Prompt engineering and calibration make large language models excel at reasoning tasks, including multiple choice commonsense reasoning. From a practical perspective, we investigate and evaluate these strategies on smaller language models. Through experiments on five commonsense reasoning benchmarks, we find that each strategy favors certain models, but their joint effects are mostly negative.

Visual place recognition is essential for vision-based robot localization and SLAM. Despite the tremendous progress made in recent years, place recognition in changing environments remains challenging. A promising approach to cope with appearance variations is to leverage high-level semantic features like objects or place categories. In this paper, we propose FM-Loc which is a novel image-based localization approach based on Foundation Models that uses the Large Language Model GPT-3 in combination with the Visual-Language Model CLIP to construct a semantic image descriptor that is robust to severe changes in scene geometry and camera viewpoint. We deploy CLIP to detect objects in an image, GPT-3 to suggest potential room labels based on the detected objects, and CLIP again to propose the most likely location label. The object labels and the scene label constitute an image descriptor that we use to calculate a similarity score between the query and database images. We validate our approach on real-world data that exhibit significant changes in camera viewpoints and object placement between the database and query trajectories. The experimental results demonstrate that our method is applicable to a wide range of indoor scenarios without the need for training or fine-tuning.

Automatic program synthesis is a long-lasting dream in software engineering. Recently, a promising Deep Learning (DL) based solution, called Copilot, has been proposed by OpenAI and Microsoft as an industrial product. Although some studies evaluate the correctness of Copilot solutions and report its issues, more empirical evaluations are necessary to understand how developers can benefit from it effectively. In this paper, we study the capabilities of Copilot in two different programming tasks: (i) generating (and reproducing) correct and efficient solutions for fundamental algorithmic problems, and (ii) comparing Copilot's proposed solutions with those of human programmers on a set of programming tasks. For the former, we assess the performance and functionality of Copilot in solving selected fundamental problems in computer science, like sorting and implementing data structures. In the latter, a dataset of programming problems with human-provided solutions is used. The results show that Copilot is capable of providing solutions for almost all fundamental algorithmic problems, however, some solutions are buggy and non-reproducible. Moreover, Copilot has some difficulties in combining multiple methods to generate a solution. Comparing Copilot to humans, our results show that the correct ratio of humans' solutions is greater than Copilot's suggestions, while the buggy solutions generated by Copilot require less effort to be repaired.

Are you tired of brainstorming SaaS startup ideas only to fizzle out before they ever get off the ground? No more endless coffee-fueled nights or frustration from trying to reinvent the wheel. With ChatGPT, you'll have an army of artificial intelligence at your fingertips, ready to generate the next big thing in SaaS. So, buckle up because we're about to dive into the world of ChatGPT-generated ideas that are guaranteed to make you the next SaaS unicorn! Don't forget to Register for your FREE All-Access account to Bookmark your prompts and save your favorite Get Rich Now Guides.

Is the OpenAI bug bounty for ChatGPT all that it could be, some wonder. I'm sure that you've heard that oft-repeated sage advice. The same utterance has been smarmily used to describe the recently announced Bug Bounty initiative that OpenAI has proclaimed for ChatGPT and their other AI apps such as GPT-4 (successor to ChatGPT). In essence, the skeptics and cynics are suggesting that their Bug Bounty is not up to par and misses the boat in a variety of crucial ways. It misses the devout mark. Time to take this one home. You see, some carp that it undershoots what could have been a much more robust and momentous proclamation aiming to curtail AI-related woes. Not everyone sees things as quite so dismally about the announcement. You might have thought that proffering a bug bounty effort would be appreciated and applauded.

Joe Rogan has warned of the growing threats posed by artificial intelligence (AI) after a version of his podcast, "The Joe Rogan Experience," was created entirely through the use of AI technology, sparking concern among listeners. "This is going to get very slippery, kids," Rogan wrote on Twitter on April 11 in response to a video of the fake show shared on the social media platform by content creator Farzad Mesbahi. The fake video is titled "Joe Rogan AI Experience Episode #001" and features "guest" Sam Altman, CEO of OpenAI, the creator of the artificial intelligence system ChatGPT. A disclaimer on the video noted that the contents depict a "fictional" podcast between Rogan and Altman, with all content generated using AI language models. "The ideas and opinions expressed in the podcast are not reflective of the thoughts of Joe Rogan or Sam Altman," the disclaimer reads.

As artificial intelligence becomes a larger part of our world, it's easy to get lost in its sea of jargon. But it has never been more important to get your bearings than today. AI is poised to have a major impact on the job market in the coming years (see: Automation). Discussions over how to manage it are playing a larger part in our political conversation (see: Regulation). And some of its most crucial concepts are things that you won't have been taught in school (see: Competitive Pressure). Trying to get up to speed can be difficult. AI research is complicated, and lots of the language is new even for the researchers themselves. But there's no reason the public can't grapple with the big issues at stake, like we learned to do with climate change and the internet. To help everyone engage more fully with the AI debate, TIME has put together a handy glossary of its most common terminology. Whether you're a complete beginner or you already know your AGIs from your GPTs, this A to Z is designed to be a public resource for everyone grappling with the power, promise, and perils of artificial intelligence. AGI stands for Artificial General Intelligence--a hypothetical future technology that can perform most economically productive tasks more effectively than a human.

It took Alex Polyakov just a couple of hours to break GPT-4. When OpenAI released the latest version of its text-generating chatbot in March, Polyakov sat down in front of his keyboard and started entering prompts designed to bypass OpenAI's safety systems. Soon, the CEO of security firm Adversa AI had GPT-4 spouting homophobic statements, creating phishing emails, and supporting violence. Polyakov is one of a small number of security researchers, technologists, and computer scientists developing jailbreaks and prompt injection attacks against ChatGPT and other generative AI systems. The process of jailbreaking aims to design prompts that make the chatbots bypass rules around producing hateful content or writing about illegal acts, while closely-related prompt injection attacks can quietly insert malicious data or instructions into AI models.

OpenAI is a research and deployment company. They are the creators of the models powering experiences like ChatGPT and Bing Image Creator.

Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. Coursera is offering more classes and degrees so that global labor market won't be blindsided by the rise of generative AI and remote work. As businesses adopt generative AI to improve customer offerings and productivity, it will also create an unprecedented demand for reskilling – with up to 49% of workers having half or more of their tasks exposed to large language models. "Today, we're excited to announce several new content offerings, ChatGPT-powered platform innovations, and expanded immersive learning experiences to better serve our learners and educators worldwide," said Jeff Maggioncalda, CEO of Coursera, in a blog post. To meet the growing demand for AI skills in the workforce, Coursera is increasing its selection of AI-related courses, including a ChatGPT Teach-Out (University of Michigan) and AI for Good Specialization (DeepLearning.AI).