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.

Recurrent neural networks have proven effective in modeling sequential user feedbacks for recommender systems. However, they usually focus solely on item relevance and fail to effectively explore diverse items for users, therefore harming the system performance in the long run. To address this problem, we propose a new type of recurrent neural networks, dubbed recurrent exploration networks (REN), to jointly perform representation learning and effective exploration in the latent space. REN tries to balance relevance and exploration while taking into account the uncertainty in the representations. Our theoretical analysis shows that REN can preserve the rate-optimal sublinear regret even when there exists uncertainty in the learned representations. Our empirical study demonstrates that REN can achieve satisfactory long-term rewards on both synthetic and real-world recommendation datasets, outperforming state-of-the-art models.

Deep learning, big data, the cloud, and the rest of the pieces of the developing AI world are becoming familiar, being visualized by marketers who understand we need images and ideas that fit into our worldview. We already love our cars and our dogs, so falling in love with an AI personal assistant who actually talks, and listens to us, seems like an easy jump. They pay attention to us, care for us, are interested and responsive, and leave us in charge. They don't judge us or pressure us, but actually seem to like us: Robots and human proximity. The heart of AI and robotic development, real and fictional, has always been how the brain works.

Recently, graph collaborative filtering methods have been proposed as an effective recommendation approach, which can capture users' preference over items by modeling the user-item interaction graphs. In order to reduce the influence of data sparsity, contrastive learning is adopted in graph collaborative filtering for enhancing the performance. However, these methods typically construct the contrastive pairs by random sampling, which neglect the neighboring relations among users (or items) and fail to fully exploit the potential of contrastive learning for recommendation. To tackle the above issue, we propose a novel contrastive learning approach, named Neighborhood-enriched Contrastive Learning, named NCL, which explicitly incorporates the potential neighbors into contrastive pairs. Specifically, we introduce the neighbors of a user (or an item) from graph structure and semantic space respectively. For the structural neighbors on the interaction graph, we develop a novel structure-contrastive objective that regards users (or items) and their structural neighbors as positive contrastive pairs. In implementation, the representations of users (or items) and neighbors correspond to the outputs of different GNN layers. Furthermore, to excavate the potential neighbor relation in semantic space, we assume that users with similar representations are within the semantic neighborhood, and incorporate these semantic neighbors into the prototype-contrastive objective. The proposed NCL can be optimized with EM algorithm and generalized to apply to graph collaborative filtering methods. Extensive experiments on five public datasets demonstrate the effectiveness of the proposed NCL, notably with 26% and 17% performance gain over a competitive graph collaborative filtering base model on the Yelp and Amazon-book datasets respectively. Our code is available at: https://github.com/RUCAIBox/NCL.

In recent years, the Internet of Things (IoT), has seen more popularity in cyber markets. Statista projects the IoT industry worldwide. The Internet of Things (IoT), which is a collection of connected devices such as smart home and office gadgets and others, has seen more popularity in recent years. Statista predicts that the IoT market will exceed one trillion dollars worldwide by 2030. Businesses can now use IoT technology to increase their revenue.