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Business Standard
a day ago
- Business
- Business Standard
Power ministry launches task force to develop India energy stack platform
The Ministry of Power on Saturday announced the launch of a task force to conceive the India Energy Stack (IES), an initiative aimed at creating a unified, secure, and interoperable digital public infrastructure (DPI) for the energy sector. The IES will play a vital role in integrating renewable energy, enhancing DISCOM efficiency, and delivering transparent, reliable, and future-ready power services, an official statement quoted Power Minister Manohar Lal Khattar as saying. There is an urgent need for developing robust DPI in the power sector to manage the country's rising demand, ensure grid stability, and empower consumers. What Aadhaar did for identity and UPI achieved for digital payments, the India Energy Stack will accomplish for the power sector - unlocking seamless, secure, and consumer-centric energy services for every citizen," he added. As India charts its path to becoming a USD 5 trillion economy and advances towards its net-zero commitments, the power sector faces both unprecedented opportunities and complex challenges, the statement said. Rapid growth in renewable energy, electric vehicles, and consumer participation in energy markets is transforming the sector, but fragmented systems and a lack of seamless digital integration remain key barriers, it pointed out. To address these issues, the ministry is reimagining the sector's digital foundation through the IES - a DPI that will provide a standardised, secure, and open platform to manage, monitor, and innovate across the electricity value chain. The IES will offer unique IDs for consumers, assets, and transactions; real-time, consent-based data sharing; open APIs(Application Programming Interface) for seamless system integration; and tools for consumer empowerment, market access, and innovation. Apart from conceiving the IES, the ministry will undertake a 12-month Proof of Concept (PoC) to demonstrate IES through real-world use cases in partnership with selected utilities. This includes piloting the Utility Intelligence Platform (UIP), a modular, analytics-driven application built on IES to support utilities, policymakers, and consumers with real-time insights and smarter energy management. To guide these initiatives, the ministry has constituted a dedicated task force comprising experts from the technology, power sector, and regulatory domains, which will steer the development, pilot implementation, and nationwide scale-up of the IES.
The Hindu
2 days ago
- Automotive
- The Hindu
Modern technology and enhanced surveillance across national highways for curtailing road safety violations
The National Highways Authority of India (NHAI) will implement advanced traffic management system (ATMS) with real-time tracking of safety violations and simultaneous issuance of challans for enhancing surveillance and road safety across all national highways with four or more lanes totalling 30,000 km in the next 2-3 years, according to senior officials of NHAI. The ATMS installation on a stretch of 56.46 km of Dwarka Expressway in the national capital region has been concluded, senior NHA officials told a group of journalists during a visit organised to the command and control centre of the Dwarka project. This stretch is the maiden project to be executed under the revised policy for ATMS brought out in October 2023 that enhances enforcement across 14 categories of violations such as triple riding, helmet and seatbelt violations, wrong lane or direction driving, presence of animals on the highway, and pedestrian crossings.. 'The focus of the new policy is to incorporate enforcement in addition to surveillance and integrate the police department so that challans can be issued. Earlier the end-user was NHAI, now it is the enforcement agencies,' explained one NHAI official who didn't want to be quoted. The new policy allows local police to generate e-challans through an API [Application Programming Interface]-based system that enables software applications to talk to each other. 'A specific incident about a road safety violation is created at our centre. Subsequently, the National Informatics Centre, which is the technology partner of the Central government, pushes this information to the police department and a challan is generated,' the above quoted official elaborated. On June 26, IHMCL also wrote to Delhi Police to start issuing challans based on the ATMS installed on Dwarka Expressway. The ATMS system ensures the highways are enabled with technology for 'real-time intelligence and incident management and analytics,' according to a handout from the IHMCL. Implementation of the ATMS system is already underway for other projects, which include Delhi-Agra Expressway, Lucknow Ring Road and the upcoming third ring road for New Delhi, the Urban Extension Road 2 (UER-2). A stretch of 20,000 kms was equipped with an older ATMS prior to the 2023 guidelines. At Dwarka Expressway, the ATMS system is supported by 110 pan-tilt-zoom cameras with each camera at every 1 km for real time monitoring that are also equipped with basic AI intelligence to detect accidents and breakdowns. There are also 15 Video Incident Detection and Enforcement Systems (VIDES) or a system of cameras installed on gantries with each such system deployed at a distance of 10 km which is capable of detecting 14 kinds of violations with high level of accuracy and is further connected to NIC and police departments. VIDES also allows vehicle counting and classification for monitoring traffic data. There are also six Vehicle Acutated Speed Display on LED screens at intervals of 20 km that use radar based detection to warn road users if they are nearing the upper speed limit and serves as a speed calming measure. Ten variable messaging sign boards or screens have also been installed for real-time updates to road users on traffic conditions, diversions, weather, and incidents to improve driver awareness.
