➀ The article presents a novel technique for the on-demand nanoengineering of in-plane ferroelectric topologies; ➁ The technique involves a combination of scanning probe microscopy and machine learning algorithms; ➂ The research demonstrates the ability to create complex patterns and functionalities by manipulating the polarization in ferroelectric materials; ➃ The findings could lead to new applications in electronics, sensors, and energy storage.

➀ Nuvoton Technology launches the NuEzAIM55M1 development board; ➁ The board is designed to simplify endpoint AI development; ➂ It is built on the Arm CortexM55 core and includes a model training tool.

➀ The cost of training AI models has surged in the past year, as indicated by data from Epoch AI; ➁ The training cost for Gemini, a large language model from Google, ranges from $30 to $191 million; ➂ OpenAI's ChatGPT-4, released in 2023, had a technical creation cost between $41 million and $78 million.

➀ Sentienz's Akiro IoT platform addresses challenges in e-transportation with advanced analytics and MQTT+; ➁ Akiro optimises IoT outcomes through messaging, analytics, and AI/ML; ➂ The platform supports smart meters, EV chargers, and battery monitoring systems, enhancing EV charging infrastructure and data management.

➀ Cadence's Tensilica HiFi 5 DSPs are integrated into NXP's new automotive audio DSP family; ➁ The integration supports advanced audio capabilities for next-generation software-defined vehicles; ➂ This development caters to the increasing demand for sophisticated audio processing.

➀ Google has rebranded TensorFlow Lite to LiteRT, allowing for ML and AI model deployment on Android, iOS, and embedded devices; ➁ LiteRT supports multiple frameworks including PyTorch, JAX, and Keras; ➂ The name change is part of the Google AI Edge suite and will be progressively rolled out with the LiteRT name appearing more in developer documentation.

➀ Meta Platforms identifies a widening gap between computing power and interconnect bandwidth at the 2022 OCP Global Summit. ➁ Broadcom proposes SCIP (Silicon Photonics in Package) as a solution to bridge this gap, focusing on low-cost, high-performance, and low-power interconnects. ➂ SCIP utilizes TSV technology and detachable optical connectors to achieve shorter interconnect distances and higher energy efficiency, targeting AI and machine learning applications.

➀ The article discusses the importance of selecting the right storage for machine learning projects, emphasizing the need for scalability, availability, security, and performance. ➁ It reviews various storage options including local file storage, NAS, SAN, DFS, and object storage, comparing their suitability for ML applications. ➂ The article concludes that object storage is the best choice for AI due to its massive scalability, handling of unstructured data, RESTful APIs, encryption capabilities, and cloud-native nature.

1. Tenstorrent introduces second-generation Wormhole accelerator cards, offering high performance with the n150 and n300 models. 2. The company launches TT-LoudBox and TT-QuietBox workstations, featuring powerful configurations and flexible deployment options. 3. Tenstorrent's roadmap includes upcoming Blackhole architecture and future chiplet designs for enhanced AI capabilities.

1. Professor Leopoldo Molina-Luna at TU Darmstadt receives his fourth ERC grant for the 'BED-TEM' project, focusing on machine learning in electron microscopy. 2. The project aims to develop a user-friendly software platform that optimizes experimental parameters using Bayesian optimization. 3. This initiative could revolutionize in situ experiments and has potential applications in nanoelectronics.

1. Professor Leopoldo Molina-Luna of TU Darmstadt receives his fourth ERC grant for the 'BED-TEM' project, focusing on machine learning applications in electron microscopy. 2. The project aims to develop a user-friendly software platform that integrates Bayesian optimization with (S)TEM image analysis to enhance experimental design. 3. The initiative addresses challenges in adapting machine learning to (S)TEM data and ensuring market demand, aiming to revolutionize in-situ experiments and contribute to material sciences.