By Samudrapom DamReviewed by Susha Cheriyedath, M.Sc.Updated on Dec 16 2024Artificial Intelligence (AI) is a type of technology that enables machines and computers to perform tasks typically associated with human intelligence. This includes problem-solving, learning, decision-making, and understanding and responding to language.
Image Credit: Shutterstock/Peshkova
For example, AI-powered tools can recognize objects, interpret spoken or written language, learn from experience, make recommendations, and even function independently without human input.1,2
The History of AI
AI has come a long way since its beginnings in 1956, when researchers J.C. Shaw, Allen Newell, and Herbert Simon developed the first AI program, Logic Theorist, and coined the term “artificial intelligence.” Over the next few decades, milestones included the creation of ELIZA, an early chatbot capable of basic natural language processing, and the first autonomous land vehicle.
AI gained wider attention in 1997 when IBM’s Deep Blue computer defeated world chess champion Garry Kasparov. In 2011, IBM Watson® demonstrated its capabilities by outperforming champions Brad Rutter and Ken Jennings on the quiz show Jeopardy. More recently, Baidu’s Minwa supercomputer showed exceptional accuracy in image recognition, surpassing human performance in 2015.
Today, AI continues to advance with models that combine data from multiple sources like text, speech, and images, creating more flexible and capable systems. Researchers are also working on smaller, more efficient models as they explore alternatives to the large-scale systems developed in previous years.1,3
How AI Works: Technologies and Methods
AI includes a variety of techniques that allow machines to learn and make decisions. Two key areas within AI are machine learning (ML) and deep learning (DL), which focus on creating systems that get better at tasks over time by learning from data.
Machine Learning
ML is a way for computers to learn from data and experiences without needing to be explicitly programmed. It works by analyzing datasets and using algorithms to make predictions or classifications. The more data the system is exposed to, the more accurate its predictions become.
There are three main types of ML:
- Supervised learning: In this approach, the system is trained with labeled data—essentially examples with clear inputs and outputs. This helps the model learn to predict outcomes for new data based on what it has seen before.
- Unsupervised learning: Here, the system works with unlabeled data, finding patterns or relationships without any guidance. It’s often used for tasks like clustering and association.
- Reinforcement learning: This is a feedback-based system where the model learns by trial and error. It gets rewarded for correct actions and penalized for mistakes, gradually improving its decisions over time.
Popular ML techniques include decision trees, random forests, k-nearest neighbors (KNN), and support vector machines (SVM).1,2
Deep Learning
DL takes machine learning a step further by using deep neural networks (DNNs), which are designed to mimic the way the human brain processes information. These networks consist of layers—an input layer, multiple hidden layers, and an output layer—all working together to analyze data and make predictions.
Unlike traditional ML models with one or two hidden layers, DNNs can have hundreds, enabling them to tackle highly complex problems. This makes DL especially useful for tasks like image recognition and natural language processing, where systems need to understand intricate patterns in large datasets.
There are different types of deep neural networks, including feed-forward networks, recurrent neural networks (RNNs), and convolutional neural networks (CNNs). These models are particularly good at recognizing patterns, extracting features, and making sense of unstructured data.1,2
Generative AI: The Creative Side of AI
Generative AI is an exciting area of AI that focuses on creating new content, such as realistic images, videos, audio, or text, in response to user prompts. Built on ML and DL techniques, it enables systems to generate outputs that are both creative and highly detailed.
At its core, generative AI works by analyzing large datasets to create simplified representations of the information it processes. These representations are then used to synthesize new outputs that share similarities with the original data. For example, a generative AI model trained on images can produce entirely new visuals based on patterns it has learned.
The generative AI process typically involves three key phases:
- Training: Developing a foundation model, such as a large language model (LLM), by processing vast amounts of data to understand patterns and structures.
- Tuning: Customizing the foundation model for specific tasks using methods like fine-tuning or reinforcement learning with human feedback (RLHF).
- Generation, evaluation, and refinement: Producing outputs, evaluating their quality and relevance, and iteratively refining the model to improve its performance.
Generative AI has become a prominent tool for producing high-quality, original content, with applications in areas like art, content creation, and personalized marketing. Its ability to combine creativity with precision has made it one of the most impactful advancements in AI technology.1,4
The Relationship Between Artificial Intelligence and Machine Learning
Benefits of AI
AI offers many practical benefits that are making a real difference across industries. It can reduce physical risks, automate repetitive tasks, analyze data more efficiently, minimize human error, improve decision-making, and operate 24/7 without interruptions. These advantages are reflected in a wide range of real-world applications.
For example, AI is transforming customer service by powering chatbots and virtual assistants that provide quick, personalized support. It’s also helping businesses detect fraud by spotting patterns that might otherwise go unnoticed. In recruitment, AI streamlines hiring processes, while in marketing, it delivers tailored campaigns that resonate with individual customers. AI is also widely used in predictive maintenance, where it analyzes data to identify potential equipment failures before they happen.1,2
In healthcare, AI’s impact is especially significant. Medtronic, for instance, teamed up with IBM Watson to create the SugarIQ App, a tool designed to help diabetic patients monitor their blood sugar levels. The app doesn’t just track data; it offers personalized guidance on diet and healthy habits, making life easier for users.2
Similarly, GE Healthcare, in collaboration with NVIDIA, introduced AI-enhanced CT scans. These scans allow doctors to capture and analyze fine details in images that might be missed during traditional examinations, leading to more accurate diagnoses.2
AI’s ability to tackle complex tasks, adapt to individual needs, and provide insights in real-time is proving invaluable across industries, from healthcare to marketing to customer support. Its growing use is helping organizations work smarter, faster, and more effectively.
Challenges/Risks of AI
While AI provides many advantages, it also comes with challenges. Risks include:
- Data integrity issues: Bias, tampering, and cyberattacks can compromise the reliability of AI systems.
- Model vulnerabilities: Theft, manipulation, or reverse engineering can reduce accuracy and security.
- Ethical concerns: AI may unintentionally produce biased outcomes or violate privacy if not carefully designed and monitored.
Operational challenges, like model drift and governance failures, can also lead to system inefficiencies. Addressing these risks is critical for ensuring AI’s responsible use.1
The Future of AI
The future of AI is filled with opportunity. From improving healthcare and optimizing energy systems to driving innovation across industries, its potential is immense. However, realizing this requires a balanced approach. Developers must prioritize transparency and fairness, researchers need to push boundaries responsibly, and policymakers must establish regulations to safeguard ethical use.
Collaboration between industry, academia, and government will be critical to address challenges like bias and data privacy while amplifying AI’s benefits. With thoughtful implementation, AI can continue to transform industries, tackle global challenges, and improve lives responsibly.
References and Further Reading
- Kavlakoglu, E., Stryker, C. (2024). What is AI? [Online] Available at https://www.ibm.com/topics/artificial-intelligence (Accessed on 16 December 2024)
- Ghosh, M., Arunachalam, T. (2021). Introduction to Artificial Intelligence. Artificial Intelligence for Information Management: A Healthcare Perspective, 23-44. DOI: 10.1007/978-981-16-0415-7_2., https://www.researchgate.net/publication/351758474_Introduction_to_Artificial_Intelligence
- Monahan, J. Artificial Intelligence, Explained. [Online] Available at https://www.heinz.cmu.edu/media/2023/July/artificial-intelligence-explained (Accessed on 16 December 2024)
- Principles of Generative AI [Online] Available at https://www.cmu.edu/intelligentbusiness/expertise/genai-principles.pdf (Accessed on 16 December 2024)
Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.