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Last Updated: 02 July 2021

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Are you thinking of Chappie, Terminator, and Lucy? Sentient, self - aware robots are closer to becoming reality than you think. Developing computer systems that equal or exceed human intelligence is the crux of Artificial Intelligence. Artificial Intelligence is a study of Computer Science focusing on developing software or machines that exhibit human intelligence. Simple enough definition, right? Obviously, there is a lot more to it. Ai is broad topic ranging from simple calculators to self - steering technology to something that might radically change in the future. Primary goals of AI include deduction and reasoning, knowledge representation, planning, natural language processing, learning, perception, and ability to manipulate and move objects. Long - term goals of AI Research include achieving Creativity, Social Intelligence, and General Intelligence. Ai has heavily influenced different sectors that we may not recognize. Ray Kurzweil says many thousands of AI applications are deeply embed in the infrastructure of every industry. John McCarthy, one of the founders of AI, once said that as soon as it work, no one call it AI anymore. While there are various forms of AI as its broad concept, we can divide it into the following three categories based on AI capabilities: weak AI, which is also referred to as narrow AI, focuses on one task. There is no self - awareness or genuine intelligence in case of weak AI. Ios Siri is a good example of weak AI combining several weak AI techniques to function. It can do a lot of things for the user,ss and youll see how narrow it exactly is when you try having conversations with a virtual assistant. Strong AI, which is also referred to as True AI, is a computer that is as smart as the human brain. This sort of AI will be able to perform all tasks that humans could do. There is a lot of research going on in this field, but we still have to do. It You should be imagining Matrix or I, Robot here. Artificial Superintelligence is going to blow your mind if strong AI impresses you. Nick Bostrom, leading AI thinker, defines it as intellect that is much smarter than best human brain in practically every field, including scientific creativity, general wisdom and social skills. Artificial Superintelligence is the reason why many prominent scientists and technologists, including Stephen Hawking and Elon Musk, have raised concerns about the possibility of human extinction. The first thing you need to do is learn a Programming Language. Though there are lot of languages that you can start with, Python is what many prefer to start with because its libraries are better suited to Machine Learning. Codeacademy learns Python the hard way coursera Python Introduction to Computer Science BOT is the most basic example of weak AI that can do automated tasks on your behalf. Chatbots were one of the first automated programs to be called bots. You need AI and ML for your chatbots.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

What is Artificial Intelligence (AI)?

Artificial Intelligence is simulation of Human Intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, Natural Language Processing, Speech Recognition and Machine Vision. Ai Programming focuses on three cognitive skills: Learning, Reasoning and Self - correction. Learning process. This aspect of AI Programming focuses on acquiring data and creating rules for how to turn data into actionable information. Rules, which are called algorithms, provide computing devices with step - by - step instructions for how to complete specific task. Reasoning processes. This aspect of AI Programming focuses on choosing the right algorithm to reach the desired outcome. Self - correction process. This aspect of AI Programming is designed to continually fine - tune algorithms and ensure they provide the most accurate results possible.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Online Courses in Artificial Intelligence

Artificial Intelligence is a fast - growing branch of Computer Science focuses on enabling computers to perform a wide range of tasks that previously required human Intelligence. Today, AI is used to power a wide range of tasks, such as image recognition, language translation, and prioritization of email or business workflows. So, if you have a smartphone, chances are you use software with AI capabilities every day. Ai is often discussed in tandem with the closely related concept of Machine Learning. Machine Learning is the use of step - by - step processes called algorithms to allow computers to solve problems on their own - and, over time, get steadily better at doing so. Well - designed Machine Learning algorithms give computers the ability to solve a wide range of problems much more effectively and flexibly than if programmers had to provide detailed instructions for one specific use case. While Machine Learning is used to create many simple AI Applications, this approach typically requires massive, clearly - defined datasets to properly atraina program. To create more sophisticated AI Applications, advanced type of Machine Learning called Deep Learning is used. Deep Learning uses Artificial Neural Networks that, as its name implies, are pattern after the human brain and do not require such structured datasets and human guidance to be successful. Instead, AI application can feed diverse, unstructured datasets and learn itself how to achieve specified goal. Even todayas most powerful Deep Learning approaches are not capable of mimicking the complexity and creativity of the human brain and its tens of billions of neurons. However, field of Artificial Intelligence has made incredible strides in recent years, and is changing the way we live and work in ways that would have seemed outlandish a decade ago. Who knows what next decade of progress in this exciting field will yield? Students learning skills in this area today may end up producing even more radical breakthroughs. As Artificial Intelligence touches more and more areas of our daily lives, IT is becoming useful for more and more career paths. Indeed, at least some background in this field is required for the growing number of jobs, and IT can help give you a significant advantage over competition in many others. Naturally, AI and its subfields are in very high demand for popular Computer Science jobs. Data scientists rely on Machine Learning and Deep Learning skills in their daily work, applying various data mining techniques to both structured and unstructured Big Data in order to produce valuable insights for a wide variety of businesses. Skills in natural language processing are needed to create useful chatbots for customer service as well as voice - activated assistants like Amazonas Alexa. And advanced AI skills can put you at the cutting edge of Computer Programming, working on teams seeking to achieve ambitious goals like self - driving cars or autonomous robots. A background in AI can help you in more and more jobs outside the realm of Computer Science, as well - itas not much of an exaggeration to say that if jobs require human Intelligence to do, Artificial Intelligence can help.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

