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Beyond Generative AI : Rise & Risk of Co-Intelligence (CI)

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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
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Stock Market Revamp

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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 stick analysi
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Digital Reference Model

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(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 capture the cloud
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Artificial Intelligence in Electric Drives

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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?
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Artificial Intelligence (AI) is building new capabilities in manufacturing

Artificial Intelligence (AI) is building new capabilities in manufacturing
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Digital World and Artificial Intelligence

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
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Will the rain fade away the Cloud....

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 collect data and process it with
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