Integrated Gradients (IG) is a widely used algorithm for attributing the outputs of a deep neural network to its input features. Due to the absence of closed-form integrals for deep learning models, inaccurate Riemann Sum approximations are used to calculate IG. This often introduces undesirable errors in the form of high levels of noise, leading to false insights in the model's decision-making process. We introduce a framework, RiemannOpt, that minimizes these errors by optimizing the sample point selection for the Riemann Sum. Our algorithm is highly versatile and applicable to IG as well as its derivatives like Blur IG and Guided IG. RiemannOpt achieves up to 20% improvement in Insertion Scores. Additionally, it enables its users to curtail computational costs by up to four folds, thereby making it highly functional for constrained environments.

Data imputation addresses the challenge of imputing missing values in database instances, ensuring consistency with the overall semantics of the dataset. Although several heuristics which rely on statistical methods, and ad-hoc rules have been proposed. These do not generalise well and often lack data context. Consequently, they also lack explainability. The existing techniques also mostly focus on the relational data context making them unsuitable for wider application contexts such as in graph data. In this paper, we propose a graph data imputation approach called GIG which relies on graph differential dependencies (GDDs). GIG, learns the GDDs from a given knowledge graph, and uses these rules to train a transformer model which then predicts the value of missing data within the graph. By leveraging GDDs, GIG incoporates semantic knowledge into the data imputation process making it more reliable and explainable. Experimental results on seven real-world datasets highlight GIG's effectiveness compared to existing state-of-the-art approaches.

In the field of eXplainable AI (XAI) in language models, the progression from local explanations of individual decisions to global explanations with high-level concepts has laid the groundwork for mechanistic interpretability, which aims to decode the exact operations. However, this paradigm has not been adequately explored in image models, where existing methods have primarily focused on classspecific interpretations. This paper introduces a novel approach to systematically trace the entire pathway from input through all intermediate layers to the final output within the whole dataset. We utilize Pointwise Feature Vectors (PFVs) and Effective Receptive Fields (ERFs) to decompose model embeddings into interpretable Concept Vectors. Then, we calculate the relevance between concept vectors with our Generalized Integrated Gradients (GIG), enabling a comprehensive, dataset-wide analysis of model behavior. In the field of eXplainable AI (XAI), efforts have historically transitioned from Local explanation to Global explanation to Mechanistic Interpretability. While local explanation methods including Selvaraju et al. (2016); Montavon et al. (2017); Sundararajan et al. (2017); Han et al. (2024) have focused on explaining specific decisions for individual instances, global explanation methods seek to uncover overall patterns and behaviors applicable across the entire dataset (Wu et al., 2022; Xuanyuan et al., 2023; Singh et al., 2024).

With the advent of general-purpose Generative AI, the interest in discerning its impact on the labor market escalates. In an attempt to bridge the extant empirical void, we interpret the launch of ChatGPT as an exogenous shock, and implement a Difference-in-Differences (DID) approach to quantify its influence on text-related jobs and freelancers within an online labor marketplace. Our results reveal a significant decrease in transaction volume for gigs and freelancers directly exposed to ChatGPT. Additionally, this decline is particularly marked in units of relatively higher past transaction volume or lower quality standards. Yet, the negative effect is not universally experienced among service providers. Subsequent analyses illustrate that freelancers proficiently adapting to novel advancements and offering services that augment AI technologies can yield substantial benefits amidst this transformative period. Consequently, even though the advent of ChatGPT could conceivably substitute existing occupations, it also unfolds immense opportunities and carries the potential to reconfigure the future of work. This research contributes to the limited empirical repository exploring the profound influence of LLM-based generative AI on the labor market, furnishing invaluable insights for workers, job intermediaries, and regulatory bodies navigating this evolving landscape.

Almost immediately after Jeff Bezos handed Amazon to Andy Jassy in July 2021, the trouble began. The company had been soaring post-pandemic, bolstered by overwhelming interest in e-commerce and the cloud, but a return to in-person life and a broad tech drawback changed it all fast. As Jassy took over, Amazon's share price plunged, customers started buying less, easy corporate deals became hard, and hard deals fell apart. While Bezos lived the good life, his anointed CEO dealt with the fallout. Next month, Jassy will reach the two-year point in his bumpy run as CEO. He's helped bring some stability to the company, but his record's been marked by approximately 27,000 layoffs, a rush to streamline operations, slowing cloud growth, and questions about the company's focus.

Fox News Flash top entertainment and celebrity headlines are here. Check out what's clicking today in entertainment. "Jeopardy!" is once again looking for a host. A nearly-exhaustive search for late host Alex Trebek's replacement began not long after his passing with a slew of guest hosts taking a swing at the gig. After months of consideration, executive producer Mike Richards was offered the reigns with actress Mayim Bialik taking over the show's spin-off events.

"Data Science" and "Data Analytics" are the most booming fields today and a large number of the crowd is inclined towards learning the same. Starting a career in Data Science and working in this field may not be as easy as it seems in the courses that are available online. You need to have a firm hold on critical thinking and analysis and need to pay great attention to details while solving any problem. Along with solving assignments and exploring the field you might as well get paid for it! So how to start freelancing in Data Science?

Machine learning is proven to yield better underwriting results and mitigate bias in lending. But not all machine learning techniques, including the wide swath at work in unregulated uses, is built to be transparent. Many of the algorithms that get deployed generate results that are difficult to explain. Recently, researchers have proposed novel and powerful methods for explaining machine learning models, notably Shapley Additive Explanations (SHAP Explainers) and Integrated Gradients (IG). These methods provide mechanisms for assigning credit to the data variables used by a model to generate a score.

On Saturday morning, I used an app on my phone to unlock a vehicle from Gig, a car sharing startup, and set off for a Valentine's Day weekend trip to northern California with my partner. By late Sunday afternoon, we were sitting on the side of a remote highway, a software issue on our smart car rendering it unusable. It was getting dark, we had no way of getting home, and I was contemplating the limits of the sharing economy and the ultimate costs of convenience. Gig is a company that rents a fleet of hybrid Toyota Priuses and electric Chevrolet Bolts in the Bay Area and Sacramento to 65,000 users, according to a spokesman for the company. It is part of a growing field of car-sharing services – including Zipcar, the now-defunct Share Now, and recently Uber and Lyft – that allow users to rent standardized vehicles on the go.

Machine learning is proven to yield better underwriting results and mitigate bias in lending. But not all machine learning techniques, including the wide swath at work in unregulated uses, is built to be transparent. Many of the algorithms that get deployed generate results that are difficult to explain. Recently, researchers have proposed novel and powerful methods for explaining machine learning models, notably Shapley Additive Explanations (SHAP Explainers) and Integrated Gradients (IG). These methods provide mechanisms for assigning credit to the data variables used by a model to generate a score.