scholarly journals Toward autonomous design and synthesis of novel inorganic materials

2021 ◽  
Author(s):  
Nathan Szymanski ◽  
Yan Zeng ◽  
Haoyan Huo ◽  
Chris Bartel ◽  
Haegyum Kim ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Recent Progress ◽  
Inorganic Materials ◽  
Design And Synthesis ◽  
Exciting Opportunity

Autonomous experimentation driven by artificial intelligence (AI) provides an exciting opportunity to revolutionize inorganic materials discovery and development. Herein, we review recent progress in the design of self-driving laboratories, including...

Recent progress in the design and synthesis of zeolite-like metal–organic frameworks (ZMOFs)

2021 ◽  
Author(s):  
Xinyao Liu ◽  
Yunling Liu
Keyword(s):  
Building Block ◽  
High Stability ◽  
Recent Progress ◽  
Metal Organic Frameworks ◽  
Excellent Performance ◽  
Design And Synthesis ◽  
Molecular Building Block ◽  
Secondary Building Units ◽  
Metal Organic

ZMOFs are a subset of MOFs that exhibit zeolite-like topologies. Using molecular building block strategy, many ZMOFs with high stability and excellent performance can be rationally designed and synthesized using different secondary building units.

scholarly journals Deep learning-enabled medical computer vision

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Andre Esteva ◽  
Katherine Chou ◽  
Serena Yeung ◽  
Nikhil Naik ◽  
Ali Madani ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Medical Imaging ◽  
Real World ◽  
Recent Progress ◽  
Medical Applications ◽  
Modern Computer ◽  
Medical Computer

AbstractA decade of unprecedented progress in artificial intelligence (AI) has demonstrated the potential for many fields—including medicine—to benefit from the insights that AI techniques can extract from data. Here we survey recent progress in the development of modern computer vision techniques—powered by deep learning—for medical applications, focusing on medical imaging, medical video, and clinical deployment. We start by briefly summarizing a decade of progress in convolutional neural networks, including the vision tasks they enable, in the context of healthcare. Next, we discuss several example medical imaging applications that stand to benefit—including cardiology, pathology, dermatology, ophthalmology–and propose new avenues for continued work. We then expand into general medical video, highlighting ways in which clinical workflows can integrate computer vision to enhance care. Finally, we discuss the challenges and hurdles required for real-world clinical deployment of these technologies.

scholarly journals In defence of machine learning: Debunking the myths of artificial intelligence

2018 ◽  
Vol 14 (4) ◽  
pp. 734-747 ◽  
Author(s):  
Constance de Saint Laurent
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Recent Progress ◽  
Life Time ◽  
Clear Understanding ◽  
Social Scientists ◽  
Scientific Publications ◽  
The Past ◽  
Potential Benefits ◽  
The Media

There has been much hype, over the past few years, about the recent progress of artificial intelligence (AI), especially through machine learning. If one is to believe many of the headlines that have proliferated in the media, as well as in an increasing number of scientific publications, it would seem that AI is now capable of creating and learning in ways that are starting to resemble what humans can do. And so that we should start to hope – or fear – that the creation of fully cognisant machine might be something we will witness in our life time. However, much of these beliefs are based on deep misconceptions about what AI can do, and how. In this paper, I start with a brief introduction to the principles of AI, machine learning, and neural networks, primarily intended for psychologists and social scientists, who often have much to contribute to the debates surrounding AI but lack a clear understanding of what it can currently do and how it works. I then debunk four common myths associated with AI: 1) it can create, 2) it can learn, 3) it is neutral and objective, and 4) it can solve ethically and/or culturally sensitive problems. In a third and last section, I argue that these misconceptions represent four main dangers: 1) avoiding debate, 2) naturalising our biases, 3) deresponsibilising creators and users, and 4) missing out some of the potential uses of machine learning. I finally conclude on the potential benefits of using machine learning in research, and thus on the need to defend machine learning without romanticising what it can actually do.

scholarly journals Artificial intelligence for diagnosis of inherited retinal disease: an exciting opportunity and one step forward

