(MENAFN - EIN Presswire)

Activeloop user interface and toolset work with NVIDIA processing to help InteinAir achieve great ML results

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Y Combinator alum achieves better aerial data pipelines for IntelinAir in an industry-leading Agriculture Tech solution

MOUNTAIN VIEW, CA, USA, August 4, 2020 / EINPresswire.com / -- In a case study now available online, Activeloop ( [To enable links contact MENAFN] ), a Y Combinator-backed startup, is announcing a major success in helping an early customer, IntelinAir , improve the efficiency of their AI analysis of aerial footage. Activeloop's software builds plug-and-play data pipelines for unstructured data. The software helps data scientists streamline their data aggregation and preparation, and automates and optimizes their training of machine learning models. Together with NVIDIA , Activeloop has achieved a massive reduction in the time-to-value and cost of machine learning / deep learning efforts. The case study documents a breakthrough in the field of aerial imagery with their joint customer IntelinAir, a leading crop intelligence firm.

Activeloop's solution is becoming available just in time for the exploding artificial intelligence and advanced machine learning market, projected to grow up to $281.24 billion by 2026 with CAGR of 37.95%. This coincides with the massive growth of data available to be analyzed by AI. All data generated by the end of 2020 will be about 40 trillion gigabytes (40 zettabytes), with IBM estimating that 90% of it has been created over the past 2 years. As data gets bigger faster than ever, translating it into actionable insights is becoming increasingly difficult and expensive. As a result, the effort needed to set up a new model and get it running efficiently can be beyond the reach of many teams who could otherwise benefit from machine learning. Existing solutions often have large cloud storage and processing costs. These solutions can't be made more efficient without radical changes.

'Unstructured data - including text, images, or videos, comprises about 80-90% of the data people generate today', says Davit Buniatyan, Activeloop Founder and CEO. 'As it comes in different forms, sizes, and even shapes, analyzing and managing it is an extremely difficult and costly task. In fact, data scientists spend about 50 to 80% of their time setting up their unstructured dataset rather than analyzing it via machine or deep learning. We're changing that by creating a fast, simple platform for building and scaling data pipelines for machine learning.'

'We operate in an agile fashion: we want to focus on building high-quality models instead of fighting with data pipelines, infrastructure, and deployment challenges' says Jennifer Hobbs, Director of Machine Learning at IntelinAir. 'Thanks to Activeloop, we've been able to deploy new models in a matter of days instead of weeks. With the help of Activeloop's platform and NVIDIA's powerful GPUs, we were able to increase the inference speed threefold and improve the accuracy of the trained models at half the cost."

You can read more about the success story here: [To enable links contact MENAFN] .

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About Activeloop

Activeloop ( [To enable links contact MENAFN] ), is a startup backed by Y Combinator and prominent Silicon Valley investors. The company has already been featured by major outlets including TechCrunch and is now coming out of stealth mode to make its product available to the machine learning community. Formerly named Snark AI, Activeloop aims to optimize the way machine and deep learning models are trained and streamline the huge amounts of data required for this work. Activeloop is a member of NVIDIA's Inception program for AI/ML development.

About IntelinAir

IntelinAir ( [To enable links contact MENAFN] ) is a full-season and full-spectrum crop intelligence company focused on agriculture that delivers actionable intelligence to help farmers make data-driven decisions to improve operational efficiency, yields, and ultimately their profitability.

Mikayel HarutyunyanActiveloop.ai+1 415-876-5667email us here Visit us on social media:Facebook Twitter LinkedIn

Activeloop introduction and demo

Link:
Preparing new machine learning models used to take weeks Activeloop teams up with NVIDIA to reduce that time to hours - MENAFN.COM

Artificial Intelligence and Machine Learning Market 2020-2026 Industry research report covers the market landscape and its growth prospects over the coming years, the report also brief deals with the product life cycle, comparing it to the relevant products from across industries that had already been commercialized details the potential for various applications, discussing about recent product innovations and gives an overview on potential regional market.

