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Goals of Artificial Intelligence

AI can be achieved by reading the behavior of humans and using the results to develop intelligent systems. For example, they learn, make decisions and act in certain situations. Observing humans while problem-solving in simple tasks and using its results to develop intelligent systems.

The overall research goal of artificial intelligence is to create technology that allows computers and machines to work intelligently. The general problem of simulating (or creating) intelligence is broken down into sub-problems.

The symptoms described below receive the most attention. These include special traits or abilities that researchers expect an intelligent system to exhibit. Eric Sandwell emphasizes planning and learning that is relevant and applicable to the given situation.

Goals of Artificial Intelligence
  • Logic, problem-solving: Early researchers developed algorithms that simulate humans' step-by-step reasoning when solving puzzles or making logical deductions. By the late 1980s and 1990s, AI research had developed methods for dealing with uncertain or incomplete information, employing concepts from probability and economics. For difficult problems, algorithms can require enormous computational resources-most experience a "combinatorial explosion": the amount of memory or computer time needed for problems of a certain size becomes astronomical. The search for more efficient problem-solving algorithms is a high priority.
  • Knowledge representation: Knowledge representation and knowledge engineering are central to AI research. Many of the problems that machines are expected to solve will require extensive world knowledge. The things AI needs to represent are objects, properties, categories, and relationships between objects; situations, events, states, and times; Cause and Effect; Knowledge about knowledge (what other people know about what we know); and many other, less well-researched domains.
    A representation of "what exists" is an ontology: the set of objects, relations, concepts, and so on about which the machine knows. The most general is upper ontology, which attempts to provide a foundation for all other knowledge.
  • Planning: Intelligent agents must be able to set goals and achieve them. They need a way to envision the future - a representation of the state of the world and make predictions about how their actions will change it - and be able to make choices that maximize the utility (or "value") of the options available.
    In classical planning problems, the agent can assume that it is the only system acting in the world, allowing the agent to be certain of the consequences of its actions.
    However, if the agent is not the only actor, it requires that the agent reason under uncertainty. It calls for an agent to assess its environment, make predictions, evaluate its predictions, and adapt based on its assessment.
  • Learning: Machine learning, a fundamental concept of AI research since the field's inception, is the study of computer algorithms that automatically improve through experience. Unsupervised learning is the ability to find patterns in a stream of input. Supervised learning includes both classification and numerical regression. After seeing several examples of things from several categories, classification is used to determine which category something falls into. Regression attempts to construct a function that describes the relationship between inputs and outputs and predicts how the outputs should change as the inputs change.
  • Social Intelligence: Effective computing is the study and development of systems that can detect, interpret, process, and simulate human It is an interdisciplinary field spanning computer science, psychology, and cognitive science. While the origins of the field can be traced to early philosophical inquiries into emotion, the more modern branch of computer science originated from Rosalind Picard's 1995 paper on "effective computing".
  • Creativity: A sub-field of AI addresses creativity theoretically (philosophical, psychological perspective) and practically (the specific implementation of systems that produce novel and useful outputs). Some related areas of computational research include artificial intuition and artificial thinking.
  • General Intelligence: Many researchers think that their work will eventually result in a machine with artificial general intelligence, combining all the skills described above and exceeding human capacity in most or all of these areas. Some believe that such a project may require anthropomorphic features such as artificial consciousness or an artificial brain.

Methods of Artificial Intelligence

After defining Artificial Intelligence, let us know about the philosophical methods lying at its core. Every research about AI falls into one of the following two categories:

  • Symbolic Method: Also known as the "top-down" approach, the symbolic method simulates intelligence without considering the biological structure of the human brain. As the name suggests, this method analyzes the thought process of the human brain by processing symbols.
  • Connectionist Method: On the other hand, the connectionist approach deals with building neural networks by imitating the biological structure of the human brain. Also known as the "bottom-up" approach, this method mobilizes more fundamental brain cells.

Both these methods compete for the approach to developing AI systems and algorithms. Although they may appear similar, they differ in their principle. Whereas the "top-down" approach focuses on symbolic details, the "bottom-up" approach considers neural activities inside the brain. We can highlight the difference between these two approaches with an example. Consider a robot that recognizes numbers through image processing.

The symbolic approach would be to write an algorithm based on the geometric pattern of each number. The program will compare and match numeric patterns of different numbers stored in its memory.

