Digitalis Ventures’ Post

Researchers have unveiled a groundbreaking innovation: a hybrid computational system powered by the dynamic Belousov-Zhabotinsky (BZ) reaction. This cutting-edge technology intertwines physical chemistry with computation, creating a platform that emulates complex behaviors seen in natural systems. With a 3D-printed grid of interconnected reactors, this system transcends traditional computing paradigms, offering new pathways for solving complex optimization problems and enhancing deep learning tasks with its inherent non-linear characteristics. #Chemistry #ComputationalChemistry  

So tonight thanks to Marine Sobas from Dataiku I learned the relationship between fungi and #AI... Yep! 😉 🧠 𝐒𝐥𝐢𝐦𝐞 𝐌𝐨𝐮𝐥𝐝 𝐚𝐧𝐝 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦 𝐃𝐞𝐬𝐢𝐠𝐧 Marine discussed how to derive a shortest path algorithm from slime moulds 1. Slime Mould as a Graph: Visualize the slime mould network as a graph. 2. Model the Flow with Maths: Apply mathematical models to represent the flow within the slime mould. 3. Compute the Edges Conductivity: Determine the conductivity of edges based on the flow model. 4. Find the Shortest Path: Use these computations to identify the shortest path It turns out that this is better than the ant colonisation optimisation algorithm. 🚀 𝐒𝐥𝐢𝐦𝐞 𝐌𝐨𝐮𝐥𝐝𝐬 𝐒𝐨𝐥𝐯𝐢𝐧𝐠 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐢𝐜 𝐏𝐫𝐨𝐛𝐥𝐞𝐦𝐬 The network of slime moulds can serve as a model to solve complex problems: - Shortest Path and Maze: Demonstrated by Andrew Adamatzky in 2012. - Traveling Salesman Problem: Tackled by Liping Zhu in 2013. - Space Exploration: Another contribution by Andrew Adamatzky. 🌱 𝐅𝐮𝐧𝐠𝐚𝐥 𝐁𝐞𝐡𝐚𝐯𝐢𝐨𝐫𝐬 𝐭𝐨 𝐈𝐦𝐩𝐫𝐨𝐯𝐞 𝐀𝐈 - Adjusting to environmental changes: Make AI models more adaptable to mitigate data drift - Self healing capacity: Make AI models more robust to opposing attacks - Merging & sharing knowledge: Create new AI models by merging existing models 🍄 𝐅𝐮𝐧𝐠𝐚𝐥 𝐂𝐨𝐦𝐩𝐮𝐭𝐞𝐫𝐬 𝐚𝐧𝐝 𝐌𝐲𝐜𝐞𝐥𝐢𝐮𝐦'𝐬 𝐂𝐨𝐦𝐩𝐮𝐭𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐂𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬 - Boolean Functions: Mycelium networks can implement logic gates like AND, OR, and NAND, similar to electronic circuits. - Turing Completeness: Mycelium can be considered to be Turing complete - Building a Fungal Computer (https://lnkd.in/gcEN4dDn) - Fungal Computer Programming: Needs the control of the geometry of the mycelium and the use of mathematical equations to control the growth of the network #MachineLearning #DataScience #Biomimicry Full meetup details: https://lnkd.in/gCJigufn

The next step in the future of molecular computing: DNA and data 🧬 Traditionally, data storage and computing functions have been separate, with different systems handling each task. However, researchers from North Carolina State University and Johns Hopkins University have created a system that uses DNA to manage the full range of data tasks: storing, retrieving, computing, and even rewriting data. 🔩How it works? At the heart of this innovation are polymer structures called dendricolloids, which allow DNA-based storage to get similar capabilities to conventional electronic devices ⚡ These structures provide an exceptional platform for storing vast amounts of information in a highly dense format, suitable for long-term storage—potentially lasting thousands of years 💻 Dendricolloids act as the circuit board, while DNA serves as the data, making the possibilities of molecular computing vast. It’s a data center in a unit no larger than a pencil eraser. That’s the power of DNA data storage. 🧐What about tests? The researchers have successfully used this technology to solve puzzles like Sudoku and chess, demonstrating its computational potential. I believe that while DNA-based computing is still in its infancy, it holds immense promise. Do you agree? 👇 #DNAComputing #MolecularComputing #DNATech #DataScience #NextGenComputing #BiotechInnovation #DNADataStorage #DataComputing #LongTermStorage

What is one common thing that all of living beings have? It is DNA, the ultimate computing platform. Did you ever get the thought that is it possible to build a machine to solve complex problems using DNA? Probably No, but one person had this idea and went on to prove that it is possible to do so. This gave rise to the DNA computing technology, the topic of our learning this week. There is a well known complex mathematical problem called Hamilton path problem. The gist of it is to find shortest route to be taken by a sales person going through each city once. Adleman solved this problem using test tubes and DNA. To keep our discussion simple, Adleman used test tubes and found a way to solve it using DNA genetic coding. In the paper that he wrote, he discussed the need for enormous computing resources to be able to solve complex science problems that regular computers and super computers take years to solve. DNA computing is put forth as one of the options along with quantum computing to solve this. DNA computing machines will be built using DNA molecules instead of silicon chips. Logic gates will be built by stranding DNA together. All the material used is available naturally and is non toxic, and cheap. It will be years before these become mainstream, while pros and cons are being weighed at this moment and whole field is in its infancy. Applications does not involve every day games and servers. It will be used for identifying optimal routes for shipping, airlines, and cracking complex cryptographic problems. See you all next week. #DNAComputing #innovation #learning

