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Artificial Intelligence: What Is It and Why It Matters

By Sunita Arnold, Mar 27, 2024

3D letters 'AI' standing on a circuit board, representing artificial intelligence technology and its integration with modern computing.

Imagine a future where machines not only understand our commands but also empathize with our needs and desires. A world where technology doesn’t just work for us but works with us, hand in hand, to create a better tomorrow. That’s the promise of the term artificial intelligence (AI). In this article, we’ll explore the fundamentals of what is artificial intelligence, its history, current applications, and future potential.

What is Artificial Intelligence?

AI is about giving machines the ability to think and learn like humans do. It’s about creating systems that can analyze vast amounts of structured data, describe data, recognize patterns, and make decisions based on that information. But artificial intelligence isn’t just about crunching the numbers or following algorithms. It’s about understanding the human experience and using that understanding to solve problems and make our lives better.

Think about it this way: just as we humans learn from our experiences and adapt to new situations, AI can do the same. They can take in information, process it, and then use that knowledge to tackle challenges in ways that traditional computer programs simply can’t. And the more structured data they have access to, the smarter and more capable they become.

But AI isn’t some abstract concept or futuristic technology. It’s already here, and it’s transforming industries across the board. From healthcare and finance to transportation and entertainment, artificial intelligence is making things faster, smarter, and more efficient. It’s helping doctors diagnose diseases earlier, enabling self-driving cars to navigate city streets safely, and even composing music that tugs at our heartstrings. These are just a few AI examples of how this technology is making a difference.

Types of Artificial Intelligence

As we explore the fascinating world of artificial intelligence, it’s important to understand that AI is not a monolithic entity. Instead, it comes in various forms, each with its own unique characteristics and capabilities, born from decades of AI research by pioneering computer science experts. According to Simplilearn, AI can be categorized based on capabilities, functionalities, and the technologies they employ, such as machine learning and deep neural networks. Let’s explore these different types of AI.

AI Based on Capabilities

Narrow AI (Weak AI):

Focuses on a single task or limited set of functions within a predefined scope (e.g. email spam filters, virtual assistants, game-playing programs). These systems often rely on machine learning and AI training processes to refine their performance.

Artificial General Intelligence (AGI or Strong AI):

Can perform any intellectual task that a human can, possessing the ability to understand, learn, and apply knowledge across various domains (e.g. writing novels, composing music, solving complex mathematical problems). AGI aims to replicate human intelligence, a goal that has driven researchers since the early days of AI research.

Superintelligent AI:

Surpasses human intelligence in virtually all domains, with the capacity to learn, reason, and make decisions at a level far beyond human capabilities (e.g., a hypothetical AI system that can solve global challenges like poverty, and disease more effectively than humans).

AI Based on Functionality

  • Reactive Machines: Respond to specific inputs or stimuli in real-time without relying on memory or past experiences; follow predefined rules and do not have the ability to learn or adapt (e.g., basic chatbots, industrial robots designed for specific tasks).
  • Limited Memory: Can learn from historical data and use past experiences to inform current decisions and actions; typically short-term and focused on a specific task (e.g., self-driving cars, personal assistants that learn user preferences, fraud detection systems). These systems often employ machine learning and deep learning algorithms to process data and improve performance.
  • Theory of Mind: Can understand and interpret the mental states, beliefs, and intentions of other intelligent entities; capable of engaging in social interactions and exhibiting empathy (e.g., a hypothetical system that can provide personalized mental health support or engage in complex negotiations).
  • Self-Aware: Possesses consciousness, self-awareness, and the ability to understand its own existence; has subjective experiences and can reflect on its own thoughts and actions (e.g., a hypothetical system that can contemplate its own purpose, develop its own goals, and make autonomous decisions).

AI Based on Technologies

Machine Learning:

Subset of AI that involves the development of algorithms and statistical models, enabling systems to learn and improve from experience without being explicitly programmed; trains machine learning models on large datasets to identify patterns, make predictions, and take actions (e.g., recommendation systems, credit scoring models, predictive maintenance systems). This field has been a significant focus for AI researchers and computer science experts.

Deep Learning:

Advanced form of machine learning inspired by the structure and function of the human brain; utilizes artificial neural networks with multiple layers to process and learn from vast amounts of data; achieved breakthroughs in image and speech recognition, natural language processing (NLP), and autonomous systems (e.g., facial recognition systems, voice assistants, self-driving cars). Deep neural networks are a key component of many modern artificial intelligence systems.

