The optimization of the electrode manufacturing process is important for upscaling the application of Lithium Ion Batteries (LIBs) to cater for growing energy demand. In particular, LIB manufacturing is very important to be optimized because it determines the practical performance of the cells when the latter are being used in applications such as electric vehicles. In this study, we tackled the issue of high-performance electrodes for desired battery application conditions by proposing a powerful data-driven approach supported by a deterministic machine learning (ML)-assisted pipeline for bi-objective optimization of the electrochemical performance. This ML pipeline allows the inverse design of the process parameters to adopt in order to manufacture electrodes for energy or power applications. The latter work is an analogy to our previous work that supported the optimization of the electrode microstructures for kinetic, ionic, and electronic transport properties improvement. An electrochemical pseudo-two-dimensional model is fed with the electrode properties characterizing the electrode microstructures generated by manufacturing simulations and used to simulate the electrochemical performances. Secondly, the resulting dataset was used to train a deterministic ML model to implement fast bi-objective optimizations to identify optimal electrodes. Our results suggested a high amount of active material, combined with intermediate values of solid content in the slurry and calendering degree, to achieve the optimal electrodes.

Frank Pavich is the director of "Jodorowsky's Dune," a documentary about the Chilean filmmaker Alejandro Jodorowsky's attempt to film a version of "Dune" in the mid-1970s. I was recently shown some frames from a film that I had never heard of: Alejandro Jodorowsky's 1976 version of "Tron." The actors, unfamiliar to me, looked fantastic in their roles. The costumes and lighting worked together perfectly. The images glowed with an extravagant and psychedelic sensibility that felt distinctly Jodorowskian.

Frank Pavich is the director of "Jodorowsky's Dune," a documentary about the Chilean filmmaker Alejandro Jodorowsky's attempt to film a version of "Dune" in the mid-1970s. I was recently shown some frames from a film that I had never heard of: Alejandro Jodorowsky's 1976 version of "Tron." The actors, unfamiliar to me, looked fantastic in their roles. The costumes and lighting worked together perfectly. The images glowed with an extravagant and psychedelic sensibility that felt distinctly Jodorowskian.

In this episode of the Data Exchange I speak with Alejandro Saucedo, Engineering Director at Seldon, a startup building tools for productionizing machine learning. Alejandro is also Chief Scientist at The Institute for Ethical AI & Machine Learning, a UK-based research center that conducts "research into processes and frameworks that support the responsible development, deployment and operation of machine learning systems". Our conversation covered Alejandro's work at both Seldon and the Institute for Ethical AI & Machine Learning: Our goal in this podcast is to build a community of people interested in Data, Machine Learning and AI. If you have suggestions for us on what to recommend (books, conferences, links), and guests to book, please visit TheDataExchange.media Subscribe to our Newsletter: We have an occasional newsletter where we share highlights from recent episodes, trends in AI / machine learning / data, and a collection of recommendations.

For an astronaut looking out of the International Space Station windows, city lights are brighter than the stars. To tackle light pollution citizen scientists are urged to help map out the problem on their smartphones by identifying images of cities taken from space. Astronaut pictures are the highest-resolution, colour images of night available from orbit. "The International Space Station is the best observation point humankind has for monitoring Earth at night," says Kevin Gaston, project leader of the Lost at Night project that raises awareness of light pollution. There are half a million high-resolution pictures of Earth at night in NASA's Astronaut Photography of Earth archives.

This post is the first in a three-part series we're publishing this year on artificial intelligence, written by DigitalOcean's Head of R&D, Alejandro (Alex) Jaimes. In recent months, the amount of media coverage on AI has increased so significantly that a day doesn't go by without news about it. Whether it's an acquisition, a funding round, a new application, a technical innovation, or an opinion piece on ethical and philosophical issues ("AI will replace humans, take over the world, eat software, eat the world"), the content just keeps coming. The field is progressing at amazing speeds and there's a lot of experimentation. But with so much noise, it's hard to distinguish hype from reality, and while everyone seems to be rushing into AI in one way or another, it's fair to say there is a good amount of confusion on what AI really is, what sort of value it can bring and where things will go next.

This post is the first in a three-part series we're publishing this summer on artificial intelligence, written by DigitalOcean's Head of R&D, Alejandro (Alex) Jaimes. In recent months, the amount of media coverage on AI has increased so significantly that a day doesn't go by without news about it. Whether it's an acquisition, a funding round, a new application, a technical innovation, or an opinion piece on ethical and philosophical issues ("AI will replace humans, take over the world, eat software, eat the world"), the content just keeps coming. The field is progressing at amazing speeds and there's a lot of experimentation. But with so much noise, it's hard to distinguish hype from reality, and while everyone seems to be rushing into AI in one way or another, it's fair to say there is a good amount of confusion on what AI really is, what sort of value it can bring and where things will go next.

Alejandro (Sasha) Vicente Grabovetsky, Co-founder of Avalon AI, discusses the ways in which machine learning is improving the rates of failed dementia clinical trials and improving the lives of those living with the disease. The idea for Avalon AI came together when my Co-founder Olivier van den Biggelaar and I realised that we shared the same aim, which was to help defeat ageing. Following that, what immediately came to mind was dementia because it's a disease that has not been successfully tackled yet. Lots of age related diseases like diabetes and cancer receive a lot of funding and are being heavily addressed, while dementia is under-funded partly due to failed clinical trials. Very few dementia clinical trials have succeeded and we noticed that a lot of the past trials were targeting late-stage dementia, where a lot of brain damage had already occurred.