ARTIFICIAL INTELLIGENCE(AI)0.2

For the magnitude of effect of other things, see active ingredient, micro-encapsulation, pesticide application, and agricultural intensification.

The AL EFFECT is the discounting of the behaviour of an artificial-intelligence program as not “real” intelligence.

The author Pamela McCordick writes: “Its part of the history of the field of artificial intelligence that every time somebody figured out how to make a computer do something-play good checkers, solve simple but relatively informal problems-there was a chorus of critics to say, that’s not thinking.

Researcher Rodney Brooks complains: Every time we figure out a piece of it, it stops being magical; we say, `oh, that’s just a computation.

GOALS

The general problem of simulating (or creating) intelligence has been broken into subproblems. These consist of particular traits or capabilities that researchers expect an intelligent system to display. The traits described below have received the most attention and cover the scope of AI research.

REASONING AND PROBLEM-SOLVING

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, methods were developed for dealing with uncertain or incomplete information, employing concepts from probability and economics.

KNOWLEDGE REPRESENTATION

An ontology represents knowledge as a set of concepts within a domain
and the relationships between those concepts.

Knowledge representation and knowledge engineering allow AI programs to answer questions intelligently and make deductions about real-world facts. Formal knowledge representations are used in content-based indexing and retrieval, scene interpretation, clinical decision support, knowledge discovery (mining “interesting” and actionable inferences from large databases), and other areas.

PLANNING AND DECISION-MAKING

An "agent" is anything that perceives and takes actions in the world. A rational agent has goals or preferences and takes actions to make them happen. In automated planning, the agent has a specific goal. In automated decision-making, the agent has preference- there are some situations it would prefer to be in, and some situations it is trying to avoid. The decision-making agent assigns a number to each situation (called the "utility") that measures how much the agent prefers it. For each possible action, it can calculate the "expected utility": the utility of all possible outcomes of the action, weighted by the probability that the outcome will occur. It can then choose the action with the maximum expected utility.

LEARNING

Machine learning is the study of programs that can improve their performance on a given task automatically. It has been a part of AI from the beginning. There are several kinds of machine learning. Unsupervised learning analyses a stream of data and finds patterns and makes predictions without any other guidance. Supervised learning requires a human to label the input data first, and comes in two main varieties: classification (where the program must deduce a numeric function based on numeric input).

NATURAL LANGUAGE PROCESSING

Natural language processing (NLP) allows programs to read, write and communicate in human languages such as English. Specific problems include speech recognition, speech synthesis, machine translation, information extraction, information retrieval and question answering.

PERCEPTION

Machine perception is the ability to use input from sensors (such as cameras, microphones, wireless signals, active lidar, sonar, radar, and tactile sensors) to deduce aspects of the world. Computer vision is the ability to analyse visual input. The field includes speech recognition, image classification, facial recognition, object recognition, object tracking, and robotic perception.

Kismet, a robot head which was made in the 1990s; a
machine that can recognize and simulate emotions.

Affective computing is an Interdisciplinary umbrella that comprises systems that recognize, interpret, process, or simulate human feeling, emotion, and mood. For example, some virtual assistants are programmed to speak conversationally or even to banter humorously; it makes them appear more sensitive to the emotional dynamics of human interaction, or to otherwise facilitate human–computer interaction.

GENERAL INTELLIGENCE

A machine with artificial general intelligence should be able to solve a wide variety of problems with breadth and versatility similar to human intelligence.

TECHNIQUES

AI research uses a wide variety of techniques to accomplish the goals above.

SEARCH AND OPTIMIZATION

AI can solve many problems by intelligently searching through many possible solutions. There are two very different kinds of search used in AI: state space search and local search.

STATE SPACE SEARCH

State space search searches through a tree of possible states to try to find a goal state. For example, planning algorithms search through trees of goals and sub goals, attempting to find a path to a target goal, a process called means-ends analysis.

LOCAL SEARCH

Illustration of gradient descent for 3 different starting points;
two parameters (represented by the plan coordinates) are
adjusted in order to minimize the loss function (the height)

Local search uses mathematical optimization to find a solution to a problem. It begins with some form of guess and refines it incrementally.

LOGIC

Formal logic is used for reasoning and knowledge representation. Formal logic comes in two main forms: propositional logic (which operates on statements that are true or false and uses logical connectives such as "and", "or", "not" and "implies") and predicate logic (which also operates on objects, predicates and relations and uses quantifiers such as "Every X is a Y" and "There are some Xs that are Ys").

PROBABILISTIC METHODS FOR UNCERTAIN REASONING

*A simple Bayesian network, with the associated conditional probability tables*.

Many problems in AI (including in reasoning, planning, learning, perception, and robotics) require the agent to operate with incomplete or uncertain information. AI researchers have devised a number of tools to solve these problems using methods from probability theory and economics.