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The report includes executive summary, global economic outlook and overview section that provide a coherent analysis on the Artificial Intelligence and Machine Learning market. Besides, the report in the market overview section delineates PLC analysis and PESTLE analysis to provide thorough analysis on the market. The overview section further delves into Porters Five Force analysis that helps in revealing the competitive scenario with regards to Artificial Intelligence and Machine Learning market revealing the probable scenario of the market.

Analysis of Artificial Intelligence and Machine Learning Market Key Manufacturers: IBM Corporation, BigML, Inc., SAP SE, SAS Institute Inc., Fair Isaac Corporation, Microsoft Corporation, Google, Inc., Baidu, Inc., Amazon Web Services Inc., Intel Corporation and Hewlett Packard Enterprise Development LP (HPE)

The report provides a detailed overview of the industry including both qualitative and quantitative information. It provides overview and forecast of the global Artificial Intelligence and Machine Learning market based on various segments. It also provides market size and forecast estimates from year 2020 to 2026 with respect to five major regions, namely; North America, Europe, Asia-Pacific (APAC), Middle East and Africa (MEA) and South & Central America. The Artificial Intelligence and Machine Learning market by each region is later sub-segmented by respective countries and segments. The report covers analysis and forecast of countries globally along with current trend and opportunities prevailing in the region.

Global Artificial Intelligence and Machine Learning Industry 2020 Market Research Report is spread across 101 pages and provides exclusive vital statistics, data, information, trends and competitive landscape details in this niche sector.

With tables and figures helping analyze worldwide Global Artificial Intelligence and Machine Learning Market, this research provides key statistics on the state of the industry and is a valuable source of guidance and direction for companies and individuals interested in the market.

At the same time, we classify different Artificial Intelligence and Machine Learning based on their definitions. Upstream raw materials, equipment and downstream consumers analysis is also carried out. What is more, the Artificial Intelligence and Machine Learning industry development trends and marketing channels are analyzed.

Market Segment by Type:

Hardware

Software

Services

Market Segment by Application:

BFSI

Healthcare and Life Sciences

Retail

Telecommunication

Government and Defense

Manufacturing

Energy and Utilities

Others

The report strongly emphasizes prominent participants of the Artificial Intelligence and Machine Learning Industry to provide a valuable source of guidance and direction to companies, executive officials, and potential investors interested in this market. The study focuses on significant factors relevant to industry participants such as manufacturing technology, latest advancements, product description, manufacturing capacities, sources of raw material, and profound business strategies.

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Finally, the feasibility of new investment projects is assessed, and overall research conclusions are offered. In a word, the report provides major statistics on the state of the industry and is a valuable source of guidance and direction for companies and individuals interested in the market.

Global Artificial Intelligence and Machine Learning 2020 to 2026 includes:

Trends in Artificial Intelligence and Machine Learning deal making in the industry

Analysis of Artificial Intelligence and Machine Learning deal structure

Access to headline, upfront, milestone and royalty data

Access to hundreds of Artificial Intelligence and Machine Learning contract documents

Comprehensive access to Artificial Intelligence and Machine Learning records

TOC of Artificial Intelligence and Machine Learning Market Report Includes:

1 Artificial Intelligence and Machine Learning Market Overview

2 Company Profiles

3 Global Artificial Intelligence and Machine Learning Market Competition, by Players

4 Global Artificial Intelligence and Machine Learning Market Size by Regions

5 North America Artificial Intelligence and Machine Learning Revenue by Countries

6 Europe Artificial Intelligence and Machine Learning Revenue by Countries

7 Asia-Pacific Artificial Intelligence and Machine Learning Revenue by Countries

8 South America Artificial Intelligence and Machine Learning Revenue by Countries

9 Middle East and Africa Revenue Artificial Intelligence and Machine Learning by Countries

10 Global Artificial Intelligence and Machine Learning Market Segment by Type

11 Global Artificial Intelligence and Machine Learning Market Segment by Application

12 Global Artificial Intelligence and Machine Learning Market Size Forecast (2020-2026)

13 Research Findings and Conclusion

14 Appendix

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Artificial Intelligence and Machine Learning Industry 2020 Market Manufacturers Analysis, Share, Size, Growth, Trends and Research Report 2026 -...

