machine learning

When Arm meets Intel – Overcoming the Challenges of Merging Architectures on an SoC to Enable Machine Learning

As the stakes for winning server segment market share grow ever higher an increasing number of companies are seeking to grasp the latest Holy Grail of multi-chip coherence. The approach promises to better enable applications such as machine learning...(read more)




machine learning

Machine learning, AI aiding Sempra utilities in solar energy management on the grid

This week Sempra Energy subsidiary PXiSE Energy Solutions announced that Sempra-owned development company Infraestructura Energetica Nova (IEnova) would be using its software at the 110-MW Pima Solar facility located in Mexico to help manage the integration of renewable power to the electric grid.




machine learning

Two companies align to help wind project owners maximize energy output with machine learning

This week global engineering company Emerson announced that it had formed a 3-year alliance with Vayu to combine Emerson’s Ovation automation platform with Vayu’s cloud-computing wind energy optimization technology. The new technology will optimize wind farms in the Americas, Caribbean and Europe.




machine learning

Machine learning as a diagnostic decision aid for patients with transient loss of consciousness

Background

Transient loss of consciousness (TLOC) is a common reason for presentation to primary/emergency care; over 90% are because of epilepsy, syncope, or psychogenic non-epileptic seizures (PNES). Misdiagnoses are common, and there are currently no validated decision rules to aid diagnosis and management. We seek to explore the utility of machine-learning techniques to develop a short diagnostic instrument by extracting features with optimal discriminatory values from responses to detailed questionnaires about TLOC manifestations and comorbidities (86 questions to patients, 31 to TLOC witnesses).

Methods

Multi-center retrospective self- and witness-report questionnaire study in secondary care settings. Feature selection was performed by an iterative algorithm based on random forest analysis. Data were randomly divided in a 2:1 ratio into training and validation sets (163:86 for all data; 208:92 for analysis excluding witness reports).

Results

Three hundred patients with proven diagnoses (100 each: epilepsy, syncope and PNES) were recruited from epilepsy and syncope services. Two hundred forty-nine completed patient and witness questionnaires: 86 epilepsy (64 female), 84 PNES (61 female), and 79 syncope (59 female). Responses to 36 questions optimally predicted diagnoses. A classifier trained on these features classified 74/86 (86.0% [95% confidence interval 76.9%–92.6%]) of patients correctly in validation (100 [86.7%–100%] syncope, 85.7 [67.3%–96.0%] epilepsy, 75.0 [56.6%–88.5%] PNES). Excluding witness reports, 34 features provided optimal prediction (classifier accuracy of 72/92 [78.3 (68.4%–86.2%)] in validation, 83.8 [68.0%–93.8%] syncope, 81.5 [61.9%–93.7%] epilepsy, 67.9 [47.7%–84.1%] PNES).

Conclusions

A tool based on patient symptoms/comorbidities and witness reports separates well between syncope and other common causes of TLOC. It can help to differentiate epilepsy and PNES. Validated decision rules may improve diagnostic processes and reduce misdiagnosis rates.

Classification of evidence

This study provides Class III evidence that for patients with TLOC, patient and witness questionnaires discriminate between syncope, epilepsy and PNES.




machine learning

Machine Learning Techniques for Classifying the Mutagenic Origins of Point Mutations [Methods, Technology, [amp ] Resources]

