Course outline:-
- Discuss the concept of artificial intelligence and its history.
- Why is the development of AI important?
- Discuss the various approaches to artificial intelligence development.
- Discuss the various types of artificial intelligence.
- What are the logic symbols in AI?
- Discuss the challenges and opportunities of artificial intelligence development.
Concept development of AI:-
The development of artificial intelligence (AI) is a science and technology that deals with the creation of intelligent agents. These systems can reason, learn, and act autonomously. It focused AI research on the creation of machines that can reason or understand and act on their own. The design of systems that can autonomously learn from experience.
Artificial intelligence (AI) is the intelligence of machines, and they relate it to the field of computer science. AI research deals with the question of how to create computers that are capable of intelligent behaviour.
In practical terms, it can deploy AI (artificial intelligence) applications in several ways, including:
- Robotics:- Robots are increasingly being used in manufacturing, healthcare and other settings, where they can carry out human tasks.
- Predictive analytics: This is a form of AI (artificial intelligence) that is used to make predictions about future events, trends, and behaviours.
- Machine learning: This form of AI (artificial intelligence) allows computers to learn from data without being explicitly programmed.
- Natural language processing: This is a form of AI (artificial intelligence) that enables computers to understand human language and respond in a way that is natural for humans.
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| Development of artificial intelligence |
Artificial Intelligence History.
The term “artificial intelligence” was first coined by computer scientist John McCarthy in '1955'. McCarthy defined AI as “the science and engineering of making intelligent machines.”
However, the history of the development of artificial intelligence dates back much further. Found one of the first examples of AI (artificial intelligence) in a Turing test proposed by British mathematician Alan Turing in '1950'. In '1943' Warren McCulloch was the first who introduced artificial neurons. The Turing test is a way of determining whether a machine is capable of intelligent behaviour.
In the decades that followed, AI (artificial intelligence) research made slow but steady progress. AI began to be used in the 1970s in practical applications, such as medical diagnosis and robot control. AI (artificial intelligence) experienced a boom with the advent of expert systems in the 1980s, which were designed to mimic the decision-making process of human experts.
The development of AI is important.
AI research is aimed at creating something that can match or exceed the cognitive abilities of human beings. We have described AI (artificial intelligence) as the "fourth industrial revolution" after the mechanisation of agriculture, the automotive industry, and the information technology industry.
artificial intelligence revenue and size comparison to 2018-2030. globally it will be a massive increase in productivity by AI. As per the market research, the market size of AI will expand by over one and a half trillion US dollars by 2024 and In 2030 the market will grow by about 1.5 trillion US dollars.
Various approaches to artificial intelligence development.
There are a few different approaches to developing artificial intelligence (AI), and each has its benefits and drawbacks. Here are a few of the most common approaches:
- The most common approach to AI development is reinforcement learning (RL). This method gives an agent feedback on its decisions and actions and can learn from this feedback to improve its performance in future situations. Train this AI (artificial intelligence) system using deep neural networks or other data-driven approaches.
- Another approach to AI development is machine learning (ML), which uses machine-learning algorithms to identify patterns in data sets that can be used to train an AI (artificial intelligence) system. ML systems can be trained using supervised learning or unsupervised learning techniques.
- Genetic algorithms are a type of genetic algorithm that uses an evolutionary process to generate new solutions for problems based on previous solutions given by other individuals or algorithms.
- Machine learning involves training a computer model by feeding it data sets and letting it learn from these experiences. A lot of machine learning systems use artificial neural networks as their underlying architecture because they're able to handle complex problems with relative ease.
Various types of Artificial Intelligence.
Artificial intelligence (AI) is the ability of a machine to act like a human being. AI (artificial intelligence) breaks down into four different types: reactive devices, limited memory, theory of mind and self-awareness.
1. Reactive Machines are computers that respond to input rather than predict it. They can react quickly to changes in their environment, but they cannot adapt to new situations or learn from previous mistakes.
2. Limited Memory Artificial Intelligence Systems have a limited amount of information stored in them at any given time. They can't learn anything new unless they are told what to do or shown something new.
3. Theory of Mind AI can recognize other entities and understand their intentions from their behaviour. For example, if you tell your phone "I'm going out for dinner," but don't tell it where you're going or who you're going with. It won't know where to go or who you're meeting with—it will just assume that "going out for dinner" means "going out for dinner."
4. Self-awareness means that a machine has enough knowledge about itself and its surroundings, such that it can make decisions based on past experiences, like being able to play chess after being taught how to do so by a human chess master!
The logic symbols in AI?
The logic symbols in artificial intelligence are:
▪ ∀ ▪ ∃ ▪ ∃ ▪ ∀
There are symbols which represent artificial intelligence. Logic is based on truth, falsehood, and the possibility of a truth being either true or false. They can also be used to describe a statement as a predicate or subject. The five most common logic symbols are, AND, OR, NOT, IF… THEN… ELSE and IF… THEN… ELSE.
Negation: The truth that something cannot be true.
Conjunction: That two or more things are true.
Disjunction: The truth that one or more things are true.
Implication: some other thing is true because the first thing is true.
Biconditionals: Two statements that are both true or both false (and so not replaced with their negations).
logical connectivity in AI
As machines become more capable of mimicking human thought processes, they will assist humans in many ways—but it's important to remember that this kind of intelligence also comes with challenges.
challenges:-
The most challenging is to assure that it deploys artificial intelligence systems to establish ethics and responsible. For example, we need to make sure that machines aren't used as weapons or tools against people without their consent (like autonomous weapons).
We also need to make sure that when we deploy AI (artificial intelligence) systems, whether it's for commercial purposes or national security—we consider how they might affect other parts of the world. Another challenge involves that these systems don't become too powerful, otherwise they might start thinking for themselves and make decisions without human input from time to time.
Artificial intelligence is the processing of information to perform tasks that usually require human intelligence, such as visual perception and judgment, speech recognition, decision making and translation. The development of artificial intelligence poses a number of challenges for society due to issues with data privacy, autonomous weapons systems and job loss.
Opportunities:-
Artificial Intelligence (AI) is fast becoming a ubiquitous feature of today’s society. From autonomous cars and delivery vans to smart homes and other products, AI will permeate our lives. As an entrepreneur, how do you tackle these challenges?
AI is being developed for a wide range of applications, including healthcare, education, security systems and financial services.
This technology has enormous potential for good—the potential to help us solve many of the world's biggest problems by advancing our understanding of human behaviour and helping us build better systems for both everyday tasks and complex challenges.
The opportunities in artificial intelligence include powering search engines, self-driving vehicles, and massive data integration.
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