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

Todays business is ruled by data and data-driven understanding. How you understand the data and interpret the data into business decisions has a direct impact on your business conversion and growth. For a more precise understanding of data, today we have artificial intelligence (AI) and Machine Learning (ML) technologies on our side. No doubt, these technologies that mimic human reasoning can positively transform businesses and their strategies.

We need to understand the impact of AI and ML technologies have in shaping our understanding and capability to interpret data.

Any business understands the importance of communicating with customers individually. Yes, thanks to the very nature of digital interfaces that opened up the tremendous scope of individual preferences and choices, your business communication must take into account the preferences of individual customers. The increasing importance of addressing individual choices for business conversion has forced many companies to focus on data-driven personalization measures.

Not only the large businesses but also the startups and small businesses increasingly understand the importance of having access to the relevant data for meeting the needs of visitors. AI can dig the available user data deeper and fetch out relevant patterns and insights that can be further utilized for data-driven decision making personalization. AI can also help to scale up such personalization efforts for every individual user.

A superb example of how AI can allow personalization in business operations can be found in the case of Starbucks. The global coffee chain brand designed 400,000 different types of emails created based on the data of individual preferences, tastes, and choices. Such well crafted personalized communication can help brands to create more engaging communication and conversation for business brands. The brand actually AI to decipher the volumes of data corresponding to customer preferences and choices.

When it comes to smaller businesses and little startups, such as AI-based data collection and data-centric personalization may be a little expensive. But small businesses can embrace similar approaches to create very specific data-oriented marketing campaigns with short duration to boost business conversion and customer engagement. Such AI-powered data-driven campaigns can also help to lift the brand image of any company.

For the B2B segment, business conversion highly depends on generating new leads. The B2B companies also need to depend heavily on tracking contact data and reaching out to them effectively through lead generation funnel. Most marketers agree to the humongous range of challenges B2B-based businesses face in doing this. This is where AI can play a great role in streamlining the process of lead generation through intelligent automation.

Artificial Intelligence (AI) powered lead generation and contact tracking solutions have the capability to make an analysis of the customer base along with important trends and emerging patterns. These trends, patterns, anomalies, characteristics, and various attributes can deliver important insights for optimizing websites and web apps. Thanks to AI-based optimization insights a website can venture to use better programming language, tools, features, and UI elements to generate more leads.

On the other hand, AI-based business data analysis can work hand in hand with big data analytics. This sophisticated and highly incisive approach to data utilization can easily help to discover ideal customers for a business. The interactions of users on web pages and corresponding data can be analyzed by B2B brands with the help of AI tools to produce the most relevant as well as actionable insights.

To make things easier for the businesses, AI, and machine learning technology for such analytical activities are now spotted in most of the leading analytics solutions across the spectrum. Simple Google Analytics can also offer highly result-oriented and precision-driven reports. Such technologies can easily know about the shortcomings and loopholes behind the decreasing motivation of traffic and readings of business conversion fallout.

There are also great tools like Finteza that uses AI technology for monitoring website traffic on a continuous basis besides checking other crucial issues and irregularities. These tools can also improve your data security since by detecting bad traffic they automatically point out the vulnerabilities in the web app.

Poor web traffic often results in DDoS attacks, manipulation of website cookies, and hackers or malicious programs impersonating computer bots. An AI-based lead generation solution can also reduce these security vulnerabilities.

AI optimizes the scope of personalization in a data-driven manner and that is portrayed as the principal useless of AI in dealing with data. But AI is also highly effective in optimizing the web design and improving the user experience (UX).

AI achieves this optimization and improvement by analyzing user behavior and interaction data and user feedback. Machine learning programs particularly can play a very effective role in learning from user behavior and adjusting various interactive elements accordingly.

AI and ML programs running behind the scene basically collect a lot of data corresponding to real user behavior so that real-time feedback about shortcomings and improvement needs can be communicated to the business owners. An ML-based program can also bring instant tweaks to the UX attributes for better engagement.

Another important thing in this respect that needs to be explained is the great role of AI in improving the efficiency of A/B tests. In the A/B testing process the AI and machine learning can deliver the most important insights about user demands and preferences to take further enhancement measures for UI and UX.

The most important aspect of AI in making an impact over A/B testing is that it leaves no scope for vague assessment or guessing. The data-driven insights guiding the A/B testing is more possible now as website cookies provide clear insights concerning user behavior.

Based on such insights the landing pages can reduce form fields as per user interest and preferences.

Biometrics data corresponding to direct interactions with a web app can help developers and marketers with a lot of actionable insights. There are many advanced online services right now available in the market that can help to understand and decipher website data.

Biometrics data coupled up with AI and machine learning technology opened up new possibilities for improved user experience.

Among these available services for data interpretation mostly take the help of a combination of both artificial intelligence and machine learning. These sophisticated solutions can easily track the eye movements of the users.

In addition, some of these services can also track facial expressions to assess user responses in different contexts. These services can extract the most organic kind of user data and generate the most valuable insights that can be used for UX design and performance optimization of websites.

As the trends stand, from this year onward the AI and ML-based data analytics and data-centric optimization of business apps will have more dominance. Thanks to these two technologies, there will be the least guesswork for all design, development, and optimization decisions.

