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HAL, GE near finish line to power LCA Mk2 with made-in-India engine
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The crucial Light Combat Aircraft Mark-2 (LCA Mk2) programme has got a fresh boost, with Hindustan Aeronautics Ltd (HAL) on Wednesday announcing it has signed an agreement with the American engine-maker GE Aerospace to produce the GE F414 engines intended to power the combat jet in India, with substantial transfer of technology (ToT). Certain commercial aspects of the broader agreement are still being discussed, but work to ensure a swift conclusion is ongoing, said a source who did not wish to be named.
The announcement comes at a time when the Indian Air Force (IAF) is in urgent need to
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Hindustan Times
2 minutes ago
- Hindustan Times
OpenAI rediscovers an open AI mission, with new reasoning models
It was a long wait since the GPT-2 in 2019, but OpenAI is now releasing its newest open-weight large language models (LLMs). They've been dubbed GPT-OSS, the current lot consisting of gpt-oss-120b and gpt-oss-20b, dubbed 'reasoning models' with OpenAI claiming these models outperform similarly sized open models on reasoning tasks. The importance of this brings OpenAI back, in a way, to their original mission of building AI systems that benefit all of humanity. Over the years, the artificial intelligence (AI) company has faced criticism of distraction towards that stated mission, as competition escalated rapidly. OpenAI has not released open source models. (Official Image) 'Releasing gpt-oss-120b and gpt-oss-20b marks a significant step forward for open-weight models. At their size, these models deliver meaningful advancements in both reasoning capabilities and safety. Open models complement our hosted models, giving developers a wider range of tools to accelerate leading edge research, foster innovation and enable safer, more transparent AI development across a wide range of use cases,' the company says, in a statement. Also read:Fidji Simo, OpenAI's new CEO, insists AI can put power in the hands of people Two questions that need to be answered before we get into the specifics of gpt-oss-120b and gpt-oss-20b models. First, what are open weight LLMs and are they different from LLMs you regularly use? And secondly, what are reasoning models? The former is best defined as a large language model that is released by a company publicly, in its entirety, which means all the actual model weights (read this as parameters, defined by billion or 'b' in model names) and any user can download these models completely on their own hardware. In comparison, the most popular LLMs that you may have used, including OpenAI's own GPT models as well as the likes of Google Gemini 2.5 and Anthropic's Claude Sonnet 4, are closed models — that means they are accessible through an application layer while the model weights are not in the public domain. At the same time, Meta's Llama models, as well as certain models by Mistral, have followed the open weight methodology, in recent times. Open weight AI models are not to be confused with open source models however, the fine difference being that the latter models such as the DeepSeek R1 also make training code, datasets, and linked documentation available publicly — open weight models don't. Having the training code and data sets allows a user or developer to retrain an open-source model from scratch, often for customised usage scenarios. That flexibility isn't there for open weight models, though accessible in their entirety. OpenAI has not released open source models. To the second question, reasoning models slightly differ from a few other LLMs in the sense that they are specifically designed to spend more time 'thinking through' complex problems before generating their final response. They are expected to use extended reasoning processes to work through multi-step problems. Back to gpt-oss-120b and gpt-oss-20b, and the primary difference is in the number of parameters each has. Parameters are essentially like the strength of synapses in a human brain, which determines how strongly different 'neurons' influence each other, before providing an answer for a query. In OpenAI's naming scheme this time, there is a slight confusion — the gpt-oss-120b is a 117 billion parameter model, while the smaller gpt-oss-20b has 21 billion parameters. OpenAI's benchmark scores do peg the gpt-oss-120b and gpt-oss-20b close to the o3 and o4-mini models in most tests. Take for instance the MMLU benchmark, which consists of questions across academic disciplines — the gpt-oss-120b returned 90% accuracy while gpt-oss-20b clocked 85.3% accuracy; in comparison, the o3 (93.4%), o4-mini (93%) and o3-mini (87%) bookend the new open weight models. Just in case you are wondering about the memory requirements for downloading and running these open weight models on your computing device, OpenAI confirms that the gpt-oss-120B model will need 80GB of memory on the system, while gpt-oss-20b requires at least 16GB. They say that Microsoft is also bringing GPU-optimised versions of the gpt-oss-20b model to Windows devices.
