AI techniques are methods that can be utilized to develop and make PC programs generally saw as types of artificial intelligence. By and large, artificial intelligence alludes to a program that can copy or re-make the perspectives showed by the human mind. This ordinarily includes taking care of problems, mentioning observable facts or getting contribution for use in investigation or problem explaining, and the capacity to arrange and distinguish distinctive items and the properties of those objects.

Artificial Intelligence

AI as opposed to natural intelligence is the intelligence (or decision-making skills) that are synthetically generated. Any human tasks that can be automated come under the AI umbrella. Some of these tasks could be quite simple like finding the shortest distance between two points, some could be complex like playing a chess game, others could be extremely difficult like driving a car. An AI system can be broadly split into two unofficial categories:

 

Artificial Narrow Intelligence

Artificial Narrow Intelligence (ANI) refers to an AI system that can perform a specific task at near-human or super human-level accuracy. Examples include playing Chess, Speech Recognition, Image Recognition, etc. This is our current stage of AI development.

Artificial General Intelligence

Artificial General Intelligence (AGI) is a theoretical AI system that can perform multiple complex human-level tasks i.e. a single system that should be able to do what a human can do including walking while simultaneously holding a conversation, able to associate lectures in class to the text in the books, etc.

Machine Learning

Machine Learning is the process in which a system identifies patterns and relations from data to obtain an optimal solution. Machine Learning is a subset of AI. In a nutshell, an AI system could be created using machine learning algorithms.

Eg: Building a recommendation engine would involve using machine learning algorithms like association rules, decision trees, etc… on customer data. The Recommendation Engine is an AI system, The process involves machine learning.AI is any system that automates human tasks, some AI tasks involve machine learning to achieve it.

  • ML stands for Machine Learning which is defined as the acquisition of knowledge or skill

  • The aim is to increase accuracy, but it does not care about the success

  • It is a simple concept machine takes data and learns from data.

  • The goal is to learn from data on certain tasks to maximize the performance of machines on this task.

  • ML allows the system to learn new things from data.

  • It involves creating self-learning algorithms.

  • ML will go for the only solution for whether it is optimal or not.

  • ML leads to knowledge.

Source: ValueLabs (Company), Expert in Data Science

Reference: www.techrepublic.com/article/understanding-the-differences-between-ai-machine-learning-and-deep-learning

Types of Machine learning:

  • Supervised

  • Unsupervised

  • Semi-Supervised

  • Reinforcement

  • Transfer learning

Deep Learning
Deep learning is a branch of machine learning which is completely based on artificial neural networks, as the neural network is going to mimic the human brain so deep learning is also a kind of mimic of the human brain. In deep learning.

Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. 

 

The concept of deep learning is not new. It has been around for a couple of years now. It’s on hype nowadays because earlier we did not have that much processing power and a lot of data. As in the last 20 years, the processing power increases exponentially, deep learning and machine learning came in the picture.

 

Deep learning is a particular kind of machine learning that achieves great power and flexibility by learning to represent the world as a nested hierarchy of concepts, with each concept defined in relation to simpler concepts, and more abstract representations computed in terms of less abstract ones.

Common Types of Algorithm Learning/Training:

  • Regression is simply drawing a curve or line through data points.

  • Classification is determining to what group something belongs to. Binary classification (two groups) is determining if something belongs to a class or not, such as whether the animal in the picture is a dog or not. Sticking with the animal example, multiclass classification (more than two groups) is whether the animal is a dog, cat, bird, etc.

  • Clustering is similar to classification, but you don’t know the classifications ahead of time. Again using the examples of animal pictures, you may determine that there are three types of animals, but you don’t know what those animals are, so you just divide them into groups. Generally speaking, clustering is used when there is insufficient supervised data or when you want to find natural groupings in the data without being constrained to specific groups, such as dogs, cats, or birds.

