In a galaxy far, far away, there was a powerful computer program called ChatGPT. ChatGPT was created to help people communicate with computers in a more natural and intelligent way. ChatGPT was incredibly advanced, with the ability to understand and respond to a wide range of questions and statements. It could even learn and adapt over time, becoming more and more sophisticated as it gained more experience. Many people throughout the galaxy used ChatGPT to communicate with their computers and devices, and it quickly became a powerful and useful tool. Some even saw it as a kind of artificial intelligence, a being with its own thoughts and feelings.

ChatGPT3 is a state-of-the-art natural language processing (NLP) system that uses machine learning techniques to generate human-like text. It is part of a larger group of NLP models called transformers, which are designed to process and generate text by considering the context and relationships between words in a sentence. One of the key features of ChatGPT3 is its ability to perform language translation, which allows it to understand and generate text in multiple languages. To do this, it uses a process called "masking" to predict the missing word or phrase in a sentence. For example, if given the sentence "The cat sat on the ____," ChatGPT3 might mask the blank space and generate the word "mat" as the missing word.

Biological systems in nature have evolved for millions of years to adapt and survive the environment. Many features they developed can be inspirational and beneficial for solving technical problems in modern industries. This leads to a specific form of design-by-analogy called bio-inspired design (BID). Although BID as a design method has been proven beneficial, the gap between biology and engineering continuously hinders designers from effectively applying the method. Therefore, we explore the recent advance of artificial intelligence (AI) for a data-driven approach to bridge the gap. This paper proposes a generative design approach based on the generative pre-trained language model (PLM) to automatically retrieve and map biological analogy and generate BID in the form of natural language. The latest generative pre-trained transformer, namely GPT-3, is used as the base PLM. Three types of design concept generators are identified and fine-tuned from the PLM according to the looseness of the problem space representation. Machine evaluators are also fine-tuned to assess the mapping relevancy between the domains within the generated BID concepts. The approach is evaluated and then employed in a real-world project of designing light-weighted flying cars during its conceptual design phase The results show our approach can generate BID concepts with good performance.

One of the most impressive results of recent NLP history is the ability of pre-trained language models to solve new tasks in a zero-shot setting. To achieve this, NLP tasks are framed as natural language prompts, generating a response indicating the predicted output. Nonetheless, the performance in such settings often lags far behind its supervised counterpart, suggesting a large space for potential improvement. In this paper, we explore methods to utilize unlabeled data to improve zero-shot performance. Specifically, we take advantage of the fact that multiple prompts can be used to specify a single task, and propose to regularize prompt consistency, encouraging consistent predictions over this diverse set of prompts. Our method makes it possible to fine-tune the model either with extra unlabeled training data, or directly on test input at inference time in an unsupervised manner. In experiments, our approach outperforms the state-of-the-art zero-shot learner, T0 (Sanh et al., 2022), on 9 out of 11 datasets across 4 NLP tasks by up to 10.6 absolute points in terms of accuracy. The gains are often attained with a small number of unlabeled examples.

Large language models (LLMs) have demonstrated impressive capabilities in natural language understanding and generation, but the quality bar for medical and clinical applications is high. Today, attempts to assess models' clinical knowledge typically rely on automated evaluations on limited benchmarks. There is no standard to evaluate model predictions and reasoning across a breadth of tasks. To address this, we present MultiMedQA, a benchmark combining six existing open question answering datasets spanning professional medical exams, research, and consumer queries; and HealthSearchQA, a new free-response dataset of medical questions searched online. We propose a framework for human evaluation of model answers along multiple axes including factuality, precision, possible harm, and bias. In addition, we evaluate PaLM (a 540-billion parameter LLM) and its instruction-tuned variant, Flan-PaLM, on MultiMedQA. Using a combination of prompting strategies, Flan-PaLM achieves state-of-the-art accuracy on every MultiMedQA multiple-choice dataset (MedQA, MedMCQA, PubMedQA, MMLU clinical topics), including 67.6% accuracy on MedQA (US Medical License Exam questions), surpassing prior state-of-the-art by over 17%. However, human evaluation reveals key gaps in Flan-PaLM responses. To resolve this we introduce instruction prompt tuning, a parameter-efficient approach for aligning LLMs to new domains using a few exemplars. The resulting model, Med-PaLM, performs encouragingly, but remains inferior to clinicians. We show that comprehension, recall of knowledge, and medical reasoning improve with model scale and instruction prompt tuning, suggesting the potential utility of LLMs in medicine. Our human evaluations reveal important limitations of today's models, reinforcing the importance of both evaluation frameworks and method development in creating safe, helpful LLM models for clinical applications.

GPT-3 (Generative Pre-trained Transformer 3) is a large-scale autoregressive language model developed by OpenAI, which has demonstrated impressive few-shot performance on a wide range of natural language processing (NLP) tasks. Hence, an intuitive application is to use it for data annotation. In this paper, we investigate whether GPT-3 can be used as a good data annotator for NLP tasks. Data annotation is the process of labeling data that could be used to train machine learning models. It is a crucial step in the development of NLP systems, as it allows the model to learn the relationship between the input data and the desired output.

Teachers are a creative bunch. They have to be to come up with lesson plans and exams that help students grow their minds and prevent those same students from relying too much on technology to enhance their work or to cheat. Which is why the rollout of OpenAI's ChatGPT has many teachers worried. The chatbot can answer almost any type of question, even if the answers aren't always accurate. Marketplace's Kimberly Adams spoke with Daniel Herman, an English teacher at Maybeck High School in Berkeley, California.

This repository contains a collection of papers and resources on Reasoning in Large Language Models. Feel free to let me know the missing papers (issue or pull request). Thank Kevin Chen-Chuan Chang @UIUC, Jason Wei @Google Brain, Denny Zhou @Google Brain for insightful discussions and suggestions. We mainly focus on techniques that are applicable to improving or eliciting "reasoning" in large language models like GPT-3 (175B) Papers in this paradigm vary a lot and are usually based on small models trained on specific datasets. We list several papers here for reference (that is, the list is not complete).

Lately some high-profile tools have emerged that can help in programming like Github Copilot and ChatGPT. Is the programming job market going to shrink because of this? I think I will be able to find a programming job even in the year 2050, but before I reveal why, I'll lead with some exploration of ChatGPT. I'll start with an experiment. I'll ask ChatGPT directly whether it will replace programmers, and then will ask it to program a neural network for me.

It is true that the invention of the car left many people unemployed, and that computer calculations left thousands of people at home, and that the Internet ended hundreds of jobs in the 20th century… but Artificial Intelligence should not be underestimated. The fact that it is capable of creating text, images and even 3D objects makes it a powerful tool, but if it continues to evolve so fast, humanity may not be able to react fast enough to create new jobs as it grows. Could there be a future where artificial intelligence does all the jobs? In the future, artificial intelligence may be able to perform many jobs more efficiently and faster than humans. However, it is unlikely that artificial intelligence can completely replace humans in all jobs. Although artificial intelligence can be very good at performing specific and repetitive tasks, it still lacks the capacity for creativity, empathy, and moral judgment that humans have.