AI basics
10 things you need to know about Artificial Intelligence
1. What is Artificial Intelligence
Artificial Intelligence (AI) has become a buzzword in recent years, but what exactly is AI?
Artificial Intelligence (AI) refers to the development of computer systems that can perform tasks that typically require human intelligence such as learning, reasoning, problem-solving, perception, and understanding language.
We explain the differences between Artificial General Intelligence and Artificial Narrow Intelligence and look at some of the real world applications of AI in our article : Understanding Artificial Intelligence: A Simple Guide
2. History of AI
The history of AI traces back to ancient myths, formally beginning in the 1950s with Turing's Turing Test and McCarthy's coining of "artificial intelligence" in 1955. Key developments include the Logic Theorist and ELIZA, breakthroughs in machine learning, and significant advancements like IBM's Deep Blue and AlphaGo's victories, illustrating AI's evolving capabilities. The 2020s have seen the rise of generative AI models like GPT-3, revolutionising content creation and sparking debates on ethics and privacy in AI's integration across industries.
Our article on A Short History of AI explores the fascinating journey of AI from 1950s to today and dives deeper into these important milestones.
3.Machine Learning and Rules Based Learning
Artificial intelligence and machine learning are two terms that are often used interchangeably, but they are actually quite different.
AI is an umbrella term that refers to the development of computer systems capable of performing tasks that would typically require human intelligence. Machine Learning (ML) and Rules Based Learning (RBL) are both subsets of AI.
Machine Learning focuses on enabling computers to learn and make decisions autonomously* without being explicitly programmed whereas rule-based systems use a set of predefined static rules.
Read our article : AI vs. ML vs. RBL: The differences between Artificial Intelligence and Machine Learning and Rules Based Learning
Our article AI Algorithms will help to explain algorithms and the difference between rules based and machine learning algorithms. An algorithm is a systematic set of instructions designed to perform a specific task or solve a particular problem.
4. Memory in Artificial Intelligence
Memory is essential for human intelligence, helping us to learn from past experiences and apply that knowledge to new situations. Similarly, memory is crucial for artificial intelligence because it enables it to learn from and adapt to its environment. By incorporating memory, AI can make more informed decisions, refine its understanding of the world, and evolve over time.
Read about the different types of memory in AI - replay, episodic, working and long term memory - in our blog Memory in Artificial Intelligence: The Key to Advanced Learning
5. Training an AI Model
The different methods of training an AI model depend on the task the model will be required to perform.
Traditional machine learning algorithms: these are statistical or mathematical models that learn patterns from data. Our article Training AI Systems - Learning Types explains the main ways an AI model can learn from data. These include :
- Supervised Learning: where AI models are trained on labelled datasets containing input-output pairs. AI Learning Types - Supervised Learning
- Unsupervised learning: where AI models are trained on unlabelled data without any input-output pairs. AI Learning Types - Unsupervised Learning
- Reinforcement Learning: where models learn by interacting and receiving feedback. AI Learning Types - Reinforcement Learning
Other learning paradigms: Other strategies that an AI model can use to enhance its learning process, often regardless of the learning type being used include: Transfer Learning, Multi-task Learning, Active Learning and Semi-Supervised Learning. Read about these types of learning in our article Advanced Training of AI Systems - Learning Paradigms
Deep learning algorithms: these are a subset of machine learning algorithms, but instead of relying on mathematical models, they mimic the human brain using artificial neural networks with multiple layers of interconnected nodes, hence the term "deep". AI Algorithms: Traditional Machine Learning vs. Deep Learning
6. Neural Networks
Neural networks are a type of Machine Learning model inspired by the human brain. Understanding Neural Networks in AI explains what neural networks are and how they are structured. It also explains the different types of neural networks, such as Feedforward, Convolutional, and Recurrent Neural Networks.
While crafting a neural network from scratch is feasible, it's often more practical to select a pre-trained one from libraries like Hugging Face and adapt it to your needs. Read our in dept article on creating and choosing a Neural Network.
It can be hard to understand Neural Networks and Overfitting so we've come up with an analogy based on a team of high school teachers to try and explain the concepts more clearly.
7. Natural Language Processing
Natural Language Processing (NLP) is a field within AI that deals with the interaction between computers and human languages. It enables computers to understand, interpret, and generate human language.
Blog article : Natural Language Processing: A key component of AI
8. AI Agents
AI agents are autonomous, intelligent entities that use machine learning and natural language processing to perform tasks, detect fraud, and provide customer service. Read more on our blog AI Agents.
Swarm intelligence groups of simple agents to collaboratively solve complex problems with remarkable efficiency and adaptability. Read more in our article Swarm Intelligence.
9. Applications of Artificial Intelligence.
Real-world AI applications, includeself-driving cars, virtual assistants, recommendation systems, image and speech recognition, and medical diagnosis. The following articles explore these applications in more detail:
AI in Formula One Racing; Accelerating Performance and Safety on the Track
Self Driving Cars - how Tesla is using AI to achieve autonomous driving
10. Ethics, Regulations and Challenges - the Future of AI
Ethical considerations and potential societal impacts of AI include job displacement and the need for responsible AI development.
Current challenges in AI include biases, privacy concerns, and the need for explainable AI models. The following articles explore AI ethics in education: AI Study Buddies: Revolutionizing Education or Crossing Copyright Boundaries? and healthcare: AI healthcare bias
Potential future advancements in AI include artificial general intelligence (AGI). It will be interesting to see the role AI will play in shaping our lives and society in the coming years : Understanding Intelligence in AI: From Narrow AI to Artificial General Intelligence
The regulatory approaches to AI across different regions are diverse and evolving. Our article AI Regulation in the UK, EU and US looks at the different approaches for implementing regulations.
*Autonomously = acting on its own or independently, especially without direct human control or intervention.
