It is a common misconception that artificial intelligence research focuses on creating technologies that resemble human intelligence. However, as Alan Turing wrote, the most important attributes of human intelligence are not the pursuit of mathematical knowledge and the ability to reason, but the ability to learn from experience, perceive the environment, and so on. To understand how these properties of human intelligence can be used to improve other technologies, one must understand these characteristics of human intelligence.
AI researchers and entrepreneurs use the term «artificial intelligence» to define software and algorithms that demonstrate human intelligence. The academic area has since expanded to cover related topics such as natural language processing and systems. Much of the work in this area takes place in universities, research institutes and companies, with investments from companies like Microsoft and Google.
Artificial intelligence is also used in other industries, such as the automatic control of ships, and is commonly used in the development of robotics. Examples of AI applications include speech recognition, image recognition, language processing, computer vision, decision making, robotics, and commercial products including language translation and recommendation engines. Artificial intelligence is at the center of national and international public policy such as the National Science Foundation. Research and development in artificial intelligence is managed by independent organizations that receive grants from public and private agencies. Other organizations, such as The Institute for the Future, have a wealth of information on AI and other emerging technologies and design professions, as well as the talent required to work with those technologies.
The definition of artificial intelligence has evolved since the concept was developed and it is currently not a black and white definition, but rather a continuum. From the 1950s to the 1970s, AI research focused on the automation of mechanical functions. Researchers such as John McCarthy and Marvin Minsky have explored the problems of general computing, general artificial intelligence, reasoning, and memory.
In 1973, Christopher Chabris and Daniel Simons proposed a thought experiment called The Incompatibility of AI and Human Intelligence. The problem described was that if the artificial system was so smart that it was superior to humans or superior to human capabilities, the system could make whatever decisions it wanted. This can violate the fundamental human assumption that people should have the right to make their own choices.
In the late 1970s and early 1980s, the field of activity changed from the classical orientation towards computers to the creation of artificial neural networks. Researchers began to look for ways to teach computers to learn rather than just perform certain tasks. This field developed rapidly during the 1970s and eventually moved from computing to a more scientific-oriented one, and its field of application expanded from computing to human perception and action.
Many researchers in the 1970s and 1980s focused on defining the boundaries of human and computer intelligence, or the capabilities required for artificial intelligence. The boundary should be wide enough to cover the full range of human capabilities.
While the human brain is capable of processing gigabytes of data, it was difficult for leading researchers to imagine how an artificial brain could process much larger amounts of data. At the time, the computer was a primitive device and could only process single-digit percentages of data on a human scale.
During that era, artificial intelligence scientists also began work on algorithms to teach computers to learn from their own experience a concept similar to how the human brain learns. Meanwhile, in parallel, a large number of computer scientists developed search methods that could solve complex problems by looking for a huge number of possible solutions.
Artificial intelligence research today continues to focus on automating specific tasks. This emphasis on the automation of cognitive tasks is called «narrow AI». Many researchers working in this field are working on facial recognition, language translation, playing chess, composing music, driving cars, playing computer games, and analyzing medical images. Over the next decade, narrow AI is expected to develop more specialized and advanced applications, including a computer system that can detect early stages of Alzheimers disease and analyze cancers.
The public uses and interacts with artificial intelligence every day, but the value of AI in education and business is often overlooked. AI has significant potential in almost all industries, such as pharmaceuticals, manufacturing, medicine, architecture, law and finance.
Companies are already using artificial intelligence to improve services, improve product quality, lower costs, improve customer service, and save money on data centers. For example, with robotics software, Southwest Airlines and Amadeus can better answer customer questions and use customer-generated reports to improve their productivity. Overall, AI will affect nearly every industry in the coming decades. On average, about 90% of U.S. jobs will be affected by AI by 2030, but the exact percentage varies by industry.
