6.3.2 | ARTIFICIAL INTELLIGENCE

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6.3.2 Describe the main characteristics of AI as the collection of data and the rules for using that data, the ability to reason, and it can include the ability to learn and adapt.

ALSO IN THE TOPIC
 6.1.1 SENSORS, MICROPROCESSORS AND ACTUATORS
6.1.2 AUTOMATED SYSTEMS IN ACTION
6.2.1 ROBOTICS
6.2.2 CHARACTERISTICS OF A ROBOT
 6.2.3 ROBOT ROLES
 6.3.1 WHAT IS AI
YOU ARE HERE | ​6.3.2 CHARACTERISTICS OF AI
​6.3.3 OPERATIONS AND CONTROLS OF AI
AUTOMATED SYSTEMS TERMINOLOGY
AUTOMATED SYSTEMS ANSWERS

CHARACTERISTICS OF AI

AI is a collection of data, rules for using that data, the ability to reason, and it can include the ability to learn and adapt.

• Data Collection and Usage | AI systems collect and analyze vast amounts of data. This data can come from various sources and in different formats. The collected data is then used to make predictions, recognize patterns, and make decisions. The rules for using this data are often defined by algorithms developed by data scientists.
• Reasoning | AI has the ability to reason, or the capability to solve problems through logical deduction. It can analyse the relationships between various elements in a dataset, identify patterns, and use this understanding to make informed decisions or predictions.
• Learning and Adaptation | One of the most powerful aspects of AI is its ability to learn and adapt over time. This is often achieved through machine learning, a subset of AI that involves training an algorithm on a dataset and then using that trained algorithm to make predictions on new, unseen data. As more data is collected and analysed, the AI system can refine its algorithms, improving its performance over time.

In essence, AI combines these characteristics to mimic human intelligence, enabling it to perform complex tasks that traditionally required human intellect.

SECTION 1 | CLASSIFYING AI

Artificial Intelligence (AI) can be classified into four types: Narrow AI, General AI, Strong AI, and Superintelligent AI. Here’s a brief description of each:

• Narrow AI | Also known as Weak AI, this type of AI is designed to perform a narrow task, such as voice recognition or driving a car. It operates under a limited set of constraints and is very good at the specific tasks it’s designed for. However, it can’t perform tasks outside of its specific domain.
• General AI | This type of AI can understand, learn, and apply knowledge across a broad range of tasks at the level of a human being. It has the ability to transfer knowledge from one domain to another and learn from experience. However, General AI doesn’t exist yet.
• Strong AI | This is an extension of General AI. Strong AI not only simulates human intelligence but also experiences consciousness. It understands, learns, and applies knowledge just like a human and has emotions and self-awareness. Like General AI, Strong AI doesn’t exist yet.
• Superintelligent AI | This type of AI surpasses human intelligence across most economically valuable work. It’s an intellect that is much smarter than the best human brains in practically every field, including scientific creativity, general wisdom, and social skills. Superintelligent AI is currently theoretical and doesn’t exist yet.

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Each type represents a different level of capability, complexity, and potential impact on our world.

SECTION 3 | REASONING

Reasoning in AI refers to the process by which a machine makes decisions or draws conclusions based on the information it has been given. It’s a key aspect of AI that enables systems to solve problems, make predictions, and perform tasks that would normally require human intelligence.

There are two main types of reasoning in AI:

• Deductive Reasoning | This is a type of reasoning where conclusions are reached by applying general rules to specific situations. If the general rules are true and the specific situation fits within those rules, then the conclusion must be true. For example, if we know that “All apples are fruits” (general rule) and “Granny Smith is an apple” (specific situation), we can deduce that “Granny Smith is a fruit” (conclusion).
• Inductive Reasoning | This is the opposite of deductive reasoning. It involves making broad generalizations based on specific observations. For instance, if we observe that the sun has risen every day for as long as we can remember (specific observations), we might conclude that “The sun will rise every day” (broad generalization).

In AI systems, reasoning is often achieved through algorithms that can process information and make decisions based on predefined rules or patterns in the data. These algorithms can range from simple decision trees to complex neural networks.

Remember, reasoning in AI is not about understanding or consciousness, but about processing information in a way that mimics human thought processes.

SECTION 4 | EXAMPLES OF AI

Here are some present-day instances of AI and what the future might hold:

PRESENT DAY 

• Interactive Chatbots | Advanced AI-driven chatbots, such as ChatGPT, are evolving to produce human-like interactions. They can craft articles, generate intricate code, spin engaging tales, provide culinary recipes, and address a myriad of queries.
• Enhanced Search Engines | Microsoft has infused Bing with AI capabilities, enabling it to deliver more context-aware answers.
• Digital Assistants | Everyday AI tools like Siri, Alexa, and Cortana assist users in accomplishing a range of tasks.
• Social Media Personalization | AI algorithms curate your social media content, tailoring what appears on your timeline based on your interactions.
• Email Enhancements |Solutions like Grammarly and automated spell checkers employ AI and natural language processing to ensure your emails are polished and clear.

ANTICIPATED ADVANCEMENTS

• Intelligent Robotics | Modern robots, powered by AI, can problem-solve and demonstrate rudimentary cognitive abilities. For instance, iRobot's Roomba uses AI to map rooms, detect obstacles, and recall optimal cleaning paths.
• Customised Education | AI promises tailored educational experiences for learners, adapting to individual needs.
• AI in Cybersecurity | Machine learning offers rapid and efficient solutions to bolster cybersecurity.
• Fashion Innovations | AI aids fashion brands in crafting attire that aligns more closely with individual preferences and fits.
• Healthcare Efficiency | AI streamlines various healthcare operations, enhancing patient care and administrative tasks.
• Driverless Cars | The evolution of AI is driving the growth of autonomous vehicles, making them safer and more prevalent.

These instances underscore the pervasive nature of AI in our current routines and hint at its growing significance in the coming years.

ALSO IN THIS TOPIC

 6.1.1 SENSORS, MICROPROCESSORS AND ACTUATORS
6.1.2 AUTOMATED SYSTEMS IN ACTION
6.2.1 ROBOTICS
6.2.2 CHARACTERISTICS OF A ROBOT
 6.2.3 ROBOT ROLES
 6.3.1 WHAT IS AI
​6.3.2 CHARACTERISTICS OF AI
6.3.3 OPERATIONS AND CONTROLS OF AI
AUTOMATED SYSTEMS TERMINOLOGY
AUTOMATED SYSTEMS ANSWERS