Natural Language Processing:

Focuses on enabling computers to understand, interpret, and generate human language; employed in language translation, sentiment analysis, text summarization, and chatbots; allows machines to interact with humans using natural language (e.g., machine translation tools, voice-controlled smart home devices, content analysis software).


Involves the development of intelligent machines that can perform tasks autonomously or with minimal human intervention; combines AI with advanced sensors and actuators to enable machines to perceive, make decisions, and control actions (e.g., industrial robots, humanoid robots, autonomous drones, robotic surgeons).

Computer Vision:

Artificial intelligence technology that allows computers to interpret and understand visual information from the world; encompasses AI techniques such as image recognition, object detection, and facial recognition; used in self-driving cars, medical imaging analysis, and surveillance systems (e.g., autonomous vehicles, medical diagnostic tools, security cameras with facial recognition).

Expert Systems:

AI programs that emulate the decision-making ability of a human expert in a specific domain; use a knowledge base and a set of rules to provide advice, diagnose problems, or make decisions (e.g., medical diagnosis systems, financial risk assessment tools, manufacturing process optimization software).

These different types of AI showcase the diverse ways in which artificial intelligence can be applied to solve problems, automate tasks, and enhance human capabilities.

Reference: Simplilearn (2024). Types of Artificial Intelligence That You Should Know in 2024

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A Brief History of Artificial Intelligence 

The story of artificial intelligence isn’t just a tech saga; it’s a journey that mirrors our own quest to push boundaries. Imagine a timeline where ancient thinkers are daydreaming about machines that could think and act like us. Fast forward to the mid-20th century, and that’s where things start getting real. Enter Alan Turing, the brainiac who proposed the Turing Test in 1950. It’s a simple yet profound idea: if a machine can chat with us and make us believe it’s human, then it’s got some serious AI cred. This early AI research laid the foundation for the development of more advanced AI systems.

But the real game-changer came with the introduction of neural networks. Think of these as the brain’s digital cousins, learning and adapting over time. They’re the backbone of AI, crunching numbers and patterns to mimic how we think. To train these neural networks, you need a ton of data – that’s the training data. It’s like feeding the AI a constant stream of brain food, helping it learn from past experiences.

Then there’s the surge in computer systems that could handle this heavy lifting. With beefier hardware, AI could evolve from basic tasks to more complex feats, like generating new content. This is where generative AI tools step in, creating everything from art to new snippets of text, showing off a creative side we didn’t know machines could have. These advancements in AI research have pushed the boundaries of what’s possible with artificial intelligence.

And we can’t forget about large language models (LLMs). These are the heavyweights of AI, juggling and interpreting human language in ways that blow the mind. They’re a culmination of neural networks, heaps of training data, and computing muscle, showcasing just how far we’ve come in making machines that can understand and interact with us. LLMs are a testament to the incredible progress made by AI researchers and computer science experts in the field of artificial general intelligence.

But the journey doesn’t end there. As AI continues to evolve, we’re seeing the emergence of even more advanced AI systems, like deep neural networks and recurrent neural networks. These complex networks are inspired by the structure and function of the human brain, allowing machines to learn and adapt in ways that were once thought impossible. With each passing year, AI researchers are pushing the boundaries of what is possible, bringing us closer to the dream of creating machines that can truly think and reason like humans.

The history of artificial intelligence is a story of human ingenuity, perseverance, and the relentless pursuit of knowledge. From the early days of artificial intelligence research to the cutting-edge advancements of today, we’ve witnessed the birth of a new era – one where machines and humans work side by side to solve the world’s greatest challenges. As we look to the future, we can only imagine the incredible possibilities that await us as we continue to push the boundaries of artificial intelligence.

How Does AI Work? Unpacking AI’s Core Mechanics

Let’s peel back the layers to uncover the engine driving AI’s intelligence, focusing on aspects that haven’t been highlighted yet.

