The rapid growth of information in the field of Generative Artificial Intelligence (AI), particularly in the subfields of Natural Language Processing (NLP) and Machine Learning (ML), presents a significant challenge for researchers and practitioners to keep pace with the latest developments. To address the problem of information overload, this report by the Natural Language Learning Group at Bielefeld University focuses on identifying the most popular papers on arXiv, with a specific emphasis on NLP and ML. The objective is to offer a quick guide to the most relevant and widely discussed research, aiding both newcomers and established researchers in staying abreast of current trends. In particular, we compile a list of the 40 most popular papers based on normalized citation counts from the first half of 2023. We observe the dominance of papers related to Large Language Models (LLMs) and specifically ChatGPT during the first half of 2023, with the latter showing signs of declining popularity more recently, however. Further, NLP related papers are the most influential (around 60\% of top papers) even though there are twice as many ML related papers in our data. Core issues investigated in the most heavily cited papers are: LLM efficiency, evaluation techniques, ethical considerations, embodied agents, and problem-solving with LLMs. Additionally, we examine the characteristics of top papers in comparison to others outside the top-40 list (noticing the top paper's focus on LLM related issues and higher number of co-authors) and analyze the citation distributions in our dataset, among others.

This year, we see significant progress in the field of generative models. Stable Diffusion creates hyperrealistic art. ChatGPT answers questions to the meaning of life. Galactica learns humanity's scientific knowledge but also reveals the limitations of large language models. This article is my take on the 20 most impactful AI papers of 2022.

As any Machine Learning, AI or Computer Scientist enthusiast will know, finding resources and papers on subjects you're interested in can be a hassle. Often you're required to sign up to a website and some will even try to charge you a subscription fee for reading the work of others. This is what makes the site Papers with code so great; they provide a magnitude of free resources covering a whole host of subjects. The mission of Papers With Code is to create a free and open resource with Machine Learning papers, code and evaluation tables. We believe this is best done together with the community and powered by automation.

If We Want Machines to Think, We Need to Teach Them to See. Learning and computation provides machine the ability to better understand the context of images and build visual systems which truly understand intelligence. The huge amount of image and video content urges the scientific community to make sense and identify patterns amongst it to reveal details which we aren't aware of. Computer Vision generates mathematical models from images; Computer Graphics draws in images from models and lastly image processing takes image as an input and gives an image at the output. Computer Vision is an overlapping field drawing on concepts from areas such as artificial intelligence, digital image processing, machine learning, deep learning, pattern recognition, probabilistic graphical models, scientific computing and a lot of mathematics.