One of my friends, who isn't much of a tech enthusiast and is very content with her current Android phone, recently told me that she intended to buy an iPhone. I asked if there was a specific reason behind her sudden decision to change her'calling device' (as she calls it). 'I just love Siri!' she said with enough excitement to take me by surprise. However, my friend is not the only person who feels this way. A lot of my buddies are hopelessly in love with this little digi-voice avatar called'Siri' that is available on iPhones and a few other Apple devices.

Crowd Sourcing Specialist at Appen ACTIVELY HIRING Let's connect! Transcript Data Collection for Italian speakers in Italy We are hiring! Ready to help a leading technology provider to improve the quality of day-to-day interactions with your smart home speakers for higher quality outputs? We are looking for individuals who currently own a Google Home and/ or Amazon Echo in their home, interact with it frequently, and would be willing to share recent transcripts of the voice commands you give to these smart home speakers. Requirements: · Must own either an Amazon Echo and/ or Google Home · Ability to extract 30 days of transcripts · Frequent interactions with smart home speakers · Minimum of 100 transcripts available/ submitted Please follow the steps below on how to apply for the project: 1. Visit the Appen website using the link below: https://lnkd.in/gtD6FeUh

While dating apps were once seen as a last resort for finding love, they're now the go-to option for millions of singletons around the world. But if you use Tinder, Bumble or Hinge, a new study may encourage you to reassess which photos you include. Researchers from the University of Arizona have revealed that people with sexy photos on their dating profiles are more likely to seen as incompetent and unlikeable. People who opt for saucy snaps are also less likely to be seen as looking for a long-term relationship than people with less sexy photos. The first incarnation of a dating app can be traced back to 1995 when Match.com was first launched.

Artificial intelligence (AI), like robotics, has long been seen as "future technologies". However, much as with robots, we can now affirm that AI is not just science fiction, but much more than that. AI is very much alive in our personal and professional lives, and it is swiftly catching up to mobile devices in terms of popularity. There is not a single activity in our daily activities, the use of AI is not impacting us. From Alexa, Siri to self-driving cars, AI is stepping up to assist us just like a human would.

I've been in the education business for decades as a senior lecturer, trainer and CEO. When people ask me about the biggest challenge that learners face, the first thing that comes to mind is that learners see training as something they "have to do." Now, let's think for a moment about this. How did we get here? Why aren't we talking about "want to do" or "happy to have the opportunity to do?"

There's a reason people say, "first impression is the last impression." About 51% of customers will never approach a business again after a single bad experience. This puts pressure on every interaction and missed opportunity with potential customers, new purchasers, and long–term users. Webchat is often the first impression for customer service interactions. However, while chat services initially connected consumers with actual customer service staff, chatbots have become increasingly common -- for obvious reasons and with obvious limitations. Consumers care most about solving their problems.

Valentine's Day is behind us, but romance scams remain alive and dwell online, with fake suitors' latest goal to get you to invest in cryptocurrency. Romance scams, in which an online scammer leads a person on with talk of love and then swindles them, do not end after the roses and chocolates are delivered. The FBI issued an alert last week about victims of romance fraud losing $1 billion in 2021. Similarly, romance scams reported to the Federal Trade Commission rose 80% in 2021, with victims losing $547 million. These numbers are likely low, both agencies said.

By Sreenivas Gudavalli When it comes to customer service, long wait time and continual call rerouting can be discomforting besides not being a pleasant experience for customers. Even the world's most prominent companies having high volume customer inflow struggle with sluggish customer service engagement, as well as the delayed turn-around times in handling customer queries, leading to negative outcomes, including possible damage to brand reputation. Utilizing tech to enhance UX Over the years, there has been constant evolution in the customer experience (UX) and technology. That said, the incorporation of artificial intelligence (AI), machine learning (ML)-powered speech analytics into customer service operations are transforming how businesses interact with their customers. Today, more businesses are taking advantage of AI to automate and improve their Customer Relationship Management (CRM) than ever before.

When Tony Stark needs to travel to space in the original Iron Man movie, he asks his artificial intelligent (AI) assistant J.A.R.V.I.S. to make a suit that can survive harsh conditions. As AI specialist Kamal Choudhary explains: "The way I see it, what J.A.R.V.I.S. did is, it had a database of materials, scanned the database, found a suitable material, tested it, then synthesized an alloy that could survive space conditions. "That's what we want our system to do, and that's why we called it JARVIS." Choudhary, a researcher at the National Institute of Standards and Technology (NIST), is the founder and developer of JARVIS (Joint Automated Repository for Various Integrated Simulations)--an open dataset designed to automate materials discovery and optimization. Writing in npj Computational Materials in December 2021, Choudhary and Brian DeCost (NIST) described the latest enhancements to JARVIS that apply AI to speed discovery. Combining graph neural networks with chemical and structural knowledge about materials, their Atomistic Line Graph Neural Network (ALIGNN) outperforms previously reported models on atomistic prediction tasks with very high accuracy and better or comparable model training speed. "ALIGNN can predict characteristics in seconds instead of months," Choudhary said. Beyond the inspiration from Iron Man, there was the Materials Genome Initiative. Originated in 2011 under President Obama, the initiative is a multi-federal agency effort to discover, manufacture, and deploy advanced materials twice as fast and at a fraction of the cost of traditional methods. NIST's original contribution to the initiative was the creation of a database of materials and their characteristics, obtained rigorously, using standardized, cutting-edge computing methods. Several such databases have been established, but "what's particular about the JARVIS database is that it contains modules for various kinds of computational approaches," according to David Vanderbilt, professor of physics at Rutgers University, member of the National Academy of Sciences, and a contributor to the project. "There are many different theoretical levels on which you can approach the field.

Conversational agents (CAs) have the great potential in mitigating the clinicians' burden in screening for neurocognitive disorders among older adults. It is important, therefore, to develop CAs that can be engaging, to elicit conversational speech input from older adult participants for supporting assessment of cognitive abilities. As an initial step, this paper presents research in developing the backchanneling ability in CAs in the form of a verbal response to engage the speaker. We analyzed 246 conversations of cognitive assessments between older adults and human assessors, and derived the categories of reactive backchannels (e.g. "hmm") and proactive backchannels (e.g. "please keep going"). This is used in the development of TalkTive, a CA which can predict both timing and form of backchanneling during cognitive assessments. The study then invited 36 older adult participants to evaluate the backchanneling feature. Results show that proactive backchanneling is more appreciated by participants than reactive backchanneling.