There is increasing interest in developing diagnostics that discriminate individual mutagenic mechanisms in a range of applications that include identifying population-specific mutagenesis and resolving distinct mutation signatures in cancer samples. Analyses for these applications assume that mutagenic mechanisms have a distinct relationship with neighboring bases that allows them to be distinguished. Direct support for this assumption is limited to a small number of simple cases, e.g., CpG hypermutability. We have evaluated whether the mechanistic origin of a point mutation can be resolved using only sequence context for a more complicated case. We contrasted single nucleotide variants originating from the multitude of mutagenic processes that normally operate in the mouse germline with those induced by the potent mutagen N-ethyl-N-nitrosourea (ENU). The considerable overlap in the mutation spectra of these two samples make this a challenging problem. Employing a new, robust log-linear modeling method, we demonstrate that neighboring bases contain information regarding point mutation direction that differs between the ENU-induced and spontaneous mutation variant classes. A logistic regression classifier exhibited strong performance at discriminating between the different mutation classes. Concordance between the feature set of the best classifier and information content analyses suggest our results can be generalized to other mutation classification problems. We conclude that machine learning can be used to build a practical classification tool to identify the mutation mechanism for individual genetic variants. Software implementing our approach is freely available under an open-source license.




machine learning

Harnessing Population Pedigree Data and Machine Learning Methods to Identify Patterns of Familial Bladder Cancer Risk

Background:

Relatives of patients with bladder cancer have been shown to be at increased risk for kidney, lung, thyroid, and cervical cancer after correcting for smoking-related behaviors that may concentrate in some families. We demonstrate a novel approach to simultaneously assess risks for multiple cancers to identify distinct multicancer configurations (multiple different cancer types that cluster in relatives) surrounding patients with familial bladder cancer.

Methods:

This study takes advantage of a unique population-level data resource, the Utah Population Database (UPDB), containing vast genealogy and statewide cancer data. Familial risk is measured using standardized incidence risk (SIR) ratios that account for sex, age, birth cohort, and person-years of the pedigree members.

Results:

We identify 1,023 families with a significantly higher bladder cancer rate than population controls (familial bladder cancer). Familial SIRs are then calculated across 25 cancer types, and a weighted Gower distance with K-medoids clustering is used to identify familial multicancer configurations (FMC). We found five FMCs, each exhibiting a different pattern of cancer aggregation. Of the 25 cancer types studied, kidney and prostate cancers were most commonly enriched in the familial bladder cancer clusters. Laryngeal, lung, stomach, acute lymphocytic leukemia, Hodgkin disease, soft-tissue carcinoma, esophageal, breast, lung, uterine, thyroid, and melanoma cancers were the other cancer types with increased incidence in familial bladder cancer families.

Conclusions:

This study identified five familial bladder cancer FMCs showing unique risk patterns for cancers of other organs, suggesting phenotypic heterogeneity familial bladder cancer.

Impact:

FMC configurations could permit better definitions of cancer phenotypes (subtypes or multicancer) for gene discovery and environmental risk factor studies.




machine learning

Want a Really Hard Machine Learning Problem? Try Agriculture, Says John Deere Labs

John Deere, the nearly 200-year-old tractor manufacturer, now considers itself a software company



  • robotics
  • robotics/artificial-intelligence

machine learning

GSK hires computational drug design expert Dr Kim Branson as new head of machine learning and AI

British multinational GlaxoSmithKline have hired computational drug design expert Dr Kim Branson as the company’s new Senior Vice President, Global Head of Artificial Intelligence and Machine Learning.

In his new role, the biotech veteran will oversee projects which use AI to identify novel targets for potential medicines.

Dr Branson brings to the role more than 15 years’ worth of experience in biotech and academia having held positions at a number of Silicon Valley firms including Gliimpse, Lumia and Hessian Informatics.

read more




machine learning

Accumulating Evidence Using Crowdsourcing and Machine Learning: A Living Bibliography about Existential Risk and Global Catastrophic Risk

The study of existential risk — the risk of human extinction or the collapse of human civilization — has only recently emerged as an integrated field of research, and yet an overwhelming volume of relevant research has already been published. To provide an evidence base for policy and risk analysis, this research should be systematically reviewed. In a systematic review, one of many time-consuming tasks is to read the titles and abstracts of research publications, to see if they meet the inclusion criteria. The authors show how this task can be shared between multiple people (using crowdsourcing) and partially automated (using machine learning), as methods of handling an overwhelming volume of research.




machine learning

Accumulating Evidence Using Crowdsourcing and Machine Learning: A Living Bibliography about Existential Risk and Global Catastrophic Risk