The robot would train its artificial neural network by repeatedly tuning it to recognize numbers in the connectionist approach. In a way, The Connectionist approach more closely emulates the human mind and its thought process than the symbolic approach.

Researchers use both these methods of AI implementation when developing algorithms. While the symbolic approach is famous for simple problems, researchers prefer the connectionist method for complex, real-world problems. Despite showing immense potential, both of these approaches have produced limited results.

In addition to these two major classifications, researchers have coined several approaches to implementing AI.

  • Logic-based AI uses formal logic to represent knowledge, planning, and learning in the human mind. Rather than imitating human thought, this approach focuses on determining the basis for logical reasoning and abstract thinking.
  • Anti-logic AI: Some researchers argue that it is impossible to capture every aspect of human behavior using simple general logic. Rather than using simple logic, the anti-logic approach deals with ad hoc solutions for machine learning and vision processing.
  • Knowledge-Based AI: With important memories computers becoming available around the 1970s, people began to add AI applications. As a result, systems architecture incorporated facts and rules to depict algorithms in their systems.
  • Statistical learning: In recent years, researchers worldwide have combined advanced mathematical and statistical models such as information theory, decision theory, etc., to develop AI algorithms. This approach has resulted in greater accuracy and reproducibility in data mining.

Application areas of artificial intelligence

Modern AI-based technologies are relevant in any intelligent task, and the list of its applications continues to grow significantly. Let's take a quick look at some of them.

  • Healthcare: Artificial intelligence has proven to be a life-saving aid in healthcare. For example, the AI algorithm in a smartwatch monitors a person's vital activity to detect heart problems and even alerts emergency services. Additionally, AI has helped increase the speed and accuracy of drugs.
  • Financial Services: Financial services have benefited from AI for the past three decades. AI is useful for bookkeeping management, stock forecasting, and even fraud prevention in banking. Large enterprises use AI to process and audit transactions under strict compliance.
  • Retail Sector: In the retail business, AI algorithms can combat supply-chain problems by managing inventory. Apart from predicting future trends in the apparel business, AI can also help forecast demand and enhance customer experience through real-time data analysis.
  • Automobile Industry: An important example of using AI is the automobile industry. An autonomous or self-driving car is the latest research area, and every carmaker is investing heavily in it. Several carmakers have already used AI features such as voice-control, lane-switch, collision-detection, and improved driver safety.

AI as a Rewarding Career Possibility

We hope that the brief introduction to Artificial Intelligence in this blog has given you a taste of its technology and capabilities, as you must have understood by now that AI opens up an ocean of opportunities for your career. By visiting the Indian career portal, you can know about various courses and job opportunities to make a successful career in AI.

Benefits of Artificial Intelligence

  • Artificial intelligence is difficult for beginners, yet it offers great opportunities to develop intelligent machines that can turn computer science on its head.
  • Reduce human errors and perform various tasks with greater efficiency using intelligent systems.
  • Intelligent systems can perform challenging tasks that are beyond human reach. Foreign currency; Explore the ocean and perform various difficult, painstaking tasks with ease.
  • A lot of applications have been developed using Artificial Intelligence. iPhones, Siri and Microsoft Cortana, evolved on the phenomenon of artificial intelligence. These are interactive robots that help you access the smartphone.
  • With the help of artificial intelligence technology, a digital assistant can be developed, which reduces the number of employees. These assistants can do the job with amazing efficiency.
  • Radiosurgery has been used in the medical field for tumor treatments developed using artificial intelligence.
  • Use artificial intelligence and improve your products' productivity, efficiency, and accuracy.

Disadvantages of Artificial Intelligence

Artificial intelligence looks promising, and it is quite futuristic. It is gradually being implemented in many areas. There are many drawbacks of Artificial Intelligence which are;

Artificial Intelligence is slowly making its way into real-time applications. AI offers a lot of possibilities, but it is really expensive. Smaller organizations cannot afford the high-end machines, softwares, and resources required to implement AI.

Artificial intelligence systems can replace humans in performing tasks in terms of productivity, but they cannot make decisions. Robots cannot decide what is right and what is wrong.

With intelligent systems, you won't get creative with everyday experience. Human beings display creative ideas with everyday experience.

Replacing humans with intelligent systems can increase unemployment which leads to poor GDP.

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