Artificial intelligence

Artificial Intelligence

What is Artificial intelligence?

According to the father of Artificial Intelligence John McCarthy, it is “The science and

engineering of making intelligent machines, especially intelligent computer programs”.

                                 Artificial Intelligence is a way of making a computer, a computer-controlled robot, or a software think intelligently, in the similar manner the intelligent humans think.

               

Thus, Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. Some of the activities computers with artificial intelligence are designed for include:

• Speech recognition
• Learning
• Planning
• Problem solving

                 AI is accomplished by studying how human brain thinks, and how humans learn, decide, and

work while trying to solve a problem, and then using the outcomes of this study as a basis of

developing intelligent software and systems.

Artificial intelligence is a branch of computer science that aims to create intelligent machines. It has become an essential part of the technology industry.

                         Research associated with artificial intelligence is highly technical and specialized. The core problems of artificial intelligence include programming computers for certain traits such as:

• Knowledge
• Reasoning
• Problem solving
• Perception
• Learning
• Planning
• Ability to manipulate and move objects

Knowledge engineering is a core part of AI research. Machines can often act and react like humans only if they have abundant information relating to the world. Artificial intelligence must have access to objects, categories, properties and relations between all of them to implement knowledge engineering. Initiating common sense, reasoning and problem-solving power in machines is a difficult and tedious approach.

Machine learning is another core part of AI. Learning without any kind of supervision requires an ability to identify patterns in streams of inputs, whereas learning with adequate supervision involves classification and numerical regressions. Classification determines the category an object belongs to and regression deals with obtaining a set of numerical input or output examples, thereby discovering functions enabling the generation of suitable outputs from respective inputs. Mathematical analysis of machine learning algorithms and their performance is a well-defined branch of theoretical computer science often referred to as computational learning theory.

The birth of Artificial intelligence

In the 1940s and 50s, a handful of scientists from a variety of fields (mathematics, psychology, engineering, economics and political science) began to discuss the possibility of creating an artificial brain. The field of artificial intelligence research was founded as an academic discipline in 1956.

Cybernetics and early neural networks

The earliest research into thinking machines was inspired by a confluence of ideas that became prevalent in the late 30s, 40s and early 50s. Recent research in neurology had shown that the brain was an electrical network of neurons that fired in all-or-nothing pulses. Norbert Wiener's cybernetics described control and stability in electrical networks. Claude Shannon's information theory described digital signals (i.e., all-or-nothing signals). Alan Turing's theory of computation showed that any form of computation could be described digitally. The close relationship between these ideas suggested that it might be possible to construct an electric brain.

Walter Pitts and Warren McCulloch analyzed networks of idealized artificial neurons and showed how they might perform simple logical functions. They were the first to describe what later researchers would call a neural network. One of the students inspired by Pitts and McCulloch was a young Marvin Minsky, then a 24-year-old graduate student. In 1951 (with Dean Edmonds) he built the first neural net machine, the SNARC. Minsky was to become one of the most important leaders and innovators in AI for the next 50 years.

Turing's test

In 1950 Alan Turing published a landmark paper in which he speculated about the possibility of creating machines that think. He noted that "thinking" is difficult to define and devised his famous Turing Test. If a machine could carry on a conversation (over a teleprinter) that was indistinguishable from a conversation with a human being, then it was reasonable to say that the machine was "thinking". This simplified version of the problem allowed Turing to argue convincingly that a "thinking machine" was at least plausible and the paper answered all the most common objections to the proposition. The Turing Test was the first serious proposal in the philosophy of artificial intelligence.

Game AI

In 1951, using the Ferranti Mark 1 machine of the University of Manchester, Christopher Strachey wrote a checkers program and Dietrich Prinz wrote one for chess. Arthur Samuel's checkers program, developed in the middle 50s and early 60s, eventually achieved sufficient skill to challenge a respectable amateur. Game AI would continue to be used as a measure of progress in AI throughout its history.

Symbolic reasoning and the Logic Theorist

When access to digital computers became possible in the middle fifties, a few scientists instinctively recognized that a machine that could manipulate numbers could also manipulate symbols and that the manipulation of symbols could well be the essence of human thought. This was a new approach to creating thinking machines.

In 1955, Allen Newell and (future Nobel Laureate) Herbert A. Simon created the "Logic Theorist" (with help from J. C. Shaw). The program would eventually prove 38 of the first 52 theorems in Russell and Whitehead's Principia Mathematica, and find new and more elegant proofs for some. Simon said that they had "solved the venerable mind/body problem, explaining how a system composed of matter can have the properties of mind." (This was an early statement of the philosophical position John Searle would later call "Strong AI": that machines can contain minds just as human bodies do.)

Dartmouth Conference 1956: the birth of AI

The Dartmouth Conference of 1956 was organized by Marvin Minsky, John McCarthy and two senior scientists: Claude Shannon and Nathan Rochester of IBM. The proposal for the conference included this assertion: "every aspect of learning or any other feature of intelligence can be so precisely described that a machine can be made to simulate it". The participants included Ray Solomonoff, Oliver Selfridge, Trenchard More, Arthur Samuel, Allen Newell and Herbert A. Simon, all of whom would create important programs during the first decades of AI research.At the conference Newell and Simon debuted the "Logic Theorist" and McCarthy persuaded the attendees to accept "Artificial Intelligence" as the name of the field. The 1956 Dartmouth conference was the moment that AI gained its name, its mission, its first success and its major players, and is widely considered the birth of AI.

History of AI

Here is the history of AI during 20th century:

Year Milestone / Innovation

1923-Karel Čapek’s play named “Rossum's Universal Robots” (RUR) opens in London,

first use of the word "robot" in English.