  • Feature Extraction: Before AI can learn or make decisions, it needs to understand the data it’s working with. This is where feature extraction comes in. It’s a process where AI identifies key elements from raw data—like distinguishing a dog’s features from a photo—laying the groundwork for further analysis or learning.
  • Algorithm Optimization: AI doesn’t just use algorithms; it optimizes them. Through a process called optimization, artificial intelligence systems fine-tune their internal settings to improve performance. Think of it as an artificial intelligence continually tweaking its approach to get better at predicting the weather or playing a video game.
  • Reinforcement Learning: Beyond just learning from data, there’s reinforcement learning, where AI learns through trial and error, much like we do. It makes decisions, observes the outcomes, and adjusts its strategies based on rewards or penalties. This method is key for artificial intelligence systems that need to navigate dynamic environments or complex problems where the right actions aren’t always clear.
  • Transfer Learning: Imagine not having to learn everything from scratch. That’s what transfer learning is for artificial intelligence. It allows a machine learning model trained in one area (like recognizing cars in images) to apply its knowledge to a different but related task (like identifying trucks). This not only speeds up the learning process but also makes AI more adaptable.
  • Explainable AI: As artificial intelligence systems get more complex, understanding how they make decisions becomes crucial. Explainable AI focuses on making AI’s decision-making process clear and understandable to humans. This transparency is vital for trust and accountability, especially in critical applications like healthcare or finance.
  • Ethical AI : Lastly, there’s a growing emphasis on ethical AI, which involves designing and using artificial intelligence systems in a way that respects ethical standards and societal values. This includes ensuring privacy, fairness, and non-discrimination in AI applications, guiding AI development towards positive impacts while mitigating risks and potential human error.

By diving into these facets, we get a fuller picture of how AI works, not just in terms of its technical capabilities but also in how it integrates into our world responsibly and transparently. This exploration offers a nuanced understanding of AI’s mechanics, emphasizing the importance of both its intelligent functions and its alignment with ethical standards.

10 Benefits of Artificial Intelligence

The capabilities of AI stretch beyond the traditional boundaries of human intellect, driving efficiencies and creating new possibilities across diverse sectors. It touches everything from our day-to-day tasks to complex problem-solving in various industries. By understanding these advantages, we can better appreciate the value and influence of AI in today’s world:

  1. Task Automation: AI streamlines mundane tasks, freeing up time for us to engage in more complex and innovative endeavors, boosting overall productivity and efficiency.
  2. Informed Decision-Making: With its ability to process and analyze vast amounts of data, AI acts as a decision-support tool, providing nuanced insights that help in making smarter, data-driven choices.
  3. Tailored Experiences: Whether it’s shopping online or streaming music, AI’s knack for learning from our preferences delivers a customized experience, making our digital interactions more relevant and enjoyable.
  4. Accessibility Advances: AI-powered applications, like speech-to-text or enhanced visual recognition, are game-changers for individuals with disabilities, offering new ways to interact with technology and the world.
  5. Innovation Acceleration: AI is a catalyst for breakthroughs across fields, from medical research to environmental science, speeding up discoveries and the application of new knowledge.
  6. Enhancing Safety: AI’s vigilant eye assists in everything from monitoring financial transactions to ensuring the safe operation of autonomous vehicles, adding an extra layer of security to our daily lives.
  7. Smarter Communication: By refining natural language processing, AI makes digital interactions smoother, whether you’re chatting with a virtual assistant or translating languages on the fly.
  8. Optimization: AI fine-tunes processes across sectors, optimizing resource use and operational workflows, which translates into cost savings and a smaller ecological footprint.
  9. Entertainment and Media: AI knows what we like, curating content on streaming platforms and social media to align with our tastes, keeping us engaged and entertained.
  10. Personalized Learning: In education, AI tailors learning to the individual, adapting content and pacing to the learner’s needs, which enhances understanding and retention.

The benefits of AI are significant in various domains, reflecting a notable shift in our interaction with technology and its integration into our daily routines. AI’s contributions range from improving operational efficiencies to enriching user experiences, highlighting its broad impact and the ongoing opportunities it presents for innovation and improvement in multiple fields.

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Examples of AI Technology and How It is Used Today 

From simple everyday tasks to complex scientific and business challenges, AI technology is being adopted across various sectors. Let’s explore some examples and discover how AI is impacting our world.

Smart Home Devices 

Imagine your home as an intuitive partner in your daily life. With AI-powered devices like Amazon’s Alexa, Google Home, and Apple’s HomePod, your house becomes more efficient and comfortable. These digital assistants can control your lighting, heating, and security systems with a simple voice command, answer your questions, remind you of important tasks, and even take care of your shopping list.