Probabilistic algorithms can also be used for filtering, prediction, smoothing, and finding explanations for streams of data, thus helping perception system analyse processes that occur over time (e.g., hidden Markov models or Kalman filters).

Expectation-maximization clustering of Old Faithful eruption data
starts from a random guess but then successfully converges on an
accurate clustering of the two physically distinct modes of eruption.

CLASSIFIERS AND STATISTICAL LEARNING METHODS

The simplest AI applications can be divided into two types: classifiers (e.g., "if shiny then diamond"), on one hand, and controllers (e.g., "if diamond then pick up"), on the other hand. Classifiers are functions that use pattern matching to determine the closest match. They can be fine-tuned based on chosen examples using supervised learning. Each pattern (also called an "observation") is labelled with a certain predefined class. All the observations combined with their class labels are known as a data set. When a new observation is received, that observation is classified based on previous experience.

ARTIFICIAL NEURAL NETWORKS

A neural network is an interconnected group of nodes,
akin to the vast network of neurons in the human brain.

An artificial neural network is based on a collection of nodes also known as artificial neurons, which loosely model the neurons in a biological brain. It is trained to recognize patterns; once trained, it can recognize those patterns in fresh data. There is an input, at least one hidden layer of nodes and an output. Each node applies a function and once the weight crosses its specified threshold, the data is transmitted to the next layer. A network is typically called a deep neural network if it has at least 2 hidden layers.

DEEP LEARNING

Deep learning uses several layers of neurons between the networks inputs and outputs. The multiple layers can progressively extract higher-level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits, letters, or faces.

GPT

Generative pre-trained transformers (GPT) are large language models (LLMs) that generate text based on the semantic relationship between words in sentences. Text-based GPT models are pretrained on a large corpus of text that can be from the internet.

HARDWARE AND SOFTWARE

Main articles: Programming languages for artificial intelligence and Hardware for artificial intelligence.

In the late 2010s, graphics processing units (GPUs) that were increasingly designed with AI-Specific enhancements and used with specialized TensorFlow software had replaced previously used central processing unit (CPUs) as the dominant means for large-scale (commercial and academic) machine learning models` training. Specialized programming languages such as Prolong were used in early AI research, but general-purpose programming languages like Python have become predominant.

APPLICATIONS

Main article: Applications of artificial intelligence.

AI and machine learning technology is used in most of the essential applications of the 2020s, including: search engines (such as Google Search), targeting online advertisements, recommendation systems (offered by Netflix, YouTube or Amazon), driving internet traffic, targeted advertising (AdSense, Facebook), virtual assistants (such as Siri or Alexa), autonomous vehicles (including drones, ADAS and self-driving cars), automatic language translation (Microsoft Translator, Google Translate), facial recognition (Apples Face ID or Microsoft`s Deep Face and Google`s Face Net) and image labelling (used by Facebook, Apples iPhoto and TikTok). The deployment of AI may be overseen by a Chief automation officer (CAO).

HEALTH AND MEDICINE

Main article: Artificial intelligence in healthcare

The application of AI in medicine and medical research has the potential to increase patient care and quality of life. Through the lens of the Hippocratic Oath, medical professionals are ethically compelled to use AI, if applications can more accurately diagnose and treat patients.

GAMES

Main article: Game artificial intelligence.

Game playing programs have been used since the 1950s to demonstrate and test AIs most advanced techniques. Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov, on 11 May 1997. In 2011, in a jeopardy! quiz show exhibition match, IBM`s question answering system, Watson, defeated the two greatest jeopardy! champions, Brad Rutter and Ken Jennings, by a significant margin. In March 2016, AlphaGo won 4 out of 5 games of Go in a match with Go champion Lee Sedol, becoming the first computer Go-playing system to beat a professional Go player without handicaps.

MATHEMATICS

In mathematics, special forms of formal step-by-step reasoning are used. In contrast, LLMs such as GPT-4 Turbo, Gemini Ultra, Claude Opus, Llama-2 or Mistral Large are working with probabilistic models, which can produce wrong answers in the form of hallucinations.

FINANCE

Finance is one of the fastest growing sectors where applied AI tools are being deployed: from retail online banking to investment advice and insurance, where automated "robot advisers" have been in use for some years.

MILITARY

Main article: Military artificial intelligence.

Various countries are deploying AI military applications. The applications enhance command and control, communications, sensors, integration and interoperability. Research is targeting intelligence collection and analysis, logistics, cyber operations, information operations, and semiautonomous and autonomous vehicles.

GENERATIVE AI

Main article: Generative artificial intelligence.

In the early 2020s, generative AI gained widespread prominence. GenAI is AI capable of generating text, image, videos, or other data using generative models, often in response to prompts.

ARTIFICIAL INTELLIGENCE(AI)0.2