Artificial intelligence can dramatically improve many aspects of our lives. There is a lot of potential for improving health and treating illness and injury, restoring the environment, personal safety, and more. This potential has generated a lot of discussion and debate about its impact on humanity. AI has been shown to be far superior to humans in a variety of tasks such as machine vision, speech recognition, machine learning, language translation, computer vision, natural language processing, pattern recognition, cryptography, chess.
Many of the fundamental technologies developed in the 1960s were largely abandoned by the late 1990s, leaving gaps in this area. Fundamental technologies that define AI today, such as neural networks, data structures, and so on. Many modern artificial intelligence technologies are based on these ideas and are much more powerful than their predecessors. Due to the slow pace of change in the tech industry, while current advances have produced some interesting and impressive results, there is little to distinguish them from each other.
Early research in artificial intelligence focused on learning machines that used a knowledge base to change their behavior. In 1970, Marvin Minsky published a concept paper on LISP machines. In 1973, Turing proposed a similar language called ML, which, unlike LISP, recognized a subset of finite and formal sets for inclusion.
In the decades that followed, researchers were able to refine the concepts of natural language processing and knowledge representation. This advance has led to the development of the ubiquitous natural language processing and machine translation technologies in use today.
In 1978, Andrew Ng and Andrew Hsey wrote an influential review article in the journal Nature containing over 2,000 papers on AI and robotic systems. The paper covered many aspects of this area such as modeling, reinforcement learning, decision trees, and social media.
Since then, it has become increasingly difficult to involve researchers in natural language processing, and new advances in robotics and digital sensing have surpassed the state of the art in natural language processing.
In the early 2000s, a lot of attention was paid to the introduction of machine learning. Learning algorithms are mathematical systems that learn by observation.
In the 1960s, Bendixon and Ruelle began to apply the concepts of learning machines to education and beyond. Their innovations inspired researchers to further explore this area, and many research papers were published in this area in the 1990s.
In the 1960s, Bendixon and Ruelle began to apply the concepts of learning machines to education and beyond. Their innovations inspired researchers to further explore this area, and many research papers were published in this area in the 1990s.
Sumit Chintals 2002 article, Learning with Fake Data, discusses a feedback system in which artificial intelligence learns by experimenting with the data it receives as input.
In 2006, Judofsky, Stein, and Tucker published an article on deep learning that proposed a scalable deep neural network architecture.
In 2007, Rohit described" hyperparameters». The term "hyperparameter" is used to describe a mathematical formula that is used in computer learning. While it is possible to design systems with tens, hundreds, or thousands of hyperparameters, the number of parameters must be carefully controlled because overloading the system with too many hyperparameters can degrade performance.
Google co-founders Larry Page and Sergey Brin published an article on the future of robotics in 2006. This document includes a section on developing intelligent systems using deep neural networks. Page also noted that this area would not be practical without a wide range of underlying technologies.
In 2008, Max Jaderberg and Shai Halevi published «Deep Speech». In it was presented the technology «Deep Speech», which allowed the system to determine the phonemes of spoken language. The system entered four sentences and was able to output sentences that were almost grammatically correct, but had the wrong pronunciation of several consonants. Deep Speech was one of the first programs to learn to speak and had a great impact on research in the field of natural language processing.
In 2010, Jeffrey Hinton describes the relationship between human-centered design and the field of natural language processing. The book was widely cited because it introduced the field of human-centered AI research.
Around the same time, Clifford Nass and Herbert A. Simon emphasized the importance of human-centered design in building artificial intelligence systems and laid out a number of design principles.
In 2014, Hinton and Thomas Kluver describe neural networks and use them to build a system that can transcribe a person with a cleft lip. The transcription system has shown significant improvements in speech recognition accuracy.
In 2015, Neil Jacobstein and Arun Ross describe the TensorFlow framework, which is now one of the most popular data-driven machine learning frameworks.
In 2017, Fei-Fei Li highlights the importance of deep learning in data science and describes some of the research that has been done in this area.