Self-Driving Cars 

Step outside, and you’ll find AI is evolving the way we move. Self-driving cars, developed by companies like Tesla and Waymo, can navigate roads, recognize traffic signals, and avoid obstacles, all without human input. This technology has the potential to make our commutes more relaxing, our roads safer, and our traffic flow more efficient.

Recommendation Systems  

AI is also transforming the way we consume digital content. Recommendation systems, like those used by Netflix and Spotify, analyze your past behavior and preferences to create a personalized experience just for you. It’s like having a virtual concierge who understands your tastes and always has the perfect suggestion.

Predictive Text and Voice Recognition  

Even the way we communicate is being shaped by AI. Predictive text and voice recognition, found in AI tools like Google Search and smartphone dictation, use AI to anticipate what you’re trying to say or type. By learning from your unique patterns and habits, these AI tools make digital communication faster, easier, and more natural.

Social Media Algorithms 

Behind the scenes of your social media feeds, AI algorithms are working to curate content that’s tailored to your interests. Platforms like TikTok and Instagram analyze your likes, shares, and comments to keep you engaged and connected. While this helps you stay informed about the people and topics you care about, it also raises important questions about privacy and the influence of algorithms on our perception of the world.

These examples are just a glimpse into the vast potential of AI. As this technology continues to evolve, it will undoubtedly reshape industries and change the way we live and work. It’s up to us to harness its power responsibly and create a future that’s not just more efficient, but also more equitable and human-centered.

Reference: CIO. (2023). 12 Most Popular AI Use Cases in the Enterprise Today.

Challenges of Artificial Intelligence (AI) 

As we embrace the incredible potential of AI, it’s important to acknowledge and address the challenges that come with this transformative technology. Like any powerful tool, AI can be misused or have unintended consequences if not developed and deployed with care and foresight.


One of the most significant challenges facing AI is the issue of bias. If the data AI systems are trained to contain human biases, the AI can inadvertently learn and replicate those biases, leading to unfair or discriminatory practices. It’s a reminder that AI is not inherently objective, and that we must be vigilant in ensuring the data we use is fair and representative.

Transparency and Explainability 

Another challenge is the opacity of many AI systems, especially those based on deep learning. These “black box” models can make decisions or predictions, but it can be incredibly difficult to understand how they arrived at those outputs. If an AI system makes a high-stakes decision, like recommending a medical treatment or prison sentence, we need to be able to understand and justify those decisions. Developing transparent and interpretable AI systems is a key challenge to address.


Like any technology, AI systems can be misused or exploited for malicious purposes, from creating deepfakes to fooling or manipulating AI systems. In a world where AI is increasingly embedded in critical systems, ensuring their security is paramount.

Privacy Concerns

AI’s reliance on vast amounts of data raises significant privacy concerns. As AI becomes more prevalent, the risks of personal data being misused or leaked only increase. Data use and privacy protection must be balanced in the right way.


The hardware required to run AI systems consumes significant energy and resources, risking the generation of growing electronic waste. Addressing this challenge will require developing more efficient and sustainable AI hardware, as well as better e-waste management practices.

These challenges underscore the complexity of AI and the careful balance we must strike as we develop and deploy these systems. By proactively addressing these challenges, we can work towards a future where AI lives up to its incredible potential while avoiding its potential harms.

The Future of Artificial Intelligence (AI)

As we stand at the precipice of a new era in AI, the future is not just about the advancements in technology, but about how we navigate the challenges and opportunities that come with it. The AI landscape is evolving rapidly, with AI trends like multimodal AI, smaller language models, and increased accessibility shaping the direction of the field.

Human-AI Collaboration: Seamless human-AI collaboration will become the norm, with powerful virtual agents assisting us in a wide range of tasks, augmenting human capabilities and helping us tackle complex problems more efficiently.

Personalized AI: AI will adapt to individual users’ needs and preferences, with advances in natural language processing and multimodal AI enabling more natural and intuitive interactions, and providing tailored recommendations across various domains.

Ethics and Regulation: As AI becomes more influential and accessible, robust ethical guidelines and regulatory frameworks will be crucial to address potential misuse, such as deepfakes, bias, and privacy violations. Governments and legal cases will shape the future of AI regulation.

