Real-World Examples Of AI In Engineering Solutions

I built an entire PCB from scratch in 35 minutes using AI. FLUX.AI COPILOT transformed my engineering workflow. Starting point: A student's basic block diagram End result: Complete schematic + 3D layout The AI asked intelligent questions: "Which USB serial IC - CH340G or FT232RL?" "Internal or external oscillator?" "Do you want an RC filter on your inputs?" Real engineering decisions while AI handled the grunt work. Technical breakdown: POWER SUPPLY • Generated optimal rail voltages • Added protection circuitry • Selected efficient regulators MICROCONTROLLER • Automated pin assignments • Optimized peripheral routing • Generated decoupling network COMMUNICATION • USB serial interface • I2C expansion ports • Debug headers placement SENSORS • Light-dependent resistors • Temperature monitoring • Motion detection The most powerful part for me were these: AI suggested improvements I wouldn't consider if I were a beginner or even intermediate, but are common for advanced design: • Better ground plane distribution • Reduced EMI through strategic routing • Thermal optimization via component placement This tool cuts my design time by 80%. Engineering evolves. Tools improve. We adapt or fall behind. I've documented the entire process in a free roadmap video. I'll share it with anyone who comments below. Serious about accelerating your PCB design workflow? Drop "Flux" in the comments. Like this post if you believe AI assistants will revolutionize hardware design - or at least make it A LOT easier, faster and more accurate.

𝐀𝐈-𝐩𝐨𝐰𝐞𝐫𝐞𝐝 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐓𝐰𝐢𝐧𝐬, where AI plays a crucial role in creating and enhancing these virtual replicas, is one of the most exciting combos for the future of business and technology. Example one: Predictive Maintenance. Predictive maintenance is one of the most essential uses of artificial intelligence in engineering. AI systems can detect equipment breakdowns by evaluating real-time sensor data and optimizing maintenance plans, resulting in reduced downtime and operational expenses. Combining Digital Twins with AI enhances these advantages. AI improves the capability of Digital Twins by offering predictive analytics for real-time simulations and scenario modeling. This combination dramatically increases operational insights and decision-making capabilities. Example 2: Industry 4.0 (Cars) Consider the development of self-driving autos as an example. Training an AI-empowered Digital Twins model to mimic virtually billions of kilometers of driving scenarios is significantly faster, safer, and less expensive than physical testing. The AI model may predict behavior that contradicts physical laws, such as a car speeding suddenly or cornering impossibly. However, physics-based digital twin simulations provide the required safeguards, guaranteeing these virtual tests generate valid and actionable results and reassuring us of the safety and cost-effectiveness of this technology. Example 3: Healthcare/Medicines It is a computer-generated heart, or digital twin, used to test implantable cardiovascular devices such as stents and prosthetic valves, which, once proven safe, will be placed on actual patients. Using artificial intelligence and massive amounts of data, they constructed a variety of hearts. These AI-generated synthetic hearts may be customized to match not just biological characteristics such as weight, age, gender, and blood pressure but also health conditions and ethnicities. Because these disparities are frequently not represented in clinical data, Digital Twin Hearts can assist device manufacturers in conducting trials over a broader range of populations than human trials or trials utilizing only digital twins and no AI. Example 4: Education. The potential of AI and digital Twins has particularly piqued the interest of many in the EdTech industry. Creating accurate digital clones to support human educators is more than just a faddish trend. These AI-powered counterparts are highly trained productivity and support boosters who can free educators from demanding work schedules. Their outputs go beyond simple automated responses; they are crafted & capable of engaging the client in meaningful conversations, all while making well-informed decisions and capturing the intricate nuances of an individual's personality. The examples here can go on and on. It's fascinating (at least in my eyes) to see the combination of #IoT, #AI, #DigitalTwins, and #SaaS intertwined in such an innovative and productive means in the future.

LLMs and Agents alone cannot solve real-world problems that exist in enterprises.   The right mix of data understanding, operations expertise, choice of the right AI model, last-mile custom engineering, and operational change is critical to drive the proper outcomes. But let me focus on the last-mile custom engineering today.   For example, a global logistics company manually processes large volumes (20-50k daily) of unstructured and complex documents as part of the billing process. This resulted in escalating costs, delays in the process, and little insights about things billing inaccuracies and leakage.   Deploying an off-the-shelf LLM-powered intelligent document processing solution helped, but not completely.    So EXL reengineered the solution leveraging the NVIDIA stack across multiple dimensions: ✅ 70% cost reduction – Migrated from a closed-source model, which relied on OCR + 2 small LLMs (fragmented & unscalable) to a simplified fine-tuned multi-modal model. Leveraged Nemo Curator & Customizer to do this conversion within 1 week and with a nimble training effort ✅ 50% lower latency – Optimized deployment on NVIDIA NIMS to improve user experience ✅ 5% accuracy uplift – Smarter processing and more accurate output for humans in the loop   ROI from enterprise AI needs more than just models or tooling – It needs practical engineering. Gaurav Iyer, Piyush Aggarwal, Somya Rai, Wyatt Bennett, Joseph Richart, Vivek Vinod, Arturo Devesa #AI #LLM #EnterpriseAI #NVIDIA #GenAI #Data #XtraktoAI

  AI is a game-changer for sustainability at work. At Amazon, our culture is rooted in innovation and speed. AI can enable both, and we’re using it in ways big and small to make progress. Here are just a few examples: 📦 The Package Decision Engine - we created this AI model to make sure items arrive on your doorstep safely, in the most efficient packaging possible. It makes decisions using deep machine learning, natural language processing and computer vision. What does this mean for sustainability? So far, along with other packaging innovations, the Package Decision Engine has helped us avoid over 2-million tons of packaging material worldwide. 🏢 AI Tools for Buildings - You may be surprised to hear that buildings and their construction account for 40% of the world's greenhouse gas emissions. We’re using a suite of AI tools to help manage energy and water use in more than 100 of our buildings. One example: a tool built by Amazon Web Services (AWS) called FlowMS led engineers at a logistics facility to an underground leak, and fixing it helped prevent the loss of over 9-million gallons of water per year. Other AI tools help us monitor our HVAC systems, refrigeration units, and dock doors. These seemingly simple solutions add up, and we're making meaningful progress in saving energy. 🤖 Maximo - Arguably one of the coolest-looking examples, Maximo is an AI-powered robot developed by The AES Corporation helping build solar farms, including projects backed by Amazon. It uses computer vision to lift heavy panels, makes decisions with real-time construction intelligence, and helps construction crews avoid dangerous heat. All told, Maximo can reduce solar construction timelines and costs by as much as 50%. This is just the beginning, and I’m excited about all the ways AI can help us reach our goals. If you’d like to dive deeper into how we’re using it in our buildings, you’ll find more details here: https://lnkd.in/gU_UmWbq