Artificial neural networks and genetic algorithms
Artificial neural networks (ANNs), commonly referred to simply as deep learning algorithms, represent a paradigm shift in artificial intelligence. They have the ability to explore concepts and relationships without any predefined parameters. ANNs are also capable of studying unstructured information that goes beyond the requirements of established rules. Initial ANN models were built in the 1960s, but research has intensified in the last decade.
The rise in computing power opened up a new world of computing through the development of convolutional neural networks (CNNs) in the early 1970s. In the early 1980s, Stanislav Ulam developed the symbolic distance function, which became the basis for future network learning algorithms.
By the late 1970s, several CNNs were deployed on ImageNet. In the early 2000s, floating point GPUs provided exponential performance and low power consumption for data processing. The emergence of deep learning algorithms is a consequence of the application of more general computational architectures and new methods for training neural networks.
With the latest advances in multi-core and GPU technology, training neural networks with multiple GPUs is possible at a fraction of the cost of conventional training. One of the most popular examples is GPU deep learning. Training deep neural networks on GPUs is fast, scalable, and requires low-level programming capabilities to implement modern deep learning architectures.
Optimization of genetic algorithms can be an effective method for finding promising solutions to computer science problems.
Genetic algorithm techniques are usually implemented in a simulation environment, and many common optimization problems can be solved using standard library software such as PowerMorph or Q-Learning.
Traditional software applications based on genetic algorithms require a trained expert to program and customize their agent. To enable automatic scripting, genetic algorithm software can be distributed as executable source code, which can then be compiled by ordinary users.
Genetic algorithms are optimized for known solutions that can be of any type (e.g. integer search, matrix factorization, partitioning, etc.). In contrast, Monte Carlo optimization requires that an optimal solution can be generated by an unknown method. The advantage of genetic algorithms over other optimization methods lies in their automatic control over the number of generations required, initial parameters, evaluation function, and reward for accurate predictions.
An important property of a genetic algorithm is its ability to create a «wild» configuration of parameters (for example, alternating hot and cold endpoints) that correspond to a given learning rate (learning rate times the number of generations). This property allows the user to analyze and decide if the equilibrium configuration is unstable.
The downside of genetic algorithms is their dependence on distributed memory management. While extensive optimization techniques can be used to handle large input sets and multiple processor / core configurations, the complexity of this operation can make genetic algorithm decisions vulnerable to resource constraints that impede progress. Even with the genetic algorithm code, in theory, programs based on genetic algorithms can only find solutions to problems when run on the appropriate computer architecture. Examples of problems associated with a genetic algorithm running on a more limited architecture include memory limits for storing representations of the genetic algorithm, memory limits imposed by the underlying operating system or instruction set, and memory limits imposed by the programmer, such as limits on the amount of processing power, allocated for the genetic algorithm and / or memory requirements.
Many optimization algorithms have been developed that allow genetic algorithms to run efficiently on limited hardware or on a conventional computer, but implementations of genetic algorithms based on these algorithms have been limited due to their high requirements for specialized hardware.
Heterogeneous hardware is capable of delivering genetic algorithms with the speed and flexibility of a conventional computer, while using less energy and computer time. Most implementations of genetic algorithms are based on a genetic architecture approach.
Genetic algorithms can be seen as an example of discrete optimization and computational complexity theory. They provide a short explanation of evolutionary algorithms. Unlike search algorithms, genetic algorithms allow you to control changes in parameters that affect the performance of a solution. For this, the genetic algorithm can study a set of algorithms for finding the optimal solution. When an algorithm converges to an optimal solution, it can choose an algorithm that is faster or more accurate.
In the mathematical language of programmatic analysis, a genetic algorithm is a function that maps states into transitions to the next states. A state can be a single location in a shared space or a collection of states. «Generation» is the number of states and transitions between them that must be performed to achieve the target state. The genetic algorithm uses the transition probability to find the optimal solution, and uses a small number of new mutations each time a generation ends. Thus, most mutations are random (or quasi-random) and therefore can be ignored by the genetic algorithm to test behavior or make decisions. However, if the algorithm can be used to solve the optimization problem, then this fact can be used to implement the mutation step.