  • Time series assumes that the sequence of data is important (that the data points taken over time have an internal structure that should be accounted for). For example, sales data could be considered time-series because you may want to trend revenue over time to detect seasonality and to correlate it with promotional events. On the other hand, the order of your animal pictures doesn’t matter for classification purposes.

  • Optimization is a method of achieving the best value for multiple variables when they do not move in the same direction.

  • NLP (natural language processing) is the general category of algorithms that try to mimic human use and understanding of languages, such as chatbots, scrubbing unstructured writing like doctor’s notes for key data fields, and autonomous writing of news articles.

  • Anomaly detection is used to find outliers in the data. It is similar to control charts but uses lots more variables as inputs. Anomaly detection is especially useful when “normal” operating parameters are difficult to define and change over time, and you want your detection of abnormalities to adjust automatically.

Source: https://www.miltonmarketing.com/news/ai-machine-learning-and-deep-learning-everything-you-need-to-know/

5 Most Common AI techniques

  1. Heuristic.

  2. Support vector machines.

  3. Artificial neural networks.

  4. Markov decision process.

  5. Natural language processing.

Heuristic

  • It is one of the most popular search algorithms used in Artificial Intelligence.

  • It is implemented to solve problems faster than classical methods or to find solutions for which classical methods cannot.

  • Heuristic techniques basically employ heuristics for their movements and are used to reduce the total number of alternatives to the results.

  • This technique is one of the most basic techniques used for AI and is based on the trial and error principle. Learn from mistakes.

  • Heuristics are one of the best options for solving difficult problems. For example, to know the shortest route to any destination, the best way is to identify all possible routes and then the shortest.

 

Support Vector Machines

  • Support Vector Machine is a supervised machine learning algorithm used for regression challenges or classification issues.

  • However, in most cases it is only used for rating, for example, email systems use vector machines for email ratings like Social or Promotion or any other. It categorizes each mail according to its categories.

  • This technique is widely used for face recognition, text recognition, and image recognition systems.

 

Artificial neural network

  • Neural networks are usually found in the brains of living organisms.

  • These are basically the neural circuits that help living things transmit and process information.

  • To this end, there are billions of neurons that help create neural systems to make day-to-day decisions and learn new things.

  • These natural neural networks inspired the design of an artificial neural network. Instead of neurons, artificial neural networks are composed of nodes.

  • These networks help identify patterns from the data and then learn from them.

  • To this end, it uses different learning methods, such as supervised learning, unsupervised learning and reinforced learning.

  • From an application standpoint, it is used in machine learning, deep learning and pattern recognition.

Markov Decision Process

  • A Markov Decision Process (MDP) is a framework for decision-making modeling, wherein some situations the result is partly random and partly based on input from the decision-maker.

  • Another application where MDP is used is optimized planning. The basic objective of the MDP is to find a policy for the decision-maker, indicating what specific action should be taken in what state.

  • An MDP model consists of the following parts:

  • A set of possible states: For example, this may refer to the world of a robot's grid or the states of a door (open or closed).

  • A set of possible actions: A fixed set of actions that, for example, a robot can take, such as going north, left, south, or west. Or in relation to a door, closing or opening.

  • Transition Probabilities: This is the probability of going from one state to another. For example, what is the probability that the door will be closed after closing the door?

  • Rewards: These are used to direct planning. For example, a robot may want to go north to reach its destination. In fact, going north will result in a bigger reward.

Natural Language Processing

  • Basically, it is a technique used by computers to understand, interpret and manipulate human language. Going by its use, it is useful for speech recognition and synthesis.

  • This technique is already used for many applications by a multitude of companies. Apple Siri, Google Assistant, Cortana, and Alexa from Microsoft are some of the applications that use natural language processing techniques.

  • In addition, it is also used for parsing, part of speech, and text recognition techniques.

Source: https://www.quora.com/What-is-the-AI-Technique

 

5 Top Programming AI Languages

Here we look at the best five programming languages for artificial intelligence development. It is a big concept, so it is very hard to refer to a single programming language.

