Abstract. Quantum information technologies, on the one side, and intelligent learning systems, on the other, are both emergent technologies that will likely have a transforming impact on our society in the future. The respective underlying fields of basic research – quantum information (QI) versus machinelearning and artificialintelligence (AI) – have their own specific questions and challenges, which have hitherto been investigated largely independently. However, in a growing body of recent work, researchers have been prob- ing the question to what extent these fields can indeed learn and benefit from each other. QML explores the interaction between quantum computing and machinelearning, inves- tigating how results and techniques from one field can be used to solve the problems of the other. In recent time, we have witnessed significant breakthroughs in both directions of influence. For instance, quantum computing is finding a vital application in providing speed-ups for machinelearning problems, critical in our “big data” world. Conversely, machinelearning already permeates many cutting-edge technologies, and may become instrumental in advanced quantum technologies. Aside from quantum speed-up in data analysis, or classical machinelearning optimization used in quantum experiments, quan- tum enhancements have also been (theoretically) demonstrated for interactive learning tasks, highlighting the potential of quantum-enhanced learning agents. Finally, works exploring the use of artificialintelligence for the very design of quantum experiments, and for performing parts of genuine research autonomously, have reported their first successes. Beyond the topics of mutual enhancement – exploring what ML/AI can do for quantum physics, and vice versa – researchers have also broached the fundamental issue of quantum generalizations of learning and AI concepts. This deals with questions of the very meaning of learning and intelligence in a world that is fully described by quantum mechanics. In this review, we describe the main ideas, recent developments, and progress in a broad spectrum of research investigating machinelearning and artificialintelligence in the quantum domain.
Machinelearning is an application of ArtificialIntelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machinelearning focuses on the development of computer programs that can access data and use it learn for themselves. Machinelearning is a category of algorithm that allows software applications to become more accurate in predicting outcomes without being explicitly programmed
AI (ArtificialIntelligence), in a broader view is making machines capable of carrying out tasks in ‘smart’ moves. ML (MachineLearning) on the other hand, is an application of AI where machines can have access to data and are permitted to learn by themselves. Through ML and application of data, program can accomplish defined tasks. At present, our world is going through its worst times, since World War 2. Dealing with Covid-19 pandemic is proving to be one of the most difficult and biggest crisis of all time. In India alone, this rapidly growing disease has already infected more than two lakh people.
Healthcare industry is currently undergoing a digital transformation, and ArtificialIntelligence (AI) is the latest buzzword in the healthcare domain. The accuracy and efficiency of AI-based decisions are already been heard across countries. Moreover, the increasing availability of electronic clinical data can be combined with big data analytics to harness the power of AI applications in healthcare. Like other countries, the Indian healthcare industry has also witnessed the growth of AI-based applications. A review of the literature for data on AI and machinelearning was conducted. In this article, we discuss AI, the need for AI in healthcare, and its current status. An overview of AI in the Indian healthcare setting has also been discussed.
People might have hurt their joints or for some other reason and cannot use their limbs to the full extent, such as after a fall, a stroke, or an accident. There is a need to develop an application to distinguish normal person and affected person using Artificialintelligence and Machinelearning and to provide the need of physiotherapy to the affected people. We are proposing an idea of Automated system which will track human Range of motion while physiotherapy of patient. Background
Many authors apply the Raspberry PI for Home Automation, in this paper Raspberry PI is used for Self-driving cars with artificialintelligence. The work described in this paper was initiated to develop a device forcontinuous safety distance monitoring and proper movement of the traffic. The aim of this paper is to give an overview about raspberry pi based on intelligent systems and also to use for self-driving car. The main aim of future technology is to automate everything but coming to cars, automation is not sufficient to take real time decisions to drive the car because humans are able to learn from previous experiences but cars can’t do it, so cars had to learn how to drive similar to humans. This can be achieved by using MachineLearning Algorithms and one of the ways to achieve machinelearning is artificial neural networks. These are similar to neural networks in our brain.
In his US senate hearing in April 2018, Mark Zuckerberg stressed the necessary capabilities of Facebook’s “AI tools (…) to (…) identify hate speech (…)” or “ (…) terrorist propaganda” . Researchers would typically describe such tasks of identifying specific instances within social media platforms as classification tasks within the field of (supervised) machinelearning –. However, with rising popularity of artificialintelligence (AI) , the term AI is often used interchangeably with machinelearning–not only by Facebook’s CEO in the example above or in other interviews , but also across various theoretical and application-oriented contribu- tions in recent literature –. Carner (2017) even states that he still uses AI as a synonym for machinelearning although knowing this is not correct . Such ambiguity, though, may lead to multiple imprecisions both in research and practice when conversing about methods, concepts, and results.
