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Introductory Chapter: Machine Learning in Finance-Emerging Trends and Challenges [chapter]

Jaydip Sen, Rajdeep Sen, Abhishek Dutta
2021 Artificial Intelligence  
The factors that contribute to the increased complexity in feature engineering in machine learning are as follows: The first and the most obvious reason is the large number of features involved in machine  ...  Cognitive Robotics: The robots in the cognitive domain have the power of automating several tasks which are currently done by humans.  ... 
doi:10.5772/intechopen.101120 fatcat:r274n77acfdqdaei4rsngxzw44

Smart Production Workers in Terms of Creativity and Innovation: The Implication for Open Innovation

Bożena Gajdzik, Radosław Wolniak
2022 Journal of Open Innovation: Technology, Market and Complexity  
To what extent do the profile (portfolio) of metallurgy graduates of Polish technical universities turn their attention to the issues related to creativity and innovation?  ...  The aim of the study was to develop a framework for the profile of an employee working in an innovative company transforming to I4.0.  ...  The authors are aware of the limitations of the prepared work, which are narrow in scope and area of research.  ... 
doi:10.3390/joitmc8020068 fatcat:5v2cxivmkbf6ph25psvner7gya

Advances in Artificial Intelligence Require Progress Across all of Computer Science [article]

Gregory D. Hager, Randal Bryant, Eric Horvitz, Maja Mataric, and Vasant Honavar
2017 arXiv   pre-print
Advances in Artificial Intelligence require progress across all of computer science.  ...  The demands of machine learning has led to advances in algorithms, especially for optimization of complex objective functions, reasoning about complex probability distributions e.g., using factorized representations  ...  Theoretical CS: Analysis of Algorithms, Combinatorics, and Complexity AI has influenced, and benefited from, advances in algorithms in a number of areas including automated reasoning, search, planning,  ... 
arXiv:1707.04352v1 fatcat:ibbuhjf5mfbihi4cyxsfibcfvm

Steps Toward Robust Artificial Intelligence

Thomas G. Dietterich
2017 The AI Magazine  
These issues are fascinating, because they touch on the fundamental question of how finite systems can survive and thrive in a complex and dangerous world  ...  Such applications require AI methods to be robust to both the known unknowns (those uncertain aspects of the world about which the computer can reason explicitly) and the unknown unknowns (those aspects  ...  I apologize that I was not able to weave in all of the great work that folks described. I also thank the AI Magazine editor Ashok Goel for his suggestions and editorial improvements.  ... 
doi:10.1609/aimag.v38i3.2756 fatcat:bvtji32735ayfeenfhchba4kee

Pervasive Intelligence

Sebastian Vehlken
2018 Digital Culture & Society  
As is shown on the basis of some generative examples from the field of UAS, robot swarms are imagined to literally penetrate space and control it.  ...  This article seeks to situate collective or swarm robotics (SR) on a conceptual pane which on the one hand sheds light on the peculiar form of AI which is at play in such systems, whilst on the other hand  ...  deduction and reasoning, machine learning, robotics and planning.  ... 
doi:10.14361/dcs-2018-0108 fatcat:rkuidqpnfjehpaychdcs3ccure

A Look Into The Artificial Intelligence And Its Applications In Various Fields Of Life

PROF. DEEPAK SINGH, PROF. ANKIT JAIN
2018 Zenodo  
, robot control, and remote sensing.  ...  More specifically, it is Weak AI, the form of AI where programs are developed to perform specific tasks, that is being utilized for a wide range of activities including medical diagnosis, electronic trading  ...  This class of financial advisers work based on algorithms built to automatically develop a financial portfolio according to the investment goals and risk tolerance of the clients.  ... 
doi:10.5281/zenodo.1411025 fatcat:3myqiyudpzcutddanaanb3ggki

Artificial intelligence and machine learning for future army applications

John M. Fossaceca, Stuart H. Young, Tien Pham, Michael A. Kolodny, Dietrich M. Wiegmann
2018 Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IX  
Based on current trends in artificial intelligence (AI) and machine learning (ML), we provide an overview of novel algorithms intended to address Army-specific needs for increased operational tempo and  ...  for humans and robots to communicate via natural language.  ...  and explanations of their research.  ... 
doi:10.1117/12.2307753 fatcat:mtpmrsiixjfsta6av32kd23lly

Implementation of Competencies by Smart Ethical Artificial Intelligence in Different Environments

Evgeny Bryndin
2020 Software Engineering (Science Publishing Group)  
Consciousness, intelligence and empathy would be worthy design goals that can be engineered in machines.  ...  We present a computational framework for engineering intelligence, empathy and consciousness in machines.  ...  The data is fed to the machine learning algorithms that identify patterns and gradually learn to predict problems and design solutions in a faster, more efficient manner.  ... 
doi:10.11648/j.se.20200804.11 fatcat:jlpivhmvb5fbncuq6jux6tpmxi

