9/14/2019 Self Programming Artificial Intelligence
Getting started with Artificial Intelligence. Where should I start if I want to get into AI programming? (self.artificial) submitted 5 years ago * by gautamadude. About this Course. This class will teach you basic methods in Artificial Intelligence, including: probabilistic inference, planning and search, localization, tracking and control, all with a focus on robotics. Extensive programming examples and assignments will apply these methods in the context of building self-driving cars.
AI programming is an elevation of technology that has brought efficiency and optimum benefits to different company’s operations and peoples lives. AI has brought another level of smart technology to different industries and the prospects of its potential still grows with the expectation that it would reach the human intelligence.
This is because developers are willing to explore, experiment and implement its capabilities to satisfy more of the human and organization necessities. After all, necessity is the mother of invention.
Revenue of the AI market is expected to grow 170% in 2018 in comparison to 2017. Source: Statista Just like in the, a developer has a variety of languages to use in writing AI.
However, there is no perfect programming language to point as the best programming language used in artificial intelligence. The development process depends on the desired functionality of the AI application being developed.
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AI has so far achieved biometric intelligence, autopilots for self-driving cars and other applications that required different artificial intelligence coding language for their development projects. At, we love AI programming, our AI and neural network based handwriting character recognition application case study. Debates about best language for AI programming languages never stop.
Because of that, we decided to compare languages we commonly use for artificial intelligence projects to outline the pros and cons of each one. Table of Contents. Java, Python, Lisp, Prolog, and C are major AI programming language used for artificial intelligence capable of satisfying different needs in the development and designing of different software. It is up to a developer to choose which of the AI languages will gratify the desired functionality and features of the application requirements.
As a result, this article seeks to provide you with closure on the appropriate artificial intelligence programming language. It answers the question, ‘what is the language used for artificial intelligence?’ WHICH COMPUTER LANGUAGES ARE USED FOR ARTIFICIAL INTELLIGENCE PYTHON Python (official website) is among developers favorites programming languages in AI development because of its syntax simplicity and versatility. Python is very encouraging for machine learning for developers as it is less complex as compared to C and Java. It also a very portable language as it is used on platforms including Linux, Windows, Mac OS, and UNIX.
It is also likable from its features such as Interactive, interpreted, modular, dynamic, portable and high level which make it more unique than Java. Also, Python is a Multi-paradigm programming supporting object-oriented, procedural and functional styles of programming. Python supports neural networks and development of NLP solutions thanks to its simple function library and more so ideal structure. Advantages.
Java has a rich and extensive variety of library and tools. Supports algorithm testing without having to implement them.
Python supporting object-oriented design increases a programmer’s productivity. Compared to Java and C, Python is faster in development. Drawbacks. Developers accustomed to using Python face difficulty in adjusting to completely different syntax when they try using other languages for AI programming. Unlike C and Java, python works with the help of an interpreter which makes compilation and execution slower in AI development.
Not suitable for mobile computing. For AI meant for mobile applications, Python unsuitable due to its weak language for mobile computing. C C is the fastest computer language, its speed is appreciated for AI programming projects that are time sensitive. It provides faster execution and has less response time which is applied in search engines and development of computer games.
In addition, C allows extensive use of algorithms and is efficient in using statistical AI techniques. Another important factor is that C supports re-use of programs in development due to inheritance and data-hiding thus efficient in time and cost saving. C is appropriate for machine learning and neural network.
Advantages. Good for finding solutions for complex AI problems. Rich in library functions and programming tools collection. C is a multi-paradigm programming that supports object-oriented principles thus useful in achieving organized data. Drawbacks.
Poor in multitasking; C is suitable only for implementing core or the base of specific systems or algorithms. It follows the bottom-up approach thus, highly complex making it hard for newbies developers at using it for writing AI programs.
