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

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

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

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

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.

Read more from the original source:
New AI diagnostic tool knows when to defer to a human, MIT researchers say - Healthcare IT News