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How much of today's AI capabilities are driven by hardware? We will explore the answer to this question, in plain English, in Episode 16 of "Tuesday Trivia". https://lnkd.in/gTxtNigd #ai #artificialintelligence #gpu #innovation

Wonder what the form factor of such a microgrid will be. Truth be told, what fascinates me more here is the microgrid itself. The AI capabilities mentioned to manage the microgrid are ones that can be applied across a wide gamut of scenarios. https://lnkd.in/g2sHxE7k A wearable microgrid is a combination of: a. Energy harvesters (solar, thermoelectric, bioenergy, motion, etc.) b. Energy storage (batteries or supercapacitors) c. Power management circuits (regulating, balancing, switching between sources) Apparently, the grid operates “on the body” (in/on skin, textile, or worn modules). That makes the design aspect of a tangible product much more interesting in my opinion. The authors propose a framework in three incremental “generations” of wearable microgrids, each more advanced in autonomy and function but I think the most critical aspect in each generation will be to design a product that can be worn without coming across as carrying a microgrid. This can be a "Maverick Mondays" topic, so stay tuned 😃 . The authors highlight several roles AI can play in improving the performance of these microgrids. Again, all these can be leveraged not only for a grid of any size, but beyond power grids. a. Prediction of energy demand: estimating how much power the sensing, processing, and communication will require, given current/future conditions (activity, ambient energy, etc.). b. Energy budgeting & scheduling: deciding when to harvest, when to store, when to spend power (e.g. when to sample more, when to reduce frequency). c. Adaptive operation: adjusting to environmental changes (light, temperature), behavioral patterns of user, mobility, etc. d. Sustainable energy harvesting optimization: choosing and combining harvesters, tuning them (e.g. orientation, exposure) to maximize useful harvest. #ai #artificialintelligence #electricalengineering #energymanagement 

Back in January of this year, when China imposed critical mineral export restrictions, I postulated that AI can help with the task of developing self-sufficiency. https://lnkd.in/gyyvhkUz One path, as suggested in a few of my posts, including this one https://lnkd.in/gN9aZ2nR, is to explore alternatives and use AI as one of the tools in the exploration. Another path is to leverage AI across the value chain. This paper (https://lnkd.in/gPZ2JAaC) explores that area. There is no novel application here since the usage can be generalized for any type of mining operations. Yet, the crux is that AI can significantly impact processes across the chain. AI has the potential to reduce both the duration of mining project development (by improving exploration, mapping, modeling, and prediction of ore bodies) and the uncertainty/risk in later stages. That includes better prediction of recoverable grades, optimizing equipment, environmental risk forecasting, etc. If AI is deployed well, it could lower the back-ended risk premium, making investment in critical minerals more attractive, which would help narrow the gap between demand (for clean energy transitions) and supply. A good read! #ai #artificialintelligence #mining #technology #criticalmineral

Our definition of smart homes is as flawed as our definition of smart cities. After all, the most important element of a home is the house. But our smart home paradigm is centered around smart fixtures and devices inside the house. So how do we rethink the idea of smart homes? We discuss this in Episode 16 of "Maverick Mondays". Decided to do it in a more informal way today, since I wanted to enjoy a bit more of sun and shine, while it lasts. https://lnkd.in/gr6qY8xb Enjoy your Monday, folks!!! #ai #artificialintelligence #iot #industry4_0 #innovation #smarthomes

Neutral-atom quantum computing which is an attractive modality due to its flexibility (you can arrange many atoms with optical tweezers, relatively simpler cooling compared to some other platforms). This development helps move it closer to practical large‐scale quantum machines. https://lnkd.in/gKVafeYt Developments like these are always exciting and interesting since Quantum computing is the only answer to some of the ambitious visions we have for AI. But we can't wait till the technology matures to fathom how we will leverage it. As we soak in and celebrate such advancements, we should also start connecting the dots in our minds in terms of how we need to steer these into the computing capabilities we will need in the future. The good news is, there are few different paths that quantum computing can take, and all of them will help us leverage it to build extraordinary solutions in areas ranging from space exploration to industrial manufacturing (and hundreds of others in between). So what is happening in this article? Physicists at Harvard (led by Mikhail Lukin) have developed a scheme to address a key limitation in neutral-atom quantum computing: atom loss during computation. They’ve built a “conveyor belt” system of atoms: a way to continuously replace and replenish atoms in the qubit array so that lost atoms don’t degrade the computation. Why is it important? Why it matters Improved reliability and stability: One of the biggest problems in neutral-atom qubit systems is that atoms occasionally are lost (due to spontaneous losses, background gas collisions, imperfections). By having a way to refresh atoms continuously, the system can maintain high fidelity over longer operations. Scalability: Because you’re not limited by how long you can keep every atom alive, the approach supports scaling up to larger and longer quantum computations. #ai #artificialintelligence #quantumcomputing #future