Who you are together is more important than who you are alone.

What makes us happy in a romantic relationship? The question might seem too complex to answer, too varied couple to couple. But a new study in the Proceedings of the National Academy of Sciences attempts to answer just that - using machine learning.

Previous studies on romantic satisfaction were limited in size. By using machine learning, however, researchers were able to analyze a massive amount of data, which included over 11,000 different couples from 43 data sets. Individual studies are many times limited - it is difficult and expensive to recruit couples for the studies. Its also exhausting for the participants. Using machine learning to analyze a large amount of data from pre-existing studies bypasses these problems.

The researchers looked at variables that could predict happiness within a relationship. Some of these, such as neuroticism, political orientation, conscientiousness or family history were qualities of the individuals involved. Others, such as appreciation, affection and perceived partner commitment were qualities of the relationship.

Of these, qualities of the relationship, rather than the individuals involved, contributed more to overall satisfaction. The five most important were how much they believed their partner was committed to the relationship, how much they appreciated their partner, sexual satisfaction, how much they believed their partner was happy in the relationship, and not fighting often.

Appreciation and commitment are key for a fulfilling relationship.

Qualities of the individuals contribute too - but not as much. In fact, 45% of the variability in a relationship is due to the qualities of the relationship. 21% were due to the individuals themselves. In addition, once qualities of the relationship were taken into account, the differences due to the individuals were not as important.

Experiencing negative affect, depression, or insecure attachment are surely relationship risk factors. But if people nevertheless manage to establish a relationship characterized by appreciation, sexual satisfaction, and a lack of conflictand they perceive their partner to be committed and responsivethose individual risk factors may matter little, say the authors.

In other words, for a happy relationship, its more important who you are together than who you are apart.

Excerpt from:
Machine Learning Reveals What Makes People Happy In A Relationship - Forbes

An array of new methods along with unexpected new pressures cast todays IoT automation efforts in an utterly new light.

Progress today in IoT automation is based on fresh methods employing big data, machine learning, asset intelligence and edge computing architecture. It is also enabled by emerging approaches to service orchestration and workflow, and by ITOps efforts that stress better links between IT and operations.

On one end, advances in IoT automation includerobotic process automation(RPA) tools that use sensor data to inform backroom and clerical tasks. On the other end are true robots that maintain the flow of goods onfactory floors.

Meanwhile, nothing has focused business leaders on automation like COVID-19. Automation technologies have gained priority in light of 2020s pandemic, which is spurring use of IoT sensors, robots and software to enable additional remote monitoring. Still, this work was well underway before COVID-19 emerged.

Cybersecurity Drives Advances in IoT Automation

In particular, automated discovery of IoT environments for cybersecurity purposes has been an ongoing driver of IoT automation. That is simply because there istoo much machine information to manually track,according to Lerry Wilson, senior director for innovation and digital ecosystems at Splunk. The target is anomalies found in data stream patterns.

Anomalous behavior starts to trickle into the environment, and theres too much for humans to do, Wilson said. And, while much of this still requires a human somewhere in the loop, the role of automation continues to grow.

Wilson said Splunk, which focuses on integrating a breadth of machine data, has worked with partners to ensure incoming data can now kick off useful functions in real time. These kinds of efforts are central to emerging information technology/operations technology (IT/OT) integration. This, along with machine learning (ML), promises increased automation of business workflows.

Today, we and our partners are creating machine learning that will automatically set up a work order people dont have to [manually] enter that anymore, he said, adding that what once took the form of analytical reports now is correlated with historic data for immediate execution.