Democratization of AI : Open-source models and techniques like Low Rank Adaption (LoRA), quantization, and Direct Preference Optimization (DPO) will make AI more accessible to a wider range of individuals and organizations, empowering startups, researchers, and hobbyists to innovate across various industries.

Advanced Natural Language Processing: Advancements in NLP and more efficient, explainable, and accessible language models will enable sophisticated virtual agents, improved translation, and AI-generated content nearly indistinguishable from human-created content.

Reference: IBM (2024). The most important AI trends in 2024

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Harnessing the Power of AI for a Better Tomorrow

The story of AI is an evolving tapestry of human curiosity, ingenuity, and ambition. From the early days of symbolic AI to the cutting-edge advancements in machine learning, deep learning, and neural networks, we have embarked on a remarkable journey of discovery and innovation. AI’s impact is evident in countless aspects of our lives, from personalized recommendations and smart home devices to self-driving cars and advanced healthcare solutions.

However, as we marvel at the benefits AI brings, we must also acknowledge and address the challenges that come with this transformative technology. Issues such as bias, transparency, privacy, and the ethical use of AI require our careful attention and proactive efforts. It is our responsibility to ensure that the development and deployment of AI systems align with our values and contribute to the greater good.

As we stand at the precipice of a new era in AI, the future is not just about the advancements in technology, but about how we navigate the opportunities and challenges that come with it. The democratization of AI, coupled with the rise of multimodal AI and advanced natural language processing, promises to empower individuals and organizations across various industries to harness the power of this technology.

At the same time, the need for robust ethical guidelines and regulatory frameworks has never been more pressing. The essence of AI lies not just in creating intelligent machines, but in enhancing human life and creating a world that reflects our highest ideals and aspirations. By fostering human-AI collaboration, developing personalized AI solutions, and ensuring the responsible use of this technology, we can work towards a future where the benefits of AI are shared by all.

Have thoughts or experiences to share on this topic? Dive into the discussion and leave your insights in the comments section below!

This material, provided by Linqto, is for informational purposes only and is not intended as investment advice or any form of professional guidance. Before making any investment decision, especially in the dynamic field of private markets, it is recommended that you seek advice from professional advisors. The information contained herein does not imply endorsement of any third parties or investment opportunities mentioned. Our market views and investment insights are subject to change and may not always reflect the most current developments. No assumption should be made regarding the profitability of any securities, sectors, or markets discussed. Past performance is not indicative of future results, and investing in private markets involves unique risks, including the potential for loss. Historical and hypothetical performance figures are provided to illustrate possible market behaviors and should not be relied upon as predictions of future performance.

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  • Jann

    AI is intriguing. My concern is how easy it would be to creat an army of warrors. You give them/it a specific attitude about whatever or whomever you want. Then we have a Holocast type pandemic that is world encompassing. How long after do the AI’s realize we are no longer needed and must be removed. Poof, people are a footnote in history.

    There must be a “kill code” or a “back door” something that the AI is not aware of to safegaurd humanilty. Something that your regular “Joe citizen” can use.

    Overall this is an exciting concept, used correctly has potential to elevate the human experience.

  • Carol

    Many aspects of Ai are very interesting and shows us that anything is possible to input into a computer, robot, or any other form of technology, so how can we ensure this to be SAFE , if this kind of brain machine is some how taken over by a cyber attack as we all know has once took over our largest collection of hospital , medical reports, ect … Them what would happen , we would all be controlled by a AI system!! The way we live our life’s and learn should be from human resources and NOT intelligence , it could all go wrong , that’s my opinion cheers for reading 👍

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Sunita Arnold

Sunita Arnold

As Director of Content Strategy at Linqto, Sunita deftly merges creative and strategic planning. Guiding a talented team, she aligns content with marketing objectives to deliver compelling, brand-consistent materials. Her diverse 15+ year career spans fintech, healthcare, and public relations, with highlights including managing investor relations for an alternate investment platform, steering business development in a leading PR firm's sports and entertainment sectors, and overseeing operations for a wealth management data service. Her past achievements include significantly contributing to the growth and efficiency of a top global fund administrator. Keeping pace with industry trends, Sunita applies her strategic acumen and market insight to advance Linqto's content strategy.