AI with Python Tutorial

Python is an AI programming language that has gained huge popularity. The main reasons are simple syntax, less coding and a large number of available libraries ready for use. Simple syntax means you can focus on the core value of programming, thinking, or problem - solving. Earlier mentioned libraries include NumPy, SciPy, Matplotlib, nltk, SimpleAI. Python is an open - source AI programming language. Thats why it has a huge fan base among programmers. Because it can be used broadly, to make small scripts and up to enterprise applications, it is suitable for AI. Where other AI programming languages use punctuation, Python Uses English keywords. Its design is to be readable. It has only a few keywords and has a clearly defined syntax. If you are a student, you will pick up language quickly. Libraries are portable across platforms such as UNIX, Windows, and Macintosh. It also provides interfaces for all major commercial databases. When it comes to scalability, it provides better structure and support for large enterprise programs than it does for simple shell scripts. Python supports Object - oriented programming, dynamic type checking, automatic garbage collection, and can be integrated with C +, C, Java, Cobra, and many other languages. The bottom line is that Python is considered the best AI programming language because of its simplicity.


Artificial Intelligence With Python:

Artificial Intelligence, often dubbed AI, is Intelligence machine demonstrates. With machine intelligence, it is possible to give a device the ability to discern its environment and act to maximize its chances of success in achieving its goals. In other words, AI is when a machine can mimic cognitive functions like learning and problem - solving. Ai is whatever n't been done yet. As we say, AI takes in its environment and acts to maximize its chances of success in achieving its goals. Goal can be simple or complex, explicit or induce. It is also true that many algorithms in AI can learn from data, learn new heuristics to improve and write other algorithms. One difference to humans is that AI does not possess features of human commonsense reasoning and folk psychology. This makes it end up making different mistakes than humans do.


Machine Learning With Python

Python is a popular and general - purpose programming language. We can write machine learning algorithms using Python, and it works well. The reason why Python is so popular among data scientists is that Python has a diverse variety of modules and libraries already implemented that make our life more comfortable. Let us have a brief look at some exciting Python libraries. Numpy: It is a math library to work with n - dimensional arrays in Python. It enables us to do computations effectively and efficiently. Scipy: It is a collection of numerical algorithms and domain - specific tool - box, including signal processing, optimization, statistics, and much more. Scipy is a functional library for scientific and high - performance computations. Matplotlib: It is a trendy plotting package that provides 2D plotting as well as 3D plotting. Scikit - learn: It is a free machine learning library for Python programming language. It has most of the classification, regression, and clustering algorithms, and works with Python numerical libraries such as Numpy, Scipy. Supervise Learning algorithms Unsupervised Learning algorithms


What Is A Perceptron?

In Multilayer Perceptron, weights assigned to each input at the beginning are updated in order to minimize resultant errors in computation. This is because initially we randomly assign weight values for each input, These weight values obviously do not give us the desired outcome,. Therefore, it is necessary to update weights in such a manner that the output is precise. This process of updating weights and training networks is know as Backpropagation. Backpropagation is the logic behind Multilayer Perceptrons. This method is used to update weights in such a way that the most significant input variable gets maximum weight, thus reducing error while computing output. So that was the logic behind Artificial Neural Networks. If you wish to learn more, make sure you give this, Neural Network Tutorial - Multi - Layer Perceptron blog read.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