1943- Foundations for neural networks laid.

1945- Isaac Asimov, a Columbia University alumni, coined the term Robotics.

1950-Alan Turing introduced Turing Test for evaluation of intelligence and published

Computing Machinery and Intelligence. Claude Shannon published Detailed

Analysis of Chess Playing as a search.

1956-John McCarthy coined the term Artificial Intelligence. Demonstration of the first

running AI program at Carnegie Mellon University.

1958- John McCarthy invents LISP programming language for AI.

1964-Danny Bobrow's dissertation at MIT showed that computers can understand

natural language well enough to solve algebra word problems correctly.

1965-Joseph Weizenbaum at MIT built ELIZA, an interactive problem that carries on

a dialogue in English.

1969-Scientists at Stanford Research Institute Developed Shakey, a robot, equipped

with locomotion, perception, and problem solving.

1973- The Assembly Robotics group at Edinburgh University built Freddy, the Famous

Scottish Robot, capable of using vision to locate and assemble models.

1979- The first computer-controlled autonomous vehicle, Stanford Cart, was built.

1985-Harold Cohen created and demonstrated the drawing program, Aaron.

1990-Major advances in all areas of AI:

 Significant demonstrations in machine learning

 Case-based reasoning

 Multi-agent planning

 Scheduling

 Data mining, Web Crawler

 natural language understanding and translation

 Vision, Virtual Reality

 Games

1997- The Deep Blue Chess Program beats the then world chess champion, Garry

Kasparov.

2000-Interactive robot pets become commercially available. MIT displays Kismet, a

robot with a face that expresses emotions. The robot Nomad explores remote

regions of Antarctica and locates meteorites.

The 5 best programming languages for AI development

Artificial Intelligence is a huge field. With so much to cover, it is really hard to refer one single programming language. Clearly, there are many programming languages that can be used, but not every programming language offers you the best value of your time and effort. And there's no authoritative answer as to which programming language you should use for AI project.

Python

Python is one of the most widely used programming languages in the AI field of Artificial Intelligence thanks to its simplicity. It can seamlessly be used with the data structures and other frequently used AI algorithms.

Java

Java is also a great choice. It is an object-oriented programming language that focuses on providing all the high-level features needed to work on AI projects, it's portable, and it offers in-built garbage collection. The Java community is also a plus point as there will be someone to help you with your queries and problems.

Java is also a good choice as it offers an easy way to code algorithms, and AI is full of algorithms, be they search algorithms, natural language processing algorithms or neural networks. Not to mention that Java also allows for scalability, which is a must-have feature for AI projects.

Lisp

Lisp fares well in the AI field because of its excellent prototyping capabilities and its support for symbolic expressions. It's a powerful programming language and is used in major AI projects, such as Macsyma, DART, and CYC.

The Lisp language is mostly used in the Machine Learning/ ILP sub-field because of its usability and symbolic structure.

Prolog

Prolog stands alongside Lisp when it comes to usefulness and usability. According to the literature, Prolog Programming for Artificial Intelligence, Prolog is one of those programming languages for some basic mechanisms, which can be extremely useful for AI programming.

Prolog is extensively used in expert systems for AI and is also useful for working on medical projects.

C++

C++ is the fastest programming language in the world. Its ability to talk at the hardware level enables developers to improve their program execution time. C++ is extremely useful for AI projects, which are time-sensitive.

In AI, C++ can be used for statistical AI techniques like those found in neural networks. Algorithms can also be written extensively in the C++ for speed execution, and AI in games is mostly coded in C++ for faster execution and response time.

Based on Forrester’s analysis, here’s my list of the 10 hottest AI technologies:

1.       Natural Language Generation: Producing text from computer data. Currently used in customer service, report generation, and summarizing business intelligence insights.

2.      Speech Recognition: Transcribe and transform human speech into format useful for computer applications. Currently used in interactive voice response systems and mobile applications.

3.      Virtual Agents: “The current darling of the media,” says Forrester (I believe they refer to my evolving relationships with Alexa), from simple chatbots to advanced systems that can network with humans. Currently used in customer service and support and as a smart home manager.

4.      Machine Learning Platforms: Providing algorithms, APIs, development and training toolkits, data, as well as computing power to design, train, and deploy models into applications, processes, and other machines. Currently used in a wide range of enterprise applications, mostly `involving prediction or classification.

5.      AI-optimized Hardware: Graphics processing units (GPU) and appliances specifically designed and architected to efficiently run AI-oriented computational jobs. Currently primarily making a difference in deep learning applications.

6.      Decision Management: Engines that insert rules and logic into AI systems and used for initial setup/training and ongoing maintenance and tuning. A mature technology, it is used in a wide variety of enterprise applications, assisting in or performing automated decision-making. 

7.      Deep Learning Platforms: A special type of machine learning consisting of artificial neural networks with multiple abstraction layers. Currently primarily used in pattern recognition and classification applications supported by very large data sets.

8.      Biometrics: Enable more natural interactions between humans and machines, including but not limited to image and touch recognition, speech, and body language. Currently used primarily in market research.

9.      Robotic Process Automation: Using scripts and other methods to automate human action to support efficient business processes. Currently used where it’s too expensive or inefficient for humans to execute a task or a process. 

10.   Text Analytics and NLP: Natural language processing (NLP) uses and supports text analytics by facilitating the understanding of sentence structure and meaning, sentiment, and intent through statistical and machine learning methods. Currently used in fraud detection and security, a wide range of automated assistants, and applications for mining unstructured data.