NYS mental health committee member Patricia Canzoneri-Fitzpatrick on the bipartisan efforts to pass legislation to protect children online. Every state has its own set of strange laws still technically on the books that may surprise you, including New York. When it comes to strange laws in a state, many have little evidence to back them, with origins unknown, while others are ones you can still find in written law. Strange laws are often head-scratchers in terms of trying to figure out why the law was created in the first place. Below are a few examples of strange laws in New York.

Gone are the days of simply meeting people in the pub or through friends and when Hinge was merely a joint attached to a door. Instead, we're forced to swipe right endlessly to search for our soulmate. But with two million Brits estimated to be living with undiagnosed ADHD, being unlucky in love online could be a potential sign of the condition, new research suggests. Experts found over a fifth of singletons with the mental health condition are more likely to be feel overwhelmed by dating apps than non-ADHD daters. According to the probe, by dating app Hinge, three in four ADHD daters also report feeling misunderstood using them.

This paper describes a highly developed personalised recommendation system using multimodal, autonomous, multi-agent systems. The system focuses on the incorporation of futuristic AI tech and LLMs like Gemini-1.5- pro and LLaMA-70B to improve customer service experiences especially within e-commerce. Our approach uses multi agent, multimodal systems to provide best possible recommendations to its users. The system is made up of three agents as a whole. The first agent recommends products appropriate for answering the given question, while the second asks follow-up questions based on images that belong to these recommended products and is followed up with an autonomous search by the third agent. It also features a real-time data fetch, user preferences-based recommendations and is adaptive learning. During complicated queries the application processes with Symphony, and uses the Groq API to answer quickly with low response times. It uses a multimodal way to utilize text and images comprehensively, so as to optimize product recommendation and customer interaction.

Kernelized bandit (KB) problem [Srinivas et al., 2010], also called Gaussian process bandit or Bayesian optimization, is one of the important sequential decision-making problems where one seeks to minimize the regret under an unknown reward function via sequentially acquiring function evaluations. As the name suggests, in the KB problem, the underlying reward function is assumed to be an element of reproducing kernel Hilbert space (RKHS) induced by a known fixed kernel function. KB has been applied in many applications, such as materials discovery [Ueno et al., 2016], drug discovery [Korovina et al., 2020], and robotics [Berkenkamp et al., 2023]. In addition, the near-optimal KB algorithms, whose regret upper bound matches the regret lower bound derived in Scarlett et al. [2017], have been shown [Camilleri et al., 2021, Salgia et al., 2021, Li and Scarlett, 2022, Salgia et al., 2024]. Non-stationary KB [Bogunovic et al., 2016] considers the optimization under a non-stationary environment; that is, the reward function may change over time within some RKHS. This modification is crucial in many practical applications where an objective function varies over time, such as financial markets [Heaton and Lucas, 1999] and recommender systems [Hariri et al., 2015]. For example, Zhou and Shroff [2021], Deng et al. [2022] have proposed upper confidence bound (UCB)-based algorithms for the non-stationary KB problem and derived the upper bound of the cumulative regret. Recently, Hong et al. [2023] have proposed an optimization-based KB

LLMs are frequently used tools for conversational generation. Without additional information LLMs can generate lower quality responses due to lacking relevant content and hallucinations, as well as the perception of poor emotional capability, and an inability to maintain a consistent character. Knowledge graphs are commonly used forms of external knowledge and may provide solutions to these challenges. This paper introduces three proposals, utilizing knowledge graphs to enhance LLM generation. Firstly, dynamic knowledge graph embeddings and recommendation could allow for the integration of new information and the selection of relevant knowledge for response generation. Secondly, storing entities with emotional values as additional features may provide knowledge that is better emotionally aligned with the user input. Thirdly, integrating character information through narrative bubbles would maintain character consistency, as well as introducing a structure that would readily incorporate new information.

