AI.pdf
- 1. BAHIRDAR UNIVERSITY BAHIRDAR INSTITUTE OF TECHNOLOGY FACULITY OF ELECTRICAL AND COMPUTER ENGINEERING DEPARTMENT OF COMPUTER ENGINEERING SECTION B ARTIFICIAL INTELLIGENT GROUP ASSIGNMENT Name ID No. 1. Getaye Aysheshim 1010919 2. 3. 4. 5. Submit to Asaminew Submit date 12/03/2022
- 2. 1 What is artificial intelligence? Artificial intelligence is the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. In other word Artificial Intelligence (AI) is the branch of computer sciences that emphasizes the development of intelligence machines, thinking and working like humans. For example, speech recognition, problem-solving, learning and planning. Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving. Types of Artificial Intelligence AI types are categorized according to their capacity and functionality. Based on capacity or ability: A. Artificial Narrow Intelligence(ANI) It has a narrow range of abilities. Artificial Narrow Intelligence is also referred to as Narrow AI or weak AI. it is the only type of AI that is achieved to date. Weak AI or Narrow AI is specifically goal-oriented, that is used to designed to perform singular tasks such as face recognition, driving car, speech recognition/voice assistants, or browsing the Internet. It is very quick and accurate at completing the specified task for which it is programmed to do. It is referred to as weak AI because though the machines look intelligent but they function under a narrow set of constraints and limitations. Narrow AI cannot replicate human intelligence, it just simulates human behavior which is based on a narrow range of contexts and parameters or factors. Narrow AI makes use of Natural Language Processing(NLP) to perform tasks, which means that it understands text and speech in natural language and it is programmed to communicate with humans in a personalized manner. The examples of narrow AI include Rankbrain by Google, manufacturing and drone robots, IBM’s Watson, Siri by Apple, Alexa by Amazon, Cortana by Microsoft, and other virtual assistants. It also includes disease mapping and prediction tools, image/facial recognition software, recommendations that are based on listen, watch, purchase history, etc. B. Artificial General Intelligence(AGI) It has capabilities as in humans. Also referred to as Strong AI or Deep AI. AGI is a concept of a machine that possesses general intelligence that can mimic human behavior or intelligence and is capable of learning and applying this knowledge to solve any problem. AGI is capable of thinking, understanding, and acting in a way that is identical to humans in any given scenario.
- 3. 2 AGI is not yet achieved by AI researchers and scientists. To achieve AGI, they need to program all the cognitive abilities into the machine. Machines are required to take up experiential learning to the higher levels and apart from improving efficiency in singular tasks, they also need to be capable of applying knowledge to a wider area of solving problems. Strong AI makes use of the theory of mind AI framework. It is the ability to anticipate the needs, beliefs, emotions, thought processes of other intelligent entities. It focuses not on simulation or replication, but on truly understanding humans. The example of strong AI includes Fujitsu-built K, a supercomputer, which is one of the most remarkable attempts towards achieving strong AI, but considering some disappointing factors, it is not easy to say that strong AI can be achieved in near future. C. Artificial Super Intelligence(ASI) It has capability more than that of humans. Apart from understanding human behavior, AI where machines surpass the capability of human intelligence and become self-aware is the area that is called Artificial Super Intelligence. The concept of ASI includes evoking emotions, requirements, beliefs, and impulses of its own. Besides replicating human intelligence, it is anticipated that ASI would theoretically be far better than humans in areas like math, sports, science, medicine, art, hobbies, emotional relationships, or simply everything. If such super intelligent machines came into existence, it will impact humanity, our survival, or our lifestyle. Based on functionality: A. Reactive Machines Reactive Machines paved the way for the field of AI. It is the oldest of the four types and served as the foundation for ‘Conditional Intelligence’. Reactive Machines deal with a simple set of behaviors that run in response to the environment. They are unable to draw conclusions about their future actions based on the data. Simply put, it is a complex network of nested if-else cases that does not learn from past experiences. It simply responds to the settings that are provided. Deep Blue, the famous IBM Chess programme that defeated Garry Kasparov, is an example of Reactive AI. Alpha Go is example of a reactive machine in Google. B. Limited Memory Limited memory machines are purely reactive machines with the ability to learn from historical data and make decisions. In layman's terms, people with limited memory have a small memory that they can use to make observations and judge a situation before responding. As previously stated, the only capability that distinguishes it from reactive machines is its ability to learn.