National Mapping agencies (NMA) are frequently tasked with providing highly accurate geospatial data for a range of customers. Traditionally, this challenge has been met by combining the collection of remote sensing data with extensive field work, and the manual interpretation and processing of the combined data. Consequently, this task is a significant logistical undertaking which benefits the production of high quality output, but which is extremely expensive to deliver. Therefore, novel approaches that can automate feature extraction and classification from remotely sensed data, are of great potential interest to NMAs across the entire sector. Using research undertaken at Great Britain’s NMA; Ordnance Survey (OS) as an example, this paper provides an overview of the recent advances at an NMA in the use of artificialintelligence (AI), including machinelearning (ML) and deep learning (DL) based applications. Examples of these approaches are in automating the process of feature extraction and classification from remotely sensed aerial imagery. In addition, recent OS research in applying deep (convolutional) neural network architectures to image classification are also described. This overview is intended to be useful to other NMAs who may be considering the adoption of similar approaches within their workflows.
The National Science Foundation (NSF), through its Combined Research and Curriculum Development program, has recently funded a project involving the integration of machinelearning into the engineering curriculum 4 . The project involved two phases, one that integrates machinelearning modules into a variety of first and second year engineering courses and the second phase that involves the development of two upper level courses in machinelearning. Our current project, also funded by the National Science Foundation, is an adaptation of the above project. Our target audience is different. Our material targets juniors and seniors who have a strong computer science background, including programming, data structures and algorithms, and discrete mathematics. Thus, we can concentrate on machinelearning concepts and use them as a unifying theme for introducing the core concepts of artificialintelligence. In addition, the framework being proposed is adaptable to allow instructors to extend it based on local needs. Our project incorporates machinelearning as a unifying theme for the AI course through a set of hands-on lab projects. Machinelearning is inherently connected with the AI core topics and provides methodology and technology to enhance real-world applications within many of these topics. Machinelearning also provides a bridge between AI technology and modern software engineering. As Mitchell 12 points out, machinelearning is now considered as a technology for both software development (especially suitable for difficult-to-program applications or for customizing software) and building intelligent software (i.e., a tool for AI programming). Planning algorithms and machinelearning techniques are important in several areas of AI and hence their in-depth coverage is important in such a course. While at times an agent may be able to react immediately, there are times where planning and evaluating potential actions is
There are thousands of real estate technology firms out there, including the 2,000+ on the lists that were used as a basis for this research, and that number is growing quickly. However, through the process of identifying companies that employ machinelearning or artificialintelligence
From the 1970s onward, development in artificialintelligence took another step. Many leading compa- nies have started their research in areas such as machinelearning, expert system, pattern recognition, and robotics. The WABOT-1 was the first full-scale humanoid robot built in 1972, which was able to walk and communicate with people in Japanese. In 1974, the first autonomous vehicle was created in the Stanford AI lab. By the same year, the Internet came in use for the first time. (Mijwil 2015.) Since the 1980s, AI has expanded into a more extensive study of the interaction between the body, brain, and environment, and how intelligence rises from such interaction. In computer science and psychology, many algorithms have been applied to many learning problems. In 1982, Japan began a project to de- velop fifth-generation technology. The Ministry of International Trade and Industry of Japan had started the project to create computers using massively parallel computing and logical programming. The pro- ject aimed to build an intelligent machine with listening and speaking abilities. (Bala 2019.) Simi- larly, much of the work was done using neural networks. After much research and development in neural networks, ALAVIN was introduced as the first driverless self-driving vehicle using neural networks in 1986. Self-driving vehicles may seem like a recent technological phenomenon, but the engineers and researchers have been building self-driving vehicles for decades. ALAVIN was considered as the fore- father of today’s self- driving cars. (Hawkins 2016.)
Artificialintelligence (AI) and machinelearning (ML) have seen widespread adoption by organizations seeking to identify and hire high-quality job applicants. Yet the volume, variety, and velocity of professional involvement among I-O psychologists remains relatively limited when it comes to developing and evaluating AI/ML applications for talent assessment and selection. Furthermore, there is a paucity of empirical research that investigates the reliability, validity, and fairness of AI/ML tools in organizational contexts. To stimulate future involvement and research, we share our review and perspective on the current state of AI/ML in talent assessment as well as its benefits and potential pitfalls; and in addressing the issue of fairness, we present experimental evidence regarding the potential for AI/ML to evoke adverse reactions from job applicants during selection procedures. We close by emphasizing increased collaboration among I-O psychologists, computer scientists, legal scholars, and members of other professional disciplines in developing, implementing, and evaluating AI/ML applications in organizational contexts.
In recent years, research into the use of artificialintelligence (AI) in the field of medicine has increased. Machinelearning is a type of AI that allows computers to learn without being explicitly programmed for a given task. Using machinelearning algorithms (MLAs) such as support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF), highly efficient, object- ive, and accurate disease diagnosis models can be con- structed. Based on structural MRI data, Bisenius et al. applied the SVM method for predicting primary pro- gressive aphasia subtypes. Their results showed that the method provided a high degree of accuracy of between 91 and 97%. Forghani et al.  used the RF method to design a model for predicting lymph node metastasis of squamous cell carcinoma of the head and neck, achiev- ing a diagnostic accuracy of 88%. Kim et al. used decision tree, RF, KNN and SVM methods to construct several models for the diagnosis of glaucoma . The sensitivity, specificity, and accuracy of these four models reached 95% and higher. The application of MLAs in the diagnosis of TPE is uncommon , and comparisons between the diagnostic performances of various algorithmic models have not been drawn. The diagnostic performances of pfADA and MLAs have also not been compared.