We moved past reporting to action, Wilson said.

Notable use cases Splunk has encountered include systems that collect signals to monitor and optimize factory floor and campus activity as well as to correlate asset information, Wilson indicated.

To read the complete article, visit IoT World Today.

Excerpt from:
IoT automation trend rides the next wave of machine learning, Big Data - Urgent Communications

FORT WORTH, Texas, Aug. 4, 2020 /PRNewswire/ --What worries today's prospective college students most? Topping the list are two life-altering decisions they don't want to flub: selecting the right college and choosing the right major.

For students, finding a trusted source for college and degree information to make those huge decisions means sifting through websites that rank colleges without giving users a clear sense for the data, algorithms, and formulas that generated those rankings. In the end, a question lingers: Are these options really the best?

Students need a trustworthy, science-based means to measure genuine excellence. Now they can find it at

https://AcademicInfluence.com

"AcademicInfluence.com uses machine learning and search routines to evaluate the real-world influence of noteworthy individuals, institutions, and other rankable entities," says Dr. Jed Macosko, academic director of AcademicInfluence.com and professor of physics at Wake Forest University. "Never before have inquirers had such a customizable, objective, online tool for discovering the people and institutions that are changing our world. While there is no limit to what can be ranked with our Influence Engine technology, the AcademicInfluence.com website will focus on meeting the needs of students, with rankings of the world's top colleges and most influential thought leaders. Look for sites that explore other topics using our Influence Engine soon."

Founded in October 2016, with funding assistance from the Defense Advanced Research Projects Agency (DARPA), Influence Networksnow part of the EducationAccess groupcreated proprietary, real-time technology that maps lines of influence through constantly updated data repositories online, including Wikipedia and Crossref. Because they consist of billions of open-sourced, crowd-edited data points, these databases result in analysis that resists being gamed or undermined by single-source editorial bias.

"Our results aren't spin, promotion, or paid advertisement but instead reflect true, objective, real-world influence," says Macosko. "The objectivity of AcademicInfluence.com stands in stark contrast to major ranking sites that typically gather data by faculty survey, student opinions, and an over-reliance on self-reportingall of it at least a year out-of-date before it can be used to derive rankings. The difference at AcademicInfluence.com is enlightening."

With its interactive search and Custom College Match tools, AcademicInfluence.com offers students the capabilities they need to find the answers they want. The site also delivers influence rankings to researchers exploring the most authoritative voices in a gamut of disciplines and over time.

AcademicInfluence.com puts to rest the question of the best. And it does so by the best means possible: science. When coupled with the influence of individual choice, it's a powerful combination.

AcademicInfluence.comis the preeminent technology-driven rankings site dedicated to students, researchers, and inquirers from high school through college and beyond, offering resources that connect learners to leaders. AcademicInfluence.com is a part of the EducationAccess group, a family of sites dedicated to lifelong learning and personal growth, including Influence Networks, InfluencePublishers.com (nonfiction publishing and publishers of Bright Notes), IntelligentEducation.com (instructional video library and easy instructional video creation with 3D elements), AlexandriaLibrary.com (free, online library and reader), and soon, Success Portraits (personalized strengths inventory for college and career).

Contact:

Dr. Jed MacoskoAcademic DirectorAcademicInfluence.com[emailprotected](682) 302-4945

SOURCE AcademicInfluence.com

https://AcademicInfluence.com

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AcademicInfluence.com Unveils Machine-Learning Technology for Ranking the World's Top Colleges and Influential Thought Leaders - PRNewswire

Machine learning researchers at MIT's Computer Science and Artificial Intelligence Lab, or CSAIL, have developed a new AI diagnostic system they say can do two things: make a decision or diagnosis based on its digital findings or, crucially, recognize its own limitations and turn to a carbon-based lifeform who might make a more informed decision.