The study of existential risk — the risk of human extinction or the collapse of human civilization — has only recently emerged as an integrated field of research, and yet an overwhelming volume of relevant research has already been published. To provide an evidence base for policy and risk analysis, this research should be systematically reviewed. In a systematic review, one of many time-consuming tasks is to read the titles and abstracts of research publications, to see if they meet the inclusion criteria. The authors show how this task can be shared between multiple people (using crowdsourcing) and partially automated (using machine learning), as methods of handling an overwhelming volume of research.




machine learning

Accumulating Evidence Using Crowdsourcing and Machine Learning: A Living Bibliography about Existential Risk and Global Catastrophic Risk

The study of existential risk — the risk of human extinction or the collapse of human civilization — has only recently emerged as an integrated field of research, and yet an overwhelming volume of relevant research has already been published. To provide an evidence base for policy and risk analysis, this research should be systematically reviewed. In a systematic review, one of many time-consuming tasks is to read the titles and abstracts of research publications, to see if they meet the inclusion criteria. The authors show how this task can be shared between multiple people (using crowdsourcing) and partially automated (using machine learning), as methods of handling an overwhelming volume of research.




machine learning

Accumulating Evidence Using Crowdsourcing and Machine Learning: A Living Bibliography about Existential Risk and Global Catastrophic Risk

The study of existential risk — the risk of human extinction or the collapse of human civilization — has only recently emerged as an integrated field of research, and yet an overwhelming volume of relevant research has already been published. To provide an evidence base for policy and risk analysis, this research should be systematically reviewed. In a systematic review, one of many time-consuming tasks is to read the titles and abstracts of research publications, to see if they meet the inclusion criteria. The authors show how this task can be shared between multiple people (using crowdsourcing) and partially automated (using machine learning), as methods of handling an overwhelming volume of research.




machine learning

Accumulating Evidence Using Crowdsourcing and Machine Learning: A Living Bibliography about Existential Risk and Global Catastrophic Risk

The study of existential risk — the risk of human extinction or the collapse of human civilization — has only recently emerged as an integrated field of research, and yet an overwhelming volume of relevant research has already been published. To provide an evidence base for policy and risk analysis, this research should be systematically reviewed. In a systematic review, one of many time-consuming tasks is to read the titles and abstracts of research publications, to see if they meet the inclusion criteria. The authors show how this task can be shared between multiple people (using crowdsourcing) and partially automated (using machine learning), as methods of handling an overwhelming volume of research.




machine learning

Accumulating Evidence Using Crowdsourcing and Machine Learning: A Living Bibliography about Existential Risk and Global Catastrophic Risk

The study of existential risk — the risk of human extinction or the collapse of human civilization — has only recently emerged as an integrated field of research, and yet an overwhelming volume of relevant research has already been published. To provide an evidence base for policy and risk analysis, this research should be systematically reviewed. In a systematic review, one of many time-consuming tasks is to read the titles and abstracts of research publications, to see if they meet the inclusion criteria. The authors show how this task can be shared between multiple people (using crowdsourcing) and partially automated (using machine learning), as methods of handling an overwhelming volume of research.




machine learning

Accumulating Evidence Using Crowdsourcing and Machine Learning: A Living Bibliography about Existential Risk and Global Catastrophic Risk

The study of existential risk — the risk of human extinction or the collapse of human civilization — has only recently emerged as an integrated field of research, and yet an overwhelming volume of relevant research has already been published. To provide an evidence base for policy and risk analysis, this research should be systematically reviewed. In a systematic review, one of many time-consuming tasks is to read the titles and abstracts of research publications, to see if they meet the inclusion criteria. The authors show how this task can be shared between multiple people (using crowdsourcing) and partially automated (using machine learning), as methods of handling an overwhelming volume of research.




machine learning

Accumulating Evidence Using Crowdsourcing and Machine Learning: A Living Bibliography about Existential Risk and Global Catastrophic Risk

