scholarly journals Why the Data Train Needs Semantic Rails

AI Magazine ◽  
2015 ◽  
Vol 36 (1) ◽  
pp. 5-14 ◽  
Author(s):  
Krzysztof Janowicz ◽  
Frank Van Harmelen ◽  
James A. Hendler ◽  
Pascal Hitzler
Keyword(s):  
Machine Learning ◽  
Predictive Analytics ◽  
Statistical Techniques ◽  
Common Theme ◽  
Data Intensive ◽  
Making Sense ◽  
Smart Dust ◽  
Thematic Resolution ◽  
Smart Data

While catchphrases such as big data, smart data, data-intensive science, or smart dust highlight different aspects, they share a common theme: Namely, a shift towards a data-centric perspective in which the synthesis and analysis of data at an ever-increasing spatial, temporal, and thematic resolution promises new insights, while, at the same time, reducing the need for strong domain theories as starting points. In terms of the envisioned methodologies, those catchphrases tend to emphasize the role of predictive analytics, that is, statistical techniques including data mining and machine learning, as well as supercomputing. Interestingly, however, while this perspective takes the availability of data as a given, it does not answer the question how one would discover the required data in today’s chaotic information universe, how one would understand which datasets can be meaningfully integrated, and how to communicate the results to humans and machines alike. The semantic web addresses these questions. In the following, we argue why the data train needs semantic rails. We point out that making sense of data and gaining new insights works best if inductive and deductive techniques go hand-in-hand instead of competing over the prerogative of interpretation.

Current Trends

2019 ◽  
pp. 181-198
Author(s):  
Jayanthi Jagannathan ◽  
Anitha Elavarasi S.
Keyword(s):  
Machine Learning ◽  
Manufacturing Industry ◽  
Predictive Analytics ◽  
Process Industry ◽  
Healthcare Industry ◽  
Current Trends ◽  
Smart Campus ◽  
Industry Automation ◽  
The Internet Of Things

This chapter addresses the key role of machine learning and artificial intelligence for various applications of the internet of things. The following are the most significant applications of IoT: (1) manufacturing industry: automation of industries is on the rise; there is an urge for analyzing the energy in the process industry; (2) anomaly detection: to detect the existing fault and abnormality in functioning by using ML algorithms thereby avoiding the adverse effect during its operation; (3) smart campus: in-order to efficiently handle the energy in buildings, smart campus systems are developed; (4) improving product decisions: with the help of the predictive analytics system products are designed and developed based on the user's requirements and usability; (5) healthcare industry: IoT with machine learning provides numerous ways for the betterment of the human wellbeing. In this chapter, the most predominant approaches to machine learning that can be useful in the IoT applications to achieve a significant set of outcomes will be discussed.

scholarly journals Navigating through the Maze of Homogeneous Catalyst Design with Machine Learning

2020 ◽  
Author(s):  
Gabriel dos Passos Gomes ◽  
Robert Pollice ◽  
Alan Aspuru-Guzik
Keyword(s):  
Machine Learning ◽  
Chemical Reactions ◽  
Catalyst Design ◽  
New Drugs ◽  
Homogeneous Catalyst ◽  
Chemical Bonds ◽  
Catalytic Systems ◽  
Data Intensive ◽  
The Past

<div><div><div><p>The ability to forge difficult chemical bonds through catalysis has transformed society on all fronts, from feeding our ever-growing populations to increasing our life-expectancies through the synthesis of new drugs. However, developing new chemical reactions and catalytic systems is a tedious task that requires tremendous discovery and optimization efforts. Over the past decade, advances in machine learning have revolutionized a whole new way to approach data- intensive problems, and many of these developments have started to enter chemistry. However, similar progress in the field of homogenous catalysis are only in their infancy. In this article, we want to outline our vision for the future of catalyst design and the role of machine learning to navigate this maze.</p></div></div></div>

scholarly journals Navigating through the Maze of Homogeneous Catalyst Design with Machine Learning

2020 ◽  
Author(s):  
Gabriel dos Passos Gomes ◽  
Robert Pollice ◽  
Alan Aspuru-Guzik
Keyword(s):  
Machine Learning ◽  
Chemical Reactions ◽  
Catalyst Design ◽  
New Drugs ◽  
Homogeneous Catalyst ◽  
Chemical Bonds ◽  
Catalytic Systems ◽  
Data Intensive ◽  
The Past

