In optimizing performance and extending the lifespan of lithium batteries, accurate state prediction is pivotal. Traditional regression and classification methods have achieved some success in battery state prediction. However, the efficacy of these data-driven approaches heavily relies on the availability and quality of public datasets. Additionally, generating electrochemical data predominantly through battery experiments is a lengthy and costly process, making it challenging to acquire high-quality electrochemical data. This difficulty, coupled with data incompleteness, significantly impacts prediction accuracy. Addressing these challenges, this study introduces the End of Life (EOL) and Equivalent Cycle Life (ECL) as conditions for generative AI models. By integrating an embedding layer into the CVAE model, we developed the Refined Conditional Variational Autoencoder (RCVAE). Through preprocessing data into a quasi-video format, our study achieves an integrated synthesis of electrochemical data, including voltage, current, temperature, and charging capacity, which is then processed by the RCVAE model. Coupled with customized training and inference algorithms, this model can generate specific electrochemical data for EOL and ECL under supervised conditions. This method provides users with a comprehensive electrochemical dataset, pioneering a new research domain for the artificial synthesis of lithium battery data. Furthermore, based on the detailed synthetic data, various battery state indicators can be calculated, offering new perspectives and possibilities for lithium battery performance prediction.

Artificial intelligence has made great strides in many areas lately, yet it has had comparatively little success in general-use robotics. We believe one of the reasons for this is the disconnect between traditional robotic design and the properties needed for open-ended, creativity-based AI systems. To that end, we, taking selective inspiration from nature, build a robust, partially soft robotic limb with a large action space, rich sensory data stream from multiple cameras, and the ability to connect with others to enhance the action space and data stream. As a proof of concept, we train two contemporary machine learning algorithms to perform a simple target-finding task. Altogether, we believe that this design serves as a first step to building a robot tailor-made for achieving artificial general intelligence.

Novel deep-learning (DL) architectures have reached a level where they can generate digital media, including photorealistic images, that are difficult to distinguish from real data. These technologies have already been used to generate training data for Machine Learning (ML) models, and large text-to-image models like DALL E 2, Imagen, and Stable Diffusion are achieving remarkable results in realistic high-resolution image generation. Given these developments, issues of data authentication in monitoring and verification deserve a careful and systematic analysis: How realistic are synthetic images? How easily can they be generated? How useful are they for ML researchers, and what is their potential for Open Science? In this work, we use novel DL models to explore how synthetic satellite images can be created using conditioning mechanisms. We investigate the challenges of synthetic satellite image generation and evaluate the results based on authenticity and state-of-the-art metrics. Furthermore, we investigate how synthetic data can alleviate the lack of data in the context of ML methods for remote-sensing. Finally we discuss implications of synthetic satellite imagery in the context of monitoring and verification.

Supply chain networks in enterprises are typically composed of complex topological graphs involving various types of nodes and edges, accommodating numerous products with considerable demand and supply variability. However, as supply chain networks expand in size and complexity, traditional supply chain planning methods (e.g., those found in heuristic rule-based and operations research-based systems) tend to become locally optimal or lack computational scalability, resulting in substantial imbalances between supply and demand across nodes in the network. This paper introduces a novel Generative AI technique, which we call Generative Probabilistic Planning (GPP). GPP generates dynamic supply action plans that are globally optimized across all network nodes over the time horizon for changing objectives like maximizing profits or service levels, factoring in time-varying probabilistic demand, lead time, and production conditions. GPP leverages attention-based graph neural networks (GNN), offline deep reinforcement learning (Offline RL), and policy simulations to train generative policy models and create optimal plans through probabilistic simulations, effectively accounting for various uncertainties. Our experiments using historical data from a global consumer goods company with complex supply chain networks demonstrate that GPP accomplishes objective-adaptable, probabilistically resilient, and dynamic planning for supply chain networks, leading to significant improvements in performance and profitability for enterprises. Our work plays a pivotal role in shaping the trajectory of AI adoption within the supply chain domain.

rollama is an R package that wraps the Ollama API, which allows you to run different Generative Large Language Models (GLLM) locally. The package and learning material focus on making it easy to use Ollama for annotating textual or imagine data with open-source models as well as use these models for document embedding. But users can use or extend rollama to do essentially anything else that is possible through OpenAI's API, yet more private, reproducible and for free.

For Bill Gates, artificial intelligence is the most important invention since the internet or the personal computer. For Google boss Sundar Pichai, it will have a more profound impact than electricity and fire. Already, though, there are signs the AI revolution may be faltering. Since OpenAI released its landmark GPT-4 system in March 2023, new large language models like Google's Gemini have offered only incremental improvements. GPT-5 could change this tomorrow, of course.

Amazon has decided to cut off paid perks for Alexa developers. The company confirmed to Engadget on Wednesday that it will end the Alexa Developer Rewards Program at the end of June. A second program that rewards developers for using Amazon Web Services as the backend for their Alexa apps will wrap up at the same time. With the emergence of generative AI, the pioneering voice assistant's third-party apps ("skills") no longer appear to be a central focus for the company. The news was first reported by Bloomberg and confirmed by Engadget with the company.

As far as AI has come in the last few years there are still a few things that machines can't do as well as humans. However, all of that might soon change as OpenAI and Meta are both reported to be on the brink of releasing AIs capable of reasoning and planning. Leaders of both companies suggest that the latest versions of their AI models are coming soon and will be a lot more powerful. According to their reports, ChatGPT-5 and Llama-3 will not just generate text but start to do something that looks a lot more like thinking. Joelle Pineau, vice-president of AI research at Meta says: 'We are hard at work in figuring out how to get these models not just to talk, but actually to reason, to plan... to have memory.'

As a six-year-old growing up in Barcelona, Spain, during the 1940s, Maria would visit a neighbor's apartment in her building when she wanted to see her father. From there, she could try and try to catch a glimpse of him in the prison below, where he was locked up for opposing the dictatorship of Francisco Franco. There is no photo of Maria on that balcony. But she can now hold something like it: a fake photo--or memory-based reconstruction, as the Barcelona-based design studio Domestic Data Streamers puts it--of the scene that a real photo might have captured.The studio uses generative image models, such as OpenAI's DALL-E, to bring people's memories to life. The fake snapshots are blurred and distorted, but they can still rewind a lifetime in an instant.

OpenAI, Google, and the French artificial intelligence startup Mistral have all released new versions of their frontier AI models within 12 hours of one another, as the industry prepares for a burst of activity over the summer. The unprecedented flurry of releases come as the sector readies for the expected launch of the next major version of GPT, the system that underpins OpenAI's hit chatbot Chat-GPT. The first came only hours after Nick Clegg appeared on stage at an event in London, where he confirmed that the third version of Meta's own AI model, Llama, would be published in a matter of weeks. Seven hours after Clegg left the stage, Google's Gemini Pro 1.5, his competitor's most advanced large language model, was released to the general public, with a free tier limited to 50 requests a day. An hour later, OpenAI released its own frontier model, the final version of GPT-4 Turbo.