Indian Express
2 minutes ago
- Indian Express
Why Trump's tariff threats to India over purchase of Russian oil mark just another chapter in US hegemony
US President Donald Trump's trade war, which began with the aim of fixing the trade deficit with partner countries, has entered a new phase of economic coercion, with India in the eye of the storm. To force Russia into signing a peace deal with Ukraine and India to agree to stiff US terms in the ongoing trade talks, Trump on Monday widened the tariff war to achieve geopolitical goals by warning India of additional tariffs for profiting from sales of Russian oil amid the Ukraine war. While New Delhi has called the targeting of India over the purchase of Russian oil 'unjustified and unreasonable' and vowed to take 'all necessary measures' to safeguard its 'national interests and economic security', Indian exporters are in a fix, scrambling to retain access to the US — their most valuable export market, accounting for nearly 20 per cent of India's total outbound shipments. Globally, experts on trade and geopolitics have said the new tariff threats by the US directed at India could undermine 25 years of US-India relations. Trump's latest threats to India follow the American President's multiple blunt remarks over India's association with the BRICS grouping, Apple's manufacturing operations in India, and, topping it all, the invitation extended to Pakistan's Army Chief to the White House weeks after the Pahalgam attack and later offering a lower 19 per cent tariff to Pakistan. However, using economic coercion to achieve geopolitical goals has been a longstanding American policy and is only expanding under Trump. A working paper titled 'Asphyxiation by Sanctions: Harm, Fear and Smog' by former Reserve Bank of India governor Urjit R. Patel calls the US the 'hegemonic sanctioner', arguing that India should view the emerging international financial architecture around BRICS and the Asian Infrastructure Investment Bank (AIIB) as a 'risk mitigant' and a rational response to the ever-expanding sanctions regime. India's sharp response to Trump's coercion on Monday comes after the US for decades sanctioned multiple countries for exporting oil, complicating New Delhi's strategy to diversify its energy imports. Over the years, the US has sanctioned oil exports from Venezuela, Iran, Iraq, Libya, Sudan and Syria. India resumed oil imports from Venezuela in 2023, after a three-year pause, only after the US eased sanctions on the country. Patel's paper said out of 1,325 global sanctions since 1949, 486 have been imposed by the US, which currently administers over 30 sanctions programmes — making it responsible for 'three times as many sanctions as any other country or international body'. Moreover, US-led sanctions have surged in recent decades, partly due to the collapse of the Soviet Union, the paper said. Patel wrote that the US has 'pioneered secondary sanctions on an industrial scale', often in coordination with allies such as the G7 grouping and the EU, forming a 'posse'. These extraterritorial sanctions are enforced to impede economic and commercial activity by third countries that would not otherwise violate a primary sanctioner's rules, he said. The former RBI Governor wrote that the effectiveness and reach of US secondary sanctions are heavily reliant on the centrality of the American financial system and the US dollar's status as the global numeraire and principal currency for settling cross-border transactions. However, the overuse of the US dollar correspondent banking network as a 'switch' on payments has prompted many countries to explore alternatives — a trend that could undermine the dollar's dominance, he warned. Patel said the US economic sanctions have imposed a significant burden on emerging economies such as India. The hegemonic position of the US means that its sanctions, particularly secondary ones, cause 'collateral damage' to third countries — economic losses that are especially severe for emerging economies and often 'underappreciated' by the sanctioners. Patel, who has also served as a Director at the Bank for International Settlements, argues that the imposition of sanctions by hegemonic powers often occurs without adequate public debate or transparency regarding their costs. Unlike wars, where human and financial costs are evident, the 'cost-benefit of sanctions, counter sanctions and secondary sanctions is a black box — the layered and complex scope is a mystery to most'. The paper also pointed out