_{Bhargavastra has been designed and developed by Solar Defence and Aerospace Limited (SDAL), signifying a substantial leap in countering the escalating threat of drone swarms. The Bhargavastra is meant for quick interception of drones for an assured hard kill using tiny, guided missiles with swarm engagement capability. This solution offers mobile protection against drone attacks with near-simultaneous engagement of multiple drones.}

Highlights of ‘Bhargavastra’:

Multi-layered defence:

Layer 1: Unguided micro rockets with a 20-meter lethal radius to neutralise drone swarms up to 2.5 km.

Layer 2: Guided micro-missiles for precision targeting, already successfully tested.

China said on Wednesday it would further enhance capital market support for science and technology innovation enterprises and would expand bank credit support for firms in the sector.

The move comes as the nation steps up efforts to reduce dependence on foreign technology amid rising China-U.S. geopolitical tensions.

China would encourage qualified tech firms to go public domestically and abroad in accordance with the law, it said.

China would also expand its pilot programme for financial asset investment companies to 18 cities and provinces, which aims to guide more equity investment in tech innovation firms. Banks in pilot cities would be allowed to extend the loan term of merger and acquisition loans for tech firms to up to 10 years, it said.

BRITISH Airways’ £7billion investment in AI has led to the airline’s flight punctuality soaring to record levels - with 86 per cent of jets now taking off on time.

The airline achieved more than 90% on-time departures on 38 of the 89 operational days in the first quarter. AI-driven improvements include real-time weather routing to avoid delays, smart aircraft stand allocation based on live passenger data, and new digital tools for pilots and crew, collectively saving hundreds of thousands of minutes in delays.

In the world of artificial intelligence, autoencoders are like the quiet heroes working behind the scenes. They’re not as flashy as robots or chatbots, but they play a key role in helping machines understand and clean up data.

So, what is an autoencoder?

Imagine you have a photo of a cat. The autoencoder works in two main steps: encoding and decoding.

Step 1: The Encoder
The encoder is like a machine that shrinks your picture down to just the important parts. It turns your cat photo into a small list of numbers — just enough to capture the shape, color, and key features. Think of it like turning a big, detailed image into a tiny summary.

Step 2: The Decoder
Now, the decoder takes that small summary and tries to rebuild the original picture. It uses those numbers to draw the cat again. If the model is trained well, the newly created image looks very similar to the original.

This entire process helps the AI learn how to compress and understand data efficiently. During training, it compares its output with the real image and gradually improves by learning from its mistakes.

Autoencoders are used in many practical tasks like removing noise from images, compressing files, and detecting patterns or outliers in data. They're simple, powerful, and a great starting point for anyone exploring how deep learning models work.

Whether you're new to AI or just curious about how machines learn to "see," autoencoders offer a fascinating look into how smart systems simplify and reconstruct the world around them.

Unscramble the jumbled letters to find the tech word matching each clue. Check your answers afterward!

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