The study of existential risk — the risk of human extinction or the collapse of human civilization — has only recently emerged as an integrated field of research, and yet an overwhelming volume of relevant research has already been published. To provide an evidence base for policy and risk analysis, this research should be systematically reviewed. In a systematic review, one of many time-consuming tasks is to read the titles and abstracts of research publications, to see if they meet the inclusion criteria. The authors show how this task can be shared between multiple people (using crowdsourcing) and partially automated (using machine learning), as methods of handling an overwhelming volume of research.




machine learning

Accumulating Evidence Using Crowdsourcing and Machine Learning: A Living Bibliography about Existential Risk and Global Catastrophic Risk

The study of existential risk — the risk of human extinction or the collapse of human civilization — has only recently emerged as an integrated field of research, and yet an overwhelming volume of relevant research has already been published. To provide an evidence base for policy and risk analysis, this research should be systematically reviewed. In a systematic review, one of many time-consuming tasks is to read the titles and abstracts of research publications, to see if they meet the inclusion criteria. The authors show how this task can be shared between multiple people (using crowdsourcing) and partially automated (using machine learning), as methods of handling an overwhelming volume of research.




machine learning

Accumulating Evidence Using Crowdsourcing and Machine Learning: A Living Bibliography about Existential Risk and Global Catastrophic Risk

The study of existential risk — the risk of human extinction or the collapse of human civilization — has only recently emerged as an integrated field of research, and yet an overwhelming volume of relevant research has already been published. To provide an evidence base for policy and risk analysis, this research should be systematically reviewed. In a systematic review, one of many time-consuming tasks is to read the titles and abstracts of research publications, to see if they meet the inclusion criteria. The authors show how this task can be shared between multiple people (using crowdsourcing) and partially automated (using machine learning), as methods of handling an overwhelming volume of research.




machine learning

Accumulating Evidence Using Crowdsourcing and Machine Learning: A Living Bibliography about Existential Risk and Global Catastrophic Risk

The study of existential risk — the risk of human extinction or the collapse of human civilization — has only recently emerged as an integrated field of research, and yet an overwhelming volume of relevant research has already been published. To provide an evidence base for policy and risk analysis, this research should be systematically reviewed. In a systematic review, one of many time-consuming tasks is to read the titles and abstracts of research publications, to see if they meet the inclusion criteria. The authors show how this task can be shared between multiple people (using crowdsourcing) and partially automated (using machine learning), as methods of handling an overwhelming volume of research.




machine learning

Helping journalists understand the power of machine learning

Editor’s note: What impact can AI and machine learning have on journalism? That is a question the Google News Initiative is exploring through a partnership with Polis, the international journalism think tank at the London School of Economics and Political Science. The following post is written by Mattia Peretti, who manages the program, called JournalismAI.

In the global survey we conducted last year about the use of artificial intelligence (AI) by news organizations, most respondents highlighted the urgent need to educate and train their newsroom on the potential offered by machine learning and other AI-powered technologies. Improving AI literacy was seen as vital to change culture and improve understanding of new tools and systems:

AI literacy is crucial. The more the newsroom at large embraces the technology and generates the ideas and expertise for AI projects, the better the outcome. New powers, new responsibilities:
A global survey of journalism and AI

The message from newsrooms was loud and clear. So we decided to do something about it. That’s why we’re announcing a free training course produced by JournalismAI in collaboration with VRT News and the Google News Initiative. 

This Introduction to Machine Learning is built by journalists, for journalists, and it will help answer questions such as: What is machine learning? How do you train a machine learning model? What can journalists and news organizations do with it and why is it important to use it responsibly?

The course is available in 17 different languages on the Google News Initiative Training Center. By logging in, you can track your progress and get a certificate when you complete the course. The Training Center also has a variety of other courses to help you find, verify and tell news stories online.


The Introduction to Machine Learning is available on the Google News Initiative Training Center in 17 different languages.

