We present PyFCG, an open source software library that ports Fluid Construction Grammar (FCG) to the Python programming language. PyFCG enables its users to seamlessly integrate FCG functionality into Python programs, and to use FCG in combination with other libraries within Python's rich ecosystem. Apart from a general description of the library, this paper provides three walkthrough tutorials that demonstrate example usage of PyFCG in typical use cases of FCG: (i) formalising and testing construction grammar analyses, (ii) learning usage-based construction grammars from corpora, and (iii) implementing agent-based experiments on emergent communication.

One of today's most significant and transformative technologies is the rapidly developing field of artificial intelligence (AI). Deined as a computer system that simulates human cognitive processes, AI is present in many aspects of our daily lives, from the self-driving cars on the road to the intelligence (AI) because some AI systems are so complex and opaque. With millions of parameters and layers, these system-deep neural networks in particular-make it difficult for humans to comprehend accountability, prejudice, and justice are raised by the opaqueness of its decision-making process. AI has a lot of potential, but it also comes with a lot of difficulties and moral dilemmas. In the context of explainable artificial intelligence (XAI), trust is crucial as it ensures that AI systems behave consistently, fairly, and ethically. In the present article, we explore XAI, reliable XAI, and several practical uses for reliable XAI. Once more, we go over the three main components-transparency, explainability, and trustworthiness of XAI-that we determined are pertinent in this situation. We present an overview of recent scientific studies that employ trustworthy XAI in various application fields. In the end, trustworthiness is crucial for establishing and maintaining trust between humans and AI systems, facilitating the integration of AI systems into various applications and domains for the benefit of society.

A technology by itself is never enough. In order for it to be of use, it needs to be accompanied by other elements, such as popular understanding, good habits, and acceptance of shared responsibility for its consequences. Without that kind of societal halo, technologies tend to be used ineffectively or incompletely. A good example of this might be the mRNA vaccines created during the COVID epidemic. They were an amazing medical achievement--and yet, because of widespread incomprehension, they didn't land as well as they might have.

ChatGPT exploded into the world in the fall of 2022, sparking a race toward ever more advanced artificial intelligence: GPT-4, Anthropic's Claude, Google Gemini, and so many others. But with every passing month, tech corporations appear more and more stuck, competing over millimeters of progress. The most advanced and attention-grabbing AI models, having consumed most of the text and images available on the internet, are running out of training data, their most precious resource. This, along with the costly and slow process of using human evaluators to develop these systems, has stymied the technology's growth, leading to iterative updates rather than massive paradigm shifts. As researchers are left trying to wring water from stone, they are exploring a new avenue to advance their products: They're using machines to train machines.

This paper investigates the information encoded in the embeddings of large language models (LLMs). We conduct simulations to analyze the representation entropy and discover a power law relationship with model sizes. Building upon this observation, we propose a theory based on (conditional) entropy to elucidate the scaling law phenomenon. Furthermore, we delve into the auto-regressive structure of LLMs and examine the relationship between the last token and previous context tokens using information theory and regression techniques. Specifically, we establish a theoretical connection between the information gain of new tokens and ridge regression. Additionally, we explore the effectiveness of Lasso regression in selecting meaningful tokens, which sometimes outperforms the closely related attention weights. Finally, we conduct controlled experiments, and find that information is distributed across tokens, rather than being concentrated in specific "meaningful" tokens alone.

In 2023, Aylin Caliskan was recognized as one of the 100 Brilliant Women in AI Ethics. At this year's International Joint Conference on Artificial Intelligence (IJCAI 2023) she gave an IJCAI Early Career Spotlight talk about her work. I met with Aylin at the conference and chatted to her about AI ethics. We spoke about bias in generative AI tools and the associated research and societal challenges. Andrea Rafai: We've seen generative AI tools become mainstream recently.

Multi-choice Machine Reading Comprehension (MRC) is a challenging extension of Natural Language Processing (NLP) that requires the ability to comprehend the semantics and logical relationships between entities in a given text. The MRC task has traditionally been viewed as a process of answering questions based on the given text. This single-stage approach has often led the network to concentrate on generating the correct answer, potentially neglecting the comprehension of the text itself. As a result, many prevalent models have faced challenges in performing well on this task when dealing with longer texts. In this paper, we propose a two-stage knowledge distillation method that teaches the model to better comprehend the document by dividing the MRC task into two separate stages. Our experimental results show that the student model, when equipped with our method, achieves significant improvements, demonstrating the effectiveness of our method.

The writing sounded like the typical 3 a.m. It was the sort of paper that usually makes me wonder: Did this student even come to class? Did I communicate anything of any value to them at all? Except there were no obvious tells that this was the product of an all-nighter: no grammar errors, misspellings, or departures into the extraneous examples that seem profound to students late at night but definitely sound like the product of a bong hit in the light of day. Perhaps, just before the end of the semester, I was seeing my very first student essay written by ChatGPT?

Artificial Intelligence poses both risks and rewards, and developers should be weary of "scary" outcomes, AI technologist says. Artificial Intelligence will never be able to truly understand the feeling of some human emotions, a humane technologist told Fox News. "The more integrated AI gets into our lives, the more we will see a difference between human and computer," Alexa Eden, a humane technologist at AlgoAI Tech, told Fox News. "And one of these impenetrable differences will be human emotions, as well as empathy, intuition and other intelligences only humans have. "Empathy is not anything that AI will ever be able to really, truly understand.

There once was a fascinating kingdom with wizards and mystical animals in a not too distant globe. These were the most incredible mysteries of the Land of Artificial Intelligence (AI). We'll go out on a voyage today to discover these mysteries and discover AI, Neural Networks, and Deep Learning in a way that even a young child can comprehend. Imagine coming into a knowledgeable owl by the name of AI while exploring a forest full of talking creatures. The owl has various magical abilities, including recognizing faces, playing chess, and solving problems.