role of the artist and its impact on the art market. As AI-generated artworks gain attention and recognition, it becomes crucial to navigate the implications for artists, collectors, and the broader art ecosystem.

Overall, while AI offers exciting opportunities to enhance visual arts and push creative boundaries, ethical considerations must be addressed. Ongoing discussions and guidelines are necessary to ensure transparency, authenticity, and fair practices in the creation and dissemination of AI-generated art.

3. AI in Content Generation

• AI-Powered Content Creation Tools and Platforms: The advent of AI has revolutionized content generation, offering powerful tools and platforms that enable efficient and creative production. AI-powered content creation tools leverage natural language processing, machine learning, and other advanced techniques to automate various aspects of content creation, such as writing, editing, and even design. These tools provide content creators with valuable assistance, streamlining their workflow and enhancing their productivity.

• Automating Content Creation Processes With AI: AI has the ability to automate content creation processes, reducing manual labor and accelerating production timelines. For instance, AI algorithms can generate written articles, blog posts, or product descriptions based on specific parameters and data inputs. This automation frees up time for content creators to focus on higher-level tasks such as strategy, ideation, and creative thinking.

AI can also automate tedious tasks like proofreading, grammar checking, and fact verification, ensuring the accuracy and quality of content. By relieving content creators of repetitive tasks, AI empowers them to allocate their time and energy toward more strategic and impactful endeavors.

• Personalization and Customization Through AI Algorithms: One of the key strengths of AI in content generation lies in its ability to deliver personalized and customized experiences. AI algorithms can analyze vast amounts of data about user preferences, behaviors, and demographics to create tailored content recommendations. This personalization enhances user engagement and satisfaction by presenting content that aligns with individual interests and needs.

AI-powered recommendation systems can suggest relevant articles, videos, or products based on user preferences, browsing history, and contextual information. These algorithms continually learn and adapt, refining their recommendations to provide users with increasingly accurate and valuable content.

• Implications for Content Creators and Businesses: The integration of AI in content generation has profound implications for both content creators and businesses. For content creators, AI serves as a powerful ally, streamlining their workflows and enhancing their productivity. It frees up their time from repetitive tasks, allowing them to focus on creativity, strategy, and storytelling. Content creators can leverage AI-powered tools and platforms to amplify their output, produce high-quality content at scale, and meet the demands of an ever-growing audience.

For businesses, AI-driven content generation offers opportunities to enhance customer experiences and drive engagement. Personalized content recommendations powered by AI algorithms can increase user satisfaction, improve conversion rates, and foster customer loyalty. AI also enables businesses to optimize content for search engines, ensuring greater visibility and reach.

However, challenges exist as well. Critics raise concerns about the potential impact of AI-generated content on the job market and the authenticity of human-authored content. There is a need for transparency to distinguish AI-generated content from human-created content to maintain trust and ethical standards.

Overall, AI’s integration into content generation presents a transformative landscape, empowering content creators with automation, personalization, and customization capabilities. It provides businesses with tools to enhance user experiences and drive business outcomes. As AI continues to evolve, collaboration between AI and human creators will be essential to strike the right balance, leveraging the strengths of both to deliver compelling, relevant, and authentic content in an increasingly AI-driven world.

4. Augmenting Human Creativity with AI

• The Symbiotic Relationship Between AI and Human Creativity: The relationship between AI and human creativity is not one of substitution but of augmentation. AI serves as a powerful tool that enhances and amplifies human creativity, expanding the horizons of what is possible. Rather than replacing human creativity, AI acts as a catalyst, fueling inspiration, and unlocking new realms of imagination. This symbiotic relationship between AI and human creativity opens doors to uncharted territories and empowers artists to reach new heights.

• AI as a Tool for Inspiration and Ideation: AI has the capacity to inspire and ignite the creative process. By analyzing vast amounts of data and recognizing patterns, AI algorithms can offer unique insights and generate novel ideas. Artists can leverage AI-powered tools to explore new artistic concepts, experiment with different styles, and discover fresh perspectives that they may not have considered otherwise. AI acts as a wellspring of inspiration, infusing the creative process with new possibilities and pushing the boundaries of human imagination.

