In response to these issues, tech companies have taken some steps. To prevent data leakage and to enhance security, some have engineered generative A.I. products so they do not keep a company's data and have instructed the A.I. models to answer only questions based on the source of data. When Salesforce last month introduced AI Cloud, a service with nine generative A.I.-powered products for businesses, the company included a "trust layer" to help obfuscate sensitive corporate information and promised that what users typed into these products would not be used to retrain the underlying A.I. model. Similarly, Oracle said that customer data would be kept in a secure environment while training its A.I. model and added that it would not be able to see the information. Salesforce offers AI Cloud starting at $360,000 annually, with the cost rising depending on the amount of usage. Microsoft charges for Azure OpenAI Service based on the version of OpenAI technology that a customer chooses, as well as the amount of usage.

Mona Awad, whose books include Bunny and 13 Ways of Looking at a Fat Girl, and Paul Tremblay, author of The Cabin at the End of the World, filed the class action complaint to a San Francisco federal court last week. ChatGPT allows users to ask questions and type commands into a chatbot and responds with text that resembles human language patterns. The model underlying ChatGPT is trained with data that is publicly available on the internet. Sample summaries are included in the lawsuit as exhibits. The lawsuit will explore the uncertain "borders of the legality" of actions within the generative AI space, he adds.

To prevent this theoretical but cataclysmic outcome, mission-driven labs like DeepMind, OpenAI and Anthropic are racing to build a good kind of AI programmed not to lie, deceive or kill us. Meanwhile, donors such as Tesla CEO Elon Musk, disgraced FTX founder Sam Bankman-Fried, Skype founder Jaan Tallinn and ethereum co-founder Vitalik Buterin -- as well as institutions like Open Philanthropy, a charitable organization started by billionaire Facebook co-founder Dustin Moskovitz -- have worked to push doomsayers from the tech industry's margins into the mainstream.

The convergence of generative large language models (LLMs), edge networks, and multi-agent systems represents a groundbreaking synergy that holds immense promise for future wireless generations, harnessing the power of collective intelligence and paving the way for self-governed networks where intelligent decision-making happens right at the edge. This article puts the stepping-stone for incorporating multi-agent generative artificial intelligence (AI) in wireless networks, and sets the scene for realizing on-device LLMs, where multi-agent LLMs are collaboratively planning and solving tasks to achieve a number of network goals. We further investigate the profound limitations of cloud-based LLMs, and explore multi-agent LLMs from a game theoretic perspective, where agents collaboratively solve tasks in competitive environments. Moreover, we establish the underpinnings for the architecture design of wireless multi-agent generative AI systems at the network level and the agent level, and we identify the wireless technologies that are envisioned to play a key role in enabling on-device LLM. To demonstrate the promising potentials of wireless multi-agent generative AI networks, we highlight the benefits that can be achieved when implementing wireless generative agents in intent-based networking, and we provide a case study to showcase how on-device LLMs can contribute to solving network intents in a collaborative fashion. We finally shed lights on potential challenges and sketch a research roadmap towards realizing the vision of wireless collective intelligence.

AI Image generators based on diffusion models are widely discussed recently for their capability to create images from simple text prompts. But, for practical use in civil engineering they need to be able to create specific construction plans for given constraints. Within this paper we explore the capabilities of those diffusion-based AI generators for computational design at the example of floor plans and identify their current limitation. We explain how the diffusion-models work and propose new diffusion models with improved semantic encoding. In several experiments we show that we can improve validity of generated floor plans from 6% to 90% and query performance for different examples. We identify short comings and derive future research challenges of those models and discuss the need to combine diffusion models with building information modelling. With this we provide key insights into the current state and future directions for diffusion models in civil engineering.