With rapid transformation of technologies, the fusion of Artificial Intelligence (AI) and Machine Learning (ML) in finance is disrupting the entire ecosystem and operations which were followed for decades. The current landscape is where decisions are increasingly data-driven by financial institutions with an appetite for automation while mitigating risks. The segments of financial institutions which are getting heavily influenced are retail banking, wealth management, corporate banking & payment ecosystem. The solution ranges from onboarding the customers all the way fraud detection & prevention to enhancing the customer services. Financial Institutes are leap frogging with integration of Artificial Intelligence and Machine Learning in mainstream applications and enhancing operational efficiency through advanced predictive analytics, extending personalized customer experiences, and automation to minimize risk with fraud detection techniques. However, with Adoption of AI & ML, it is imperative that the financial institute also needs to address ethical and regulatory challenges, by putting in place robust governance frameworks and responsible AI practices.

The world has witnessed rapid technological transformation, past couple of decades and with Advent of Cloud computing the landscape evolved exponentially leading to efficient and scalable application development. Now, the past couple of years the digital ecosystem has brought in numerous innovations with integration of Artificial Intelligence commonly known as AI. This paper explores how AI and cloud computing intersect to deliver transformative capabilities for modernizing applications by providing services and infrastructure. Harnessing the combined potential of both AI & Cloud technologies, technology providers can now exploit intelligent resource management, predictive analytics, automated deployment & scaling with enhanced security leading to offering innovative solutions to their customers. Furthermore, by leveraging such technologies of cloud & AI businesses can reap rich rewards in the form of reducing operational costs and improving service delivery. This paper further addresses challenges associated such as data privacy concerns and how it can be mitigated with robust AI governance frameworks.

Conventional algorithms for training language models (LMs) with human feedback rely on preferences that are assumed to account for an "average" user, disregarding subjectivity and finer-grained variations. Recent studies have raised concerns that aggregating such diverse and often contradictory human feedback to finetune models results in generic models that generate outputs not preferred by many user groups, as they tend to average out styles and norms. To address this issue, we draw inspiration from recommendation systems and propose ComPO, a method to personalize preference optimization in LMs by contextualizing the probability distribution of model outputs with the preference provider. Focusing on group-level preferences rather than individuals, we collect and release ComPRed, a question answering dataset with community-level preferences from Reddit. This dataset facilitates studying diversity in preferences without incurring privacy concerns associated with individual feedback. Our experiments reveal that conditioning language models on a community identifier (i.e., subreddit name) during preference tuning substantially enhances model performance. Conversely, replacing this context with random subreddit identifiers significantly diminishes performance, highlighting the effectiveness of our approach in tailoring responses to communities' preferences.

In human conversations, short backchannel utterances such as "yeah" and "oh" play a crucial role in facilitating smooth and engaging dialogue. These backchannels signal attentiveness and understanding without interrupting the speaker, making their accurate prediction essential for creating more natural conversational agents. This paper proposes a novel method for real-time, continuous backchannel prediction using a fine-tuned Voice Activity Projection (VAP) model. While existing approaches have relied on turn-based or artificially balanced datasets, our approach predicts both the timing and type of backchannels in a continuous and frame-wise manner on unbalanced, real-world datasets. We first pre-train the VAP model on a general dialogue corpus to capture conversational dynamics and then fine-tune it on a specialized dataset focused on backchannel behavior. Experimental results demonstrate that our model outperforms baseline methods in both timing and type prediction tasks, achieving robust performance in real-time environments. This research offers a promising step toward more responsive and human-like dialogue systems, with implications for interactive spoken dialogue applications such as virtual assistants and robots.

This paper presents LightFusionRec, a novel lightweight cross-domain recommendation system that integrates DistilBERT for textual feature extraction and FastText for genre embedding. Important issues in recommendation systems, such as data sparsity, computational efficiency, and cold start issues, are addressed in methodology. LightFusionRec uses a small amount of information to produce precise and contextually relevant recommendations for many media formats by fusing genre vector embedding with natural language processing algorithms. Tests conducted on extensive movie and book datasets show notable enhancements in suggestion quality when compared to conventional methods. Because of its lightweight design, the model can be used for a variety of purposes and allows for ondevice inference. LightFusionRec is a noteworthy development in cross-domain recommendation systems, providing accurate and scalable recommendations to improve user experience on digital content platforms.