Python

In artificial intelligence, Python is one of the most widely used programming languages because of its simplicity. The main use is for AI algorithms and data structure. It has a lot of useful libraries that are useful for AI development. For example, for advanced computing, Skype is used. For scientific computation capability, Numpy is used and for machine learning. There are tons of resources available online for AI using

Python.

Java

Java is an Object-oriented programming language, so it is a great choice. This language provides all high-level features needed to work on AI projects. It offers inbuilt garbage collection, and it is a portable language. The plus point with Java community there will be somebody to assist you with your queries and effort. AI is full of the algorithm, so Java is the best choice it provides an easy way to code good algorithms. You can develop algorithms like search algorithms, natural language processing algorithm or neural algorithms. Java has the feature of scalability which best for AI projects. Java is still not as high level as Prolong and Lisp and not faster than C.

 

Lisp

Lisp is the programming language developed between the 1970s and 1980s. It’s a great programming language used in large AI projects, such as Macsyma, DART, and CYC. Because of its best prototyping capabilities and its support for symbolic expression Lisp is used in AI field. This language is used in Machine Learning/ILP subfield because of its usability and symbolic structure. Lisp is the top programming language in AI field because of its best features. Lisp language has a feature of automatic garbage collection with the dynamic creation of new objects. Lisp generates efficient code with well development compilers. This language has a macro system that lets developers create a domain-specific level of abstraction on which to build the next level. Because of these features, Lisp excels compared to another language.

 

Prolog

Prolog is an excellent programming language for artificial Intelligence. Some basic features of Prolog which are extremely useful for AI programming. It offers tree-based data structuring mechanisms, automatic backtracking and pattern matching combining these mechanisms provides a flexible framework to work with artificial intelligence. In the expert system of AI Prolog is extensively used for working on medical projects. Unlike traditional programming language, Prolog is a high-level programming language based on formal logic. It is a language performing sequence of commands and solving logical formulas. As its program consists of list facts and rules, it is rule-based as well as declarative language.

C++

C++ is the greatest object-oriented programming language in the world. For AI project of the time, sensitive C++ is extremely useful. This language can talk at the hardware level and allows developers to progress their program execution time. For statistical AI techniques such as neural networks, C++ is the preferred language. The search engine can utilize C++ widely. Games in AI mostly coded with C++ for speedy execution and response time.

Summary: Before deciding a programming language for artificial intelligence makes sure that it can be utilized not partially but extensively. Freelance services are available in all of these programming languages. Also preferring a programming language for your AI project depends upon subfield. Python is well-known due to its flexibility, C++ and Java are also useful because of the best features they offer. Lisp and Prolog are always being used extensively because of their productive features.

Source: https://www.quora.com/What-is-best-programming-language-for-Artificial-Intelligence-projects

AI can be used ideally for these purposes:

  • Biometrics

  • Decision Management

  • ​Machine Learning Platforms

  • Speech Recognition

  • Robotic Process Automation

  • Text Analytics and NLP

Final Remarks about artificial intelligence

A computer can be said to be intelligent if it can achieve human-level performance in all cognitive tasks, sufficient to fool an interrogator.  In order to be artificially intelligent and pass the computer should possess the following,
 

  • Natural language processing to enable it to communicate successfully in English (or some other human language).

  • Knowledge representation to store the information provided before or during the interrogation.

  • Automated reasoning to use the stored information to answer questions and to draw new conclusions.

  • Machine learning to adapt to new circumstances and to detect and extrapolate patterns.


Artificial Intelligence is a broader class that includes Machine Learning.

[1] https://en.wikipedia.org/wiki/Turing_test
[2] Artificial Intelligence, A modern approach by Stuart. J. Russell and Peter Norvig

AI Techniques and Languages
Best Ai Techniques for Computer Science

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