The Future: Artificial Intelligence

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans.

The field of AI research was born at a workshop at Dartmouth College in 1956 where attendees Allen Newell (CMU), Herbert Simon (CMU), John McCarthy (MIT), Marvin Minsky (MIT) and Arthur Samuel (IBM) became the founders and leaders of AI research. They and their students produced programs that the press described as “astonishing”: computers were learning checkers strategies, solving word problems in algebra, proving logical theorems and speaking English.

By the middle of the 1960s, AI’s founder Herbert Simon said about the future:

“machines will be capable, within twenty years, of doing any work a man can do”

Recent studies suggest that up to 50% of jobs are threatened in the next 5–10 years. Regardless of whether we can fully simulate our minds or not, AI will have significant impact on our lives.

AI Types

AI can be categorized in any number of ways, but here are two examples.

1. Weak AI

Weak AI, also known as narrow AI, is an AI system that is designed and trained for a particular task. Virtual personal assistants, such as Google’s Google Assistance, Apple’s Siri, Window’s Cortana, Amazon’s Alexa are a form of weak AI.

2. Strong AI

Strong AI, also known as artificial general intelligence, is an AI system with generalized human cognitive abilities so that when presented with an unfamiliar task, it has enough intelligence to find a solution.

General Problems

The problem of simulating intelligence has been broken down into sub-problems. These consist of particular traits or capabilities that researchers expect an intelligent system to display.

1. Reasoning

Early researchers developed algorithms that imitated step-by-step reasoning that humans use when they solve puzzles or make logical deductions. By the late 1980s and 1990s, AI research had developed methods for dealing with uncertain or incomplete information, employing concepts from probability. These algorithms proved to be insufficient for solving large reasoning problems, they became exponentially slower as the problems grew larger.

2. Planning

Intelligent agents must be able to set goals and achieve them. They need a way to visualize the future like:

· A representation of the state of the world and be able to make predictions about how their actions will change it.

· Make choices that maximize the utility of available choices.

3. Machine Learning

It’s a fundamental concept of AI research since the field’s inception, is the study of computer algorithms that improve automatically through experience. Deep learning is a subset of machine learning that, in very simple terms, can be thought of as the automation of predictive analytics.

There are three types of machine learning algorithms: Supervised learning, in which data sets are labeled so that patterns can be detected and used to label new data sets; Unsupervised learning, in which data sets aren’t labeled and are sorted according to similarities or differences; and Reinforcement learning, in which data sets aren’t labeled but, after performing an action or several actions, the AI system is given feedback.

4. Natural language processing

NLP gives machines the ability to read and understand human language. A sufficiently powerful natural language processing system would enable natural-language user interfaces and the acquisition of knowledge directly from human-written sources, such as newswires texts. Some applications of natural language processing include information retrieval, text mining, question answering and machine translation.

5. Perception

Machine perception is the ability to use input from sensors to deduce aspects of the world. Applications include speech recognition, facial recognition, and object recognition. Computer vision is the ability to analyze visual input.

6. Motion and manipulation

AI is heavily used in robotics. Robotics is a field of engineering focused on the design and manufacturing of robots. Robots are often used to perform tasks that are difficult for humans to perform or perform consistently. They are used in assembly lines for car production or by NASA to move large objects in space. More recently, researchers are using machine learning to build robots that can interact in social settings.

In the 21st century, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding, and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science. This will grow far batter than what we have in AI today.

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