A law professor has been falsely accused of sexually harassing a student in reputation-ruining misinformation shared by ChatGPT, it has been alleged. US criminal defence attorney, Jonathan Turley, has raised fears over the dangers of artificial intelligence (AI) after being wrongly accused of unwanted sexual behaviour on a Alaska trip he never went on. To jump to this conclusion, it was claimed that ChatGPT relied on a cited Washington Post article that had never been written, quoting a statement that was never issued by the newspaper. The chatbot also believed that the'incident' took place while the professor was working in a faculty he had never been employed in. In a tweet, the George Washington University professor said: 'Yesterday, President Joe Biden declared that "it remains to be seen" whether Artificial Intelligence (AI) is "dangerous."

The advent of ChatGPT seems to represent a quantum leap in the capacity of artificial intelligence (AI) to reshape the world we live in. Things are moving quickly, and organisations must begin now to think about how they can harness the power of AI to improve their performance beyond what basic process automation has long made possible. Customer service is especially ripe for the kind of improvements that AI can bring. Whether for providers of consumer goods or services ranging from healthcare to insurance and financial products--customer service operations have long been plagued by chronic inefficiencies. AI can help to reverse that.

ChatGPT can readily traverse the vast amounts of information on the internet to answer almost any ad-hoc question users pose. That it does so via natural language, in close to real-time, is indicative of the immense advancements of Generative Artificial Intelligence--and of Natural Language Generation, in particular. ChatGPT's practical utility spans most tasks associated with language, including creating annotated training datasets for data scientists, to creating highly specific reports, emails, or papers for almost any facet of business or academia. Not surprisingly, vendors of all types are rushing to implement this language model to improve solutions for everything from Business Intelligence to content services. "It's still got its own set of limitations," admitted Abhishek Gupta, Principal Data Scientist, and Engineer at Talentica.

Recent months have seen a surge of interest and activity from advocates, politicians, and scholars from various disciplines due to the extensive public deployment of large language models (LLMs). While this focus is warranted in light of the pressing concerns that new technology brings, it can also overlook some crucial factors. Recently, there has been much interest from journalists, policymakers, and scholars across disciplines in large language models and products built on them, such as ChatGPT. Nevertheless, because this technology surprises in so many ways, it is easy for concise explanations to gloss over key details. The recent increase in research and investment in LLMs may largely be attributed to the results of scaling laws.

Private investment in AI decreased for the first time in a decade. Global private investment in AI has been climbing for years but decreased by 26.7 percent from 2021 to $91.9 billion. in 2022. Training big AI models has environmental costs. A 2022 paper estimates that training a large AI language model called BLOOM emitted 25 times as much carbon as that of flying one passenger from New York to San Francisco and back. By comparison, OpenAI's GPT-3 was estimated to have a carbon cost 20 times that of BLOOM.

It's not trivial to set up, but super exciting to run your own model. Let me tell you how to start out and what outcome you can expect. BLOOM -- BigScience Large Open-science Open-access Multilingual Language Model is a transformer-based language model created by 1000 researchers (more on the BigScience project). It was trained on about 1,6 TB pre-processed multilingual text. It is free -- everybody who wants to, can try it out.

Recent improvements in machine learning and deep learning algorithms, as well as the accessibility of enormous amounts of data and processing power, have fuelled the rapid evolution of AI technology. Large-scale language models like GPT-3, as well as research in other fields like robotics and computer vision, are just a few of the substantial contributions that OpenAI has made to the field of artificial intelligence. Without any prior experience of AI, we will learn how to use the OpenAI API and build an AI application in this tutorial. OpenAI is a research organisation that aims to advance artificial intelligence in a way that is safe and beneficial for humanity. Founded in 2015, the organisation has quickly established itself as a leader in the field of AI research and development.

Over the past few months, AI chatbots like ChatGPT have captured the world's attention due to their ability to converse in a human-like way on just about any subject. But they come with a serious drawback: They can present convincing false information easily, making them unreliable sources of factual information and potential sources of defamation. Why do AI chatbots make things up, and will we ever be able to fully trust their output? We asked several experts and dug into how these AI models work to find the answers. AI chatbots such as OpenAI's ChatGPT rely on a type of AI called a "large language model" (LLM) to generate their responses. An LLM is a computer program trained on millions of text sources that can read and generate "natural language" text--language as humans would naturally write or talk.

Chain-of-Thought (CoT) prompting can effectively elicit complex multi-step reasoning from Large Language Models~(LLMs). For example, by simply adding CoT instruction ``Let's think step-by-step'' to each input query of MultiArith dataset, GPT-3's accuracy can be improved from 17.7\% to 78.7\%. However, it is not clear whether CoT is still effective on more recent instruction finetuned (IFT) LLMs such as ChatGPT. Surprisingly, on ChatGPT, CoT is no longer effective for certain tasks such as arithmetic reasoning while still keeping effective on other reasoning tasks. Moreover, on the former tasks, ChatGPT usually achieves the best performance and can generate CoT even without being instructed to do so. Hence, it is plausible that ChatGPT has already been trained on these tasks with CoT and thus memorized the instruction so it implicitly follows such an instruction when applied to the same queries, even without CoT. Our analysis reflects a potential risk of overfitting/bias toward instructions introduced in IFT, which becomes more common in training LLMs. In addition, it indicates possible leakage of the pretraining recipe, e.g., one can verify whether a dataset and instruction were used in training ChatGPT. Our experiments report new baseline results of ChatGPT on a variety of reasoning tasks and shed novel insights into LLM's profiling, instruction memorization, and pretraining dataset leakage.

Orchestrating a high-quality data preparation program is essential for successful machine learning (ML), but it is known to be time and effort consuming. Despite the impressive capabilities of large language models like ChatGPT in generating programs by interacting with users through natural language prompts, there are still limitations. Specifically, a user must provide specific prompts to iteratively guide ChatGPT in improving data preparation programs, which requires a certain level of expertise in programming, the dataset used and the ML task. Moreover, once a program has been generated, it is non-trivial to revisit a previous version or make changes to the program without starting the process over again. In this paper, we present ChatPipe, a novel system designed to facilitate seamless interaction between users and ChatGPT. ChatPipe provides users with effective recommendation on next data preparation operations, and guides ChatGPT to generate program for the operations. Also, ChatPipe enables users to easily roll back to previous versions of the program, which facilitates more efficient experimentation and testing. We have developed a web application for ChatPipe and prepared several real-world ML tasks from Kaggle. These tasks can showcase the capabilities of ChatPipe and enable VLDB attendees to easily experiment with our novel features to rapidly orchestrate a high-quality data preparation program.