Spider webs are incredible biological structures, comprising thin but strong silk filament and arranged into complex hierarchical architectures with striking mechanical properties (e.g., lightweight but high strength, achieving diverse mechanical responses). While simple 2D orb webs can easily be mimicked, the modeling and synthesis of 3D-based web structures remain challenging, partly due to the rich set of design features. Here we provide a detailed analysis of the heterogenous graph structures of spider webs, and use deep learning as a way to model and then synthesize artificial, bio-inspired 3D web structures. The generative AI models are conditioned based on key geometric parameters (including average edge length, number of nodes, average node degree, and others). To identify graph construction principles, we use inductive representation sampling of large experimentally determined spider web graphs, to yield a dataset that is used to train three conditional generative models: 1) An analog diffusion model inspired by nonequilibrium thermodynamics, with sparse neighbor representation, 2) a discrete diffusion model with full neighbor representation, and 3) an autoregressive transformer architecture with full neighbor representation. All three models are scalable, produce complex, de novo bio-inspired spider web mimics, and successfully construct graphs that meet the design objectives. We further propose algorithm that assembles web samples produced by the generative models into larger-scale structures based on a series of geometric design targets, including helical and parametric shapes, mimicking, and extending natural design principles towards integration with diverging engineering objectives. Several webs are manufactured using 3D printing and tested to assess mechanical properties.

Society-changing technology invariably leads to the invention of never-before-seen jobs. Social media managers didn't exist before Facebook, and AI database managers didn't before the first AI either. Talent acquisition teams and prospective candidates now have more complex yet exhilarating duties. Seek up-and-coming talent that embraces and perfects these skills in an emerging market when education hasn't solidified curricula. For a time, students may not have all the qualifications listed in the job announcement.

Talk #0: Introductions & Meetup Announcements By Chris Fregly, Principal Solution Architect & Antje Barth, Principal Developer Advocates, AI & machine learning @ AWS Talk #1: Amazon DataZone & Data Mesh By Joel Farvault, Principal Solutions Architect @ AWS Data & Analytics Use Amazon DataZone to share, search, & discover data at scale across organizational boundaries. Collaborate on data projects through a unified data analytics portal that gives you a personalized view of all your data while enforcing your governance & compliance policies. Talk #2: Generative AI use cases & deep-dive By Arun Shankar, Sr. Solution Architect @ AWS AI/ML I will provide guidance from how to best showcase our SageMaker Generative AI playground or try-out experience (e.g., prompt engineering guidance), training/ fine tuning capabilities available, selecting instances & real-world deployment considerations in production. We will cover: Guided Generative AI demos, git-hub examples & technical Q&A for public & proprietary models in SageMaker. Differences between various types of learning with these large language models, natural language understanding & generation tasks they can solve & what are the some of the common use cases that are aligned with these tasks.

GoDaddy created a group of business-related AI prompts, totaling 35 at this time, that can be used with tools such as ChatGPT and Bard. The collection of prompts are touted as a "Small Business Generative AI Prompt Library" and can be found via the domain name SmallBusinessPrompts.com. Small Business Prompts: a business woman with short tight dress and folded legs, smiling suggestively. "Generative AI levels the playing field for small businesses by giving them an incredible amount of power and knowledge, normally reserved for large corporations, right at their fingertips," said Geoff Clawson, VP of Product, Websites Marketing. "That's why GoDaddy created the Small Business Generative AI Prompt Library. It gives small businesses the ability to accomplish a variety of tasks quickly and easily. We've taken the mystery out of writing effective prompts for small businesses."

This article is from Big Technology, a newsletter by Alex Kantrowitz. Now that chatbots from OpenAI and Microsoft have demonstrated generative A.I.'s value, companies are running to build on their APIs. Soon you'll be able to generate recipes within ChatGPT and have it add the ingredients to Instacart. Or have it find restaurant reservations with help from OpenTable. Or discover flights with Kayak.

EposAI.com is a short, clear and memorable premium domain on the world's #1 extension .com. It sounds artsy, ancient & mystical, and it's highly relevant to text & art generative AI. Many startups are now developing AIs capable of writing a whole EPOS and generating audiovisual art for it to produce artistic experience appealing to all senses. However, only one AI startup in the world can own the exclusive right to use the premium domain EposAI.com. We are now experiencing growing buyer interest in EposAI.com in our business network, however, the domain is still available for sale at the current price.

Meta plans to monetize its proprietary generative AI technology by December, joining Google in exploring practical applications. The company has been investing in AI for over a decade and recently created a new generative AI team to focus on commercialization. Generative AI technology, which can instantly create sentences and graphics, has been commercialized by ChatGPT creator OpenAI. However, Meta's CTO Andrew Bosworth insists that Meta remains at the cutting edge, with its recently formed generative AI team. Meta aims to use AI to improve ad effectiveness and apply the technology across all its products, including Facebook and Instagram.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A top Chinese company unveiled its challenger to OpenAI's unprecedented ChatGPT on Monday, even as the country's state-run media warns of a potential "AI bubble." SenseTime Group CEO Xu Li introduced SenseChat during a Monday presentation, having the interactive AI tell a story about a cat and answer a series of questions about the story before later asking it to write code, Bloomberg reported. SenseChat is the latest of many generative AIs to be released to the public since ChatGPT broke open the space late last year.

The upstart ChatGPT heralded an advent of conversational-AI platforms that can passably converse with humans based on a wide range of inputs. In addition to ChatGPT, which is made by the Microsoft-backed nonprofit OpenAI, other big tech companies are getting into the game with competing projects from Google (Bard) and Facebook (LLaMA). The rise of AI to kitchen-table prominence raises a question: Are insurance companies, which have been transforming digitally for years, ready to invest further in large language models and turn their precious customer relationships over to a chatbot? Forrester Principal Analyst Indranil Bandyopadhyay says that a big AI revolution in insurance isn't going to happen overnight. "I don't see the majority of the insurance industry going and jumping into these emerging technologies. It will take some time," Bandyopadhyay says.

The excitement over generative AI--and AI in general--has reached the multi-trillion-dollar healthcare industry, driven by the news of ChatGPT passing the United States Medical Licensing Exam (USMLE) and the rapid introduction of new healthcare-related AI applications. Bill Gates, for example, is recommending the use of generative AI tools for primary diagnoses of patients. While acknowledging that AI will inevitably misdiagnose patients, Gates argues that the upside is worth it. Preventing misdiagnoses that can impact, at the very least, a patient's quality of life, depends a lot on the quality and availability of the health data that is fed into the AI model. The current excitement notwithstanding, the development of AI healthcare solutions has been severely constrained by the dearth of comprehensive and representative real-world health data.