LISP

Short for List Processing, it is the second oldest Programming Language next to Fortran. Call as one of the Founding Fathers of AI, LISP was created by John McCarthy in 1958. Build as practical mathematical notation for programs, LISP soon become choice of AI Programming Language for developers very quickly. Below are some of LISP features that make it one of the best options for AI projects on Machine Learning: with major improvements in other competing Programming Languages, several features specific to LISP have made their way into other Languages. Some of notable projects that involve LISP at some point in time are Reddit and HackerNews.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

C++

If it like you are working on a new Artificial Intelligence project and still have not decided which language you should use to program it, then you are in the right place. Artificial Intelligence is a branch of engineering, which basically aims to making computers which can think intelligently, in similar manner intelligent humans think. Here are the top languages that are most commonly used for making AI projects: 1. Python Python is considered to be in first place in the list of all AI development languages due to simplicity. Syntaxes belonging to Python are very simple and can be easily learnt. Therefore, many AI algorithms can be easily implemented in it. Python has short development time in comparison to other languages like Java, C + or Ruby. Python supports object - orient, functional as well as procedure - oriented styles of programming. There are plenty of libraries in Python, which make our tasks easier. For example: Numpy is a library for Python that helps us to solve many scientific computations. Also, we have Pybrain, which is for using Machine learning in Python. 2. R R is one of the most effective languages and environments for analyzing and manipulating data for statistical purposes. Using R, we can easily produce designer publication - quality plot, including mathematical symbols and formulae where needed. Apart from being a general purpose Language, R has numerous packages like RODBC, Gmodels, Class and Tm which are used in the field of Machine learning. These packages make implementation of Machine learning algorithms easy, for cracking business associate problems. 3. Lisp LISP is one of oldest and most suited languages for development in AI. It was invented by John McCarthy, father of Artificial Intelligence, in 1958. It has the capability of processing symbolic information effectively. It is also known for its excellent prototyping capabilities and easy dynamic creation of new objects, with automatic garbage collection. Its development cycle allows interactive evaluation of expressions and recompilation of functions or files while the program is still running. Over the years, due to advancement, many of these features have migrated into many other languages, thereby affecting the uniqueness of LISP. 4. Prolog this Language stays alongside LISP when we talk about development in the AI field. Features provided by it include efficient pattern matching, tree - base data structuring and automatic backtracking. All these features provide a surprisingly powerful and flexible programming framework. Prolog is widely used for working on medical projects and also for designing expert AI systems. 5. Java Java can also be considered as a good choice for AI development. Artificial Intelligence has a lot to do with search algorithms, Artificial neural networks and genetic programming. Java provides many benefits: easy use, debugging ease, package services, simplified work with large - scale projects, graphical representation of data and better user interaction. It also has incorporation of Swing and SWT. These tools make graphics and interfaces look appealing and sophisticated.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Java

Table

Advantages of JavaDisadvantages of Java
1. Security: Security is integral for a Java design. The Java compiler, interpreter, and runtime environment are secured.1. Performance Issues: Java consumes more memory and is slower when compared to compiled languages such as C or C++ hence faces performance issues.
2. Stack Allocation: Java follows a LIFO (Last in First Out) system which helps in storing and retrieving the data easily.2. Complex Codes : Java codes are long and complex and are difficult to read and understand. The overly complex codes require one to explain everything in detail.
3. Multithreaded: Using Javas multithreading capability, a programmer can perform several tasks simultaneously in a program.
4. Rich APIs: Java offers APIs and a set of commands for database connection, networking, I/O, XML parsing, utilities, and much more.
5. Rapid Development Tools: The open-source development IDEs used for the coding of Java languages such as Eclipse and Netbeans provide a base for powerful application development with efficient coding and debugging.

Undoubtedly, artificial Intelligence has taken technology to another level in different industries. There is no perfect programming language used in AI; different applications require different coding languages for their development. The debate about which programming language to choose between Java and Python is unending, but here is brief of two: Python is a high - level programming language used for complex scenarios as well as a general - purpose language that is used across various domains. It is the favourite language among developers because of its simplicity and less complex syntax. It is Open - source and available for all operating systems and is platform - independent and has an extensive Library of Python programming code. Java is an Object - oriented language and also multi - paradigm just like the Python programming language. It is one of the most commonly used languages that came into existence way before Python. It is still ranks among the five top languages for AI programming. It has relatively complex syntax than Python, but the speed of execution is quite higher. Both these languages Support Neural Networks and NLP development Solutions. Differences in functionalities of languages will be discussed further.


