keyword
https://read.qxmd.com/read/34467642/when-is-psychology-research-useful-in-artificial-intelligence-a-case-for-reducing-computational-complexity-in-problem-solving
#21
JOURNAL ARTICLE
Sébastien Hélie, Zygmunt Pizlo
A problem is a situation in which an agent seeks to attain a given goal without knowing how to achieve it. Human problem solving is typically studied as a search in a problem space composed of states (information about the environment) and operators (to move between states). A problem such as playing a game of chess has <mml:math xmlns:mml="https://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:msup><mml:mn>10</mml:mn> <mml:mn>120</mml:mn></mml:msup> <mml:annotation>$10^{120}$</mml:annotation></mml:semantics> </mml:math> possible states, and a traveling salesperson problem with as little as 82 cities already has more than <mml:math xmlns:mml="https://www...
August 31, 2021: Topics in Cognitive Science
https://read.qxmd.com/read/34136801/the-applicability-of-self-play-algorithms-to-trading-and-forecasting-financial-markets
#22
REVIEW
Jan-Alexander Posth, Piotr Kotlarz, Branka Hadji Misheva, Joerg Osterrieder, Peter Schwendner
The central research question to answer in this study is whether the AI methodology of Self-Play can be applied to financial markets. In typical use-cases of Self-Play, two AI agents play against each other in a particular game, e.g., chess or Go. By repeatedly playing the game, they learn its rules as well as possible winning strategies. When considering financial markets, however, we usually have one player-the trader-that does not face one individual adversary but competes against a vast universe of other market participants...
2021: Frontiers in artificial intelligence
https://read.qxmd.com/read/33817031/tkfim-top-k-frequent-itemset-mining-technique-based-on-equivalence-classes
#23
JOURNAL ARTICLE
Saood Iqbal, Abdul Shahid, Muhammad Roman, Zahid Khan, Shaha Al-Otaibi, Lisu Yu
Frequently used items mining is a significant subject of data mining studies. In the last ten years, due to innovative development, the quantity of data has grown exponentially. For frequent Itemset (FIs) mining applications, it imposes new challenges. Misconceived information may be found in recent algorithms, including both threshold and size based algorithms. Threshold value plays a central role in generating frequent itemsets from the given dataset. Selecting a support threshold value is very complicated for those unaware of the dataset's characteristics...
2021: PeerJ. Computer Science
https://read.qxmd.com/read/33361790/mastering-atari-go-chess-and-shogi-by-planning-with-a-learned-model
#24
JOURNAL ARTICLE
Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, Timothy Lillicrap, David Silver
Constructing agents with planning capabilities has long been one of the main challenges in the pursuit of artificial intelligence. Tree-based planning methods have enjoyed huge success in challenging domains, such as chess1 and Go2 , where a perfect simulator is available. However, in real-world problems, the dynamics governing the environment are often complex and unknown. Here we present the MuZero algorithm, which, by combining a tree-based search with a learned model, achieves superhuman performance in a range of challenging and visually complex domains, without any knowledge of their underlying dynamics...
December 2020: Nature
https://read.qxmd.com/read/33300493/the-impact-of-artificial-intelligence-on-the-chess-world
#25
JOURNAL ARTICLE
Delia Monica Duca Iliescu
This paper focuses on key areas in which artificial intelligence has affected the chess world, including cheat detection methods, which are especially necessary recently, as there has been an unexpected rise in the popularity of online chess. Many major chess events that were to take place in 2020 have been canceled, but the global popularity of chess has in fact grown in recent months due to easier conversion of the game from offline to online formats compared with other games. Still, though a game of chess can be easily played online, there are some concerns about the increased chances of cheating...
December 10, 2020: JMIR Serious Games
https://read.qxmd.com/read/33014180/balanced-difficulty-task-finder-an-adaptive-recommendation-method-for-learning-tasks-based-on-the-concept-of-state-of-flow
#26
JOURNAL ARTICLE
Anis Yazidi, Asieh Abolpour Mofrad, Morten Goodwin, Hugo Lewi Hammer, Erik Arntzen
An adaptive task difficulty assignment method which we reckon as balanced difficulty task finder (BDTF) is proposed in this paper. The aim is to recommend tasks to a learner using a trade-off between skills of the learner and difficulty of the tasks such that the learner experiences a state of flow during the learning. Flow is a mental state that psychologists refer to when someone is completely immersed in an activity. Flow state is a multidisciplinary field of research and has been studied not only in psychology, but also neuroscience, education, sport, and games...
October 2020: Cognitive Neurodynamics
https://read.qxmd.com/read/32530098/introduction-to-artificial-intelligence-in-ultrasound-imaging-in-obstetrics-and-gynecology
#27
JOURNAL ARTICLE
L Drukker, J A Noble, A T Papageorghiou
Artificial intelligence (AI) uses data and algorithms to aim to draw conclusions that are as good as, or even better than, those drawn by humans. AI is already part of our daily life; it is behind face recognition technology, speech recognition in virtual assistants (such as Amazon Alexa, Apple's Siri, Google Assistant and Microsoft Cortana) and self-driving cars. AI software has been able to beat world champions in chess, Go and recently even Poker. Relevant to our community, it is a prominent source of innovation in healthcare, already helping to develop new drugs, support clinical decisions and provide quality assurance in radiology...