It’s a tough time for journalists and news organizations worldwide, as they try to assess the impact that COVID-19 will have on the business and editorial side of the industry. With JournalismAI, we want to play our role in helping to minimize costs and enhance opportunities for the industry through these new technologies. This course complements our recently launched collaborative experiment, as well as our effort to highlight profiles and experiments that show the transformative potential of AI and machine learning in shaping the journalist, and the journalism, of the future.

At the end of the course, you’ll find a list of recommended resources, produced by journalism and technology experts across the world, that have been instrumental in designing our Introduction to Machine Learning and will help you dive even deeper in the world of AI and automation. 

And we are not done. After this course, and the previous training module with strategic suggestions on AI adoption, we are planning to design more training resources on AI and machine learning for journalists later this year. Sign up for the JournalismAI newsletter to stay updated.



  • Google News Initiative

machine learning

Machine Learning at Arraignments can Cut Repeat Domestic Violence

In the United States, the typical pre-trial process proceeds from arrest to preliminary arraignment to a mandatory court appearance, when appropriate.




machine learning

Google is Offering Journalists Free Courses in AI, Machine Learning

The course is available in 17 different languages on the Google News Initiative Training Centre.




machine learning

[ASAP] From Absorption Spectra to Charge Transfer in Nanoaggregates of Oligomers with Machine Learning

ACS Nano
DOI: 10.1021/acsnano.0c00384




machine learning

Machine learning with PySpark : with natural language processing and recommender systems [Electronic book] / Pramod Singh.

[Berkeley, CA] : Apress, [2019]




machine learning

Machine Learning, Optimization, and Data Science [Electronic book] : 5th International Conference, LOD 2019, Siena, Italy, September 10-13, 2019, Proceedings / Giuseppe Nicosia, Panos Pardalos, Renato Umeton, Giovanni Giuffrida, Vincenzo Sciacca (eds.).

Cham : Springer, c2019.




machine learning

Machine Learning and AI for Healthcare : Big Data for Improved Health Outcomes [Electronic book] / Arjun Panesar.

[Berkeley, CA] : Apress, [2019]




machine learning

Intelligence science and big data engineering : big data and machine learning : 9th International Conference, IScIDE 2019, Nanjing, China, October 17-20, 2019, proceedings. Part II [Electronic book] / Zhen Cui, Jinshan Pan, Shanshan Zhang, Liang Xiao, Jia

Cham, Switzerland : Springer, [2019]




machine learning

Machine learning for microbial phenotype prediction / Roman Feldbauer

Online Resource




machine learning

Statistical modelling and machine learning principles for bioinformatics techniques, tools, and applications K. G. Srinivasa, G. M. Siddesh, S. R. Manisekhar, editors

Online Resource




machine learning

Integration of ultra-high-pressure liquid chromatography-tandem mass spectrometry with machine learning for identifying fatty acid metabolite biomarkers of ischemic stroke

Chem. Commun., 2020, Accepted Manuscript
DOI: 10.1039/D0CC02329A, Communication
Lijian Zhang, Fei Ma, Ao Qi, Lulu Liu, Junjie Zhang, Simin Xu, Qisheng Zhong, Yusen Chen, Chun-yang Zhang, Chun Cai
We report for the first time the integration of ultra-high-pressure liquid chromatography-tandem mass spectrometry with machine learning for identifying fatty acid metabolite biomarkers of ischemic stroke. Especially, we develop an...
The content of this RSS Feed (c) The Royal Society of Chemistry




machine learning

Big data analytics with Spark : a practitioner's guide to using Spark for large-scale data processing, machine learning, and graph analytics, and high-velocity data stream processing / Mohammed Guller

Guller, Mohammed, author




machine learning

Machine learning methods to predict the crystallization propensity of small organic molecules

