Thoughts

  In this ever-evolving world of technologies, people are getting scared that Artificial Intelligence-based bots will take away jobs from humans. Do you really think so? Let us step back and put our rational thinking to work. Why Humans Work: Understanding Our Core Motivations We humans have been working for ages, doing multiple different types of work based on our capabilities, knowledge, and sometimes based on what we love to do. But have you ever thought about why humans work in the first place? Humans work because they have needs to be fulfilled. We work because we want food and shelter as minimum basics. Over and above that, we want comfort, luxury, and hence we aspire to grow and increase our earnings. In the process of climbing corporate ladders, sometimes humans work against other humans because their aspirations push them to do so. We also want to get satisfaction by achieving something – for example, by establishing a company and seeing it grow, or by playing an important...

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...

The book introduced concepts of Modular Neural Networks and how to reduce rule base for fuzzy logic to reduce CPU processing time leading to less consumption of energy. https://www.amazon.com/Artificial-Intelligence-Electric-Pradeep-Chatterjee/dp/6134996068/ref=sr_1_1?ie=UTF8&qid=1524325362&sr=8-1&keywords=artificial+intelligence+in+electric+drives Buy the Book?

Artificial Intelligence (AI) is building new capabilities in manufacturing

Words "Digital" and "Artificial Intelligence" have become very popular in the industry today. Every organization is moving towards same. Digital solutions comprise of Industrial Automation, Information Technology and Artificial Intelligence. These concepts existed many decades ago. Then what has really changed and why organizations are doing it now?  Evolution of these technologies dates back in the history: Industry Automation (IA) with control systems started as early as 1920 - early 1930. With factory electrification, control systems were built using conventional relay logic where on-off actions can be taken based on set desired values. Numeric Control (NC) machines started during 1950 and subsequently Computerized Numeric Control (CNC) machines evolved. First computing machine dates back to 17th century when the first calculating machine was developed by Blaise Pascal. Charles Babbage and Ada Lovelace worked on development of analytical engine with pun...

The concept of IoT is catching up fast though it may take some time to speed up its adoption in industries. If it can show real value in short time span, industry will adopt it fast. The apprehension with most industries is the infrastructure which they already have and how to match it with new technologies, which is slowing down its adoption to large extent.   However, the concept existed for years and it was there in isolation in some other form in the name of machine automation or operational technologies. What really changed is introduction of Artificial Intelligence (AI) and the machines expected to send data to some centralised location for processing along-with other machine data, which will help in decision making or decision automation. But with embedded technologies gaining prominence and improvement in chipset development and manufacturing, the trend is more towards development of intelligent sensors and devices. That means device will coll...

With companies moving from being national to multi-national and now becoming global organizations, it has become imperative to adopt to global ways of working. Employees and teams spread across the globe managing functions across the World is truly a global way of working. Preconceived notions of management to have all people located in single location or some identified location hubs are waning out. More and more organizations are adopting to policies as work from home or flexi timings to support employees adopting the global way of working. Development of collaboration technologies supported by communication technologies are paving the path to take this movement forward. But the question is, will the offices exist 10 years down the line or will it become virtual offices giving birth to development of new technologies and new business ventures for many? If we look back 100 years from now, let us understand why the concept of people going to offices started. We are leaving out f...