Over the weekend, experimenters discovered that OpenAI's new chatbot, ChatGPT, can hallucinate simulations of Linux shells and role-play dialing into a bulletin board system (BBS). The chatbot, based on a deep-learning AI model, uses its stored knowledge to simulate Linux with surprising results, including executing Python code and browsing virtual websites. Last week, OpenAI made ChatGPT freely available during a testing phase, which has led to people probing its capabilities and weaknesses in novel ways. I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show.

As the capabilities of natural language processing technology continue to advance, there is a growing hype around the potential of chatbots and conversational AI systems. One such system, ChatGPT, claims to be able to engage in natural, human-like conversation and even provide useful information and advice. However, there are valid concerns about the limitations of ChatGPT and other conversational AI systems, and their ability to truly replicate human intelligence and interaction. No, I didn't write that. It was actually written by ChatGPT itself, a conversational AI software program, after I asked it to create "an opening paragraph to an article sceptical about the abilities of ChatGPT in the style of Kenan Malik". And it is not difficult to see why there has been such excitement, indeed hype, about the latest version of the chatbot since it was released a week ago.

Artificial intelligence: What is ChatGPT? Will artificial intelligence lead to plagiarism?

ChatGPT launched few days ago and it already has over 1 million users . ChatGPT is a cutting-edge natural language processing (NLP) technology developed by OpenAI. It uses advanced machine learning algorithms to generate human-like responses to user inputs in real time.

Did a human write that, or ChatGPT? It can be hard to tell -- perhaps too hard, its creator OpenAI thinks, which is why it is working on a way to "watermark" AI-generated content. In a lecture at the University of Austin, computer science professor Scott Aaronson, currently a guest researcher at OpenAI, revealed that OpenAI is developing a tool for "statistically watermarking the outputs of a text [AI system]." Whenever a system -- say, ChatGPT -- generates text, the tool would embed an "unnoticeable secret signal" indicating where the text came from. OpenAI engineer Hendrik Kirchner built a working prototype, Aaronson says, and the hope is to build it into future OpenAI-developed systems.

… artificial general intelligence — machine intelligence that can solve problems … These specific learning technologies are self catalyzing; …

I remember the days before Google: Finding answers on the internet was tedious and clunky, I had to switch between search engines or run meta-searches to get workable results, and it still felt like I wasn't finding all the information that was out there. Then Google came along and everything changed – I felt like I had gained new super-powers. Using ChatGPT feels at least as transformative as switching from AltaVista to Google. After only a few days of working with ChatGPT, I feel like it has made me much more effective. It'd be hard to go back to pre-ChatGPT life.

The goal of building dialogue agents that can converse with humans naturally has been a long-standing dream of researchers since the early days of artificial intelligence. The well-known Turing Test proposed to judge the ultimate validity of an artificial intelligence agent on the indistinguishability of its dialogues from humans'. It should come as no surprise that human-level dialogue systems are very challenging to build. But, while early effort on rule-based systems found limited success, the emergence of deep learning enabled great advance on this topic. In this thesis, we focus on methods that address the numerous issues that have been imposing the gap between artificial conversational agents and human-level interlocutors. These methods were proposed and experimented with in ways that were inspired by general state-of-the-art AI methodologies. But they also targeted the characteristics that dialogue systems possess.

Reasoning is central to human intelligence. However, fallacious arguments are common, and some exacerbate problems such as spreading misinformation about climate change. In this paper, we propose the task of logical fallacy detection, and provide a new dataset (Logic) of logical fallacies generally found in text, together with an additional challenge set for detecting logical fallacies in climate change claims (LogicClimate). Detecting logical fallacies is a hard problem as the model must understand the underlying logical structure of the argument. We find that existing pretrained large language models perform poorly on this task. In contrast, we show that a simple structure-aware classifier outperforms the best language model by 5.46% on Logic and 4.51% on LogicClimate. We encourage future work to explore this task as (a) it can serve as a new reasoning challenge for language models, and (b) it can have potential applications in tackling the spread of misinformation. Our dataset and code are available at https://github.com/causalNLP/logical-fallacy

The OpenAI Startup Fund is investing $100 million to help AI companies have a profound, positive impact on the world. We're looking to partner with a small number of early-stage startups in fields where artificial intelligence can have a transformative effect--like health care, climate change, and education--and where AI tools can empower people by helping them be more productive. The fund is managed by a dedicated team with expertise in investing, ML, engineering, talent and operations, including members of OpenAI leadership and technical staff. The fund's investors consist of Microsoft and other OpenAI partners. In addition to capital, companies in the OpenAI Startup Fund will get early access to future OpenAI systems, support from the fund's team and credits on Azure.