JAVA Java (official website) is another programming language to answer ‘which computer language is used for artificial intelligence?’ Java is also a multi-paradigm language that follows object-oriented principles and the principle of Once Written Read/Run Anywhere (WORA). It is an AI programming language that can run on any platform that supports it without the need for recompilation. Java is one of the most commonly used and not just in AI development. It derives a major part of its syntax from C and C in addition to its lesser tools that them. Java is not only appropriate for NLP and search algorithms but also for neural networks. Advantages. Very portable; it is easy to implement on different platforms because of Virtual Machine Technology.
Unlike C, Java is simple to use and even debug. Has an automatic memory manager which eases the work of the developer. Disadvantages. Java is, however, slower than C, it has less speed in execution and more response time. Though highly portable, on older platforms, java would require dramatic changes on software and hardware to facilitate. Java is also a generally immature programming AI language as there are still some developments ongoing such as JDK 1.1 in beta. LISP LISP is another language used for artificial intelligence development.
It is a family of computer programming language and is the second oldest programming language after Fortran. LISP has developed over time to become strong and dynamic language in coding. Some consider LISP as the best AI programming language due to the favour of liberty it offers developers. LISP is used in AI because of its flexibility for fast in prototyping and experimentation which in turn facilitate LISP to grow to a standard AI language. For instance, LISP has a unique macro system which facilitates exploration and implementation of different levels of Intellectual Intelligence.
LISP, unlike most AI programming languages, is more efficient in solving specific as it adapts to the needs of the solutions a developer is writing. It is highly suitable in inductive logic projects and machine learning.
Advantages. Fast and efficient in coding as it is supported by compilers instead of interpreters. Automatic memory manager was invented for LISP, therefore, it has a garbage collection. LISP offers specific control over systems resulting to their maximum use. Drawbacks. Few developers are well acquainted with Lisp programming.
Being a vintage programming language artificial intelligence, LISP requires configuration of new software and hardware to accommodate it use. PROLOG Prolog is also one of the oldest programming languages thus also suitable for the development of programming AI. Like Lisp, it is also a primary computer language for artificial intelligence. It has mechanisms that facilitate flexible frameworks developers enjoy working with.
It is a rule-based and declarative language as it contains facts and rules that dictate its artificial intelligence coding language. Prolog supports basic mechanisms such as pattern matching, tree-based data structuring, and automatic backtracking essential for AI programming. Other than its extensive use in AI projects, Prolog is also used for creation of medical systems.
Advantages. Prolog has a built-in list handling essential in representing tree-based data structures. Efficient for fast prototyping for AI programs to be released modules frequently. Allows database creation simultaneous with running of the program. Drawbacks. Despite prolog old age, it has not been fully standardized in that some features differ in implementation making the work of the developer cumbersome.
WHAT IS INSTALL FOR AI IN 2018 In 2017 most of us learned about AI from frequent talks by individuals in the tech world such as. The Artificial Intelligence debate over time. Credits: Artificial Lawyer Nevertheless, there have also been impactful developments such as:. – the English Language Speech Assistant which understands a person’s native language and corrects their pronunciations. – it enables companies and individuals make better predictions of future events.
It uses neural network to comprehensively describe what is around us and even distink between different objects at the same place. It uses a neural network to comprehensively describe what is around us and even indicate the distinction between different objects. It is a safer and more accurate source analytical information to organizations than humans. However, in 2018, AI technology will be at a more tangible level to many individuals and impact our lives at the core basis.
This graph illustrates the percentage of market players who plan to adopt AI in next two years across various business verticals. Source: Infosys Survey Here are changes to expect in the AI technology:. Availability of an individual’s virtual assistant with information of the person’s daily life routine thus facilitating them in their day to day goals and needs. Availability of multiple voice-based gadgets. This is where most of the basic items such as cars and television will be customized with to allow their listening and providing solutions to individuals. Replacement of credit cards with the facial recognition technology thanks to the biometrics capabilities. In Media; there are prospects of AI creating media platforms in which the viewer or listener can choose the specific of their needs such as the genre of music to listen to.