Artificialintelligence is an intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. Machinelearning is a field of ArtificialIntelligence in which it studies algorithms and different statistical models which can be used by machines or computers to perform tasks without explicit instructions. ArtificialIntelligence and MachineLearning can be used to fight piracy by using content monitoring solutions. Data mining is the process. It is used for discovering patterns in large data sets. Data mining techniques will be used to search the web for the media content and identify piracy threats. It will give data of searched media content. This data can be used by Private Organizations or Government Agencies to get rid of the Pirated content of the Web.
Artificialintelligence (AI g ) and machinelearning (ML g ) applications have been developed to generate and inter- rogate large, accumulating knowledge bases using onto- logical approaches. In the HBCP, building computer programs to extract and process knowledge from text documents at a level that is usable by experts in the do- main, requires several elements that can generally be equated with intelligence, such as advanced reading abil- ity and significant domain understanding. In this respect, a computer program performing this task can be thought of as artificially intelligent.
Abstract— Machinelearning is a branch of artificialintelligence science i.e. the systems that can learn data. For example, a machinelearning system can learn e-mail receiving and distinguish the difference between spam and non-spam message from each other. After training, the system can put new messages in their folders using classification. Currently, we do not know how to program computers in order to human learn more efficient. Although the methods that have been discovered operate very effectively for certain purposes, not suitable for all purposes. For example, machinelearning algorithms are commonly used in data mining. Even in areas where data are concerned, these algorithms operate and result much better than other methods. For example, in issues such as speech recognition, algorithms based on machinelearning resulted much better than the other methods. Apparently, it seems that our knowledge of computers will improve gradually. Certainly, it can be said that the topic of machinelearning play a highly significant role in the field of computer science and game technology. This paper describes machinelearning algorithms, feature selection methods, dimensions reduction, and deleting of useless data.
Debugging the intelligent system is an integral part of research. This help developers in a number of ways — correct the mistakes and shortcomings in the system, trust the system, etc. In  authors Kulesza et al., performed an illusive study on how the models equipped with explainable models should explain their prediction the users. They analysed their study with two important aspects of explanation — ‘Soundness’ and ‘Completeness’. They interpreted Soundness as the truthfulness associated with each component of the system and Completeness as the ability to describe all the intrinsic systems. In their attempt to answer some of the research questions, they performed the experiment on a music recommendation system under four different circumstances, described as High Soundness and High Completeness (HH), Medium Soundness and Medium Completeness (MM), High Soundness and Low Completeness (HSLC) and Low Soundness and High Completeness (LSHC). They tried to answer queries relating to impact of soundness and completeness on the model’s mental health; beneficial information; obstacles; cost-benefit trade off and trust. Most complete models are proven to be beneficial for the mental health of the model. Complete systems also proven to be associated with low cost and high benefit incurred. Complete systems are also associated with the trust associated with the model. Hence, the end result indicated that completeness is inferior to soundness when the model tries to explain how it arrived at the prediction. With the development of the concept of explainability as a new learning technology, various myths and misconceptions are also associated with the concept. Most of these remain unobserved, which may pose a threat to the conclusions made by the model. Most of the misconception, as highlighted by many research studies, including the Defense Advanced Research Project Agency (DARPA) [13, 52] and others, such as , is the trade-off existing between the accuracy computed by the model along with the explanation provided by it. The accuracy of a model is, in most cases, independent of the complexity of the model. Though most of the research community believes that more complex structured algorithms produce more accurate results, i.e. the more complex is the model, the higher is the accuracy. This is not always true when one has to deal with the structured and meaningful data. When dealing with such data, both the simple and complex structured models show similar accuracy; but their explainability may vary.
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Digital image processing and machinelearning have been extensively used in medical applications and especially for cancer diagnosis and classification. Deep learning is spreading its roots in the field of biomedical image analysis and these systems are being used as a machine aid for human experts. These technologies give access to huge amount of data thereby increasing the possibility of achieving high accuracy classification systems. Given different tumor types and the many categories within the tumor type, it is essential to have substantial computational resources including processor power, memory space for reducing the time consumed to a manageable limit once we have designed automated system and the machinelearning algorithms with statistical analysis for classifying the tumor stage/grade
and deductively these algorithms relies upon the information structures utilized and additionally hypotheses of learning subjective and hereditary structures. Yet characteristic method for learning gives awesome exposures for understanding and great degree for wide range of kinds of conditions. Numerous machinelearning calculation are by and large being obtained from current reasoning in intellectual science and neural systems. General we can state that learning is characterized as far as enhancing execution in light of some measure. To know whether an operator has learned, we should characterize a measure of accomplishment. The measure is generally not how well the operator performs on the preparation encounters, yet how well the specialist performs for new encounters. In this examination paper we will consider the two primary sorts of algorithms i.e. supervised and unsupervised learning.