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Artificial Intelligence (AI) is revolutionising the way we live and work by enabling machines to perform tasks that typically require human intelligence. From healthcare and finance to manufacturing and logistics, AI is enhancing efficiency and innovation through technologies like big data, robotics, and the Internet of Things (IoT).
While AI has been around for decades, recent advancements—especially tools like ChatGPT and Google Bard, have sparked mainstream interest. A 2023 IBM survey found that 42% of large enterprises already use AI, with another 40% planning adoption. Moreover, 38% are exploring generative AI specifically. This blog explores what AI is, how it works, and how it's shaping our world.
Artificial Intelligence allows computers to perform tasks that typically require human intelligence, such as identifying images, understanding spoken language, making decisions, and translating text.
AI improves through learning, much like humans do. AI systems learn from data and repeated experiences.
AI does not need step-by-step instructions for every task, and it uses algorithms to learn independently. The more information it receives, the more accurate and efficient it becomes. This ability to learn and adapt makes AI highly effective in solving complex problems. Now that we know the artificial intelligence basics, let's move on to understanding the terminologies associated with AI.
When learning the artificial intelligence fundamentals, it isn't easy to understand the often-used technical terms. Let us know what each term means with respect to AI:
| Terminology | Short Description | Examples | Usage |
| Large Language Models (LLMs) | AI systems are trained to understand and generate human-like language. | ChatGPT, GPT-4, BERT | Writing text, answering questions, summarising content |
| Datasets | Collections of structured or unstructured data used for training AI. | Image collections, sensor readings, labelled texts | Teaching AI models to recognise patterns or make decisions |
| Machine Learning | A sub field of AI used to create self-learning models. | Email spam filters, recommendation systems | Used in self-improving systems like fraud detection or predictive typing |
| Algorithm | A set of rules or instructions that a computer follows to solve a task. | Sorting algorithms, route-finding algorithms | Powering search engines, social media feeds, or game mechanics |
| Neural Networks | AI models are designed to mimic how human brains process information. | Image recognition, speech-to-text tools | Used in face recognition, diagnostics, or voice assistants |
| Natural Language Processing (NLP) | A branch of AI that deals with understanding and generating human language. | Siri, Google Translate, Grammarly | Enables machines to talk, translate, or analyse text like humans do |
| Big Data | Very large and complex datasets are used for analysis and decision-making. | Social media data, healthcare records | Helps in finding insights in trends, customer behaviour, or scientific research |
| Deep Learning | A subset of machine learning that uses layered neural networks for complex tasks. | Voice recognition, autonomous driving | Used in object detection, advanced robotics, or realistic text-to-image generation |
| Structured Query Language (SQL) | A language used to communicate with and manage data in relational databases. | Query example: SELECT name, age FROM customers WHERE age > 30; This query retrieves names and ages of customers over 30 years old from a database. |
Used to fetch and update data in applications like banking, e-commerce, and HR systems |
| Jira Query Language (JQL) | A language for querying and managing tasks in Jira. | Query example: project = "AI Project" AND status = "In Progress" ORDER BY priority DESC This query finds all in-progress tasks in the "AI Project" and sorts them by priority. |
Helps project managers filter, find, and manage issues in software development |
| AI Agents | Independent AI systems that perform tasks or make decisions with minimal human input. | Travel booking assistants, AI personal schedulers | Automate workflows and solve problems without step-by-step instructions |
| Agentic AI | A system composed of multiple coordinated AI agents working toward a larger goal. | Multi-agent planning for business operations or automated scientific research | Solves complex, goal-driven tasks across multiple systems or platforms |
| Generative AI | AI that creates original content such as text, images, audio, or code from learned patterns. | ChatGPT, DALL·E, Midjourney, Copilot | Used in creative tasks like writing stories, generating art, composing music, or writing software code |
When we search for information online—whether it's a recipe, a fact, or a product—AI algorithms analyse our query and return the most relevant results. This not only makes searching faster but also more tailored to our needs. Below, we have outlined some of the most prominent artificial intelligence applications around us:
| Application Area | Description | Examples/Use Cases |
| Search Engines | AI algorithms process queries to deliver fast, relevant, and personalised results. | Google Search, Bing |
| Streaming Services | AI analyses viewing/listening habits to recommend personalised content. | Netflix suggests shows; Spotify recommends songs |
| Voice Assistants | AI understands and responds to voice commands, enabling hands-free help. | Siri, Alexa, Google Assistant |
| Fraud Detection | Machine learning detects unusual patterns in transactions to flag fraud. | Banking apps alert users of suspicious activity |
| Retail & Marketing | AI personalises product recommendations and marketing messages based on customer behaviour. | E-commerce platforms suggesting products; targeted promotions via email |
| Recruitment & Hiring | AI tools screen resumes, match candidates to job roles, and conduct initial interviews using video analysis. | AI-based hiring platforms like HireVue or LinkedIn Talent Solutions |
Starting with AI can feel overwhelming, but we can break it down into manageable steps. Here’s a guide curated to help AI for beginners:
Before diving into classes, we should map out our learning plan. Consider these questions:
Q1: What do we already know?
Are we beginners, or do we have some background in math or programming?
Q2: Why are we learning AI?
Are we looking to switch careers, enhance our current job, or explore?
Q3: How much time and money can we invest?
Do we prefer full-time, part-time, or self-paced learning?
Automation and Innovation :The future of Artificial Intelligence (AI) promises significant transformations across various industries. AI is expected to further automate processes, especially in customer service, through chatbots and digital assistants, which handle simple tasks like answering queries. Self-driving cars and AI-powered travel planning are just the beginning of AI's role in transforming transportation.
Additionally, AI's ability to analyse large datasets and present insights will expedite decision-making, allowing company leaders to make informed choices quickly. AI is poised to revolutionise industries, enhance efficiency, and drive innovation.
Ethical and Social Considerations :AI raises important questions regarding ethics, data privacy, job disruption, and environmental footprint, which will require careful consideration and regulation in the coming years. Data privacy is one of the top issues since AI models require lots of personal data.
Regulatory Landscape: It is the initiatives such as the AI Bill of Rights that points out that there should be greater transparency and privacy of data, and that the handling of consumer data needs to be treated with special care.
Although the future of artificial intelligence is promising, it also calls for stricter regulations, especially regarding intellectual property and ethical concerns.
Artificial intelligence is indisputably transforming the world with breakthroughs in various health, finance and transport sectors. AI makes things less complicated, more efficient and personalised with the use of generative AI and machine learning innovations. As more businesses and industries adopt AI, it will continue to drive progress, from enhancing decision-making to automating routine processes.
A1: The four main types of AI are Reactive Machines, Limited Memory Machines, Theory of Mind, and Self-Aware AI.
A2: John McCarthy is widely considered the ‘Father of Artificial Intelligence'.
A3: AI is used for various applications across industries, such as improving automation, optimising decision-making, analysing data, and enhancing multiple tasks.
A4: AI is needed because it offers a unique combination of speed, accuracy, and adaptability that humans can't easily match. This allows for automating tasks, analysing vast datasets, and generating insights to improve decision-making across various sectors.