Thoughts

Artificial Intelligence (AI) is no longer a buzzword in manufacturing — it’s the silent revolution reshaping how factories function, evolve, and compete globally. While generative AI models, like text and image generation, often grab the headlines, the real game-changing impact of AI in manufacturing lies far beyond content creation. Let’s explore how deep AI applications are transforming manufacturing at every layer of the value chain. 1. Predictive Maintenance: Keeping Machines Healthy Imagine a plant where machines never fail unexpectedly. AI-powered predictive maintenance makes this possible. By analysing sensor data in real-time, AI algorithms detect early signs of wear and tear, allowing maintenance teams to intervene before breakdowns occur. This not only extends equipment lifespan but also reduces downtime, saving millions in operational costs. Key Technologies: Machine learning anomaly detection IoT sensor data analytics Real-time monitoring systems 2. Supply Chain Optimizat...

AI for sustainability is a rapidly growing field, focusing on leveraging Artificial Intelligence (AI) to address environmental, social, and economic challenges. Here are some usage examples: Environmental Sustainability: 1. Climate Modeling: AI helps predict climate patterns, identifying areas vulnerable to climate change. 2. Renewable Energy Optimization: AI optimizes solar and wind energy production, reducing waste and increasing efficiency. 3. Wildlife Conservation: AI-powered monitoring systems track and protect endangered species. 4. Sustainable Agriculture: AI-driven precision farming reduces water and fertilizer usage. 5. Waste Management: AI optimizes waste collection routes and recycling processes. Social Sustainability: 1. Education: AI-powered adaptive learning platforms improve access to quality education. 2. Healthcare: AI-driven diagnostic tools enhance healthcare accessibility and efficiency. 3. Social Justice: AI helps identify and mitigate bias in decision-making proce...

  - LLMs can understand like humans: LLMs work on pattern matching using algorithms. LLMs cannot understand the questions as humans do and so the response might not be always 100% as expected. - Accuracy of LLMs:While models try for accuracy, the information given might be incorrect or misleading. Model does not verify facts of the response - More data will improve response of LLMs:While more data can improve quality to some extent, fine tuning, transfer learning, tunning some parameters, modifying architecture of data flow, etc. can improve performance of LLMs. - LLMs can be creative like humans: LLMs can generate text, images or videos. While LLMs can augment human creativity but it cannot replace complexity of human creativity. - LLMs are invulnerable to errors or manipulation: LLMs can make mistakes and can be manipulated with adverse inputs to generate misleading information. - LLMs will surpass human intelligence: Artificial general intelligence (AGI) is still far off from re...

Credit: ProStockStudio / Shutterstock.com © 2019 / er.educause.edu Generative AI :   In recent past the most widely searched terminology over internet is "Generative AI" (GAI). With LLM (Large Language Models) based solutions coming to exposure, every individual got interested, as one can directly converse with an AI engine and get human like responses. Easy accessibility for general public to interact and test out an AI engine made it popular. However, today "Generative AI" is the most non-optimally used terminology (purposefully not saying "mis-used") in general.  With Generative AI (GAI) coming in, we should not undermine the capabilities of erstwhile AI as Generative AI cannot replace many of those purpose specific AI (Narrow AI) solutions. Generative AI (GAI) is specific to solutions which require content generation as conversation, speech, code generation, image description or creation of image from description and many such use cases. Since GAI is a...

While talking of stock market, I keep wondering, what does the price of a stock indicate? Does it indicate health of the organisation or it indicates what stock traders want others to believe is the health  of the organisation.  Stock traders with lot of funds available can manage to make the stock market behave the way they want others to see it. They have the power either to raise the stock price by creating demand or make a fall in the stock price by setting up high selling spree, irrespective of the actual health of the organisation. This becomes misleading for investors who gets carried away with the flow of the market and can end up making losses.  Is there a way to address it? What looks to me is the problem lies in the option to bid a price for a stock. Since people have option to bid and they have funds, they can either take a price upwards or fall the price by pushing higher volumes of stock in the market. We hear that investors do technical analysis like candle...

(C) Copyright Pradeep Chatterjee (L-80336/2019) The Digital Reference Model has been developed to help designers in design of digital solutions. The Digital Levers -  Block Chain Augmented Realty / Virtual Realty Artificial Intelligence/ Machine Learning Image / Video Analytics  Cobots Additive Manufacturing Digital Twin Advanced Analytics  Virtual Assistants  help to create the digital layer. Data captured from external sources or enterprise applications or operations technologies through integrations can help to create digital solutions using this digital layer. These are supported by cloud computing with immense computing power on demand, communication networks and protected with information security. While design of solutions, designer can highlight the data sources used, type of integrations, highlight the digital levers relevant for the solution design and details of each of the digital levers to be used. In addition it can...