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First Post
22-06-2025
- Business
- First Post
Trump's Truth Social crashed moments after he announced strikes on Iranian nuclear strikes
Trump's social media platform Truth Social faced disruption across US soon after he posted about strikes on Iranian nuclear sites read more US President Donald Trump's Truth Social platform suffered a major outage early Sunday (June 22), moments after he announced the bombing of Iranian nuclear sites. Downdetector reported that thousands of users faced problems and experienced server issues while trying to access the social media platform on both Windows and mobile phones. The platform crashed soon after Trump posted this: 'We have completed our very successful attack on the three Nuclear sites in Iran, including Fordow, Natanz, and Esfahan. All planes are now outside of Iran's airspace. A full payload of BOMBS was dropped on the primary site, Fordow. All planes are safely on their way home." STORY CONTINUES BELOW THIS AD In a separate post, Trump wrote: 'Fordow is gone", referring to the underground nuclear facility near Natanz. The strikes followed the deployment of B-2 stealth bombers to Guam earlier on Saturday. In announcing the operation, President Trump confirmed that B-2 bombers executed the attacks, calling it a 'historic moment' for the US, Israel, and the global community, stressing that the strike was a critical step in addressing the ongoing regional crisis. What caused Truth Social to crash? Some screenshots posted by users showed that API (Application Programming Interfaces) congestion may have caused the disruption. It's a situation when the volume of requests being sent to an API exceeds its capacity to handle them efficiently, causing drop in performance quality. Simply put, it's a traffic jam for API requests. Despite Trump's massive popularity and following on all other social media platforms, he exclusively uses his Truth Social for making announcements and posts on issues of national and international importance. His stake in Truth Social's parent company TMTG has earned him a significant portion of his net worth. Just a few days back, the platform urged its users to following Trump by sending them an email. 'If you aren't following President Donald J. Trump on Truth Social, you're missing an essential resource for navigating world affairs today,' the e-mail read.
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Business Standard
11-06-2025
- Business
- Business Standard
OpenAI delays launch of its open-weight AI model: All you need to know
OpenAI has delayed the release of its open-weight model, which was initially expected to be released in early summer. CEO Sam Altman announced the delay through a post on X (formerly Twitter) on June 11, stating 'we are going to take a little more time with our open-weights model, i.e. expect it later this summer but not June." Altman shared that the delay comes as a result of unexpected progress made by OpenAI's research team. He explained 'Our research team did something unexpected and quite amazing, and we think it will be very, very worth the wait, but needs a bit longer.' we are going to take a little more time with our open-weights model, i.e. expect it later this summer but not june. our research team did something unexpected and quite amazing and we think it will be very very worth the wait, but needs a bit longer. — Sam Altman (@sama) June 10, 2025 OpenAI's open-weight model: What to expect The upcoming open model is said to feature reasoning capabilities on par with OpenAI's o-series models and aims to outperform other leading open reasoning models like DeepSeek's R1. OpenAI is exploring advanced features to strengthen its open model. According to a report by TechCrunch, one possibility includes integrating the model with OpenAI's cloud-based systems for handling complex tasks. However, it remains uncertain whether such capabilities will be part of the final release. What are open-weight language models? Open-weight language models are AI systems whose trained parameters—the numerical values that guide how the model operates—are openly shared with the public. This enables researchers, developers, and organisations to download and use the models on their own devices, without needing access to a cloud-based API (Application Programming Interface). However, such models can still come with licensing limitations, especially around modifications and commercial applications. How are they different from other models? Open-weight models strike a balance between fully open-source and entirely closed AI systems. Unlike open-source models that offer complete access to both code and model weights with minimal restrictions, open-weight models typically release only the trained weights and may include limitations on how they can be modified or used commercially. On the other hand, closed models like OpenAI's GPT-4 and Google's Gemini keep both their code and weights private, maintaining full proprietary control over their technology.
