The advent of digital technologies has significantly influenced customer service methodologies, with a notable shift towards integrating chatbots for handling customer support inquiries. This trend is primarily observed on business websites, where chatbots serve to facilitate customer queries pertinent to the business's domain. These virtual assistants are instrumental in providing essential information to customers, thereby reducing the workload traditionally managed by human customer support agents. In the realm of chatbot development, recent years have witnessed a surge in the employment of generative artificial intelligence technologies to craft customized responses. Despite this technological advancement, certain enterprises continue to favor a more structured approach to chatbot interactions. In this perspective, the content of responses is predetermined rather than generated on-the-fly, ensuring accuracy of information and adherence to the business's branding style. The deployment of these chatbots typically involves defining specific classifications known as intents. Each intent correlates with a particular customer inquiry, guiding the chatbot to deliver an appropriate response. Consequently, a pivotal challenge within this system lies in accurately identifying the user's intent based on their textual input to the chatbot.

TL;DR: Get hands-on with AI tools like ChatGPT, Gemini, and GPT-4, and more with these courses on sale for 24.97 through October 27. AI is taking over the world -- but don't worry, you can still be in charge. With this ChatGPT and Gemini AI course bundle for 25, you'll learn to harness the power of AI. This course dives into the generative AI fundamentals, giving you the skills to use tools like ChatGPT, Gemini AI, GPT-4, and even DALL-E 2 for text, image, video, and audio creation. Learn how to leverage AI for productivity gains by automating everything from inbox management to content production.

Generative AI has transformed the fields of Design and Manufacturing by providing efficient and automated methods for generating and modifying 3D objects. One approach involves using Large Language Models (LLMs) to generate Computer- Aided Design (CAD) scripting code, which can then be executed to render a 3D object; however, the resulting 3D object may not meet the specified requirements. Testing the correctness of CAD generated code is challenging due to the complexity and structure of 3D objects (e.g., shapes, surfaces, and dimensions) that are not feasible in code. In this paper, we introduce CADCodeVerify, a novel approach to iteratively verify and improve 3D objects generated from CAD code. Our approach works by producing ameliorative feedback by prompting a Vision-Language Model (VLM) to generate and answer a set of validation questions to verify the generated object and prompt the VLM to correct deviations. To evaluate CADCodeVerify, we introduce, CADPrompt, the first benchmark for CAD code generation, consisting of 200 natural language prompts paired with expert-annotated scripting code for 3D objects to benchmark progress. Our findings show that CADCodeVerify improves VLM performance by providing visual feedback, enhancing the structure of the 3D objects, and increasing the success rate of the compiled program. When applied to GPT-4, CADCodeVerify achieved a 7.30% reduction in Point Cloud distance and a 5.0% improvement in success rate compared to prior work

Recent advances in generative AI technologies like large language models have boosted the incorporation of AI assistance in writing workflows, leading to the rise of a new paradigm of human-AI co-creation in writing. To understand how people perceive writings that are produced under this paradigm, in this paper, we conduct an experimental study to understand whether and how the disclosure of the level and type of AI assistance in the writing process would affect people's perceptions of the writing on various aspects, including their evaluation on the quality of the writing and their ranking of different writings. Our results suggest that disclosing the AI assistance in the writing process, especially if AI has provided assistance in generating new content, decreases the average quality ratings for both argumentative essays and creative stories. This decrease in the average quality ratings often comes with an increased level of variations in different individuals' quality evaluations of the same writing. Indeed, factors such as an individual's writing confidence and familiarity with AI writing assistants are shown to moderate the impact of AI assistance disclosure on their writing quality evaluations. We also find that disclosing the use of AI assistance may significantly reduce the proportion of writings produced with AI's content generation assistance among the top-ranked writings.

OpenAI continues to plug new features and options into its AI-powered ChatGPT bot, and one of the latest to arrive is'memories'. They're exactly what they sound like: things ChatGPT will remember about what you know, what you like, and how you want it to respond. "Remembering things you discuss across all chats saves you from having to repeat information and makes future conversations more helpful," says OpenAI. The feature is now available to all ChatGPT users on both free and paid plans. For the privacy-conscious, this might set off a few alarm bells--but if you'd rather every conversation with ChatGPT was a blank slate, you can disable memories.

In November 2022, ChatGPT took the world by storm, demonstrating that a chatbot could be sufficiently refined to be practical and useful (unlike earlier attempts like Microsoft's disastrous Tay chatbot back in 2016). Now, after just two short years, generative AI technology has advanced by leaps and bounds, progressing quickly from simple text and image generation tools to advanced multimodal models that produce highly competent outputs across text, images, video, audio, and code. Generative AI is not just generating content and art and code, however. It also is generating tons of problems for business and society, especially when it comes to figuring out who owns the outputs produced by these systems. After all, individuals and companies are increasingly using generative AI to generate sophisticated, commercially valuable outputs.

OpenAI announced this week that it has raised 6.6 billion in new funding and that the company is now valued at 157 billion overall. This is quite a feat for an organization that reportedly burns through 7 billion a year--far more cash than it brings in--but it makes sense when you realize that OpenAI's primary product isn't technology. Case in point: Last week, CEO Sam Altman published an online manifesto titled "The Intelligence Age." In it, he declares that the AI revolution is on the verge of unleashing boundless prosperity and radically improving human life. "We'll soon be able to work with AI that helps us accomplish much more than we ever could without AI," he writes.

At this point, you probably either love the idea of making realistic videos with generative AI, or you think it's a morally bankrupt endeavor that devalues artists and will usher in a disastrous era of deepfakes we'll never escape from. It's hard to find middle ground. Meta isn't going to change minds with Movie Gen, its latest video creation AI model, but no matter what you think of AI media creation, it could end up being a significant milestone for the industry. Movie Gen can produce realistic videos alongside music and sound effects at 16 fps or 24 fps at up to 1080p (upscaled from 768 by 768 pixels). It can also generative personalized videos if you upload a photo, and crucially, it appears to be easy to edit videos using simple text commands.

Meta, the owner of Facebook and Instagram, announced on Friday it had built a new artificial intelligence model called Movie Gen that can create realistic-seeming video and audio clips in response to user prompts, claiming it can rival tools from leading media generation startups like OpenAI and ElevenLabs. Samples of Movie Gen's creations provided by Meta showed videos of animals swimming and surfing, as well as clips using people's real photos to depict them performing actions like painting on a canvas. Movie Gen also can generate background music and sound effects synced to the content of the videos, Meta said in a blogpost. Users can also edit existing videos with the model. In one such video, Meta had the tool insert pompoms into the hands of a man running by himself in the desert, while in another it changed a parking lot on which a man was skateboarding from dry ground into one covered by a splashing puddle.

Third-year student Jack Quinlan was confident he knew what I was going to ask before we conducted our interview. He wasn't psychic, and I hadn't fed him questions – he'd just done a trial run on ChatGPT. Prior to our meeting, the software engineering and neuroscience undergraduate logged on to the program to generate the kinds of questions a "professional journalist at the Guardian" would ask a student about artificial intelligence at universities. "What prompted your university to begin using generative AI tools in education?" the software version of me began. "How have students and educators at your university responded to the introduction of generative AI? Have there been any challenges and concerns raised?"