Atman Rathod is the Co-founder at CMARIX TechnoLabs Pvt. Ltd., a leading web and mobile app development services company with 16+ years of experience. He loves to write about technology, startups, entrepreneurship, and business. His creative abilities, academic track record and leadership skills made him one of the key industry influencers as well.

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How Can AI and ML Transform the Way We Read and Understand Data? - ReadWrite

When OpenAI first published a paper on their new language generation AI, GPT-3, the hype was slow to build. The paper indicated GPT-3, the biggest natural language AI model yet, was advanced, but it only had a few written examples of its output. Then OpenAI gave select access to a beta version of GPT-3 to see what developers would do with it, and minds were blown.

Developers playing with GPT-3 have taken to Twitter with examples of its capabilities: short stories, press releases, articles about itself, a search engine. Perhaps most surprising was the discovery GPT-3 can write simple computer code. When web developer, Sharif Shameem, modified it to spit out HTML instead of natural language, the program generated code for webpage layouts from prompts like a button that looks like a watermelon.

I used to say that AI research seemed to have an odd blind spot towards automation of programming work, and I suspected a subconscious self-preservation bias, tweeted John Carmack, legendary computer game developer and consulting CTO at Oculus VR. The recent, almost accidental, discovery that GPT-3 can sort of write code does generate a slight shiver.

While the discovery of GPT-3s coding skills may have been somewhat serendipitous, there is, in fact, a whole field dedicated to the development of machine learning algorithms that can code. The research has been making progress, and a new algorithm just recently took another step.

The algorithm, called machine inferred code similarity (MISIM), is the brainchild of researchers from Intel, Georgia Institute of Technology, University of Pennsylvania, and MIT. Trained on the huge amount of code already publicly available on the web, MISIM can figure out what a program is supposed to do. Then, after finding other similar programs and comparing it to them, MISIM can offer ways to make the program faster or more efficient.

It isnt the first machine learning algorithm to make recommendations or compare similarity, but according to the researchers in a new preprint paper on MISIM, it was up to 40 times more accurate at the task when it went head to head with several of its most advanced competitors.

Near term, the AI could be a useful sidekick for todays programmers. Further out, the field could open programming to anyone who can describe what they want to create in everyday language or bring machines that write and maintain their own code.

The pursuit of computers that can code is almost as old as modern computer science itself. While there have been advances in programming automation, the recent explosion in machine learning is accelerating progress in a field called machine programming.

In a 2018 paper on the field, a group of Intel and MIT researchers wrote, The general goal of machine programming is to remove the burden of writing correct and efficient code from a human programmer and to instead place it on a machine.

Researchers are pursuing systems that can automate the steps required to transform a persons intentthat is, what they want a piece of software to dointo a working program. Theyre also aiming to automate the maintenance of software over time, like, for instance finding and fixing bugs, keeping programs compatible, or updating code to keep up with hardware upgrades.

Thats easier said than done, of course. Writing software is as much art as it is science. It takes a lot of experience and creativity to translate human intent into the language of machines.

But as GPT-3 shows, language is actually a skill machine learning is rapidly mastering, and programming languages are not so different from English, Chinese, or Swahili. Which is why GPT-3 picking up a few coding skills as a byproduct of its natural language training is notable.

While algorithmic advances in machine learning, like GPT-3, are key to machine programmings success, theyd be useless without good training data. Luckily, theres a huge amount of publicly available code on sites like GitHubreplete with revision histories and notesand code snippets and comment threads on sites like Stack Overflow. Even the internet at large, with accessible webpages and code, is an abundant source of learning material for AI to gobble up.

In theory, just as GPT-3 ingests millions of example articles to learn how to write, machine programming AIs could consume millions of programs and learn to code. But how to make this work in practice is an open question. Which is where MISIM comes in.

MISIM advances machine programming a step by being able to accurately identify what a snippet of code is supposed to do. Once its classified the code, it compares it to millions of other snippets in its database, surfaces those that are most similar, and suggests improvements to the code snippet based on those other examples.

Because MISIM classifies the codes purpose at a high level, it can find code snippets that do the same thing but are written differentlytheres more than one way to solve the same problemand even snippets in other programming languages. Simplistically, this is a bit like someone reading a New Yorker article, identifying its topic, and then finding all the other articles on that topicwhether theyre in Der Spiegel or Xinhua.

Another benefit of working at that higher level of classification is the program doesnt need the code to be compiled. That is, it doesnt have to translate it into the machine code thats executed by the computer. Since MISIM doesnt require a compiler, it can analyze code snippets as theyre being written and offer similar bits of code that could be faster or more efficient. (This is a bit like an email autocomplete feature finishing your sentences.)

Intel plans to offer MISIM to internal developers for just this purpose. The hope is itll prove a useful sidekick, making the code-writing process faster, easier, and more effective. But theres potentially more it can do. Translation between computer languages, for example, could also be a valuable application. It could perhaps help coders update government software written in archaic languages to something more modern.

But Justin Gottschlich, director of machine programming at Intel, has an even grander vision: the full democratization of coding.

Combine MISIM (or something like it) with natural language AI, and future programmers could simply write down what they want a piece of software to do, and the computer whips up the code. That would open programming to anyone with a decent command of their native language and a desire to make something cool.

As Gottschlich told MIT Technology Review, I would like to see 8 billion people create software in whatever way is most natural for them.

Image credit: Markus Spiske /Unsplash

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This AI Could Bring Us Computers That Can Write Their Own Software - Singularity Hub