- 4. 3 Algorithms use prior knowledge to understand a situation and respond appropriately to it.They are trained, like all modern AI systems, by massive amounts of data that are stored in their memory to form a reference model for problem-solving. To teach image recognition AI to name objects it scans, for example, it is trained on thousands of pictures with labels. As a result, when scanning an image, it uses the training images as references to understand the contents of the image. It labels new images with increasing accuracy over time based on its learning experience. C. Theory of Mind The first two types of AI that we saw are common, but the next two are either a concept or a work in progress. The Theory of Mind, according to many researchers, will be the next big breakthrough. Theory of mind will be able to understand and have a point of view, just like a human being. Not only that, but it will be able to express emotions as well.The theory of mind is the next level of AI systems being researched right now. If it ever exists, it will be the most similar to human behaviour. By deducing the thought processes, needs, beliefs, and emotions of the entities with which it interacts, this AI will be able to better understand them. To achieve Theory of Mind, other branches of AI must be developed as well. Artificial emotional intelligence, for example, is a burgeoning industry for leading AI researchers. This is because AI machines will need to perceive humans as individuals whose minds can be shaped by a variety of factors in order to understand human needs. D. Self-Awareness Most technocrats are afraid of the state of AI known as self-awareness. But, for the time being, it is only a speculative possibility. Self-aware AI, which is self-explanatory, is an AI that evolved to be so similar to the human brain that it develops self-awareness. Self-aware AI is capable of having ideas like self-preservation, which could directly or indirectly spell the end of humanity. In simple terms, a Self-Aware AI system will have complete access and understanding of its own. This type of AI will be an exact replica of human intelligence. This type of AI is centuries away from becoming a reality, but it will always be the ultimate goal of all AI research. However, if we are successful in developing this AI system, it will have its own needs and even beliefs. For example, it may have likes and dislikes, as well as human-like characteristics such as stubbornness. This is the kind of AI you might have seen in sci- fi movies.
- 5. 4 Machine Learning Machine learning is the use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyse and draw inferences from patterns in data. Machine learning is a sub field of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior. Artificial intelligence systems are used to perform complex tasks in a way that is similar to how humans solve problems. Machine learning is about computers being able to perform tasks without being explicitly programme, but the computers still think and act like machines. Their ability to perform some complex tasks still falls far short of what humans are capable of. The function of a machine learning system can be descriptive, meaning that the system uses the data to explain what happened; predictive, meaning the system uses the data to predict what will happen; or prescriptive, meaning the system will use the data to make suggestions about what action to take,” the researchers wrote. Machine learning mechanisms are classified into the following groups. Such as: A. Supervised machine learning models are trained with labeled data sets, which allow the models to learn and grow more accurate over time. For example, an algorithm would be trained with pictures of dogs and other things, all labeled by humans, and the machine would learn ways to identify pictures of dogs on its own. Supervised machine learning is the most common type used today. B. In unsupervised machine learning, a program looks for patterns in unlabeled data. Unsupervised machine learning can find patterns or trends that people aren’t explicitly looking for. For example, an unsupervised machine learning program could look through online sales data and identify different types of clients making purchases. C. Reinforcement machine learning trains machines through trial and error to take the best action by establishing a reward system. Reinforcement learning can train models to play games or train autonomous vehicles to drive by telling the machine when it made the right decisions, which helps it learn over time what actions it should take. D. Deep learning Deep learning introduce an extremely sophisticated approach to machine learning and are set to tackle these challenges because they've been specifically modeled after the human brain. Deep learning is modeled on the way that human brain works and powers many machine learning uses, like autonomous vehicles, chat-bots, and medical diagnostics.
- 6. 5 Deep learning algorithms make these new goals to reachable and practically function-able like Convolution Neural Networks, Recurrent Neural Networks. Natural language processing Natural language processing is a field of machine learning in which machines learn to understand natural language as spoken and written by humans, instead of the data and numbers normally used to program computers. This allows machines to recognize language, understand it, and respond to it, as well as create new text and translate between languages. Natural language processing enables familiar technology like chat-bots and digital assistants like Siri or Alexa.