The ever-growing complexity of reinforcement learning (RL) tasks demands a distributed RL system to efficiently generate and process a massive amount of data to train intelligent agents. However, existing open-source libraries suffer from various limitations, which impede their practical use in challenging scenarios where large-scale training is necessary. While industrial systems from OpenAI and DeepMind have achieved successful large-scale RL training, their system architecture and implementation details remain undisclosed to the community. In this paper, we present a novel abstraction on the dataflows of RL training, which unifies practical RL training across diverse applications into a general framework and enables fine-grained optimizations. Following this abstraction, we develop a scalable, efficient, and extensible distributed RL system called ReaLly Scalable RL (SRL). The system architecture of SRL separates major RL computation components and allows massively parallelized training. Moreover, SRL offers user-friendly and extensible interfaces for customized algorithms. Our evaluation shows that SRL outperforms existing academic libraries in both a single machine and a medium-sized cluster. In a large-scale cluster, the novel architecture of SRL leads to up to 3.7x speedup compared to the design choices adopted by the existing libraries. We also conduct a direct benchmark comparison to OpenAI's industrial system, Rapid, in the challenging hide-and-seek environment. SRL reproduces the same solution as reported by OpenAI with up to 5x speedup in wall-clock time. Furthermore, we also examine the performance of SRL in a much harder variant of the hide-and-seek environment and achieve substantial learning speedup by scaling SRL to over 15k CPU cores and 32 A100 GPUs. Notably, SRL is the first in the academic community to perform RL experiments at such a large scale.

AI Dungeon, a text-based fantasy simulation that runs on OpenAI's GPT-3, has been churning out weird tales since May 2019. Reminiscent of early text adventure games like Colossal Cave Adventure, you get to choose from a roster of formulaic settings--fantasy, mystery, apocalyptic, cyberpunk, zombies--before picking a character class and name, and generating a story. Here was mine: "You are Mr. Magoo, a survivor trying to survive in a post-apocalyptic world by scavenging among the ruins of what is left. You have a backpack and a canteen. You haven't eaten in two days, so you're desperately searching for food."

Google has updated its privacy policy to state that it can use publicly available data to help train its AI models. The tech giant has changed the wording of its policy over the weekend and switched "AI models" for "language models." It also stated that it could use publicly available information to build not just features, but full products like "Google Translate, Bard, and Cloud AI capabilities." By updating its policy, it's letting people know and making it clear that anything they publicly post online could be used to train Bard, its future versions and any other generative AI product Google develops. The tech giant has highlighted the changes to its privacy policy on its archive, but here's a copy of the pertinent part: Critics have been raising concerns about companies' use of information posted online to train their large language models for generative AI use.

Source: OpenAI's DALL·E 2 with prompt "a hyperrealistic picture of a robot reading the news on a laptop at a coffee shop" Welcome to the inaugural edition of Robo-Insight, a biweekly robotics news update! In this post, we are thrilled to present a range of remarkable advancements in the field, highlighting robotics progress in terrain traversability, shape morphing, object avoidance, mechanical memory, physics-based AI techniques, and new home robotics kits. Recently, researchers from the University of California San Diego have given four-legged robots forward-facing depth cameras to enable them to clearly analyze the environment around and below them. This data can also be compared with past images to estimate possible 3D transformation. Furthermore, their system is also self-checking, as it fuses information to give it a sort of short-term memory. Although the model does not guide the robot to a specific location, it enables the robot to traverse challenging terrain.

The advancement in generative AI could be boosted with more accessible mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in programming, algorithm discovery, and theorem proving, yet their genomics application is limited. This project introduces Math Agents and mathematical embedding as fresh entries to the "Moore's Law of Mathematics", using a GPT-based workflow to convert equations from literature into LaTeX and Python formats. While many digital equation representations exist, there's a lack of automated large-scale evaluation tools. LLMs are pivotal as linguistic user interfaces, providing natural language access for human-AI chat and formal languages for large-scale AI-assisted computational infrastructure. Given the infinite formal possibility spaces, Math Agents, which interact with math, could potentially shift us from "big data" to "big math". Math, unlike the more flexible natural language, has properties subject to proof, enabling its use beyond traditional applications like high-validation math-certified icons for AI alignment aims. This project aims to use Math Agents and mathematical embeddings to address the ageing issue in information systems biology by applying multiscalar physics mathematics to disease models and genomic data. Generative AI with episodic memory could help analyse causal relations in longitudinal health records, using SIR Precision Health models. Genomic data is suggested for addressing the unsolved Alzheimer's disease problem.