<div><div><div><p>The ability to forge difficult chemical bonds through catalysis has transformed society on all fronts, from feeding our ever-growing populations to increasing our life-expectancies through the synthesis of new drugs. However, developing new chemical reactions and catalytic systems is a tedious task that requires tremendous discovery and optimization efforts. Over the past decade, advances in machine learning have revolutionized a whole new way to approach data- intensive problems, and many of these developments have started to enter chemistry. However, similar progress in the field of homogenous catalysis are only in their infancy. In this article, we want to outline our vision for the future of catalyst design and the role of machine learning to navigate this maze.</p></div></div></div>

scholarly journals “Zhores” — Petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in Skolkovo Institute of Science and Technology

2019 ◽  
Vol 9 (1) ◽  
pp. 512-520 ◽  
Author(s):  
Igor Zacharov ◽  
Rinat Arslanov ◽  
Maksim Gunin ◽  
Daniil Stefonishin ◽  
Andrey Bykov ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Predictive Analytics ◽  
Science And Technology ◽  
Data Driven ◽  
Scientific Instrument ◽  
Science And Engineering ◽  
Data Intensive ◽  
Cluster Properties ◽  
Data Driven Modeling

AbstractThe Petaflops supercomputer “Zhores” recently launched in the “Center for Computational and Data-Intensive Science and Engineering” (CDISE) of Skolkovo Institute of Science and Technology (Skoltech) opens up new exciting opportunities for scientific discoveries in the institute especially in the areas of data-driven modeling, machine learning and artificial intelligence. This supercomputer utilizes the latest generation of Intel and NVidia processors to provide resources for the most compute intensive tasks of the Skoltech scientists working in digital pharma, predictive analytics, photonics, material science, image processing, plasma physics and many more. Currently it places 7th in the Russian and CIS TOP-50 (2019) supercomputer list. In this article we summarize the cluster properties and discuss the measured performance and usage modes of this new scientific instrument in Skoltech.

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

Creating adaptive predictions for packaging-critical quality parameters using advanced analytics and machine learning

TAPPI Journal ◽  
2019 ◽  
Vol 18 (11) ◽  
pp. 679-689
Author(s):  
CYDNEY RECHTIN ◽  
CHITTA RANJAN ◽  
ANTHONY LEWIS ◽  
BETH ANN ZARKO
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Predictive Analytics ◽  
Paper Industry ◽  
Quality Parameters ◽  
Fast Change ◽  
The Face ◽  
Model Tuning ◽  
Reducing Costs ◽  
Time Critical

Packaging manufacturers are challenged to achieve consistent strength targets and maximize production while reducing costs through smarter fiber utilization, chemical optimization, energy reduction, and more. With innovative instrumentation readily accessible, mills are collecting vast amounts of data that provide them with ever increasing visibility into their processes. Turning this visibility into actionable insight is key to successfully exceeding customer expectations and reducing costs. Predictive analytics supported by machine learning can provide real-time quality measures that remain robust and accurate in the face of changing machine conditions. These adaptive quality “soft sensors” allow for more informed, on-the-fly process changes; fast change detection; and process control optimization without requiring periodic model tuning. The use of predictive modeling in the paper industry has increased in recent years; however, little attention has been given to packaging finished quality. The use of machine learning to maintain prediction relevancy under everchanging machine conditions is novel. In this paper, we demonstrate the process of establishing real-time, adaptive quality predictions in an industry focused on reel-to-reel quality control, and we discuss the value created through the availability and use of real-time critical quality.

Exchange Spin Coupling from Gaussian Process Regression

2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Molecular Magnets ◽  
Molecular Structures ◽  
Spin Coupling ◽  
Structure Property ◽  
Data Set ◽  
Uncertainty Estimates

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.

D'Arcy Wentworth Thompson, interindividual variation, and postnatal neuronal growth

2001 ◽  
Vol 24 (2) ◽  
pp. 284-284 ◽  
Author(s):  
Terry Elliott
Keyword(s):  
Brain Size ◽  
Interindividual Variation ◽  
Statistical Techniques ◽  
Neuronal Growth ◽  
Brain Part

It is suggested that a connection between neurogenesis and brain part size is unsurprising. It is argued that neurogenesis cannot, however, be the only factor contributing to brain size. Highly individual post-natal experience radically shapes individual brains, leading to dramatic increases in brain size. The role of comparatively coarse statistical techniques in addressing these subtle biological issues is questioned.

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