Could artificial intelligence (AI) put an end to homework forever? Elon Musk certainly thinks so. That's because the new AI chatbot ChatGPT is reportedly able to quickly produce unique assignments in a style of writing dictated by the user. As a result, students across the world could use it to do their homework for them without the teacher ever knowing. OpenAI says its ChatGPT model has been trained using a machine learning technique called Reinforcement Learning from Human Feedback (RLHF). This can simulate dialogue, answer follow-up questions, admit mistakes, challenge incorrect premises and reject inappropriate requests.

A new chatbot from OpenAI took the internet by storm this week, dashing off poems, screenplays and essay answers that were plastered as screenshots all over Twitter by the breathless technoratti. Though the underlying technology has been around for a few years, this was the first time OpenAI has brought its powerful language-generating system known as GPT3 to the masses, prompting a race by humans to give it the most inventive commands. Beyond the gimmicky demos, some people are already finding practical uses for ChatGPT, including programmers who are using it to draft code or spot errors. But the system's biggest utility could be a financial disaster for Google by supplying superior answers to the queries we currently put to the world's most powerful search engine. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

CHATGPT is a variant of the GPT-3 (Generative Pre-trained Transformer 3) language model developed by OpenAI. It is designed to be able to carry out conversations with humans in a natural and coherent manner, similar to how a human chatbot might operate. The GPT-3 model is trained on a massive dataset of text, including books, articles, and websites, which is used to teach it how to generate human-like text. The model is pre-trained on this dataset using a self-supervised learning approach, which means that it is not given explicit labels or examples of what it should produce, but rather it is left to discover patterns and relationships in the data on its own. Once the GPT-3 model has been pre-trained on this large dataset, it can then be fine-tuned for specific tasks, such as translation or question answering. The CHATGPT variant is fine-tuned specifically for the task of carrying out conversations with humans.

After all the dreaming, our technologies can still take unexpected turns, amazing and alarming us. As we agonize through another year about whether, as the Christmas carol says, "the wrong shall fail, the right prevail," I've traditionally started each new year with what I've called "a massive MapReduce on the year gone by" -- a lively lightning round of overlooked moments, in a final closing ceremony for the year gone by. But in asking what was truly significant about 2022, are we also highlighting events that foreshadow things to come? Besides technology playing a role in the world's geopolitical conflicts, there was also one unmistakable trend in 2022 that was both haunting and hilarious. It was the advances in both the performance and the accessibility of AI technology.

AI has finally gone mainstream. The Turing test is no longer the primary measure of intelligence, and machine learning reigns supreme in all industries.

Instead of writing myself a bland, general introductory paragraph about what generative AI and ChatGPT are, I asked ChatGPT to explain that to me. Generative AI refers to artificial intelligence systems that are able to generate new content that is similar in style or content to a given input. This can include tasks such as generating text, images, or audio. Generative AI systems are fascinating because they have the potential to create new and creative content that can be used in a variety of applications, such as generating news articles, creating art, or even generating realistic-sounding dialogue for virtual assistants. One of the main milestones in the development of generative AI has been the development of large language models, such as GPT-3 (short for "Generative Pre-trained Transformer 3").

Educators concerned that the viral popularity of OpenAI's ChatGPT will lead to waves of generic-sounding, mostly AI-written essays might have reason to relax. Princeton student Edward Tian devoted a portion of his holiday to writing GPTZero -- an application that can identify text authored by artificial intelligence. Tian posted a couple of proof-of-concept videos on January 2nd demonstrating GPTZero's capabilities. First, it determined that a human authored a New Yorker article; then, it correctly identified ChatGPT as the author of a Facebook post. GPTZero scores text on its "perplexity and burstiness" – referring to how complicated it is and how randomly it is written.

Due to the incompleteness of knowledge graphs (KGs), zero-shot link prediction (ZSLP) which aims to predict unobserved relations in KGs has attracted recent interest from researchers. A common solution is to use textual features of relations (e.g., surface name or textual descriptions) as auxiliary information to bridge the gap between seen and unseen relations. Current approaches learn an embedding for each word token in the text. These methods lack robustness as they suffer from the out-of-vocabulary (OOV) problem. Meanwhile, models built on character n-grams have the capability of generating expressive representations for OOV words. Thus, in this paper, we propose a Hierarchical N-Gram framework for Zero-Shot Link Prediction (HNZSLP), which considers the dependencies among character n-grams of the relation surface name for ZSLP. Our approach works by first constructing a hierarchical n-gram graph on the surface name to model the organizational structure of n-grams that leads to the surface name. A GramTransformer, based on the Transformer is then presented to model the hierarchical n-gram graph to construct the relation embedding for ZSLP. Experimental results show the proposed HNZSLP achieved state-of-the-art performance on two ZSLP datasets.

Google published a groundbreaking research paper about identifying page quality with AI. The details of the algorithm seem remarkably similar to what the helpful content algorithm is known to do. Nobody outside of Google can say with certainty that this research paper is the basis of the helpful content signal. Google generally does not identify the underlying technology of its various algorithms such as the Penguin, Panda or SpamBrain algorithms. So one can't say with certainty that this algorithm is the helpful content algorithm, one can only speculate and offer an opinion about it.

ChatGPT is a variant of the GPT-3 language model that was specifically designed to support conversation and chatbot applications. It was developed by OpenAI, and is notable for its ability to generate human-like text that is coherent and appropriate for a wide range of conversation topics. One potential topic for a blog post about ChatGPT could be a review or evaluation of the model's performance. This could involve comparing ChatGPT to other chatbot models or discussing its strengths and weaknesses in terms of its ability to generate natural-sounding text and handle different types of conversation. Another possible angle for a blog post on ChatGPT could be a tutorial or guide on how to use the model to build a chatbot application.