scholarly journals Tekoäly, koneoppiminen ja teknologinen murros:

2021 ◽  
Vol 5 (2) ◽  
Author(s):  
Henriikka Vartiainen ◽  
Matti Tedre ◽  
Ilkka Jormanainen ◽  
Juho Kahila ◽  
Teemu Valtonen ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Technology Education ◽  
Computer Software ◽  
Network Connectivity ◽  
Learning Technologies ◽  
Data Driven ◽  
Technological Transformation ◽  
The Future ◽  
Design Skills

Tekoälyn ja erityisesti uudet koneoppimisen tekniikat ovat teknologisen murroksen keskeisiä ajureita. Tänä päivänä koneoppiminen on myös yhä enemmän sulautumassa osaksi kehollista ja materiaalista maailmaa sekä vuorovaikutusta. Antureiden, verkkoyhteyksien ja tietokoneohjelmistojen kautta rakennukset, esineet ja tekstiilit ovat muuttumassa älykkäiden esineiden ja toimintojen verkostoiksi. Virtuaalisen, materiaalisen ja kehollisuuden uudenlainen kohtaaminen tarjoaa myös ennennäkemättömiä mahdollisuuksia sekä haasteita koneoppimisen sekä datalähtöisen suunnittelun ja innovoinnin tukemiseen kouluopetuksessa. Tämän artikkelin tavoitteena on rakentaa näkökulmia datatoimijuuteen sekä datalähtöiseen design-ajatteluun koneoppimisen muovaamassa maailmassa.  Artikkeli esittelee digitaalisen, materiaalisen sekä kehollisuuden uudenlaisia mahdollisuuksia sekä riskejä, joka tuo koneoppimisen ajamaan murrokseen liittyviä näkökulmia osaksi käsityön ja teknologiakasvatuksen tulevaisuudesta käytävää tieteellistä ja julkista keskustelua.   Artificial intelligence, machine learning, and technological transformation: Towards data agency and design skills for the future Abstract Artificial intelligence, and especially new machine learning technologies, are key drivers of technological breakthroughs. Today, machine learning is also increasingly merging into the physical and material world as well as into social interaction. Buildings, artifacts, and textiles are transforming into networks of smart objects and activities through sensors, network connectivity, and computer software.  These novel encounters of virtual, material, and bodily interactions also offer unprecedented opportunities and challenges to enhance understandings of machine learning and data-driven design in school education. This article aims to build perspectives on data agency and data-driven design needed in the age of machine learning. It also provides perspectives on the blurring boundaries of virtual, material, and physical worlds and in a manner that brings the breakthrough of machine learning into the scientific and public discussion about the future of craft and technology education. Keywords: artificial intelligence, machine learning, data-driven design, technology education, skills for the future

scholarly journals Before and beyond trust: reliance in medical AI

2021 ◽  
pp. medethics-2020-107095
Author(s):  
Charalampia (Xaroula) Kerasidou ◽  
Angeliki Kerasidou ◽  
Monika Buscher ◽  
Stephen Wilkinson
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
New Technology ◽  
Public Trust ◽  
Learning Technologies ◽  
Data Driven ◽  
Policy Makers ◽  
The Public ◽  
Regulatory Frameworks ◽  
Practice Of Medicine

Artificial intelligence (AI) is changing healthcare and the practice of medicine as data-driven science and machine-learning technologies, in particular, are contributing to a variety of medical and clinical tasks. Such advancements have also raised many questions, especially about public trust. As a response to these concerns there has been a concentrated effort from public bodies, policy-makers and technology companies leading the way in AI to address what is identified as a "public trust deficit". This paper argues that a focus on trust as the basis upon which a relationship between this new technology and the public is built is, at best, ineffective, at worst, inappropriate or even dangerous, as it diverts attention from what is actually needed to actively warrant trust. Instead of agonising about how to facilitate trust, a type of relationship which can leave those trusting vulnerable and exposed, we argue that efforts should be focused on the difficult and dynamic process of ensuring reliance underwritten by strong legal and regulatory frameworks. From there, trust could emerge but not merely as a means to an end. Instead, as something to work in practice towards; that is, the deserved result of an ongoing ethical relationship where there is the appropriate, enforceable and reliable regulatory infrastructure in place for problems, challenges and power asymmetries to be continuously accounted for and appropriately redressed.