WHY IT MATTERSThe technology, as it learns, can also adapt how often it might defer to human clinicians, according to CSAIL, based on their availability and levels of experience.

"Machine learning systems are now being deployed in settings to [complement] human decision makers," write CSAIL researchers Hussein Mozannar and David Sontagin a new paperrecently presented at the International Conference of Machine Learningthat touches, not just on clinical applications of AI, but also on areas such as content moderation with social media sites such as Facebook or YouTube.

HIMSS20 Digital

"These models are either used as a tool to help the downstream human decision maker with judges relying on algorithmic risk assessment tools and risk scores being used in the ICU, or instead these learning models are solely used to make the final prediction on a selected subset of examples."

In healthcare, they point out, "deep neural networks can outperform radiologists in detecting pneumonia from chest X-rays, however, many obstacles are limiting complete automation, an intermediate step to automating this task will be the use of models as triage tools to complement radiologist expertise.

"Our focus in this work is to give theoretically sound approaches for machine learning models that can either predict or defer the decision to a downstream expert to complement and augment their capabilities."

THE LARGER TRENDAmong the tasks the machine learning system was trained on was the ability to assess chest X-rays to potentially diagnose conditions such as lung collapse (atelectasis) and enlarged heart (cardiomegaly).

Importantly, the system was developed with two parts, according to MIT researchers: a so-called "classifier," designed to predict a certain subset of tasks, and a "rejector" that decides whether a specific task should be handled by either its own classifier or ahuman expert.

The team performed experiments focused on medical diagnosis and text/image classification, the team showed that their approach not only achieves better accuracy than baselines, but does so with a lower computational cost and with far fewer training data samples.

While researchers say they haven't yet tested the system with human experts, they did develop"synthetic experts" to enable them to tweak parameters such as experience and availability.

They note that for the machine learning program to work with a new human expert, the algorithm would "need some minimal onboarding to get trained on the person's particular strengths and weaknesses."

Interestingly, in the case of cardiomegaly, researchers found that a human-AI hybrid model performed 8% percent better than either could on its own.

Going forward, Mozannar and Sontag plan to study how the tool works with human experts such as radiologists. They also hope to learn more about how it will process biased expert data, and work with several experts at once.

ON THE RECORD"In medical environments where doctors don't have many extra cycles, it's not the best use of their time to have them look at every single data point from a given patient's file," said Mozannar, in a statement. "In that sort of scenario, it's important for the system to be especially sensitive to their time and only ask for their help when absolutely necessary."

"Our algorithms allow you to optimize for whatever choice you want, whether that's the specific prediction accuracy or the cost of the expert's time and effort," added Sontag. "Moreover, by interpreting the learned rejector, the system provides insights into how experts make decisions, and in which settings AI may be more appropriate, or vice-versa."

"There are many obstacles that understandably prohibit full automation in clinical settings, including issues of trust and accountability," says Sontag. "We hope that our method will inspire machine learning practitioners to get more creative in integrating real-time human expertise into their algorithms."

Twitter:@MikeMiliardHITNEmail the writer:mike.miliard@himssmedia.com

Healthcare IT News is a publication of HIMSS Media.

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New AI diagnostic tool knows when to defer to a human, MIT researchers say - Healthcare IT News

Private learning group AdvTech has announced the launch of a new online school for grades R to 9.

AdvTech is the largest private education provider in Africa, and its schools division includes major brands such as Crawford Schools, Trinityhouse and Abbotts.

Its new school, which is called Evolve Online School (Evolve), will begin operations from 1 January 2021 and will offer a curriculum mapping system developed by MIT.

This IEB-aligned mapping curriculum allows learners to progress at their own deliberate or accelerated pace, Evolve states.

In this rapidly changing society, the one-size-fits-all method of teaching no longer makes any sense, said Principal Colin Northmore. Evolve starts by answering the question of how we can make learning an adventure for each child?

This system places students within subjects according to their abilities, letting them progress up to their potential in each subject.

