The Machine is Doing the Work: Interview with Nicholas Mirzoeff

2018 ◽  
pp. 4-7
Author(s):  
Lenart Kučić ◽  
Nicholas Mirzoeff
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cloud Computing ◽  
Machine Vision ◽  
Communication Technologies ◽  
The Body ◽  
X Rays ◽  
Optical Performance ◽  
The World ◽  
Learning Machine

Optical and mechanical tools were the first major “augmentation” of human senses. The microscope approached the worlds that were too small for the optical performance of the eye. The telescope touched the too far-off space; X-rays radiated the inaccessible interior of the body. Such augmentations were not innocent, as they demanded a different interpretation of the world, which would correspond to images of infinitely small, remote or hidden. Similar augmentation is now happening with cloud computing, machine vision and artificial intelligence. With these tools, it may be possible to compile and analyze billions of digital images created daily by people and machines. But who will analyze these images and for what purpose? Will they help us to better understand society and learn from past mistakes? Or have they already been hijacked by attention-merchants and political demagogues who are effectively spreading old ideologies with new communication technologies? Keywords: augmented photography, communication technologies, machine learning, machine vision, reality

Machine Learning as a Silent Observer of Advanced Geoscience Interpretation

2021 ◽  
Author(s):  
Muhammad Sajid
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Transfer Learning ◽  
Learning Performance ◽  
Learning Approach ◽  
Seismic Fault ◽  
Facies Classification ◽  
The World ◽  
Learning Machine ◽  
Fault Interpretation

Abstract Machine learning is proving its successes in all fields of life including medical, automotive, planning, engineering, etc. In the world of geoscience, ML showed impressive results in seismic fault interpretation, advance seismic attributes analysis, facies classification, and geobodies extraction such as channels, carbonates, and salt, etc. One of the challenges faced in geoscience is the availability of label data which is one of the most time-consuming requirements in supervised deep learning. In this paper, an advanced learning approach is proposed for geoscience where the machine observes the seismic interpretation activities and learns simultaneously as the interpretation progresses. Initial testing showed that through the proposed method along with transfer learning, machine learning performance is highly effective, and the machine accurately predicts features requiring minor post prediction filtering to be accepted as the optimal interpretation.

scholarly journals IMPROVING INTERNET OF THINGS PARKING SYSTEMS

2021 ◽  
Vol 19 (3) ◽  
pp. 163
Author(s):  
Dušan Bogićević
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cloud Computing ◽  
Internet Of Things ◽  
Information Needs ◽  
Vehicle Classification ◽  
The Internet ◽  
Parking Space ◽  
Automated Systems ◽  
The Internet Of Things

Edge data processing represents the new evolution of the Internet and Cloud computing. Its application to the Internet of Things (IoT) is a step towards faster processing of information from sensors for better performance. In automated systems, we have a large number of sensors, whose information needs to be processed in the shortest possible time and acted upon. The paper describes the possibility of applying Artificial Intelligence on Edge devices using the example of finding a parking space for a vehicle, and directing it based on the segment the vehicle belongs to. Algorithm of Machine Learning is used for vehicle classification, which is based on vehicle dimensions.

scholarly journals The Pros & Cons of Business Innovation at IT Organizations

2019 ◽  
Vol 7 (1) ◽  
pp. 82-85
Author(s):  
Geetha Swaminathan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Information Technology ◽  
Cloud Computing ◽  
New Technologies ◽  
Global Market ◽  
Innovative Product ◽  
Business Innovation ◽  
It Organization ◽  
Pros And Cons

In the 21st Century, the buzzword is often used in all fields is “Innovation". It is no wonder using Innovation in day to the conversation as well as striving for innovation execution at organisations in Information Technology (IT) sectors. When we need to talk about innovation in IT sectors in the fast-moving technology IT organisations, they are in a position in increasing its capability in its innovative product and services. There is a lot of benefits out of business innovations that are being reaped in IT companies; there are apparent disadvantages are also the outcome of them. It is quite common, despite all benefits and drawbacks, they are in apposition to survive in the global market. That becomes a great challenge to all IT organisations. In IT organisations which consist of departments such as Development, Testing, Consulting, Networking, Infrastructure, Process and having common platforms and legacy languages, Apart from that they are in the way of invading new technologies such as Digital, Mobile, IoT, Artificial Intelligence, Machine learning Cloud computing. In all the fields, as mentioned above and area, they need to do innovation to sustain their business. This paper will provide elaborate results on Pros and Cons of Business Innovation in IT Organization.

scholarly journals AI in global healthcare: Need for robust governance frameworks

2020 ◽  
Author(s):  
Sandeep Reddy ◽  
Sonia Allan ◽  
Simon Coghlan ◽  
Paul Cooper
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Developing Countries ◽  
Clinical Care ◽  
Ethical Guidelines ◽  
Popular Discourse ◽  
The World ◽  
Healthcare Need

The re-emergence of artificial intelligence (AI) in popular discourse and its application in medicine, especially via machine learning (ML) algorithms, has excited interest from policymakers and clinicians alike. The use of AI in clinical care in both developed and developing countries is no longer a question of ‘if?’ but ‘when?’. This creates a pressing need not only for sound ethical guidelines but also for robust governance frameworks to regulate AI in medicine around the world. In this article, we discuss what components need to be considered in developing these governance frameworks and who should lead this worldwide effort?