Empathetic computers; our so-called smart devices will no longer provide a single and simple question and discrete response instead will offer human-like responses. These are comprehensive feedback for our queries and questions and even sensible solutions. AI in healthcare provision; healthcare will adopt AI systems for instance in diagnostic specialties. Provision of news and other reports by AI; this is where systems will be able to provide individuals with demanded and in comprehensive information.
CONCLUSION When it comes to keeping up with technology, every individual, business person and organization do not want to be left behind. The emergence of AI technology is bringing changes that will permeate the core of our lives therefore understanding and using AI technology would be the best strategy right now. On the other hand, we at are here to support you fully in embedding your systems and devices with AI technology. We provide our customers with professional developers who are experts in all artificial intelligence programming language. We are always available for our customers, and get evolved together with the dynamic Artificial Intelligence technology.
No notes for slide. Hi everyone! So, this is the first Bots, AI, and Conversational UI meetup for 2017. I'm really excited to be presenting one of my favorite AI topics on self-programming artificial intelligence.
I'd like to start this off with a simple question. Is it possible for a computer program to write its own programs?. Before I get started, let me quickly introduce myself. My name is Kory Becker. I'm a software architect at The Associated Press. I design web applications and web services, although my core interest in computing has always been artificial intelligence and data science. I'm also the author of the book, 'Building Voice-Enabled Apps with Alexa' which is my first published book.
In fact, the final release was just published this past Monday! If you're interested in conversational UI, chat bots, or building your first Amazon Alexa app, take a look at my book online at Safari Books or O'Reilly. I want to briefly start this presentation off by just clearing up some media hype that has been steadily growing over the past year or two, surrounding what AI is and all of the amazing things it's going to do. The news like to say things like 'chatbots are going to take over everyone's jobs, machine learning is changing everything, etc'. Not to belittle machine learning, as it truly is an amazing branch of AI that has made significant leaps and bounds in accuracy over the past few years (largely due to massive online datasets, increased computing speed, and deep learning). However, AI is not just machine learning.
There is a lot more to it! AI encompasses many different branches. There is logical AI, which deals with representing knowledge as logical sentences. There is Symbolic AI (also called Classical AI), which uses human-readable representations of problems (my STRIPS planning library is an example of this).
There is knowledge-based AI like the Cyc database, pattern recognition such as image recognition of cats, dogs, and the CIFAR dataset. There is also AI planning (see for an example of this, where I demonstrate AI for solving Starcraft build orders! How cool is that?). There are heuristics like A. Search. But the focus of this presentation is the last topic on the slide, which is genetic algorithms, also called evolutionary computation. So, why genetic algorithms?
Genetic algorithms are a branch of AI that deal with simulating evolution among thousands of different genomes to produce the best offspring, most fit to solve a particular task. In the case of self-programming AI, this task is writing its own computer programs! I like to compare the overall idea to the Infinite Monkey Theorem.
The idea is that if you have a hundred monkeys banging on a hundred typewriters, given enough time, they'll produce a written work by Shakespeare. I know this seems crazy, but consider it.
The monkeys are hitting random keys, of course. However, at some point in an infinite time space, they'll manage to randomly type a real word. Perhaps, a real sentence. And at some point in infinite time, a complete book! Now, consider this difference. What if you gave a banana to the monkeys each time they typed a correct key?
Perhaps, you could guide the random key banging into a more structured approach, effectively training the monkeys to write out a work by Shakespeare. This is exactly the idea behind program synthesis. Program synthesis is a more formal naming for self-programming AI. It's simply computer programs that can automatically generate their own computer programs for a specific goal. Program synthesis can be done in different ways, such as neural networks, deep learning, and as we're about to see, genetic algorithms. In the case of using genetic algorithms for an AI to write its own computer programs, a programming language that is easy enough for the AI to manipulate is required.
I chose the 'Brainf-ck' programming language for this purpose. It's a very strange and esoteric language. It's very difficult for humans to understand, hence the naming. However, it's simple nature makes it perfect for a self-programming AI. First, Brainf-ck is Turing complete.