CrystEngComm, 2020, 22,2817-2826
DOI: 10.1039/D0CE00070A, Paper
Florbela Pereira
Machine learning algorithms were explored for the prediction of the crystallization propensity based on molecular descriptors and fingerprints generated from 2D chemical structures and 3D chemical structures optimized with empirical methods.
The content of this RSS Feed (c) The Royal Society of Chemistry




machine learning

Machine Learning: Living in the Age of AI

“Machine Learning: Living in the Age of AI,” examines the extraordinary ways in which people are interacting with AI today. Hobbyists and teenagers are now developing tech powered by machine learning and WIRED shows the impacts of AI on schoolchildren and farmers and senior citizens, as well as looking at the implications that rapidly accelerating technology can have. The film was directed by filmmaker Chris Cannucciari, produced by WIRED, and supported by McCann Worldgroup.




machine learning

[ASAP] Chemometric Classification of Crude Oils in Complex Petroleum Systems Using t-Distributed Stochastic Neighbor Embedding Machine Learning Algorithm

Energy & Fuels
DOI: 10.1021/acs.energyfuels.0c01333




machine learning

[ASAP] Kernel-Based Machine Learning for Efficient Simulations of Molecular Liquids

Journal of Chemical Theory and Computation
DOI: 10.1021/acs.jctc.9b01256




machine learning

Applications of machine learning in wireless communications / edited by Ruisi He and Zhiguo Ding

Online Resource




machine learning

Machine learning and medical engineering for cardiovascular health and intravascular imaging and computer assisted stenting: first International Workshop, MLMECH 2019, and 8th Joint International Workshop, CVII-STENT 2019, held in conjunction with MICCAI

Online Resource




machine learning

[ASAP] Combining SchNet and SHARC: The SchNarc Machine Learning Approach for Excited-State Dynamics

The Journal of Physical Chemistry Letters
DOI: 10.1021/acs.jpclett.0c00527




machine learning

[ASAP] Property-Oriented Material Design Based on a Data-Driven Machine Learning Technique

The Journal of Physical Chemistry Letters
DOI: 10.1021/acs.jpclett.0c00665




machine learning

Train for These 5 Machine Learning and AI Roles Now

Tech companies aren’t the only organizations actively looking for experienced talent in emerging technologies like artificial intelligence (AI) and machine learning. Leaders across a mélange of industries like financial services and various types of consulting are strategically sourcing top technical talent to power future business goals – which means competition is stiff for technologists with […]

The post Train for These 5 Machine Learning and AI Roles Now appeared first on DevelopIntelligence.




machine learning

Proceedings of the 2003 International Conference on Machine Learning and Cybernetics [electronic journal].

IEEE Computer Society




machine learning

Proceedings of the 2003 International Conference on Machine Learning and Cybernetics [electronic journal].

IEEE Computer Society




machine learning

Proceedings of the 2003 International Conference on Machine Learning and Cybernetics [electronic journal].

IEEE Computer Society




machine learning

Proceedings of the 2003 International Conference on Machine Learning and Cybernetics [electronic journal].

IEEE Computer Society




machine learning

Machine Learning in High Performance Computing Environments (MLHPC), IEEE/ACM Workshop on [electronic journal].

IEEE / Institute of Electrical and Electronics Engineers Incorporated




machine learning

Machine Learning and Knowledge Extraction [electronic journal].

Molecular Diversity Preservation International




machine learning

Applied Machine Learning (ICAML), International Conference on [electronic journal].

IEEE / Institute of Electrical and Electronics Engineers Incorporated




machine learning

2019 International Conference on Machine Learning and Data Engineering (iCMLDE) [electronic journal].

IEEE / Institute of Electrical and Electronics Engineers Incorporated




machine learning

2019 International Conference on Applied Machine Learning (ICAML) [electronic journal].

IEEE / Institute of Electrical and Electronics Engineers Incorporated




machine learning

2019 IEEE/ACM Workshop on Machine Learning in High Performance Computing Environments (MLHPC) [electronic journal].

IEEE / Institute of Electrical and Electronics Engineers Incorporated