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Foundations of AI

[AI Technologies - Legal Executive Institute]

- The Foundations of Artificial Intelligence

The foundations of artificial intelligence (AI) are the fundamental areas that AI sits at the intersection of: logic, computation, probability, programming, data science, mathematical reasoning, creative problem solving, ethics, applied experiences. 

The four basic concepts of AI are: machine learning, natural language processing, computer vision, and robotics. The Foundations of AI focuses on developing algorithms that use data and and statistical tools to solve complex human tasks.

AI is an effective tool that enables machines to learn from experience, adapt to new changes, and perform tasks like humans. It is the ability to design intelligent machines or develop software applications that can teach themselves and imitate the characteristics of the human mind with the help of reasoning, sensory application, planning, optimal decision-making and problem-solving techniques. 

The potential of AI to perform human actions with the help of knowledge discovery has attracted special attention from the research community and top enterprises, and this field has witnessed the greatest growth in the past two decades compared to any other technology. 


- Four Approaches To AI

AI simulates human intelligence by relying on algorithms to understand human goals or methods to achieve those goals. It establishes relationships between goal seeking, data processing, and acquisition to better understand goals. With that in mind, here are four approaches to artificial intelligence.

  • Acting humanity - When a computer behaves exactly like a human and it is difficult to tell the difference between the two through the use of technologies such as natural language processing, automated reasoning, machine learning and automated reasoning. Known as the Imitation Game, the Turing Test determines whether a machine can demonstrate human intelligence without any physical contact. 
  • Thinking Humanly - Computers think like humans and perform tasks typically performed by human intelligence, such as driving a car. Cognitive modeling methods are methods of determining how humans think and are based on three techniques—introspection, psychometric testing, and brain imaging. This humanistic thinking is also used in psychology and healthcare to create realistic simulations when needed. 
  • Thinking Rationally - The classic study of how humans think, using standards to help develop guidelines for human behavior. Humans are considered rational (reasonable, sensible, possessing good judgment), whereas computers think rationally and solve problems logically based on recorded actions. In other words, solving a specific problem is very different from actually solving the problem, which the computer needs to perform with the help of rational thinking. 
  • Acting Rationally – The study of how humans act in uncertainty or complexity relies entirely on rational agents. Like rational thinking, action depends on conditions, environmental factors, and available data to maximize the expected value of its performance. It often relies on a black box or engineering approach to successfully achieve its goals. 


- Defining AI: Reasoning, Interaction, and Learning

Since its inception in 1956, artificial intelligence (AI) has come a long way and has become an integral part of our everyday lives. Today, this technology is used by various software and machines to perform assessment, prediction, problem solving and many other tasks. 

And that's not all, organizations around the world are investing in technology to make their operations faster, smarter, more competitive and more efficient. However, to perform these tasks, AI uses three key components:

  • Reasoning: Inference allows AI techniques to extract key information from large amounts of structured and unstructured data, perform cluster analysis, and use statistical reasoning in ways that are beginning to approach human cognition.
  • Interaction: Interaction allows AI technologies to see using computer vision, communicate using conversational AI, and read using computational linguistics, again approaching the level of human cognition.
  • Learning: What really separates AI from intelligent automation, however, is the technology's ability to learn and become smarter over time. Only AI has this third dimension. One of the reasons AI is so promising is because it changes the paradigm of how we write software code. Instead of being programmed in every "if and then except" condition, essentially telling the computing engine what to do, the AI is telling the computing engine to learn and figure it out on its own. By doing this, AI solves last-mile problems that traditional software programming can never solve.


Each intersects with each other and allows AI machines and systems to perform tasks with accuracy and speed comparable to humans.


- Research AI Vs. Applied AI

Research AI focuses on the development and research of intelligent systems. Applied AI focuses on the practical applications of these systems to solve real-world problems.

Applied AI uses many of the same ML methods as traditional AI research. The key difference, however, is that applied AI focuses on outputs and solutions rather than its internal processes or how well it reflects human capabilities.

Applied AI often aims to create tools or systems that people can use to improve efficiency, accuracy, and decision-making. 

Here are some other differences between research AI and applied AI:

  • Researchers vs. applied AI engineers: Researchers modify equations to formulate machine learning algorithms, while applied AI engineers modify algorithms and data to achieve outputs.
  • Theoretical machine learning vs. applied machine learning: Theoretical machine learning involves the learning and research of new algorithms, while applied machine learning involves the construction of data products or the use of algorithms in data science pipelines.
[Vienna, Austria - Civil Engineering Discoveries]

- Data Science and Artificial Intelligence (AI)

Although both Data Science and AI fall in the same category and are inter-related, they are not the same. These are two popular and most sought-after technologies that have their own sets of concepts and applications. 

Data science is a broad field of study pertaining to data systems and processes, aimed at maintaining data sets and deriving meaning out of them. Data scientists use a combination of tools, applications, principles and algorithms to make sense of random data clusters. 

Since almost all kinds of organizations today are generating exponential amounts of data around the world, it becomes difficult to monitor and store this data. Data science focuses on data modelling and data warehousing to track the ever-growing data set. 

The information extracted through data science applications are used to guide business processes and reach organisational goals. Data Science is a reigning field in the IT industry and has conquered almost every industry today. Data science aims to work on these datasets to derive valuable information from them. In this domain, data acts as the fuel that helps in extracting useful and meaningful insights regarding companies and in identifying the current market trends.


- The Main Research Goals of AI

The field of artificial intelligence (AI) is concerned with methods of developing systems that display aspects of intelligent behaviour. These systems are designed to imitate the human capabilities of thinking and sensing. The goal of AI is to develop computers that can think, see, hear, walk, talk and feel. 

AI is a broad multidisciplinary area drawing from computer science, neuroscience and cognitive science, linguistics, statistics, applied mathematics and many other areas of research. There are four goals to pursue in AI: systems that think like humans, systems that think rationally, systems that act like humans and systems that act rationally.

In the field of AI, computer science is being leveraged to create intelligent machines that more closely resemble humans in their functions. The knowledge engineering that allows computers to simulate human perception, learning, and decision-making is based on access to categories, properties, and relationships between various information sets.    

Five AI technologies that you need to know. From machine learning to computer vision, these technologies are fueling the AI craze. They are: Artificial Intelligence, Machine Learning, Deep Learning, Natural Language Processing, and Computer Vision.


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