While this research is within the genetics domain, take a moment to understand what makes this research powerful. https://lnkd.in/g6dBSiWn Traditional AI tools struggle to pick out which similarities are due to shared ancestry vs. coincidental or convergent features. Without being made aware of evolutionary relationships, models can misinterpret patterns. This method helps avoid that confusion. With this approach, AI can more correctly trace how particular traits evolved, reconstruct intermediate or ancestral states, and understand evolutionary history in a more nuanced way. The model is trained using a “quartet-based” approach: it looks at groups of four species at a time and learns to arrange them into the correct structure in their ancestry tree. If all these quartets are correctly arranged, the entire ancestry tree emerges like a “puzzle.” Now, try to forget the subject matter buzzwords and understand just the fundamental mechanics of the framework. You should be able to think of a plethora of use cases, ranging from data management to data exploration. A good read! #ai #artificialintelligence

Think about this approach from a different perspective. Can you reverse engineer customer behavior and interaction data into a standardized score across beliefs? So that you can collect the scores without the need for the customer to respond to a survey? The answer is yes. However, rather than predicting nationality, you would want to predict behavior that can be monetized. As you can imagine, AI is the easy part for this reverse engineering. The core is how to creatively map user interactions into scores accurately. Once that is done, AI can easily take over. The gist is that you can repurpose this research in ways that are only constrained by your imagination. https://lnkd.in/gNj45cJA In this research, researchers used machine learning on data from the World Values Survey (a large global survey measuring beliefs, values, attitudes) to train a neural network to predict a person’s country of origin based on their responses. The model was able to correctly identify among 98 countries with ~90% accuracy based solely on responses about values, beliefs, and attitudes. #ai #artificialintelligence #deeplearning #neuralnetworks #consumerbehavior #marketing

The hybrid AI-based routing framework for wireless sensor networks (WSNs) suggested here can be extrapolated for dynamic adaptive routing beyond wireless sensor networks. Obviously, the complexity may change but the underlying approaches can definitely be leveraged. In this paper, the researchers are aiming to improve energy efficiency, reduce latency, and increase the reliability of data transmission. https://lnkd.in/grX5YECa The research combines multiple AI techniques rather than relying on a single one, so that the network can dynamically adapt to changes (topology, traffic, energy levels, etc.).  The framework suggested in this research integrates several AI and optimization approaches. Every AI method highlighted here can be leveraged to mimic this solution in a whole different context as well. Reinforcement Learning (RL), specifically Q-learning, is used for local routing decisions. Nodes learn from past experience (e.g. success/failure of transmissions, delays, energy costs). Supervised Learning, including decision tree models to classify routing situations based on features like node energy, hop count, congestion, etc. Swarm Intelligence / Metaheuristic Optimization, including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and also references to Ant Colony Optimization (ACO). These are used especially for global or periodic route optimization under resource constraints (e.g. when conditions change significantly). It is a good framework to think about AI-enabled dynamic adaptive routing in any context, beyond the packets being routed in this specific research. #ai #artificialintelligence #telecom #reinforcementlearning #swarmoptimization #deeplearning #supervisedlearning

As renewable energy installations become more distributed, the operational complexity increases (more sources, more variability). Ensuring coordination, resilience, and reliability becomes harder. Also, different kinds of storage (batteries, pumped hydro, etc.) have different dynamics; integrating them flexibly and optimally is nontrivial. Then comes the increasing market complexity (e.g. more actors, more dynamic pricing) makes forecasting, bidding, dispatch, etc., harder. Data scarcity, model validity, uncertainty quantification, safety and reliability become more important. In a nutshell, like a plethora of other business operations, the data points needed to make optimal decisions are overwhelming. This is where AI can help. And this is what the authors have researched in this paper. https://lnkd.in/e_cHg-ES The authors organize the discussion around several operational problems in renewable power systems, including: Forecasting (e.g. of wind, solar generation) Dispatch (deciding how to schedule and allocate generation resources to meet demand while minimizing cost and respecting constraints) Control (maintaining stability: frequency, voltage, handling fluctuations) Electricity markets & bidding (optimizing market behavior, decision-making under uncertainty) For a second, if you zoom out from the industry specific nature of the study, the challenge areas identified above are kind of the same across many industries. What the authors found in the study was: AI / Deep Learning methods enable more accurate forecasts of RE generation, which helps reduce the imbalance between supply and demand. RL methods are especially promising for dispatch: they help with decision making where the system has to satisfy operational constraints and cope with variability, while trying to minimize costs. For control tasks (like voltage/frequency control), AI methods (including RL) can provide real-time signals to mitigate instabilities that are more frequent in RE-rich grids. In market contexts, AI can support intelligent bidding, market behavior prediction, and adaptability to complex market rules/environments. Again, if you generalize, these tools will add value in similar operations in other industries as well. For example, RL methods can significantly transform dispatch operations in field services in many other industries. #ai #artificialintelligence #analytics #reinforcementlearning #deeplearning

Discrete event simulation (DES) tools are leveraged across a majority of industries to simplify business operations, for predictions, and to gain insight into complex business and operational processes. But before we got to the current modern simulation software available today, there was an evolution path that formed the foundation for modern simulation software. We discuss that path in Episode 15 of "Throwback Thursdays". https://lnkd.in/gH-Spjc3 #data #analytics #operationsresearch #simulation #or #discreteeventsimulation