scholarly journals Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Muhammad Javed Iqbal ◽  
Zeeshan Javed ◽  
Haleema Sadia ◽  
Ijaz A. Qureshi ◽  
Asma Irshad ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cancer Diagnosis ◽  
Disease Risk ◽  
Clinical Applications ◽  
Optimal Decision ◽  
Great Promise ◽  
Time Range ◽  
Base System ◽  
The Future

AbstractArtificial intelligence (AI) is the use of mathematical algorithms to mimic human cognitive abilities and to address difficult healthcare challenges including complex biological abnormalities like cancer. The exponential growth of AI in the last decade is evidenced to be the potential platform for optimal decision-making by super-intelligence, where the human mind is limited to process huge data in a narrow time range. Cancer is a complex and multifaced disorder with thousands of genetic and epigenetic variations. AI-based algorithms hold great promise to pave the way to identify these genetic mutations and aberrant protein interactions at a very early stage. Modern biomedical research is also focused to bring AI technology to the clinics safely and ethically. AI-based assistance to pathologists and physicians could be the great leap forward towards prediction for disease risk, diagnosis, prognosis, and treatments. Clinical applications of AI and Machine Learning (ML) in cancer diagnosis and treatment are the future of medical guidance towards faster mapping of a new treatment for every individual. By using AI base system approach, researchers can collaborate in real-time and share knowledge digitally to potentially heal millions. In this review, we focused to present game-changing technology of the future in clinics, by connecting biology with Artificial Intelligence and explain how AI-based assistance help oncologist for precise treatment.

scholarly journals Design of a dynamic and self-adapting system, supported with artificial intelligence, machine learning and real-time intelligence for predictive cyber risk analytics in extreme environments – cyber risk in the colonisation of Mars

2021 ◽  
Author(s):  
Petar Radanliev ◽  
David De Roure ◽  
Kevin Page ◽  
Max Van Kleek ◽  
Omar Santos ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Time ◽  
Cyber Security ◽  
Extreme Environments ◽  
Learning Technologies ◽  
Cyber Attacks ◽  
Edge Computing ◽  
Mathematical Formulas ◽  
Cyber Risk

AbstractMultiple governmental agencies and private organisations have made commitments for the colonisation of Mars. Such colonisation requires complex systems and infrastructure that could be very costly to repair or replace in cases of cyber-attacks. This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning and real-time intelligence in edge computing. The paper presents a new mathematical approach for integrating concepts for cognition engine design, edge computing and Artificial Intelligence and Machine Learning to automate anomaly detection. This engine instigates a step change by applying Artificial Intelligence and Machine Learning embedded at the edge of IoT networks, to deliver safe and functional real-time intelligence for predictive cyber risk analytics. This will enhance capacities for risk analytics and assists in the creation of a comprehensive and systematic understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when Artificial Intelligence and Machine Learning technologies are migrated to the periphery of the internet and into local IoT networks.

scholarly journals Reports of the 2013 AAAI Spring Symposium Series

AI Magazine ◽  
2013 ◽  
Vol 34 (3) ◽  
pp. 93-98 ◽  
Author(s):  
Vita Markman ◽  
Georgi Stojanov ◽  
Bipin Indurkhya ◽  
Takashi Kido ◽  
Keiki Takadama ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cognitive Development ◽  
Behavior Change ◽  
Autonomous Systems ◽  
Data Driven ◽  
Intelligent Robots ◽  
Symposium Series ◽  
Development Data ◽  
Weakly Supervised

The Association for the Advancement of Artificial Intelligence was pleased to present the AAAI 2013 Spring Symposium Series, held Monday through Wednesday, March 25-27, 2013. The titles of the eight symposia were Analyzing Microtext, Creativity and (Early) Cognitive Development, Data Driven Wellness: From Self-Tracking to Behavior Change, Designing Intelligent Robots: Reintegrating AI II, Lifelong Machine Learning, Shikakeology: Designing Triggers for Behavior Change, Trust and Autonomous Systems, and Weakly Supervised Learning from Multimedia. This report contains summaries of the symposia, written, in most cases, by the cochairs of the symposium.