Conclusion

2020 ◽  
pp. 97-102
Author(s):  
Benjamin Wiggins
Keyword(s):  
Artificial Intelligence ◽  
United States ◽  
Machine Learning ◽  
Risk Assessment ◽  
Big Data ◽  
The United States ◽  
Blaise Pascal ◽  
Division Problem ◽  
The World

Can risk assessment be made fair? The conclusion of Calculating Race returns to actuarial science’s foundations in probability. The roots of probability rest in a pair of problems posed to Blaise Pascal and Pierre de Fermat in the summer of 1654: “the Dice Problem” and “the Division Problem.” From their very foundation, the mathematics of probability offered the potential not only to be used to gain an advantage (as in the case of the Dice Problem), but also to divide material fairly (as in the case of the Division Problem). As the United States and the world enter an age driven by Big Data, algorithms, artificial intelligence, and machine learning and characterized by an actuarialization of everything, we must remember that risk assessment need not be put to use for individual, corporate, or government advantage but, rather, that it has always been capable of guiding how to distribute risk equitably instead.

Uber and the making of an Algopticon - Insights from the daily life of Montreal drivers

2020 ◽  
Vol 44 (2) ◽  
pp. 241-260
Author(s):  
Rabih Jamil
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Daily Life ◽  
The World ◽  
Taxi Industry ◽  
Platform Economy

Using machine learning and artificial intelligence, Uber has been disrupting the world taxi industry. However, the Uber algorithmic apparatus managed to perfectionize the scalable decentralized tracking and surveillance of mobile living bodies. This article examines the Uber surveillance machinery and discusses the determinants of its algorithmically powered ‘all-seeing power’. The latter is being figured as an Algopticon that reinvents Bentham’s panopticon in the era of the platform economy.

scholarly journals Machine learning challenges and impact: an interview with Thomas Dietterich

2017 ◽  
Vol 5 (1) ◽  
pp. 54-58 ◽  
Author(s):  
Zhi-Hua Zhou
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Driving Force ◽  
Professor Emeritus ◽  
State University ◽  
Learning Society ◽  
Learning Challenges ◽  
The World ◽  
The Usa ◽  
Oregon State University

Abstract Machine learning is the driving force of the hot artificial intelligence (AI) wave. In an interview with NSR, Prof. Thomas Dietterich, the distinguished professor emeritus of computer science at Oregon State University in the USA, the former president of Association of Advancement of Artificial Intelligence (AAAI, the most prestigious association in the field of artificial intelligence) and the founding president of the International Machine Learning Society, talked about exciting recent advances and technical challenges of machine learning, as well as its big impact on the world.

A Study on SARS-CoV-2 (COVID-19) and Machine Learning Based Approach to Detect COVID-19 Through X-Ray Images

2020 ◽  
pp. 2140010
Author(s):  
Anuj Kumar Gupta ◽  
Manvinder Sharma ◽  
Ankit Sharma ◽  
Vikas Menon
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Confusion Matrix ◽  
Training Model ◽  
X Rays ◽  
Wuhan City ◽  
X Ray ◽  
Entire World ◽  
Day By Day ◽  
A New Species

From origin in Wuhan city of China, a highly communicable and deadly virus is spreading in the entire world and is known as COVID-19. COVID-19 is a new species of coronavirus which is affecting respiratory system of human. The virus is known as severe acute respiratory syndrome (SARS) coronavirus 2 abbreviated as SARS-CoV-2 and generally known as coronavirus disease COVID-19. This is growing day by day in countries. The symptoms include fever, cough and difficulty in breathing. As there is no vaccine made for this virus and COVID-19 tests are not readily available, this is causing panic. Various Artificial Intelligence-based algorithms and frameworks are being developed to detect this virus, but it has not been tested. People are taking advantages of others by providing duplicate COVID-19 test kits. A work is carried out with deep learning to detect presence of COVID 19. With the use of Convolutional Neural networks, the model is trained with dataset of COVID-19 positive and negative X-Rays. The accuracy of training model is 99% and the confusion matrix shows 98% values that are predicted truly. Hence, the model is able to detect the presence of COVID-19.

The application of artificial intelligence and radiomics in lung cancer

2020 ◽  
Vol 3 (3) ◽  
pp. 214-227
Author(s):  
Yaojie Zhou ◽  
Xiuyuan Xu ◽  
Lujia Song ◽  
Chengdi Wang ◽  
Jixiang Guo ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Lung Cancer ◽  
Early Diagnosis ◽  
Medical Management ◽  
Causes Of Death ◽  
Current Application ◽  
The World ◽  
Great Progress ◽  
Made In

Abstract Lung cancer is one of the most leading causes of death throughout the world, and there is an urgent requirement for the precision medical management of it. Artificial intelligence (AI) consisting of numerous advanced techniques has been widely applied in the field of medical care. Meanwhile, radiomics based on traditional machine learning also does a great job in mining information through medical images. With the integration of AI and radiomics, great progress has been made in the early diagnosis, specific characterization, and prognosis of lung cancer, which has aroused attention all over the world. In this study, we give a brief review of the current application of AI and radiomics for precision medical management in lung cancer.

Down the deep rabbit hole: Untangling deep learning from machine learning and artificial intelligence

First Monday ◽  
2019 ◽  
Author(s):  
Niel Chah
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Historical Context ◽  
Historical Background ◽  
Deep Roots ◽  
Technical Guide ◽  
Learning Machine ◽  
High Level

Interest in deep learning, machine learning, and artificial intelligence from industry and the general public has reached a fever pitch recently. However, these terms are frequently misused, confused, and conflated. This paper serves as a non-technical guide for those interested in a high-level understanding of these increasingly influential notions by exploring briefly the historical context of deep learning, its public presence, and growing concerns over the limitations of these techniques. As a first step, artificial intelligence and machine learning are defined. Next, an overview of the historical background of deep learning reveals its wide scope and deep roots. A case study of a major deep learning implementation is presented in order to analyze public perceptions shaped by companies focused on technology. Finally, a review of deep learning limitations illustrates systemic vulnerabilities and a growing sense of concern over these systems.

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