October 2020: Ultrasound in Obstetrics & Gynecology
https://read.qxmd.com/read/32086661/the-influence-of-context-on-information-processing
#28
JOURNAL ARTICLE
Jana Krivec, Matej Guid
The main research question of this study is how the processing of information relates to different contextual characteristics. More specifically, how the context is associated with efficiency of information processing (success and speed), size of chunks, speed of chunk processing and the recall of a chunk. The research domain was the game of chess. The efficiency of information processing and the chunk characteristics were defined with the reconstruction of sequences of chess moves. Context variables were defined using a slightly adapted chess program...
May 2020: Cognitive Processing
https://read.qxmd.com/read/31958283/use-of-artificial-intelligence-in-imaging-in-rheumatology-current-status-and-future-perspectives
#29
REVIEW
Berend Stoel
After decades of basic research with many setbacks, artificial intelligence (AI) has recently obtained significant breakthroughs, enabling computer programs to outperform human interpretation of medical images in very specific areas. After this shock wave that probably exceeds the impact of the first AI victory of defeating the world chess champion in 1997, some reflection may be appropriate on the consequences for clinical imaging in rheumatology. In this narrative review, a short explanation is given about the various AI techniques, including 'deep learning', and how these have been applied to rheumatological imaging, focussing on rheumatoid arthritis and systemic sclerosis as examples...
January 2020: RMD Open
https://read.qxmd.com/read/31898014/ai-based-computer-aided-diagnosis-ai-cad-the-latest-review-to-read-first
#30
REVIEW
Hiroshi Fujita
The third artificial intelligence (AI) boom is coming, and there is an inkling that the speed of its evolution is quickly increasing. In games like chess, shogi, and go, AI has already defeated human champions, and the fact that it is able to achieve autonomous driving is also being realized. Under these circumstances, AI has evolved and diversified at a remarkable pace in medical diagnosis, especially in diagnostic imaging. Therefore, this commentary focuses on AI in medical diagnostic imaging and explains the recent development trends and practical applications of computer-aided detection/diagnosis using artificial intelligence, especially deep learning technology, as well as some topics surrounding it...
March 2020: Radiological Physics and Technology
https://read.qxmd.com/read/31451633/the-joint-influence-of-intelligence-and-practice-on-skill-development-throughout-the-life-span
#31
JOURNAL ARTICLE
Nemanja Vaci, Peter Edelsbrunner, Elsbeth Stern, Aljoscha Neubauer, Merim Bilalić, Roland H Grabner
The relative importance of different factors in the development of human skills has been extensively discussed. Research on expertise indicates that focused practice may be the sole determinant of skill, while intelligence researchers underline the relative importance of abilities at even the highest level of skill. There is indeed a large body of research that acknowledges the role of both factors in skill development and retention. It is, however, unknown how intelligence and practice come together to enable the acquisition and retention of complex skills across the life span...
September 10, 2019: Proceedings of the National Academy of Sciences of the United States of America
https://read.qxmd.com/read/31316011/potential-role-of-machine-learning-in-oncology
#32
JOURNAL ARTICLE
S Satish Kumar, Kamran H Awan, Shankargouda Patil, Indu Bharkavi Sk, A Thirumal Raj
Machine learning (ML) is the ability of computers to learn from data autonomously. It is a core branch of artificial intelligence (AI), which is defined as the ability of a machine to replicate the intellectual processes of humans independently.1,2 The evolution of the microprocessor for home computers resulted in increased computing speed, efficient data collection, storage, and retrieval capacity. Thus AI techniques have evolved, which led to the discovery of artificial neural networks which are computer modeling algorithms mimicking the human brain...
May 1, 2019: Journal of Contemporary Dental Practice
https://read.qxmd.com/read/30916908/-artificial-intelligence-and-the-skin-specialist
#33
JOURNAL ARTICLE
Angeliki Koulouri, François Kuonen, Olivier Gaide
Artificial intelligence's progress is spread on front pages of both lay and scientific journals. After Chess, after Go, before Dota2 and Starcraft, super-trained softwares have equaled or out-performed dermatologists. But what is the future of these computer programs and how will they change clinical practice for both the general practitioner and the skin specialist? It is time to ask these questions, even though the promises of these new technologies are not yet available.
March 27, 2019: Revue Médicale Suisse
https://read.qxmd.com/read/30876182/optimisation-of-colour-generation-from-dielectric-nanostructures-using-reinforcement-learning
#34
JOURNAL ARTICLE
Iman Sajedian, Trevon Badloe, Junsuk Rho