that US sanctions, particularly on Iran, have directly affected Indian investments. The development and operation of Iran's Chabahar Port, which involves Indian investment, faced 'a hard break' due to initial US sanctions and had 'limited operations' after the US reintroduced sanctions in 2018. Even after India signed a 10-year agreement to develop and operate the port in 2024, the US State Department issued a warning about 'the potential risk of sanctions' for deals with Iran, creating significant uncertainty and hampering progress. In another example, Patel highlights that Indian public sector oil companies have accumulated unpaid dividend income of around $900 million from their upstream 'oil equity' investments in Russia. This non-receipt of income is directly attributed to 'payment channel-related prohibitions by the US and the EU', he says, adding that this financial loss 'inter alia, affects investments by Indian oil companies and the government's budgetary revenue'. While a trade deal is yet to be finalised between the two countries, several measures already announced by Trump have begun to affect India. Indian officials have indicated that the US is unwilling to negotiate sectoral tariffs — such as those on steel and automobiles — which have already impacted nearly $5 billion worth of Indian exports. Evan A. Feigenbaum, Vice President for Studies at the Carnegie Endowment for International Peace, said on Monday that US-India relations may now become a political football, especially in New Delhi. He warned that the core understandings that enabled closer ties may be at serious risk, as New Delhi had largely assumed Washington would take political risks to strengthen the relationship — something Trump has not done and clearly will not do. Feigenbaum added that the split in relations is further underscored by Trump's effusive praise for Islamabad and recent engagement with Pakistan's army and government — developments that raise obvious concerns in New Delhi. 'The United States was roiled by India's ties to Iran, Myanmar, and later Russia. Trump and his administration are now moving to sanction and tariff India over its oil trade with Russia. This significantly shifts the bar for bilateral relations,' he said.
Mint
17 minutes ago
- Mint
HAL, BEL, Bharat Dynamics, other defence stocks in focus as DAC approves projects worth ₹67,000 crore
Hindustan Aeronautics (HAL), Bharat Dynamics, Bharat Electronics (BEL), BEML, and other defence stocks will be in focus on Wednesday after the government approved key military projects worth around ₹ 67,000 crore. The Defence Acquisition Council (DAC), chaired by Defence Minister Rajnath Singh, approved various proposals aimed at boosting India's military capabilities, including procurement of long endurance drones, mountain radars and missile systems at a cost of around ₹ 67,000 crore. For the Indian Army, Acceptance of Necessity (AoN) was accorded for the procurement of Thermal Imager-based Driver Night Sight for BMP. This would enhance night driving capability of BMP and provide higher mobility and operational advantage to the Mechanised Infantry, Ministry of Defence said in a release. For the Indian Navy, AoN was accorded for the procurement of Compact Autonomous Surface Craft, BrahMos Fire Control System & Launchers and Upgradation of BARAK-1 Point Defence Missile System. For the Indian Air Force, DAC approved the procurement of Mountain Radars and Upgradation of SAKSHAM/SPYDER Weapon System, it added. AoN was also accorded for procurement of Medium Altitude Long Endurance (MALE) Remotely Piloted Aircraft (RPAs) for the three Services. In addition, DAC has also accorded AoN for sustenance of C-17 and C-130J fleets and comprehensive annual maintenance contract of S-400 Long Range Air Defence Missile System. Analysts believe defence stocks such as HAL, BEL, BDL, Solar Industries, Data Patterns (India), MIDHANI, among others may tend to benefit from the contracts. 'Defence companies likely to benefit from the ₹ 67,000 crore DAC clearance include Bharat Electronics Ltd (BEL) and Hindustan Aeronautics Ltd (HAL), given their strong alignment with the approved project domains such as radars, drones, and air defence systems. In the short term, markets are expected to respond positively to this news,' said Prashanth Tapse, Sr VP Research – Research Analyst at Mehta Equities. However, in the long term, he believes the real value will hinge on timely execution and sustained government support to the domestic defence ecosystem.
