The growing capabilities of multimodal large language models (MLLMs) have advanced tasks like chart understanding. However, these models often suffer from hallucinations, where generated text sequences conflict with the provided visual data. To address this, we introduce Post-Hoc Visual Attribution for Charts, which identifies fine-grained chart elements that validate a given chart-associated response. We propose ChartLens, a novel chart attribution algorithm that uses segmentation-based techniques to identify chart objects and employs set-of-marks prompting with MLLMs for fine-grained visual attribution. Additionally, we present ChartVA-Eval, a benchmark with synthetic and real-world charts from diverse domains like finance, policy, and economics, featuring fine-grained attribution annotations. Our evaluations show that ChartLens improves fine-grained attributions by 26-66%.

From a high level, we need something akin to the medical Hippocratic oath, which governs doctors to do no harm. It's for others to decide whether that's regulation or something else, but we need a framing commitment. I often come at things from a narrative place, and I've always been struck by writer Isaac Asimov's Robot series, in which he weaves meditations around how societal principles and protections are included in the laws of robotics on an almost engineered basis. Similarly, we need someone to assert a foundational principle for all of us that AI shouldn't do harm. On balance, at the phase we're in right now, I see far more benefits than any actual realized negatives. I think what's going on in medicine alone should give people a lot of enthusiasm for the positive potential in AI.

This formative study investigates the impact of data quality on AI-assisted data visualizations, focusing on how uncleaned datasets influence the outcomes of these tools. By generating visualizations from datasets with inherent quality issues, the research aims to identify and categorize the specific visualization problems that arise. The study further explores potential methods and tools to address these visualization challenges efficiently and effectively. Although tool development has not yet been undertaken, the findings emphasize enhancing AI visualization tools to handle flawed data better. This research underscores the critical need for more robust, user-friendly solutions that facilitate quicker and easier correction of data and visualization errors, thereby improving the overall reliability and usability of AI-assisted data visualization processes.

Many of the causes of healthcare worker attrition are related to the stressful nature of the healthcare work industry. Many employees work long hours and often experience high burnout. Employee attrition in healthcare is an issue because it exacerbates the issue of the limited supply of workers in the space. Since the healthcare space is significantly understaffed and many healthcare employees are overworked, the quality of care and the speed to care are often negatively impacted. In general, efforts towards reducing healthcare attrition involve improving the quality of the work environment.

Let's say that you work with the sales and marketing team to reach your company's pre-set goals. While your company is doing well in terms of generating revenue and retaining customers, you can not help but think that it can do better. As things stand, the advertisements, promotions, and special offers are homogenous across all customers, which is a serious issue. Engaging with customers in a manner that they won't be receptive to is tantamount to wasting your advertising budget. After all, you don't want your company to spend its limited budget sending diaper coupons to college students or advertising gaming consoles to elderly women.

Let's say that you work with the sales and marketing team to reach your company's pre-set goals. While your company is doing well in terms of generating revenue and retaining customers, you can not help but think that it can do better. As things stand, the advertisements, promotions, and special offers are homogenous across all customers, which is a serious issue. Engaging with customers in a manner that they won't be receptive to is tantamount to wasting your advertising budget. After all, you don't want your company to spend its limited budget sending diaper coupons to college students or advertising gaming consoles to elderly women.

To display all the descriptive statistics without typing each command, simply use summary() command, then R will show us each of those stats and including the 25% and 75% quantile for every variable. This is similar to the describe() command from Pandas in Python. Other simple and interesting commands for stats analysis are the cor() and cov() commands, which will show us the correlation and covariance matrices between each variable.

When searching the keyword "machine learning" on Github, I found 246,632 machine learning repositories. Since these are top repositories in machine learning, I expect the owners and the contributors of these repositories to be experts or competent in machine learning. Thus, I decided to extract the profiles of these users to gain some interesting insights into their background as well as statistics. By removing duplicates as well as removing the profiles that are organizations like udacity, I obtain a list of 1208 users. After cleaning the data, it comes to the fun part: data visualization.

Anything that has sub-types can be presented as a tree.

Bias is known to be an impediment to fair decisions in many domains such as human resources, the public sector, health care etc. Recently, hope has been expressed that the use of machine learning methods for taking such decisions would diminish or even resolve the problem. At the same time, machine learning experts warn that machine learning models can be biased as well. In this article, our goal is to explain the issue of bias in machine learning from a technical perspective and to illustrate the impact that biased data can have on a machine learning model. To reach such a goal, we develop interactive plots to visualizing the bias learned from synthetic data.