Recently, a novel machine learning model has emerged in the field of reinforcement learning known as deep Q-learning. This model is capable of finding the best possible solution in systems consisting of millions of choices, without ever experiencing it before, and has been used to beat the best human minds at complex games such as, Go and chess, which both have a huge number of possible decisions and outcomes for each move. With a human-level intelligence, it has solved the problems that no other machine learning model has done before...
February 18, 2019: Optics Express
https://read.qxmd.com/read/30571349/from-machine-learning-to-artificial-intelligence-applications-in-cardiac-care
#35
JOURNAL ARTICLE
David Tsay, Cam Patterson
Artificial intelligence offers the potential for transformational advancement in cardiovascular care delivery, yet practical applications of this technology have yet to be embedded in clinical workflows and systems. Recent advances in machine learning algorithms and accessibility to big data sources have created the ability for software to solve highly specialized problems outside of health care, such as autonomous driving, speech recognition, and game playing (chess and Go), at superhuman efficiency previously not thought possible...
November 27, 2018: Circulation
https://read.qxmd.com/read/30564390/what-can-associative-learning-do-for-planning
#36
JOURNAL ARTICLE
Johan Lind
There is a new associative learning paradox. The power of associative learning for producing flexible behaviour in non-human animals is downplayed or ignored by researchers in animal cognition, whereas artificial intelligence research shows that associative learning models can beat humans in chess. One phenomenon in which associative learning often is ruled out as an explanation for animal behaviour is flexible planning. However, planning studies have been criticized and questions have been raised regarding both methodological validity and interpretations of results...
November 2018: Royal Society Open Science
https://read.qxmd.com/read/30523106/a-general-reinforcement-learning-algorithm-that-masters-chess-shogi-and-go-through-self-play
#37
JOURNAL ARTICLE
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, Demis Hassabis
The game of chess is the longest-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, domain-specific adaptations, and handcrafted evaluation functions that have been refined by human experts over several decades. By contrast, the AlphaGo Zero program recently achieved superhuman performance in the game of Go by reinforcement learning from self-play. In this paper, we generalize this approach into a single AlphaZero algorithm that can achieve superhuman performance in many challenging games...
December 7, 2018: Science
https://read.qxmd.com/read/30325645/will-machine-learning-end-the-viability-of-radiology-as-a-thriving-medical-specialty
#38
JOURNAL ARTICLE
Stephen Chan, Eliot L Siegel
There have been tremendous advances in artificial intelligence (AI) and machine learning (ML) within the past decade, especially in the application of deep learning to various challenges. These include advanced competitive games (such as Chess and Go), self-driving cars, speech recognition, and intelligent personal assistants. Rapid advances in computer vision for recognition of objects in pictures have led some individuals, including computer science experts and health care system experts in machine learning, to make predictions that ML algorithms will soon lead to the replacement of the radiologist...
February 2019: British Journal of Radiology
https://read.qxmd.com/read/30131686/beyond-domain-specific-expertise-neural-signatures-of-face-and-spatial-working-memory-in-baduk-go-game-experts
#39
JOURNAL ARTICLE
Wi Hoon Jung, Tae Young Lee, Youngwoo B Yoon, Chi-Hoon Choi, Jun Soo Kwon
Recent advances of neuroimaging methodology and artificial intelligence have resulted in renewed interest in board games like chess and Baduk (called Go game in the West) and have provided clues as to the mechanisms behind the games. However, an interesting question that remains to be answered is whether the board game expertise as one of cognitive skills goes beyond just being good at the trained game and how it maps on networks associated with cognitive abilities that are not directly trained. To address this issue, we examined functional activity and connectivity in Baduk experts, compared to novices, while performing a visual n-back working memory (WM) task...
2018: Frontiers in Human Neuroscience
https://read.qxmd.com/read/29339817/cooperating-with-machines
#40
JOURNAL ARTICLE
Jacob W Crandall, Mayada Oudah, Tennom, Fatimah Ishowo-Oloko, Sherief Abdallah, Jean-François Bonnefon, Manuel Cebrian, Azim Shariff, Michael A Goodrich, Iyad Rahwan
Since Alan Turing envisioned artificial intelligence, technical progress has often been measured by the ability to defeat humans in zero-sum encounters (e.g., Chess, Poker, or Go). Less attention has been given to scenarios in which human-machine cooperation is beneficial but non-trivial, such as scenarios in which human and machine preferences are neither fully aligned nor fully in conflict. Cooperation does not require sheer computational power, but instead is facilitated by intuition, cultural norms, emotions, signals, and pre-evolved dispositions...
January 16, 2018: Nature Communications
keyword
keyword
53411
2
3
Fetch more papers »
Fetching more papers... Fetching...
Remove bar
Read by QxMD icon Read
×

Save your favorite articles in one place with a free QxMD account.

×

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"

We want to hear from doctors like you!

Take a second to answer a survey question.