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Monday, 20 March 2023

Speed and ability to execute correctly will determine sucess of Indian IT

 India's IT industry is going through its biggest transitional period since the dotcom boom, with technology and business models facing disruption from newer areas like cloud computing and artifical intelligence. In an interview with , talks about the disruptions facing Indian IT firms such as TCS, Infosys and Wipro and what they need to do to adapt. Edited excerpts:

When it comes to Indian IT services, what really is happening there?

I think it is important to see the changing behaviour among end-users, the buyers of services. I think we've gone through a series of years in which the buyers, the end users in IT organisations have been looking to get out of larger, long-term services contracts and transition more to shorter contracts. I think if we look at growth, it is still very much driven around the fact that end-users are looking at that. The second thing is, it's very clear that the end user is looking at splitting up their approach to IT -we're seeing this significant drive towards what we call bimodal IT. When it comes to services, they need one set of services -it is all about stability, the robustness of what you offer and security. Those kinds of services are not growing because end users are looking to get these services cheaper, leading to stagnation of revenue and margin pressure. That is what is being reflected among Indian service providers.

 


When it comes to Indi ..

Is the traditional outsourcing model becoming redundant, amid the advent of automation and the disruption from the cloud?

I actually see substantial opportunity for a services model going forward.But it is in a world that is very different and may require a different set of skills, among the service providers. You're seeing substantial growth in core business functions around the usage of cloud -it's not just extending what you've been doing. The second thing is that you're starting to see hybrid providers that are emerging in particular verticals. If the traditional service providers don't have a way to support these business functions that are cloud-based solutions, that's when they'll start to see pressure .

How big a threat to the traditional technology services model is the rise of AI based automation?

I think looking at the few vendors that are out there, whether it's IBM with Watson or IPSoft or others that are developing products that can replace repetitive user functions, can make an impact. But if you look at the much bigger picture, the opportunity is more significant. What is really happening is that algorithms are taking over the world.

Are Indian IT services firms making the right futuristic bets right now?

I think all of them are aware of what's happening and I think all of them are looking at retooling or reskilling what they do -I don't think they are unaware and they don't know what to do. I think it's the speed and the ability to execute correctly that will determine the success.

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AI:Create a frameworks using various humanoid robotics with the help of various neural networks and LLMs on the occasiun of Diwali night from late night to peak hours to earley morning detection of fireworks reside, the pollution noice, voice and various traces of scraps found there along with fire broken and taming calming the fire broken in real time in the various neural networks of remote backward areas tow, cities, states countries and continents of the US,UK, Canada, Midle East, Africa, Korea, China, Europe, Asia and Oceania in real time. IoT-Based Automated Fire Detection and Alert System Overview This project automates fire detection and alert systems using IoT technology. It replaces traditional methods of fire reporting, ensuring quick and reliable emergency responses even in the absence of human intervention. The system leverages NodeMCU microcontrollers, environmental sensors, and the MQTT protocol to monitor conditions and trigger alarms in case of abnormalities. Features Automated Fire Detection: Monitors temperature and gas levels using DHT11 and gas sensors. Real-Time Alerts: Sends location-based notifications to receivers for immediate action. Scalable MQTT Communication: Efficient communication using a single MQTT topic for all nodes. Localized Alarm System: Receiver nodes trigger buzzers for the corresponding locations. System Architecture Source Nodes: NodeMCUs (Source_Beleghata, Source_London) equipped with: DHT11 Sensor for temperature monitoring. Gas Sensor for detecting smoke or hazardous gases. Publishes location-based messages upon detecting abnormal conditions. Receiver Nodes: NodeMCUs (Receiver_Beleghata, Receiver_London) equipped with buzzers. Subscribes to a shared MQTT topic and triggers the buzzer if the location matches incoming data. Communication Protocol MQTT Protocol: All nodes are connected to the internet for real-time communication. A shared MQTT topic ensures scalability and simplicity. Demonstration Setup Nodes: Source_Beleghata (with sensors) Receiver_Beleghata (with buzzer) Source_London (with sensors) Receiver_London (with buzzer) Workflow: Source nodes detect abnormal conditions and publish the affected location ("Beleghata" or "London") to the MQTT topic. Receiver nodes subscribe to the topic and trigger an alarm if the message matches their location. Requirements Hardware: 4 NodeMCU boards DHT11 sensors (2 units) Gas sensors (2 units) Buzzers (2 units) Software: Arduino IDE MQTT broker (e.g., Mosquitto) Internet connection for NodeMCU devices Installation and Usage Hardware Setup: Connect sensors to the Source NodeMCUs. Connect buzzers to the Receiver NodeMCUs. Software Configuration: Flash the provided code onto the NodeMCU devices using the Arduino IDE. Set up an MQTT broker and configure the nodes to publish/subscribe to the shared topic. Testing: Simulate abnormal conditions to test the system. Verify that alarms are triggered at the appropriate receiver nodes. Future Enhancements Integration with mobile applications for remote monitoring and alerts. Expansion to support additional locations and sensors. Advanced machine learning algorithms for anomaly detection.

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