Too Long; Didn't Read In this article, we learn how to leverage prompt engineering and embeddings to have an OpenAI chatbot, built with React and Node.js, respond correctly to specific contextual prompts and questions.

This paper presents the first comprehensive analysis of ChatGPT's Text-to-SQL ability. Given the recent emergence of large-scale conversational language model ChatGPT and its impressive capabilities in both conversational abilities and code generation, we sought to evaluate its Text-to-SQL performance. We conducted experiments on 12 benchmark datasets with different languages, settings, or scenarios, and the results demonstrate that ChatGPT has strong text-to-SQL abilities. Although there is still a gap from the current state-of-the-art (SOTA) model performance, considering that the experiment was conducted in a zero-shot scenario, ChatGPT's performance is still impressive. Notably, in the ADVETA (RPL) scenario, the zero-shot ChatGPT even outperforms the SOTA model that requires fine-tuning on the Spider dataset by 4.1\%, demonstrating its potential for use in practical applications. To support further research in related fields, we have made the data generated by ChatGPT publicly available at https://github.com/THU-BPM/chatgpt-sql.

This paper presents prompt design techniques for software engineering, in the form of patterns, to solve common problems when using large language models (LLMs), such as ChatGPT to automate common software engineering activities, such as ensuring code is decoupled from third-party libraries and simulating a web application API before it is implemented. This paper provides two contributions to research on using LLMs for software engineering. First, it provides a catalog of patterns for software engineering that classifies patterns according to the types of problems they solve. Second, it explores several prompt patterns that have been applied to improve requirements elicitation, rapid prototyping, code quality, refactoring, and system design.

The need for emotional inference from text continues to diversify as more and more disciplines integrate emotions into their theories and applications. These needs include inferring different emotion types, handling multiple languages, and different annotation formats. A shared model between different configurations would enable the sharing of knowledge and a decrease in training costs, and would simplify the process of deploying emotion recognition models in novel environments. In this work, we study how we can build a single model that can transition between these different configurations by leveraging multilingual models and Demux, a transformer-based model whose input includes the emotions of interest, enabling us to dynamically change the emotions predicted by the model. Demux also produces emotion embeddings, and performing operations on them allows us to transition to clusters of emotions by pooling the embeddings of each cluster. We show that Demux can simultaneously transfer knowledge in a zero-shot manner to a new language, to a novel annotation format and to unseen emotions. Code is available at https://github.com/gchochla/Demux-MEmo .

ChatGPT has been inescapable in recent months, and it looks like Microsoft is about to upgrade the AI tool with an update that could thrust it into the spotlight once again. That's because the company is set to launch GPT-4 as early as next week, and it will potentially let you create AI-generated videos from simple text prompts. The news was revealed by Andreas Braun, Chief Technology Officer at Microsoft Germany, at a recent event titled "AI in Focus -- Digital Kickoff" (via Heise). According to Braun, "We will introduce GPT-4 next week … we will have multimodal models that will offer completely different possibilities -- for example videos." GPT-4 is the underlying large language model technology that powers apps like ChatGPT.

The general buzz around AI is not fading away anytime soon. However, a technical understanding of these tools remains a complicated conversation. Large Language Models (LLMs) cannot understand and emulate human-like conversations. They are trained with huge volumes of data to give a particular output based on the specific input. But they lack the ability to comprehend the true meaning behind those words. Any response generated by LLMs will lack a basic understanding of the context.

Data is the new oil, but raw data is no good in and of itself. Like oil, data assets have to be gathered entirely and accurately and sent through different refining processes to create value for end users. This is the general data lifecycle -- an area where artificial intelligence (AI) is going to play a major role for enterprises. Initially, managing the data lifecycle was a task small enough to be handled manually by a team of experts. The volume of information was not that much, the sources were just a handful and the possible applications were also limited.

AI experts from Berkeley and beyond will explore the ramifications of ChatGPT on science and society in a spring lecture series. Since its launch last November, the artificial intelligence chatbot ChatGPT has been an international sensation, with people using the platform to do everything from writing essays, computer code, poems and research proposals to planning vacations, flirting with Tinder matches and creating malware. According to UC Berkeley computer scientist Ken Goldberg, the computer program's facility with natural language -- particularly its ability to consistently demonstrate creativity -- is forcing many AI experts to rethink what machines may be capable of and even our understanding of intelligence. "ChatGPT may catalyze a paradigm shift," said Goldberg, the William S. Floyd Jr. Distinguished Chair in Engineering. "Something changed very dramatically with the performance of ChatGPT, compared with previous large language models, and everyone, including experts, is asking, 'What does it mean? Where do we go from here?'"

AI's tantalizing promise – and its missteps – are more apparent than ever, as OpenAI's ChatGPT makes headlines for its ability to cheat on college exams or conduct an imposter job interview. But, for anyone who feels inclined to dismiss AI's potential, I would urge caution. Bill Gates called recent developments in AI "every bit as important" as the emergence of the internet – a statement that should draw the attention of innovators across every discipline. In the field of healthcare, our relationship with AI has had a mixture of successes and setbacks, particularly in applications for diagnostics. To maximize our successes and realize the potential of AI, we must make a distinction between "artificial intelligence" and "augmented intelligence" to deliver meaningful change to our healthcare system.

A new announcement by Microsoft Germany has revealed that the latest version of the Generative Pre-trained Transformer language model, GPT-4, will be released in the coming week. This upcoming version is set to be even more advanced than its predecessor, #GPT3, as it will be equipped with the ability to process and comprehend various types of data, including text, images, and audio. This feature, known as multimodality, is expected to make GPT-4 an even more versatile language model as it will be multimodal, meaning it will be able to process and comprehend various types of data, including text, images, and audio. This new feature is expected to make #GPT4 more powerful, with potential applications in various fields such as natural language processing, advanced voice recognition, and image analysis and understanding. Clemens Siebler at #Microsoft presented several real-life examples of the existing capabilities of AI.