This means that it's theoretically capable of solving any problem in the universe. That means, the AI would be theoretically capable of writing its own computer programs for any problem in the universe.
Next, it consists of just 8 instructions. This makes it easy for a genetic algorithm to manipulate, as the number of possible instructions are kept to a minimum. Third, it's easy to build an interpreter for and to expand upon. To generate a program, we first create a genome. The genome is encoded as an array of doubles (or floating point values).
Each value corresponds to a programming instruction in the language. The genomes are converted to a program, executed, and their fitness is ranked according to the output of each program. The closer a generated program comes to solving the task, the more likely it is to continue to the next generation.
At each generation epoch, we use roulette selection, crossover, and mutation to create child programs that are slightly different (and hopefully better!) than their parents. Here is an example of constructing a genome from an array of doubles. Notice how each value maps to a specific instruction in the programming language. Initially, these values are all random and the programs won't do much except throw errors and fail. Most likely immediately.
However, one or two are bound to run and do, at least, something! It's these that move on to produce offspring with code that gets better and better. To create offspring, a parent genome contributes part of its genome to the child. This is called crossover, and you can see it in action in the slide above. We can also randomly apply a little mutation, which slightly modifies the value of a particular gene, resulting in a change to the resulting programming instruction. These changes copy forward potentially beneficial parts of the parent and mutate certain instructions, which may or may not, end up making the child programs more fit.
Finally, the resulting programs that are generated get ranked according to how well they performed. You can see in this slide how the top program fails, and is removed from the pool of genomes. However, the bottom program succeeded and is carried forward to produce child programs. It just so happens, the bottom program is a valid running program that takes 2 bytes for input and prints them out. Now that we're all experts on genetic algorithms and evolutionary computational AI, let's see what the project has produced. The AI has been able to successfully generate programs for 'hello world', reversing a string, addition, subtraction, multiplication, reading input from the user, if/then conditions, printing the fibonacci sequence, and even writing a program to output the 'Bottles of Beer on the Wall' song!.
It's pretty amazing how the AI was able to produce these programs, and more! There is certainly some potential in what could be done. I've been writing software for many years and I've always felt that it's just something that humans shouldn't be doing. Computers should be writing the code.
Humans should be designing the programs. Consider a future where humans create 'lego blocks' of program designs. They give these designs to the computer, which then writes the programs to make these 'lego blocks' real software. The human then designs a higher level problem, where the computer can then utilize the building blocks as sub-modules to create even more complex programs, generating software for web applications, databases, games, and much more. If you'd like to learn more about my self-programming AI research, check out my blog at I have a full write-up of the details behind the AI, example programs that it's successfully produced, and ideas towards future work. It's really fascinating stuff! Also, be sure to check out my book, 'Building Voice-Enabled Apps for Alexa' on Safari Books.
Self Programming Artificial Intelligence. 1. Self-Programming Artificial Intelligence Computer programs that write their own programs. Sponsored By Kory Becker January 2017. Hello World, Kory Becker Software Architect at AP Author of 'Building Voice-Enabled Apps with Alexa', 2017 Bleeding Edge Press Web Apps, Artificial Intelligence, Data Science. AI!
Machine Learning Logical AI, Symbolic, Knowledge-based Pattern Recognition, Representation Inference, Common Sense, Planning Heuristics, Ontology, Artificial Life, Genetic. Infinite Monkey Theorem Profit!. Program Synthesis Automatically construct a computer program that satisfies a specific goal. Genetic Algorithms Evolutionary Computation. Brainf-ck Turing complete 8 instructions, 1 byte each:.
Crossover & Mutation + - +0.9 0.8. The Strong Survive+ Generated in 1 minute. Hello Generated in 29 minutes. Hello Generated in 30 minutes.
Outputting Text Generated in 10 hours. Bottles of Beer on the Wall Generated in 33 minutes. 'hello' +-+-+-.-. Generated in 29 minutes. Addition,-,$!-$4+,-.
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