Group conversations over videoconferencing are a complex social behavior. However, the subjective moments of negative experience, where the conversation loses fluidity or enjoyment remain understudied. These moments are infrequent in naturalistic data, and thus training a supervised learning (SL) model requires costly manual data annotation. We applied semi-supervised learning (SSL) to leverage targeted labeled and unlabeled clips for training multimodal (audio, facial, text) deep features to predict non-fluid or unenjoyable moments in holdout videoconference sessions. The modality-fused co-training SSL achieved an ROC-AUC of 0.9 and an F1 score of 0.6, outperforming SL models by up to 4% with the same amount of labeled data. Remarkably, the best SSL model with just 8% labeled data matched 96% of the SL model's full-data performance. This shows an annotation-efficient framework for modeling videoconference experience.

Prompt engineering is pivotal for harnessing the capabilities of large language models (LLMs) across diverse applications. While existing prompt optimization methods improve prompt effectiveness, they often lead to prompt drifting, where newly generated prompts can adversely impact previously successful cases while addressing failures. Furthermore, these methods tend to rely heavily on LLMs' intrinsic capabilities for prompt optimization tasks. In this paper, we introduce StraGo (Strategic-Guided Optimization), a novel approach designed to mitigate prompt drifting by leveraging insights from both successful and failed cases to identify critical factors for achieving optimization objectives. StraGo employs a how-to-do methodology, integrating in-context learning to formulate specific, actionable strategies that provide detailed, step-by-step guidance for prompt optimization. Extensive experiments conducted across a range of tasks, including reasoning, natural language understanding, domain-specific knowledge, and industrial applications, demonstrate StraGo's superior performance. It establishes a new state-of-the-art in prompt optimization, showcasing its ability to deliver stable and effective prompt improvements.

Negotiation is the complex social process by which multiple parties come to mutual agreement over a series of issues. As such, it has proven to be a key challenge problem for designing adequately social AIs that can effectively navigate this space. Artificial AI agents that are capable of negotiating must be capable of realizing policies and strategies that govern offer acceptances, offer generation, preference elicitation, and more. But the next generation of agents must also adapt to reflect their users’ experiences.      The best human negotiators tend to have honed their craft through hours of practice and experience. But, not all negotiators agree on which strategic tactics to use, and endorsement of deceptive tactics in particular is a controversial topic for many negotiators. We examine the ways in which deceptive tactics are used and endorsed in non-repeated human negotiation and show that prior experience plays a key role in governing what tactics are seen as acceptable or useful in negotiation. Previous work has indicated that people that negotiate through artificial agent representatives may be more inclined to fairness than those people that negotiate directly. We present a series of three user studies that challenge this initial assumption and expand on this picture by examining the role of past experience.      This work constructs a new scale for measuring endorsement of manipulative negotiation tactics and introduces its use to artificial intelligence research. It continues by presenting the results of a series of three studies that examine how negotiating experience can change what negotiation tactics and strategies human endorse. Study #1 looks at human endorsement of deceptive techniques based on prior negotiating experience as well as representative effects. Study #2 further characterizes the negativity of prior experience in relation to endorsement of deceptive techniques. Finally, in Study #3, we show that the lessons learned from the empirical observations in Study #1 and #2 can in fact be induced—by designing agents that provide a specific type of negative experience, human endorsement of deception can be predictably manipulated.

Horses can form deep and long lasting connections with their human keepers according to new research from France. The experiment, led by Léa Lansade of the French National Research Institute for Agriculture, Food and Environment, found that horses were able to identify their keepers when presented with a photo of them and a random human about 75 percent of the time. The results were surprising not just because they suggest horses form emotional attachments to their human companions, but because it shows horses understand photographs as symbolic representations. According to a new study from France, horses seem to be able to recognize photos of their human handlers, and can even identify former handlers they haven't seen or worked with in six months or more Past research has shown horses can identify their keepers based on smells or sound cues, according to a report in Scientific American, but Lansade's study is the first to show two-dimensional images can also have significance to horses. '[T]hese results show that horses have advanced face-recognition abilities, and are able, like humans, to differentiate between a photograph of a familiar and unfamiliar individual, even when the faces did not belong to their own species,' the team writes.

Consumers rely on search results, social media and peer reviews to perform research and gather feedback on businesses they are considering visiting or products they're thinking of purchasing. And while it can be easy to turn a blind eye on the reviews your business receives, simply ignoring those review sites can be damaging to your bottom line. While this probably comes as no surprise, 95% of shoppers read online reviews before making a purchase. As they seek out peer reviews on brands or products that they're considering doing business with, they're looking for specific things. Consumers actually look for negative reviews to discover authentic feedback from real customers.

Financial institutions have a wealth of information available to them from consumers. Due to manual and antiquated models, residential lending processes so far have had several negative experiences for both the lender and the borrower. Banks are plagued with application limitations, transaction complexities and data collection and processing challenges. The'one-size-fits-all' loan application simply does not work anymore. The newly implemented and redesigned URLA (Uniform Residential Loan Application), aims to simplify, organize and streamline the entire consumer journey – from loan request, to the underwriting and approval process.

A code tutorial in Tensorflow that uses Reinforcement Learning to take free kicks. In my previous article, I presented an AI bot trained to play the game of FIFA using Supervised Learning technique. However, the training data required to improve it further quickly became cumbersome to gather and provided little-to-no improvements, making this approach very time consuming. For this sake, I decided to switch to Reinforcement Learning, as suggested by almost everyone who commented on that article! In this article, I'll provide a short description of what Reinforcement Learning is and how I applied it to this game.

Net Promoter Score (NPS) has become a KPI at many organizations, and for good reason. It is simple to measure and has the distinctive ability to indicate how the business will perform on several other metrics, including customer retention and revenue. While NPS is easy to measure, it is difficult to improve. Organizations understand that detractors have low NPS because they have had an unsatisfactory experience at some point in their customer journey. However, identifying where or when the unsatisfactory experience occurred is a challenge.

Artificial intelligence (AI) is the tech world's golden goose. Machine learning's incredible potential for task automation and productivity has a home in almost every industry, and companies all over the world are tapping into the technology. The integration of AI-powered chatbots and data-driven analytics can improve user experiences and reduce the workload on human service agents, benefiting customers and companies alike. The future of AI technology is already here; here's how companies are leveraging the power of AI. "Artificial intelligence" is broadly defined by the development of a computer program that can "learn" on its own, without input from a human programmer. Despite decades of research dating back to as early as WWII, no computer program currently exists that can match human intelligence.

With an expected market share of $6.6 billion by the year 2019, artificial intelligence continues to grow at an astronomical rate. There have been all kinds of chatter about the implications of machine learning and its benefits and disadvantages. But many agree that AI is a blaring sign that the future is officially here. AI tech has the ability to disrupt virtually all sectors in the business world, but the difference it could make in the hiring process is truly remarkable. Since AI is able to make predictions and analyze skillsets as well as completed projects almost instantly, the technology is perfect for many areas of business, but especially for tech hiring. Let's discuss the top ways AI is guaranteed to give those time-starved HR guys at startups (if there are any in the first place) a huge reprieve from making assessments and getting buy-in and agreement with engineering in the process.