Optimisation of decision-making process in industrial robot selection

Tavo KANGRU, Jüri RIIVES, Tauno OTTO, Vladimir KUTS, Madis MOOR
2020 Journal of Machine Engineering  
The successful selection process of industrial robots (IRs) for today's Cyber-Physical Systems is an important topic and there are different possibilities to solve the task.  ...  The objective of the reverse task is to carry out the sensitivity analysis of the existing robot-based manufacturing systems.  ...  An OEE prediction study comparing different machine learning algorithms have shown better reliability and performance dealing with given data [22] .  ... 
doi:10.36897/jme/117788 fatcat:gmkecygz2rdmpmrvrcqzyqtnue

Automation and Machine Learning in Transforming the Financial Industry

Praveen Kumar Donepudi, UST-Global, Inc.
2019 Asian Business Review  
The major purpose of this study was to analyze the influence of machine learning on the digital age, particularly in the field of finance.  ...  This paper demonstrates that there is a lack of experience in the field of machine learning, even as many unskilled or semi-qualified tasks carried out by individuals are carried out by machines.  ...  Such strategies may leverage the potential of machines to perform tasks such as image recognition and the use of natural scripts through experiential learning.  ... 
doi:10.18034/abr.v9i3.494 fatcat:56ufrf4osbc6xjd2h63fkbpw4y

Building AI Applications: Yesterday, Today, and Tomorrow

Reid G. Smith, Joshua Eckroth
2017 The AI Magazine  
We then examine lessons learned during this time and distill these lessons into succinct advice for future application builders.  ...  This article focuses on changes in the world of computing over the last three decades that made building AI applications more feasible.  ...  Acknowledgments In preparing this article, we have drawn  ... 
doi:10.1609/aimag.v38i1.2709 fatcat:aqvtsqt5nffirolskk3ftiameq

Application of Artificial Intelligence in Investment Banks

R. Vedapradha, Hariharan Ravi
2018 Review of Economic and Business Studies  
Banks are automating their processes, migrating their infrastructure and applications to the cloud to create a seamless customer journey.  ...  Artificial Intelligence will focus on cognitive application in functional areas of business along with investment and compliance sectors of financial services industry.  ...  This seems to be a crucial leap in advancement from advanced robotics towards machine learning and predictive analysis.  ... 
doi:10.1515/rebs-2018-0078 fatcat:f4ea4lh6lvf4ze2iegauciq3se

Recent Advances in Deep Reinforcement Learning Applications for Solving Partially Observable Markov Decision Processes (POMDP) Problems Part 2—Applications in Transportation, Industries, Communications and Networking and More Topics

Xuanchen Xiang, Simon Foo, Huanyu Zang
2021 Machine Learning and Knowledge Extraction  
The first part of the overview introduces Markov Decision Processes (MDP) problems and Reinforcement Learning and applications of DRL for solving POMDP problems in games, robotics, and natural language  ...  In part two, we continue to introduce applications in transportation, industries, communications and networking, etc. and discuss the limitations of DRL.  ...  Acknowledgments: The authors would like to express their appreciation to friends and colleagues who had provided assistance during the preparation of this paper.  ... 
doi:10.3390/make3040043 doaj:45bf00de595c44d186fa3d200589c1c5 fatcat:qx4srh7qabgjvd5l6lj6nulhxa

Machine Learning and Formal Method (Dagstuhl Seminar 17351)

Sanjit A. Seshia, Zhu, Xianjin (Jerry), Andreas Krause, Susmit Jha, Marc Herbstritt
2018 Dagstuhl Reports  
The seminar brought together practitioners and reseachers in machine learning and related areas (such as robotics) with those working in formal methods and related areas (such as programming languages  ...  This report documents the program and the outcomes of Dagstuhl Seminar 17351 "Machine Learning and Formal Methods".  ...  In the case of finite concept classes, the corresponding sample complexity appears to be related to the VC-dimension. Some open problems and potential connections to Formal Methods are discussed.  ... 
doi:10.4230/dagrep.7.8.55 dblp:journals/dagstuhl-reports/SeshiaZKJ17 fatcat:pw2cuxb3e5eephkw4khs3fnkae

Teaching Machine Learning in K–12 Computing Education: Potential and Pitfalls

Matti Tedre, Tapani Toivonen, Henriikka Vartiainen, Ilkka Jormanainen, Teemu Valtonen, Juho Kahila, Arnold Pears
2021 IEEE Access  
Despite the central position of machine learning and AI in the field of modern computing, the computing education research body of literature contains remarkably few studies of how people learn to train  ...  This article charts the emerging trajectories in educational practice, theory, and technology related to teaching machine learning in K-12 education.  ...  The much-hyped ''second machine age'' [9] is based on the ability of machine learning techniques to automate many tasks that traditional, rule-based programming struggles with.  ... 
doi:10.1109/access.2021.3097962 fatcat:sgrc2v556be6dj7er4wnqt2vv4
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