This study examines the tendency to cite older work across 20 fields of study over 43 years (1980--2023). We put NLP's propensity to cite older work in the context of these 20 other fields to analyze whether NLP shows similar temporal citation patterns to these other fields over time or whether differences can be observed. Our analysis, based on a dataset of approximately 240 million papers, reveals a broader scientific trend: many fields have markedly declined in citing older works (e.g., psychology, computer science). We term this decline a 'citation age recession', analogous to how economists define periods of reduced economic activity. The trend is strongest in NLP and ML research (-12.8% and -5.5% in citation age from previous peaks). Our results suggest that citing more recent works is not directly driven by the growth in publication rates (-3.4% across fields; -5.2% in humanities; -5.5% in formal sciences) -- even when controlling for an increase in the volume of papers. Our findings raise questions about the scientific community's engagement with past literature, particularly for NLP, and the potential consequences of neglecting older but relevant research. The data and a demo showcasing our results are publicly available.

The conversational artificial-intelligence (AI) technology ChatGPT has become one of the most widely used natural language processing tools. With thousands of published papers demonstrating its applications across various industries and fields, ChatGPT has sparked significant interest in the research community. Reviews of primary data have also begun to emerge. An overview of the available evidence from multiple reviews and studies could provide further insights, minimize redundancy, and identify areas where further research is needed. Objective: To evaluate the existing reviews and literature related to ChatGPT's applications and its potential impact on different fields by conducting a systematic review of reviews and bibliometric analysis of primary literature. Methods: PubMed, EuropePMC, Dimensions AI, medRxiv, bioRxiv, arXiv, and Google Scholar were searched for ChatGPT-related publications from 2022 to 4/30/2023. Studies including secondary data related to the application of ChatGPT were considered. Reporting and risk of bias assesment was performed using PRISMA guidelines. Results: A total of 305 unique records with potential relevance to the review were identified from a pool of over 2,000 original articles. After multi-step screening process, 11 reviews were selected, consisting of 9 reviews specifically focused on ChatGPT and 2 reviews on broader AI topics that also included discussions on ChatGPT. We also conducted bibliometric analysis of primary data. Conclusions: While AI has the potential to revolutionize various industries, further interdisciplinary research, customized integrations, and ethical innovation are necessary to address existing concerns and ensure its responsible use. Protocol Registration: PROSPERO registration no. CRD42023417336, DOI 10.17605/OSF.IO/87U6Q.

At the end of November 2022, the San Francisco-based company OpenAI launched its prototype of ChatGPT, an artificial intelligence (AI)-based chatbot that can answer a wide range of questions in short periods of time. Since then, users worldwide have been testing the chatbot and discussing its possible applications in different fields. ChatGPT is based on a so-called large language model (LLM), a deep learning technique that employs multi-layered neural networks trained on a vast pool of texts. Over time, these models can learn to make predictions about how to compose sentences and answer specific language queries. GPT-3, the model underpinning ChatGPT, is one of the most powerful LLMs worldwide, as it includes more than 175 billion parameters and can tackle a wide range of written tasks. For instance, the chatbot can translate and summarize written texts, compose basic poems or song lyrics and offer definitions for particular terms.

I teach managing technological innovation in Simon Fraser University's Management of Technology MBA program. No matter our industry or field, we should regularly review our tools and workflows. New tools, like AI, are excellent triggers for this assessment. Sorting out how best to adjust our work, as per the values and existing norms of different fields, takes a systematic approach. My research examines how companies can adjust how they use talent, technology and technique to hit work targets and stay aligned with the times -- what I've called thinking in 5T.

ChatGPT is an advanced language model developed by OpenAI that uses state-of-the-art machine-learning techniques to generate human-like text. It is one of the largest and most sophisticated language models in existence, with 1.5 billion parameters, and has been trained on a diverse range of internet texts to understand and replicate natural language. ChatGPT is designed to understand and generate text in natural language, including grammar, syntax, and semantics. It can be used for a wide range of applications, including generating conversational responses, summarizing long texts, answering questions, and even creating original content. ChatGPT can be fine-tuned for specific tasks by training it on domain-specific data, making it a versatile tool for many different use cases.

Science and technology often facilitate one another; the latest discoveries in one will lead to new discoveries in the other. Along with innovations in engineering, medicine, and many other fields, this co-evolution can also be seen in physics. The continuing improvements in technology, in particular artificial intelligence (AI) and machine learning (ML), open doors for physics researchers to explore more precise and in-depth topics -- leading to new discoveries and a deeper understanding of our world. With roots in statistical mechanics, the mathematical foundation of AI development is shared with many branches of physics, making the two natural counterparts. Since "physics" is an extremely broad subject area and covers many different fields, each field may utilize AI differently.

A team based at Princeton University has accurately simulated the initial steps of ice formation by applying artificial intelligence (AI) to solving equations that govern the quantum behavior of individual atoms and molecules. The resulting simulation describes how water molecules transition into solid ice with quantum accuracy. This level of accuracy, once thought unreachable due to the amount of computing power it would require, became possible when the researchers incorporated deep neural networks, a form of artificial intelligence, into their methods. The study was published in the journal Proceedings of the National Academy of Sciences. "In a sense, this is like a dream come true," said Roberto Car, Princeton's Ralph W. *31 Dornte Professor in Chemistry, who co-pioneered the approach of simulating molecular behaviors based on the underlying quantum laws more than 35 years ago.

I believe that anyone who has seen the movie "Artificial Intelligence" was deeply impressed by the cute-looking, kind and soft-hearted robot, David, who longed for the love of human mother Monica. David was a robot made by a robot company that could love people. He replaced Monica's son Henry, who is terminally ill and falls into a vegetative state. When Henry wakes up, David is faced with the situation of being destroyed. He turns into a real human boy, and seeks to gain the love of his mother, Monica.

We had a lot of developments with multiple tops and turns. The sheer number and quality of the multiple papers and outcomes released in the ML space were amazing. We had innovations in GPU, newer models, lots of research into different fields, and some ground-breaking discoveries. The Machine Learning industry continued to grow by leaps and bounds. Here are some interesting stats.

This blog was written by an independent guest blogger. Historically, the idea of artificial intelligence (AI) saturating our world has been met with suspicion. Indeed, it's one of the more popular tropes of science fiction -- learning machines gain sentience that helps them take over the planet. While we're not even slightly close to that dystopian reality, we have reached a point at which AI has been significantly integrated into various aspects of our society. While this isn't without its risks, largely from a security standpoint, there are huge benefits.