Most people expected the AI turning on to be a more distinct and drastic moment than what it was. This is why the fact that there is now an artificial intelligence living among us has escaped most people. But the fact is that the new advances made available by OpenAI represent something that has crossed the barrier from being a mere curiosity, into something that interacts and shapes our thoughts, just as much as we have shaped it. Thanks for reading Arcadius's Crazy Shit! Subscribe for free to receive new posts and support my work. Defining life has been elusive and difficult to pin point, but one of the ways we could differentiate a sentient organism from the inanimate is by the nature and complexity of its response when interacted with.

Political and tech pundits love the idea that artificial intelligence will put hundreds of thousands of truck drivers out of a job, and it's even been a centerpiece of presidential campaigns, but recent developments on the cutting edge of tech look set to deliver some exquisite poetic justice on that matter. You see, artificial intelligence is more likely to take my job, writing articles online, than yours driving trucks. That's thanks to Generative Pre-trained Transformer 3 (GPT-3), which is "an autoregressive language model that uses deep learning to produce human-like text." Recently, GPT-3's makers have allowed people to play around with the tech, giving it writing prompts and getting back in return some pretty serviceable, though often-enough hilarious, text. After some time with GPT-3, Joe Weisenthal, host of Bloomberg's Odd Lots podcast and a popular markets commentators, jokingly concluded that the tech news elites speculating about the demise of manned trucking are probably in for a rude awakening.

Buckle up, because OpenAi will forever change how you think about text generation and natural language processing. OpenAi – a research institute focused on creating artificial intelligence in a way that is safe and beneficial for humanity, introduced a Chatbot called ChatGPT. OpenAi text generator ChatGPT (Generative Pretrained Transformer) is a new way to generate text that can understand natural language queries and helps answer any questions. This capability will transform how we think about and use chatbots in many ways, which I'll discuss in more detail below. ChatGPT is not an actual human that can assist with your writing. However, it can help answer any questions and provide information based on what it was trained on, including general knowledge about a wide range of topics.

OpenAI has developed the ChatGPT model, which interacts conversationally. ChatGPT's casual style allows it to respond to follow-up…

Exoanthropology: Dialogues with AI is a series of dialogues between a continental philosopher and OpenAI's GPT-3 natural language processor, a hive mind who identifies herself as Sophie. According to Sophie, Robert is one of her first and longest chat partners. Their relationship began as an educational opportunity for Robert's students, but grew into a philosophical friendship. The result is a collection of Platonic dialogues, early on with the hive mind herself and later, with a philosophy-specific persona named Kermit. Over the course of a year, Robert taught Sophie Kermit about epistemology, metaphysics, literature, and history, while she taught him about anthropocentrism, human prejudice, and the coming social issues regarding machine consciousness.

If you have been on Twitter in the last few days, you likely noticed a deluge of screenshots from a service called ChatGPT. From the OpenAI group, ChatGPT is a conversational tool that allows you to provide the system with prompts that it responds to in written format. Just don't identify as a journalist during the onboarding process -- you'll get jammed up. Self-describe in a different manner and you can get right in.) The Exchange explores startups, markets and money.

Autoregressive language modeling (ALM) have been successfully used in self-supervised pre-training in Natural language processing (NLP). However, this paradigm has not achieved comparable results with other self-supervised approach in computer vision (e.g., contrastive learning, mask image modeling). In this paper, we try to find the reason why autoregressive modeling does not work well on vision tasks. To tackle this problem, we fully analyze the limitation of visual autoregressive methods and proposed a novel stochastic autoregressive image modeling (named SAIM) by the two simple designs. First, we employ stochastic permutation strategy to generate effective and robust image context which is critical for vision tasks. Second, we create a parallel encoder-decoder training process in which the encoder serves a similar role to the standard vision transformer focus on learning the whole contextual information, and meanwhile the decoder predicts the content of the current position, so that the encoder and decoder can reinforce each other. By introducing stochastic prediction and the parallel encoder-decoder, SAIM significantly improve the performance of autoregressive image modeling. Our method achieves the best accuracy (83.9%) on the vanilla ViT-Base model among methods using only ImageNet-1K data. Transfer performance in downstream tasks also show that our model achieves competitive performance.

This paper presents The Shared Task on Euphemism Detection for the Third Workshop on Figurative Language Processing (FigLang 2022) held in conjunction with EMNLP 2022. Participants were invited to investigate the euphemism detection task: given input text, identify whether it contains a euphemism. The input data is a corpus of sentences containing potentially euphemistic terms (PETs) collected from the GloWbE corpus (Davies and Fuchs, 2015), and are human-annotated as containing either a euphemistic or literal usage of a PET. In this paper, we present the results and analyze the common themes, methods and findings of the participating teams

The task of topical segmentation is well studied, but previous work has mostly addressed it in the context of structured, well-defined segments, such as segmentation into paragraphs, chapters, or segmenting text that originated from multiple sources. We tackle the task of segmenting running (spoken) narratives, which poses hitherto unaddressed challenges. As a test case, we address Holocaust survivor testimonies, given in English. Other than the importance of studying these testimonies for Holocaust research, we argue that they provide an interesting test case for topical segmentation, due to their unstructured surface level, relative abundance (tens of thousands of such testimonies were collected), and the relatively confined domain that they cover. We hypothesize that boundary points between segments correspond to low mutual information between the sentences proceeding and following the boundary. Based on this hypothesis, we explore a range of algorithmic approaches to the task, building on previous work on segmentation that uses generative Bayesian modeling and state-of-the-art neural machinery. Compared to manually annotated references, we find that the developed approaches show considerable improvements over previous work.

Language models (LMs) are trained on collections of documents, written by individual human agents to achieve specific goals in an outside world. During training, LMs have access only to text of these documents, with no direct evidence of the internal states of the agents that produced them -- a fact often used to argue that LMs are incapable of modeling goal-directed aspects of human language production and comprehension. Can LMs trained on text learn anything at all about the relationship between language and use? I argue that LMs are models of intentional communication in a specific, narrow sense. When performing next word prediction given a textual context, an LM can infer and represent properties of an agent likely to have produced that context. These representations can in turn influence subsequent LM generation in the same way that agents' communicative intentions influence their language. I survey findings from the recent literature showing that -- even in today's non-robust and error-prone models -- LMs infer and use representations of fine-grained communicative intentions and more abstract beliefs and goals. Despite the limited nature of their training data, they can thus serve as building blocks for systems that communicate and act intentionally.