• Leveraging AI To Overcome Creative Blocks and Explore New Possibilities: Creative blocks are a common challenge faced by artists. AI can play a transformative role in overcoming these blocks and expanding artistic possibilities. AI algorithms can analyze existing works, identify gaps or areas of improvement, and generate suggestions to help artists overcome creative obstacles. Additionally, AI-driven tools can facilitate experimentation and exploration, allowing artists to break free from familiar patterns and discover innovative approaches. By serving as a collaborator and guide, AI empowers artists to push past creative barriers and embark on new creative journeys.

• Examples of Successful Collaborations Between Artists and AI: Numerous successful collaborations between artists and AI have emerged, demonstrating the power of combining human creativity with AI technologies. One prominent example is the collaboration between the artist Mario Klingemann and Google’s DeepDream algorithm. Klingemann utilized the algorithm’s image recognition capabilities to create captivating and surreal artworks that merged human artistry with AI-generated transformations.

In the music world, collaborations between human musicians and AI algorithms have led to remarkable compositions. For instance, the album “Hello World” by SKYGGE, created in collaboration with AI, showcased a seamless blend of human vocals and AI-generated music, resulting in a unique and captivating musical experience.

These collaborations highlight the potential for AI to augment human creativity, leading to groundbreaking and innovative outcomes that push the boundaries of artistic expression.

5. Transformative Potential of AI in the Creative Industry

• Disruption of Traditional Business Models and Creative Processes: The transformative potential of AI in the creative industry is profound, leading to the disruption of traditional business models and creative processes. AI technologies enable automation, optimization, and efficiency in various aspects of the creative industry, such as content creation, marketing, and distribution. This disruption challenges established norms and forces businesses to adapt to the changing landscape, where AI-driven solutions redefine how creativity is harnessed, produced, and consumed.

• Opportunities for Innovation and New Revenue Streams: AI presents vast opportunities for innovation and the creation of new revenue streams within the creative industry. By leveraging AI algorithms and tools, businesses can unlock novel ways of engaging with audiences, developing personalized experiences, and delivering content. AI-driven technologies such as virtual reality (VR) and augmented reality (AR) offer immersive and interactive platforms for storytelling and artistic expression. Additionally, AI enables data-driven insights and predictive analytics, empowering businesses to make informed decisions, optimize their strategies, and generate new sources of revenue.

• Challenges and Considerations for Adopting AI in the Creative Industry: The adoption of AI in the creative industry comes with its own set of challenges and considerations. Privacy and ethical concerns arise when dealing with vast amounts of user data and personalization. There are also challenges related to the potential bias embedded in AI algorithms and the impact on diversity and representation in creative content.

Additionally, there may be resistance or fear among creative professionals regarding the potential displacement of jobs or the perception that AI-generated content lacks the human touch. The creative industry must address these concerns by ensuring transparency, fostering collaboration between AI and human creators, and preserving the value of human creativity and expression.

6. Future Trends and Possibilities in AI-Driven Creativity

Looking ahead, AI-driven creativity holds immense promise for the future. As AI technologies continue to advance, we can expect to see even greater integration of AI in the creative industry. AI-generated content may become more sophisticated and indistinguishable from human-created content, opening up new possibilities for immersive storytelling, personalized experiences, and interactive art forms.

Moreover, the collaboration between AI and human creativity will evolve, giving rise to hybrid creations that combine the best of both worlds. AI algorithms may act as creative partners, assisting artists and innovators in generating ideas, refining concepts, and bringing their visions to life. The fusion of AI and human creativity has the potential to redefine artistic boundaries, push the frontiers of innovation, and create entirely new forms of expression that were previously unimaginable.

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There are no great limits to growth because there are no limits to human intelligence, imagination, and wonder. – Ronald Reagan

Ever wondered how is it that every person has the ability to learn and apply logic to comprehend things and situations? While it is debatable how two people, who were taught the same thing, can act differently in similar situations, it is true that intelligence is what makes them solve problems and make decisions, and actually drives their entire being. 