I'm thrilled to announce that our paper, "Cumulant Learning: Highly Accurate and Computationally Efficient Load Pattern Recognition Method for Probabilistic STLF at the LV Level", has been accepted for publication in the prestigious IEEE Transactions on Smart Grid! 🎉 This paper is a generalization of my PhD thesis and the result of a collaborative effort with my professors/mentors and co-authors: Goran Švenda, Aleksandar Erdeljan, Milan Gavric, and Darko Čapko. I’m especially proud that this paper was recognized by one of the top journals in the field of Electrical and Electronics Engineering, ranking in the top 10% according to the WoS/JCR impact factor (classified as M21a in Serbia). 🏆 This work tackles one of the biggest challenges in energy forecasting—predicting the highly variable load patterns of millions of low-voltage consumers (think about all the times we turn on and off lights, air conditioners, washing machines, and other devices at home). 💡 So, rather than trying to predict the exact behavior of every individual with overly complex models (and likely failing spectacularly), we asked ourselves, “What if we could predict the collective behavior of similar consumers and use that to estimate the future behavior of individuals with some level of probability?” 🤔 This approach still required a specially designed clustering method and a fairly deep neural network, but it paid off: our model achieved the highest average accuracy in a case study on real-world smart meter data from the UK and Australia, even outperforming more sophisticated models like graph neural networks. On top of that, it proved more robust to outliers and better at predicting load surges, with shorter execution times and lower memory consumption, making it highly suitable for managing low-voltage networks. ⚡🌍 Feel free to check out the early access link below and reach out to me for a free copy! 😉 I welcome any feedback or questions you might have! #SmartGrid #Clustering #DeepLearning #PatternRecognition #Forecasting

Had the opportunity to attend an insightful lecture by Professor Daniel Packwood from Kyoto University. The discussion covered visualizing crystal structure data, exploring crystal bands, and conducting data analysis. We delved into insights on organic semiconductors and how data visualization can predict their behavior. The lecture also highlighted the use of R language, machine learning, and the t-SNE technique for predicting molecular behavior. We also explored predicting band structure and band gap in new materials, organizing organic semiconductor materials based on band gap, and designing new materials with targeted band gaps. Feeling inspired by the advancements in this highly interdisciplinary field of modern science! #DataScience #MachineLearning #MaterialsScience #OrganicSemiconductors #KyotoUniversity #Innovation #RLanguage #DataVisualization #tSNE

A quick preview of what I worked on at Byteboost!

Is life a complex computational process? David Krakauer and Chris Kempes from the Santa Fe Institute think so: "life is starting to look a lot less like an outcome of chemistry and physics, and more like a computational process." Given the ongoing #AGI conversation/debate, their recent Aeon article Problem-Solving Matter is timely. Highly recommended! "Both computation and life involve a minimal set of algorithms that support adaptive function. These ‘algorithms’ help materials process information, from the rare chemicals that build cells to the silicon semiconductors of modern computers. And so, as some research suggests, a search for life and a search for computation may not be so different. In both cases, we can be side-tracked if we focus on materials, on chemistry, physical environments and conditions." https://lnkd.in/gD3tE8Y4 #AI #computation #complexity #life

Call for paper,【International Journal of Complexity in Applied Science and Technology】 International Journal of Complexity in Applied Science and Technology， NOTE: Excellent papers can be accepted within 30 days, and No fee for publishing papers Topics covered include Evolutionary algorithms Particle swarm optimisation Single-/multi-/many-objective optimisation Constrained optimisation Multi-modal optimisation Dynamic optimisation Data-driven optimisation Large-scale optimisation Engineering design optimisation Applications associated with intelligent computation Machine learning Big data

What is biological computing, and what role will it play when looking to the sustainable technology of the future? Sustainability and long-term resilience are becoming increasingly important - and traditional silicon-based computing may not be the best answer. One answer may lie in an approach that has been long perfected by nature: DNA, harnessed through the power of biological computing. In this article by Dr Aidong Xu and Matthew Armean-Jones, we explain what biological computing is and the role it will play when looking to the sustainable technology of the future. The article ‘The next steps in harnessing biological computing for a sustainable future’ explores: 🔎 Why we need a sustainable alternative to silicon-based computing 🔎 The progress we’ve already made in biological computing 🔎 And the next steps: what still needs to be done? Want to know more? Read the article now: https://hubs.li/Q02nnQgY0 #biologicalcomputing #biologicalcomputers #futureofcomputing #advancedcomputing #biocomputing