2. Expert Systems

The expert system is also called rules - base system. Rules are typically if - then statements; ie if this condition is meet, then perform this action. Expert systems usually comprise hundreds or thousands of nested if - then statements. Expert systems were a popular form of AI in the 1980s. They are good at modeling static and deterministic relationships; eg tax code. However, they are also brittle and they require manual modification, which can be slow and expensive. Unlike, machine - learning algorithms, they do not adapt as they are exposed to more data. They can be useful complement to machine - learning algorithm,s codifying things that should always happen in a certain way.


4. Natural Language Processing

Opennlp class DoccatTrainer can process specially formatted input text files and produce categorization models using maximum entropy, which is a technique that handles data with many features. Features that are automatically extracted from text and used in models are things like words in documents and word adjacency. Maximum entropy models can recognize multiple classes. In testing model on new text data, probablilities of all possible classes add up to value 1. For example, we will be training classifier on four categories and the probablilities of these categories for some test input text add up to the value of one: format of input file for training maximum entropy classifier is simple but has to be correct: each line starts with a category name, followed by sample text for each category which must be all on one line. Please note that I have already trained models to produce model file models / en - newscat. Bin, so you do need to run example in this section unless you want to regenerate this model file. File sample_category_training_text. Txt contains four lines, defining four categories. Here are two lines from this file: here is one training example each for the categories COMPUTERS and ECONOMY. You must format the training file perfectly. As example, if you have empty lines in your input training file then you will get errors like: OpenNLP documentation has examples for writing custom Java code to build models, but I usually just use command line tool; for example: model is written to relative file path models / en - newscat. Bin. The training file I am using is tiny, so the model is trained in a few seconds. For serious applications, more training text is better! By default, DoccatTrainer tool uses the default text feature generator which uses word frequencies in documents but ignores word ordering. As I mention in the next section, I sometimes like to mix word frequency feature generation with 2gram. In this case, you cannot simply use the DoccatTrainer command line tool. You need to write a little Java code yourself so that you can plug another feature generator into using an alternative API: in the next section, you will note that the last argument would look like a case where we combine two feature generators, one that uses abag of wordsa and the other that uses adjacent words sequences: for some purposes default word frequency feature generator is probably OK so using command line tool is good place to start because models are smaller and training time is minimal. Adding NGramFeatureGenerator increases both training time and model size but should produce better results. Here is the output from running DoccatTrainer command line tool: we will use our new trained model file en - newscat. Bin in the next section. Please note that in this simple example I use very little data, just a few hundred words for each training category.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

Table2

Advantages of PythonDisadvantages of Python
1. Seamless Integration - Python harmoniously integrates with enterprise applications, which makes it feasible to develop web services. This makes it a language to be preferred for developing high-end applications.1. Run-time Error - Python is a dynamically typed language and faces many design restrictions, requires more testing time, and shows errors when the application is running.
2. Enhanced Productivity - Pythons strong process integration, unit testing framework, and control capabilities increase the productivity of the developed applications significantly.2. Primitive Database Access Layers - Pythons database is still not much developed; thats why it is not suitable for huge enterprise applications as compared to JDBC and ODBC technologies .
* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Prolog

The book comprises of 23 chapters divided into two main parts. Part 1 focuses on Prolog Language and teaches novices how to use Prolog and experienced programmers new methods for solving problems. Chapter 1, Introduction to Prolog, discusses topics including how Prolog answers questions, defining relations by facts, defining relations by rules, and declarative and procedural Meaning of Programs. Chapter 2 addresses Syntax and Meaning of Prolog Programs, while Lists, Operators and Arithmetic are discussed in chapter 3. Chapter 4, Using Structures: Example Programs, shows how to retrieve structure information from database, how to perform data obstruction, simulating non - deterministic automation, travel agent Problem, and eight queens Problem. Chapter 5 addresses Controlling Backtracking, while communication with files is discussed in chapter 6, Input and Output. The remaining four chapters in this section present More Built - in Predictates, Programming Style and Technique, Operations on Datastructures, and advance Tree Representations. The second part of the book, Prolog in Artificial Intelligence, shows how Prolog can be used as a tool for teaching and solving AI problems. Chapter 11 addresses Basic Problem - Solving Strategies, while chapter 12 introduces Best - First Heuristic Search. The following four chapters present Problem Decomposition and and / or Graphs, Constraint Logic Programming Knowledge Representation and Expert Systems, and Expert System Shell, respectively. Chapter 17, Planning, discusses topics including representing actions, protecting goals, and goal regression. Machine Learning, Inductive Logic Programming, and Qualitative Reasoning are present in the following three chapters, while chapter 21 introduces Language Processing with Grammar Rules. The final two chapters of the book address Game Playing, and Meta - Programming, respectively. Overall, this book is a superb Introduction to Prolog Programming Language. Its focus on problems rather than procedures makes it an excellent teaching resource for engineering and computer science students. As the book concentrates on Using Prolog for Artificial Intelligence, it will be of interest to those interested or involved with Expert Systems, machine Learning, and Game Playing. Complete Programs are included in text and source code and further teaching materials can be downloaded from the companion Web site at http: / www.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Conclusion