Let’s take an example. There was a pretty famous case about a 24-year old girl who used her intellect and quick thinking to save a woman who was kidnapped. The 24-year old worked as a cashier at a gas station and on a pretty normal day was approached by a woman who asked her for help. The cashier quickly locked the doors of the store and gave this woman her phone while asking her to lock herself in the bathroom.

When the kidnappers tried to get in, instead of panicking and trying to keep the door locked, the cashier opened it and was asked the reason for locking the door, to which she said that it was to give the woman who had just gotten her period some privacy. This acted as a stalling technique until the police arrived and took the kidnappers into custody.

Putting someone else in this situation could result in a different outcome, because of the difference in understanding and decision making.

Human Intelligence is the intellectual capacity of a person that allows them to think and learn from their experiences about the ways to apply logic, recognize patterns, and communicate with other human beings.

What is Human Intelligence? 

Human intelligence is the stream of knowledge that is backed by abstract feelings and emotions such as passion and awareness. It is what enables people to handle abstract concepts and complexities. And not to mention, Human intelligence is what stimulates the widely used and deeply advanced technology – Artificial intelligence.

The definition of human intelligence points towards the mental ability of the human brain to form concepts and solve problems. In simple terms, it is what makes a person decide on what to do next. 

Whereas, the definition of Artificial Intelligence is nothing more than the simulation of human intelligence, where it applies the human techniques of the brain to a machine, in order to make decisions and mimic human actions.

Types of Human Intelligence

There are different types of human intelligence that are triggering the development and innovation of artificial intelligence in a similar fashion. Let us look at a couple of types of Human Intelligence that are currently defining the scope of innovation and business development.

• Visual or Spatial intelligence, wherein a person with higher intelligence is extremely visual and good at recognizing patterns.

• Linguistic-verbal intelligence, wherein the person is quite sensitive to the meaning and usage of words and can communicate well.

Bringing us to the application of AI in Natural Language Processing for translations and interpretation of the text, audio, video.

• Intrapersonal Intelligence is where a person knows his/her emotional state and knows how to take control of them with a sense of sensitivity towards others’ feelings.

Mental Health chatbots are powered by AI and designed in a way that they can empower people struggling with their mental health, to better understand their emotions and find a way to take better care. 

• Logical-mathematical intelligence is when the individual is skilled at problem solving and calculations and can solve complex computations.

AI is built on algorithms that can automate complex tasks and solve problems in no time. This is a stream where mathematical reasoning backs most of the actions.

So, it is clear that

human intelligence is the foundation of machine intelligence that is responsible for bringing human appropriation to a machine.

Let’s take a look- If we go on to take them head-on, then attached is a crisper analysis to the most asked

AI vs HI ( Artificial Intelligence vs Human Intelligence)

• The first and the most important distinction between human intelligence and artificial intelligence is that AI is a mimic of Human intelligence wherein the machine tries to think like a human. And HI is where a person uses his/her brain and its capacity to make decisions.

• The basis of the decision making – In AI’s case, it is the data that is fed to the system that enables it to make decisions, and in the case of HI, it is the experience or the intellect that determines what decisions are made.

• Task Automation – Since AI gives machines the power to perform tasks, it is easy for this technology to automate these processes and ensure they’re error-free. But, when it comes to human intelligence, it up to the caliber of the person that decides the ratio of error and speed have taken to perform the task.

• Learning Power – Humans have the ability to learn from their and others’ mistakes, perform trial and error, and implement those learnings to a variety of activities that they go through, during the day. But AI can only learn from what data is input and only perform activities in the way that it is directed to. It is the thinking power that is lacking in machines.

• Quick Data Interpretation – With AI, it hardly takes any time to discover hidden patterns and make sense of the data, regardless of the volume. But with HI, it can take some time to go through all the information and work on the interpretation.

Now that we have covered the types of Human Intelligence let us hop onto the Implementation level and discuss variously

APPLICATIONS OF HUMAN INTELLIGENCE IN BUSINESS

• Human Brain works on high-level tasks with RPA bots executing sequential tasks
• Human Intelligence can understand different languages, and speech recognition and translation can automate conversion.
• Human Intelligence can identify known faces for security purposes, implementing the same to facial recognition by identity verification providers. 