The result is that each students learning experience is tailored to their specific needs, and they are encouraged to grow at a pace that suits their ability and enthusiasm, the school states.

One of the key features touted by the Evolve Online School is its use of machine learning, which it says is employed to:

Evolve also offers a range of forward-looking subjects that differ depending on which phase the student is in.

The school separates students into three phases Foundation Phase, Intermediate Phase, and Senior Phase. These comprise students from Grades R-3, Grades 4-6, and Grades 7-9, respectively.

Evolve said that it plans to add a phase which caters to Grades 10-12 from 2022.

The subjects included in each phase are described as follows, according to the schools website:

Instead of teachers, Evolve states that its students will be taught by learning activators, which draw from master teachers across the country to develop curriculum content.

There will be a strong focus on foundational, social, and emotional learning skills. Our team of life coaches will focus exclusively on these skills. Our children are growing up in a world very different from the one in which we grew up, Northmore said.

Things that we, as adults, deal with and take in our stride they are already facing at a very young age. Our life coaches will play a very important role in teaching students how to deal with issues such as stress and anxiety, helping them develop coping mechanisms, resilience and a growth mindset.

Registrations for the 2021 academic year open in September, with Evolves school year set to start in 2021.

The Evolve 2021 fee structure is shown below.

It should be noted that a non-refundable registration fee of R300 is payable at the start of the online application process, and the school will supply each childs iPad with all the required books and apps they will need.

Original post:
New South African online school uses machine learning to teach children Here is how much it costs - MyBroadband

The real possibility of advancing intelligence through deep learning and other AI-driven technology applied to video is that, in the long term, were not going to be looking at the video until after something has happened. The goal of gathering this high level of intelligence through video has the potential to be automated to the point that security operators will not be required to make the decisions necessary for response. Instead, the intelligence-driven next steps will be automatically communicated to various stakeholders from on-site guards to local police/fire departments. Instead, when security leaders access the video that corresponds to an incident, it will be because they want to see the incident for themselves. And isnt the automation, the ability to streamline response, and the instantaneous response the goal of an overall, data-rich surveillance strategy? For almost any enterprise, the answer is yes.

Visit link:
Benefits Of AI And Machine Learning | Expert Panel | Security News - SecurityInformed

Established 2017 Solidus Technologies have been raising funds and working on building a data centre built for the purpose of High Power Computing and processing Blockchain networks along with Artificial Intelligence applications.

The world is changing and evolving into more tech-based living. It is facing an unparalleled growth in needs and desires for computing power across areas such as artificial intelligence, pattern recognition, face recognition, image analysis, transaction validation, deep learning and much more. High Performance Computing (HPC) is already helping in many walks of life, such as social media, healthcare, mobility, communication networks, financial services, industrial processes and scientific progress.

HPC is used to describe the deployment of a sever estate and supporting infrastructure the function of which is to carry out large volume of compute tasks very quickly. In order to get the required level of performance the use of GPU (graphic processor Units) is used, the chips are capable of processing large blocks of data very quickly due to the highly parallel structure. Primarily used to manipulate computer graphics, these processors were developed to render and process millions of polygons per second and accelerating the memory intensive work of texture mapping. The complex and matrix like nature of these calculations have led to engineers and scientists using GPU based systems to perform non-graphical calculations.

GPUs have also become priceless for the artificial intelligence (AI) and machine learning space. We have seen the demand for new algorithms and processes to help in science, technology and machine learning grow exponentially over a short space in time. With new adaptions on computers, smart phones and smart technologies such as alexa and smart TVs, AI is now shaping the way we live our lives and will be ever increasing as time goes by. Data scientists and AI researchers have used GPUs for machine learning to streamline a series of processes and applications, such as speech recognition, natural language processing, image classification, and video analytics among others. The highly parallel structure of GPUs makes them much more efficient for algorithms than CPUs, which is why the AI industry finds them indispensable. Companies like IBM, Facebook, Adobe, Baidu, and Microsoft started to use GPUs for their machine learning projects and with this through Optoelectronica which is owned by the joint venture manager Adrian Stoica Solidus Technology will be tasked to process the satellite data under a Research Programme financed by The Romanian Space agency better known as ROSA.