This tutorial has introduced you to Machine Learning. Now, you know that Machine Learning is a technique of training machines to perform activities the human brain can do, albeit a bit faster and better than the average human. Today we have seen that machines can beat human champions in games such as Chess, AlphaGO, which are considered very complex. You have seen that machines can be trained to perform human activities in several areas and can aid humans in living better lives. Machine Learning can be supervised or unsupervised. If you have less amount of data and clearly labelled data for training, opt for Supervised Learning. Unsupervised Learning would generally give better performance and results for large data sets. If you have a huge data set easily available, go for Deep Learning techniques. You also have to learn Reinforcement Learning and Deep Reinforcement Learning. You now know what Neural Networks are, their applications and limitations. Finally, when it comes to development of Machine Learning models of your own, you look at choices of various development languages, IDEs and Platforms. The next thing that you need to do is start learning and practicing each Machine Learning technique. The subject is vast, it means that there is width, but if you consider depth, each topic can be learnt in a few hours. Each topic is independent of each other. You need to take into consideration one topic at a time, learn it, practice it and implement algorithm / s in it using language choice of yours. This is the best way to start studying Machine Learning. Practicing one topic at a time, very soon you will acquire width that is eventually required of Machine Learning expert.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

R

By David Ramel 08 / 29 / 2018 Microsoft has issued new release of its enhanced edition of R programming language, one of the primary languages used for Artificial Intelligence and Machine Learning development, along with Python. Although R is also used for many other tasks, it has a secure place atop the favorite lists of many AI programmers. As Wikipedia states: R is widely used in new - style Artificial Intelligence, involving statistical computations, numerical analysis, use of Bayesian inference, neural networks and in general Machine Learning. Earlier this month, Microsoft updated its R Open offering, with improved multi - processor Support highlighting new functionality in the new release. Microsoft R Open is the company's enhanced, Open source distribution of R, commonly used for statistical analysis and data science. The company this month updated its offering to version 3. 51, which is completely compatible with R 3. 51, which was shipped last month. Highlighting new functionality coming with the update is new support for multi - threaded computations via new math libraries. These libraries make it possible for so many common R operations to use all of the processing power available, Microsoft say. Matrix operations, most notably, can compute in parallel with all available processing power to significantly reduce computation times. Companies say they automatically use all available cores and processors on machine. There's nothing special any package needs to do to benefit from these libraries either, Microsoft say. In fact, any package used for Vector / matrix operations will experience enhanced performance automatically when these libraries are installed. Another key enhancement highlighted by the R Open team is high - performance default, fix CRAN repository. This post explains how it provides a consistent and static set of packages for R Open developers and users. It's based on fixed CRAN repository snapshot date of Aug. 1 2018, and won't change until the next release. Also new is the checkpoint package. Checkpoint Package was also installed from CRAN during installation of Microsoft R Open, company say. This package, available on CRAN, is designed to make it easy to write reproducible R code by allowing you to go backward in time to retrieve exact versions of packages you need. All you need are two lines of code to access package versions from different date. More information on that feature can be found here. Many new R packages are also available in that Aug. 1 snapshot, contributed by the community surrounding the Open source offering. There are dozens of packages available for applications, Data munging, Data sources, graphics, image and natural language analysis, interfaces, Machine Learning, programming tools and more.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Haskell

Define in 1990 and named after famous mathematician Haskell Brooks Curry, Haskell is purely functional and statically Type programming language, paired with lazy evaluation and shorter code. It is considered a very safe programming language as it tends to offer more flexibility in terms of handling errors as they happen so rarely in Haskell compared to other programming languages. Even if they do occur, majority of non - syntactical errors are caught at compile - time instead of runtime. Some of the features offered by Haskell are: its features help improve the productivity of programmer.S Haskell is lot like other programming languages, just used by a niche group of developers. Putting challenges aside, Haskell can prove to be just as good as other competing languages for AI with increased adoption by the developer community.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

Sources

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions.

* Please keep in mind that all text is machine-generated, we do not bear any responsibility, and you should always get advice from professionals before taking any actions

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