Many companies such as Amazon and Google utilized HI+AI for enhancing their offerings such as identifying the right category tags for product placements and displaying the right ads in product videos.

Human Intelligence has been the foundation of Artificial Intelligence and several companies are now leveraging its power to advance their offerings or functionalities. Let us look at some of the tasks solving abilities of Human Intelligence being implemented by the Top Tech Companies around the globe?

• Duolingo

Duolingo is one of the largest language learning applications with the mission to scale education and work towards enabling people with income flows via language learning. Duolingo has more than 500 Million registered users that learn different languages and excel at them with fun and regular assessments.

Duolingo is a thorough multilingual chatbot based on augmented reality, but one very important factor that powers the assessment process of this application is human intelligence. How?

High-stake tests are often prone to cheating and impose the need for proctors. But with remote and online tests, that can be quite challenging. So, Duolingo came up with a supervision proctoring model, wherein the test sessions will undergo scrutiny by NLP and computer vision analyzing behavioral and environmental factors. And, if any suspicious activity gets detected, it anonymously goes to the human proctors, who then take the final decision on the status and certification of the digital test.

• Appen

Appen is a market leader in high-quality training data with industry-leading services of its ML-assisted annotation platform.  This platform is highly recognized in the industry for successfully training AI and ML models and the judgments that they should be making.

But how does human intelligence come into the picture here?

Appen’s data annotation platform is not just a service platform, but also a crowdsourced human network that has a crowd present in over 170 different countries and expertise in over 235 languages. It functions in 3 steps-

1. The ML algorithm first provides the best guess hypothesis, which is reviewed by human contributors.
2. To reduce the cognitive load, the ML offers in-tool efficiencies while the human contributors continue to work in a more comfortable environment.
3. The ML then verifies the human contributors’ judgment before they are finalized.

This makes the Appen platform a machine and human-powered intelligence platform with unmatched capabilities and admiration across industries, wanting to train their AI, ML models.

• UiPath

UiPath is a global leader in Robotic Processing Automation (RPA) technology, known for streamlining processes and uncovering end-to-end high-scale automation. It is the go-to software for businesses looking to undergo digital transformation and automate repetitive tasks.

This RPA-driven solutions provider is one of the largest contributors to the automation sector and is widely recognized for its innovative adoption of technology. But, it is also the company that considers human intelligence as one of the top factors of innovation. How?

UiPath Action Center is a product where automation meets humans and allows them to make important decisions such as escalations, approvals, and exceptions. Even though robotics is the core of UiPath’s offerings, with UiPath Action Center, businesses can easily hand off their processes from robots to humans and back again. It offers a central portal where users can have access to all the supporting documents and tasks and even hand over the task back to the robots. This makes the automation process highly intelligent because of the human factor involved. Human intelligence is what powers the significant decisions here.

Many other big tech giants such as Google, Facebook, and even Microsoft rely on human intelligence for the high-profile tasks that can be done by machines but are not because of the lack of several human factors. AI and HI can go hand-in-hand to empower not just the technology, but also our learning capacities.

So, the end question as an aspirant might be, How do I get an entry-level job as an Intelligence Specialist to start my journey up the AI, HI corporate ladder? Don’t worry we got you covered

Tips to get an entry-level job in the human intelligence/ artificial intelligence arena

• Understand the current industry requirements

• Identify and evaluate your key skills and drawbacks

• Undergo training to acquire needed skills

• Study the market leaders

This also brings us to the more pivotal and “need of the hour” question

What are the basic skills that you should possess to be eligible for the Human Intelligence Job/Career Band? 

• Algorithmic Understanding
• Mathematical and Statistical Skills
• Coding
• Programming Languages
• Network Modelling
• Analytical Reasoning
• Graphical Modelling
• Cognitive Science
• Computer Science
• Data Visualization

Even with the vast advancements in the technological arena, human intelligence is here to stay. The human brain is designed with the utmost capabilities that we are yet to mimic. So, it is true that tech is now trying to act like the brain, but we are yet to surpass the intelligence that a human possesses. 

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