2021 ◽  
pp. bjophthalmol-2021-319365
Author(s):  
Tien-En Tan ◽  
Hwei Wuen Chan ◽  
Mandeep Singh ◽  
Tien Yin Wong ◽  
Jose S Pulido ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Retinal Disease ◽  
Inherited Retinal Disease ◽  
Exciting Opportunity ◽  
One Step

Recent Progress in Quantum Machine Learning

2021 ◽  
pp. 232-256
Author(s):  
Amandeep Singh Bhatia ◽  
Renata Wong
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Quantum Computing ◽  
Recent Progress ◽  
Machine Learning Algorithms ◽  
Learning Models ◽  
Exciting Field ◽  
Quantum Machine Learning ◽  
The Subject ◽  
Machine Learning Models

Quantum computing is a new exciting field which can be exploited to great speed and innovation in machine learning and artificial intelligence. Quantum machine learning at crossroads explores the interaction between quantum computing and machine learning, supplementing each other to create models and also to accelerate existing machine learning models predicting better and accurate classifications. The main purpose is to explore methods, concepts, theories, and algorithms that focus and utilize quantum computing features such as superposition and entanglement to enhance the abilities of machine learning computations enormously faster. It is a natural goal to study the present and future quantum technologies with machine learning that can enhance the existing classical algorithms. The objective of this chapter is to facilitate the reader to grasp the key components involved in the field to be able to understand the essentialities of the subject and thus can compare computations of quantum computing with its counterpart classical machine learning algorithms.

Theoretical Foundations of the Automatic Production and Processing of Technical Reports

1979 ◽  
Vol 9 (3) ◽  
pp. 239-269 ◽  
Author(s):  
Robert De Beaugrande
Keyword(s):  
Artificial Intelligence ◽  
Computer Science ◽  
Computer System ◽  
Recent Progress ◽  
Automatic Production ◽  
Reading And Writing ◽  
Artificial Intelligence Research ◽  
Theoretical Foundations

The following treatise surveys the issues and approaches for designing a computer system capable of reading, understanding, and writing technical reports. Recent progress in computer science and artificial intelligence research is used to specify the nature of the modules in the system. The processing of a sample text is observed during the phases of reading and writing a report on the origin of sunspots. The author advances some proposals for correlating syntax and semantics of English from a procedural standpoint. The discussion is illustrated with structural diagrams.

scholarly journals AI enabled suicide prediction tools: a qualitative narrative review

2020 ◽  
Vol 27 (3) ◽  
pp. e100175
Author(s):  
Daniel D’Hotman ◽  
Erwin Loh
Keyword(s):  
Artificial Intelligence ◽  
At Risk ◽  
Narrative Review ◽  
Online Data ◽  
Prediction Tools ◽  
Traditional Health ◽  
People With Mental Illness ◽  
Suicide Prediction ◽  
Exciting Opportunity ◽  
Independent Review

Background: Suicide poses a significant health burden worldwide. In many cases, people at risk of suicide do not engage with their doctor or community due to concerns about stigmatisation and forced medical treatment; worse still, people with mental illness (who form a majority of people who die from suicide) may have poor insight into their mental state, and not self-identify as being at risk. These issues are exacerbated by the fact that doctors have difficulty in identifying those at risk of suicide when they do present to medical services. Advances in artificial intelligence (AI) present opportunities for the development of novel tools for predicting suicide.Method: We searched Google Scholar and PubMed for articles relating to suicide prediction using artificial intelligence from 2017 onwards.Conclusions: This paper presents a qualitative narrative review of research focusing on two categories of suicide prediction tools: medical suicide prediction and social suicide prediction. Initial evidence is promising: AI-driven suicide prediction could improve our capacity to identify those at risk of suicide, and, potentially, save lives. Medical suicide prediction may be relatively uncontroversial when it pays respect to ethical and legal principles; however, further research is required to determine the validity of these tools in different contexts. Social suicide prediction offers an exciting opportunity to help identify suicide risk among those who do not engage with traditional health services. Yet, efforts by private companies such as Facebook to use online data for suicide prediction should be the subject of independent review and oversight to confirm safety, effectiveness and ethical permissibility.

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