New Indian Express
07-06-2025
- Business
- New Indian Express
'AI models can hallucinate or misfire'
Artificial Intelligence (AI) offers immense potential, but it's not without challenges. Mohit Saxena, Co-Founder & CTO, InMobi & Glance told TNIE that there's the critical need for human oversight and that AI models can hallucinate or misfire, and in today's sensitive digital climate, ensuring responsible output is essential. 'We're investing in rigorous moderation infrastructure and developing new governance frameworks to mitigate these risks,' he said. He added that deep AI expertise is scarce. 'While surface-level applications like RAG (Retrieval-Augmented Generation) are becoming common, true innovation requires depth in data science, ML infrastructure, and systems thinking—talent that's still hard to find.' But our global presence in Bengaluru, San Francisco, and the UK gives us broader access to specialised talent pools, the co-founder said. Talking about other key challenges, he said that AI infrastructure is expensive. Running advanced models at scale demands significant compute and energy. 'Our approach is rooted in frugality—we optimize model usage, leverage pre-processing, explore alternatives like TPUs (Tensor Processing Unit), and work closely with partners like Google to get the most out of every dollar,' he said. InMobi views AI not just as a tool, but as a foundational shift and its roadmap over the next one to three years is anchored in three key areas. 'First, we are reimagining engineering productivity with AI—helping experienced engineers scale faster and empowering fresh talent to leapfrog traditional learning curves. AI is now embedded into every aspect of how we build—whether it's writing code, improving observability, or boosting efficiency,' he said. 'Second, we are building intelligent automation into our core business processes—moving from simple scripting to AI agents that can deconstruct complex workflows, predict outcomes, and take action. This isn't just automation; it's autonomous decision-making at scale. Third, we're embracing the rise of agentic architecture—where agents talk to agents, not APIs (Application Programming Interface), to get work done. This is the future of system communication, and are actively developing for it,' he further said. InMobi is setting up a dedicated unit to track and accelerate engineering efficiency with AI, with a goal to complete most of the foundational work by year-end. The company is leveraging AI to generate high-impact formats—ranging from image-based ads to audio creatives—enabling brands to engage users across multiple touchpoints. It also uses AI to generate and summarize content at scale. In the visual content space, he said the company is leveraging Contrastive Language-Image Pre-training (CLIP) to bridge the gap between AI-generated creativity and real-world commerce through its Glance AI product. 'By using CLIP, we're able to understand and interpret AI-generated fashion looks—essentially decoding the visual style and identifying apparel elements within the image. These elements are then matched to real products from our extensive catalogue of brand and retail partners,' he explained. Even before the LLM (large language model) wave, the company has been leveraging AI for content generation at Glance. 'We're onboarding fresh engineering talent through structured bootcamps where AI adoption starts from day one—including access to AI assistants and hands-on experience with applied ML tools. Simultaneously, we're deepening our bench strength by hiring top-tier data scientists—we've onboarded over 50 employees in the past year alone, across domains like LLMs, DNNs (Deep Neural Networks), and imaging. We're also shifting our hiring lens—prioritising engineers with a strong aptitude in data science and statistical thinking. Our aim is that 80% of our workforce, both new and existing, to be highly AI- and ML-savvy in the next 1–2 years,' the co-founder and CTO informed.