Network Security Policy Automation

2022 ◽  
pp. 83-112
Author(s):  
Myo Zarny ◽  
Meng Xu ◽  
Yi Sun
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Network Security ◽  
Security Policy ◽  
Learning Technologies ◽  
Use Cases ◽  
Hybrid Cloud ◽  
Cloud Environments ◽  
Enterprise Security

Network security policy automation enables enterprise security teams to keep pace with increasingly dynamic changes in on-premises and public/hybrid cloud environments. This chapter discusses the most common use cases for policy automation in the enterprise, and new automation methodologies to address them by taking the reader step-by-step through sample use cases. It also looks into how emerging automation solutions are using big data, artificial intelligence, and machine learning technologies to further accelerate network security policy automation and improve application and network security in the process.

Evolving the Materials Genome: How Machine Learning Is Fueling the Next Generation of Materials Discovery

2020 ◽  
Vol 50 (1) ◽  
pp. 1-25 ◽  
Author(s):  
Changwon Suh ◽  
Clyde Fare ◽  
James A. Warren ◽  
Edward O. Pyzer-Knapp
Keyword(s):  
Machine Learning ◽  
Materials Science ◽  
State Of The Art ◽  
Learning Technologies ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Computational Learning ◽  
Data Intensive ◽  
Research Activities ◽  
Materials Genome

Machine learning, applied to chemical and materials data, is transforming the field of materials discovery and design, yet significant work is still required to fully take advantage of machine learning algorithms, tools, and methods. Here, we review the accomplishments to date of the community and assess the maturity of state-of-the-art, data-intensive research activities that combine perspectives from materials science and chemistry. We focus on three major themes—learning to see, learning to estimate, and learning to search materials—to show how advanced computational learning technologies are rapidly and successfully used to solve materials and chemistry problems. Additionally, we discuss a clear path toward a future where data-driven approaches to materials discovery and design are standard practice.

Geophysics Bright Spots

2020 ◽  
Vol 39 (7) ◽  
pp. 518-519
Author(s):  
Jyoti Behura
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Driven ◽  
Daily Routines ◽  
Theoretical Approaches ◽  
Bright Spots ◽  
Catch Up ◽  
A Minor ◽  
Silver Lining

Welcome to the latest installment of Geophysics Bright Spots. For most of us, the pandemic has upended our daily routines. Personally, a minor silver lining in all of this chaos has been the gain of a few extra hours every week. I have used this time to catch up on the fascinating work being done in the field of artificial intelligence and its applications to numerous disciplines. An emerging trend is physics-informed machine learning, which will help us bridge the gap between traditional theoretical approaches and more recent data-driven methodologies, leading to physically plausible and meaningful results. To follow is a list of research that the editors found interesting in the latest issue of Geophysics. I sincerely hope these articles enlighten you and take your mind off some of the chaos surrounding us.

scholarly journals Automation of business processes of entrepreneurial activity in the field of real estate based on artificial intelligence technologies

2021 ◽  
Vol 18 (5) ◽  
pp. 150-159
Author(s):  
A. N. Asaul ◽  
◽  
G. F. Shcherbina ◽  
M. A. Asaul ◽  
◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Estate ◽  
Business Processes ◽  
Learning Technologies ◽  
Entrepreneurial Activity ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Sector ◽  
Entrepreneurial Activities

The article refines the concept of «business process», the essence of business processes` automation in entrepreneurial activities is considered through the use of artificial intelligence and machine learning technologies for IT integration in the real estate sector. Based on the market analysis, the state of development of artificial intelligence and machine learning in Russia, its significance and prospects for implementation in business activities in the real estate sector are studied.

scholarly journals Data Analytics and Public Safety

2021 ◽  
Vol 3 (3) ◽  
pp. 59-62
Author(s):  
Mark Masongsong
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
West Coast ◽  
Data Analytics ◽  
Public Safety ◽  
Learning Technologies ◽  
Key Points

On November 27, 2020, UrbanLogiq CEO Mark Masongsong spoke on the topic of Data Analytics and Public Safety at the 2020 CASIS West Coast Security Conference. The key points of discussion focused on the challenges of artificial intelligence and machine learning technologies and their utility towards public safety. 

Sign in / Sign up

Export Citation Format

Share Document