Artificial Intelligence Assignment Help
Artificial intelligence is a branch of computer science that deals with building smart machines. These machines are capable of performing tasks that need human intelligence. Also, AI can be defined as an interdisciplinary science with multiple approaches. However deep learning and machine learning are creating a paradigm shift in virtually all sectors of the tech industry. Our artificial intelligence assignment help handles all concepts related to this area. Hire our online artificial intelligence tutors if you need professional help with your assignment.
Can Machines Think?
Mathematician Alan Turing changed history a second time in 1950 with a simple question, “can machines think?” This was less than a decade after he had broken the Nazi encryption machine Enigma and helped the Allied forces win World War II. Turing established a fundamental goal and vision of artificial intelligence in his paper "Computing Machinery and Intelligence "(1950). Artificial Intelligence at its core aims to answer Turing's question in the affirmative. In other words, it endeavors to simulate or replicate human intelligence in machines. No singular definition of this field is accepted because the expansive goal of AI has given rise to several debates and questions. Defining AI as simply building intelligent machines is limited. It doesn't explain what AI is and what makes machines intelligent.
Authors Peter Norvig and Stuart Russell in their groundbreaking book, Artificial Intelligence: A Modern Approach, unify their work around the theme of intelligent agents in machines. The textbook defines AI as “the study of agents that receives percepts from the environment and performs various actions.”
The field of AI has been historically defined by four different approaches:
· Thinking rationally
· Thinking humanly
· Acting rationally
· Acting humanly
Russell and Norvig explore these four approaches in their book. Thinking rationally and thinking humanly concern thought process and reasoning. On the other hand, acting rationally and acting humanly deal with the behavior. The main focus of Russell and Norvig is rational agents that act to achieve the best outcome. They also say that an agent is allowed to act rationally by the skills needed for the Turing Test.
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Programming in AI majors on three cognitive skills:
· Learning processes
It is an aspect that deals with acquiring data and creating rules on how to turn the data into actionable information. The rules are known as algorithms. They provide step-by-step instructions to computing devices on how to complete a specific task.
· Reasoning process
It is an aspect that involves choosing the right algorithm to reach the desired outcome.
This aspect deals with continually fine-tuning algorithms to ensure that they lead to the most accurate results possible.
How is Artificial Intelligence used?
There are two broad categories of artificial intelligence:
· Narrow AI
This category is sometimes referred to as weak AI. Narrow AI is a simulation of human intelligence and operates within a limited context. It focuses on performing a single task perfectly well. Although these machines may seem intelligent, they operate under far more constraints and limitations than even the most basic human intelligence.
To date, narrow artificial intelligence is easily the most successful realization of AI. Its focus on performing specific tasks has enabled it to experience several breakthroughs in the last decades. These success stories have had significant societal benefits that have contributed to the economic vitality of nations.
Some of the examples of Narrow AI are:
· Google search
· Software for image recognition
· IBM’s Watson
· Self-driving cars
· Artificial General Intelligence (AGI)
Artificial general intelligence is sometimes called Strong AI. It is the category exhibited in movies, like the data from Star Trek: The Next Generation, the robots in Will Smith’s classic I Robot, and the robots from Westworld. AGI machines possess general intelligence. They are much like a human being and can apply their intelligence to solve any problem.
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Examples of artificial Intelligence
· Mobile phones smart assistants like Siri and Alexa
· Prediction and disease mapping tools
· Drone robots
· Personalized and optimized healthcare treatment recommendations
· Stock trading’s Robo-advisors
· Conversational bots used in marketing and customer service
· Spam filters on emails
· Netflix’s TV shows recommendations
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Machine Learning and Deep Learning
According to our professionals, most students find it confusing to understand the difference between machine learning, deep learning, and artificial intelligence. If you are struggling with distinguishing these topics then you can use Venture Capitalist Frank Chen's overview: "Artificial intelligence refers to a set of algorithms and intelligence that tries to mimic human intelligence. Machine learning is one of the intelligence used and deep learning is a machine learning technique."
In simple terms, machine learning uses statistical techniques to help a computer it has fed with data to learn and progressively get better at a task. The computer does not have to be specifically programmed for that task. Machine learning, therefore, eliminates the need for millions of lines of written code. It also consists of supervised (using labeled data sets) and unsupervised learning (using unlabeled data sets).
Deep learning is a technique in machine learning. It runs inputs through a biologically inspired neural network architecture. The data is processed through several hidden layers contained in the neural network. This allows the machine to go deep in its learning, weighing inputs and making connections for the best results. Get our help with artificial intelligence assignments if you need explicit definitions of these two confusing topics.
Types of Artificial Intelligence
Artificial Intelligence can be categorized into four types. We start with the task-specific intelligent systems in wide use and progress to sentient systems, which are not in existence. Here are the categories:
· Reactive Machines
These systems are task-specific but have no memory. An example is the IBM chess program (Deep Blue) that beat Garry Kasparov in the 90s. Although Deep Blue could identify pieces on the chessboard and make predictions, it cannot make informed future decisions based on past experiences.
· Limited Memory
These systems have memory. As a result, they can use their past experiences to make informed future decisions. These machines have inspired some of the decision-making functions in self-driving cars.
· Theory of mind
When applied in AI, the theory of mind becomes a psychological term. It means that the systems in this category have the social intelligence to understand emotions. This skill is necessary for AI systems to become integral members of human teams. AI systems built using the theory of the mind can infer human intentions and predict behavior.
AI systems in this category possess a sense of self. This gives them consciousness. In other words, machines built using this concept understand the theory's current state. Machines built using this type of AI do not exist yet.
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· AI in business
Several companies are integrating machine learning algorithms into analytics and customer relationship management platforms. Doing this has helped them uncover information on how to serve their customers better. Also, chatbots have been incorporated into websites and applications to offer customers immediate service.
· AI in education
Educators now have more time thanks to the automation of the grading system which can now assess students and adapt to their needs. This means that educators can now work at their own pace. Also, AI tutors can ensure that students stay on track by providing additional support. AI has changed how and where students learn. They have even replaced some teachers.
· AI in finance
Artificial intelligence has been integrated into personal finance applications like Intuit Mint and TurboTax. The applications can collect personal data and offer financial advice. Also, programs such as IBM's Watson have helped in the process of buying a home. Additionally, artificial intelligence tools today handle much of the trading on Wall Street.
· AI in law
In law, sifting through documents is often overwhelming. The automation of the legal industry’s labor-intensive processes using AI has improved client service and saved a lot of time. Also, machine learning is used by law firms to describe data and predict outcomes.
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