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Radiology Report Generation (RRG) has achieved significant progress with the advancements of multimodal generative models. However, the evaluation in the domain suffers from a lack of fair and robust metrics. We reveal that, high performance on RRG with existing lexical-based metrics (e.g. BLEU) might be more of a mirage - a model can get a high BLEU only by learning the template of reports. This has become an urgent problem for RRG due to the highly patternized nature of these reports. In this work, we un-intuitively approach this problem by proposing the Layman's RRG framework, a layman's terms-based dataset, evaluation and training framework that systematically improves RRG with day-to-day language. We first contribute the translated Layman's terms dataset. Building upon the dataset, we then propose a semantics-based evaluation method, which is proved to mitigate the inflated numbers of BLEU and provides fairer evaluation. Last, we show that training on the layman's terms dataset encourages models to focus on the semantics of the reports, as opposed to overfitting to learning the report templates. We reveal a promising scaling law between the number of training examples and semantics gain provided by our dataset, compared to the inverse pattern brought by the original formats. Our code is available at \url{https://github.com/hegehongcha/LaymanRRG}.

If you're like me, you may have been confused when you first came across Joscha Bach's ideas on cognition and the mind. Here, I hope to help the casually curious clear any confusion regarding Bach's theories by explaining how I understand them. I am not nearly an expert, but I will do my best to stay true to his work and give a reasonable depiction of his ideas. I wrote this article by primarily referencing his talk at the 31st Chaos Communication Congress in Hamburg, linked here: https://www.youtube.com/watch?v lKQ0yaEJjok. This has been a popular philosophical idea to consider for a very long time, and it can mean a lot of different things to many different people.

The term "Artificial Neural Networks" is derived from the biological structure of brain which is basically composed of interconnected network of neurons. For understanding this we must understand the functioning of brain. Suppose you are driving a car and suddenly a person comes running in the middle of Road. So now you would be thinking that the person writing the article is somewhat mad because nobody will opt for the fourth option. Let us understand the fourth option first.

Pretend you want to improve on your 3-point shooting in basketball. You specifically want to hit the corner shots consistently. You take your basketball and your accessories to a nearby court, and you start shooting 3s in the left corner of the court. You airball your first shot as you shoot with too much power. Understanding that shooting with a lot of power will cause you to miss, you shoot again.

Code is the language that every computer uses to'speak' and'understand.' When developers write code, they make it to issue instructions to a computer and tell them what to do. But as technology has evolved and everything including the way we order food to eject satellites into space has moved to digital mode, the need for coding has drastically surged. But despite its increasing demand, we are still not able to take coding to laymen. When it comes to programming languages and coding, only tech-savvy can get their hands on it.

This course explores a specific domain of data science: time series analysis. The lectures explain topics in time series from a high level perspective, so that you can get a logical understanding of the concepts without getting intimidated by the math or programming. Whether you are new to time series or an experienced data scientist, this course covers every aspect of time series. The later half of the course entails several projects for you to get your hands dirty with time series analysis in Python. You will learn about modern time series forecasting models and AI, how to build them, and implement them to do extraordinary things.

Enough with the layman terms, let's look at AI in a little more depth. This form of AI works by exposing the machine to data along with its labels. The labels associated with the data tell the machine what the data represents. When we -- humans -- have to learn something, our best approach is to revise that certain thing again and again until our brain masters it. Similarly in Machine learning, data is shown to the machine in several iterations until the machine understands the data and is able to associate labels with similar data without any external help.

Machine learning and statistics have many applications in business and the social sciences. However, the theory is often intimidating and not easily understood. In this series of articles, I aim to demystify the concepts behind the common tools used in data science and machine learning, starting with linear regression. Linear regression is a statistical method that allows us to describe relationships between variables (distinct things that can be measured or recorded, such as height, weight, and hair colour). It is an extension of the General Linear Model, a framework to describe how a variable of interest can be modelled using other predictor variables. In simple linear regression (SLR), we focus on the relationship between two continuous variables, x and y (hence, simple).

At this step, there are three dimensions to explore: whether the data imply supervised learning or unsupervised learning? Is this a classification problem, or is it a regression problem? Is this a prediction problem or an inference problem? These three sets of questions can offer a lot of guidance when solving your data science problem. There are many tools that help you understand your data quickly. You can start by checking out the first few rows of the data frame to get the initial impression of the data organization. Automatic tools incorporated in multiple libraries, such as Pandas' .describe(),