Scott Cannon, Director at Solidus has said I am delighted with the way things are moving forward for the company, we are currently speaking with some very well known organisations in regards to their Artificial Intelligence needs and with us now Helping ROSA the potential for us to expand in this space is increasing. Its a great revenue stream for Solidus and Im looking forward to gaining further partnerships as the demand in this sector is huge right now.

Since 1991, the coordination of the space activities inRomaniaand stronger collaboration in European and international space programs is achieved through the establishment of the Romanian Space Agency (ROSA), within the Ministry of Education and Technology, which wasreorganised in 1995 asa public institution entirely self-funded, operating under Government Decision and the subsequent decisions of the Ministry of Education and Research National Authority for Scientific Research and Innovation.

As a government institution, ROSA has concluded international agreements on behalf of the Romanian Government. The first agreement betweenRomaniaand the European Space Agency (ESA) on space cooperation for peaceful purposes was signed inParison11 December 1992and ratified by Law no. 40/1993, event that marked the beginning of the Romanian participation in several research projects together with other European countries. In 1999 was signed the Agreement betweenRomaniaand ESA on the Cooperation for peaceful exploration and use of outer space, an event which increased the opportunities for collaboration between the industrial community inRomaniaand ESA.

The Romanian Commission for Space Activities dealt also with international agreements.Romaniawas among the first Eastern European countries to sign collaborations with NASA in the 70s, taking and processing the images from Americans satellites.Romaniaalso had agreements with countries in westernEurope, such as the agreement withFrance, through which the Romanian specialists were sent to Toulouse to prepare in the satellite remote sensing field.

Solidus Technology are actively seeking further contracts or Letters of intent from other well known organisations to utilise there processing power for their HPC and AI needs

View original post here:
Solidus making waves in the AI sector - EnterpriseTalk

Researchers involved in aWestern University-led international study have found that the most reliable predictor of a relationships success is partners belief that the other person is fully committed.

A statement issued by the university, which is located in London Ont., said this is the first-ever systematic attempt at using machine-learning algorithms to predict peoples relationship satisfaction.

Satisfaction with romantic relationships has important implications for health, well-being and work productivity, said Western psychology professor Samantha Joel.

But research on predictors of relationship quality is often limited in scope and scale, and carried out separately in individual laboratories.

The machine-learning study is conducted by Joel, Paul Eastwick from University of California, Davis, as well as 84 other scholars internationally.

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More than 11,000 couples participated.

In the study, an application of artificial intelligence (AI) is used to comb through various combinations of predictors to find the most robust predictors of relationship satisfaction.

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It provides answers to the question: What predicts how happy I will be with my relationship partner?

According to the study, relationship-specific predictors such as perceived partner commitment, appreciation, and sexual satisfaction account for nearly half of variance in relationship quality.

Individual characteristics, which describe a partner rather than a relationship, explains 21 per cent of variance in relationship quality, the study said.

The top five individual characteristics with the strongest predictive power for relationship quality are satisfaction with life, negative affect, depression, avoidant attachment and anxious attachment.

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Joel notes she was surprised the study showed that one partners individual differences predictors like life satisfaction, depression or agreeableness explained only five per cent of variance in the other partners relationship satisfaction.

In other words, relationship satisfaction is not well-explained by your partners own self-reported characteristics, Joel said.

The current datasets were sampled from Canada, the United States, Israel, the Netherlands, Switzerland and New Zealand.

2020 Global News, a division of Corus Entertainment Inc.

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Bees do it, machines know it: Western University-led study hints at key to relationship satisfaction - Globalnews.ca