HTLS 2025: Can Doctors and Scientists trust AI with their work? Google DeepMind's Pushmeet Kohli answers

HTLS 2025: Can Doctors and Scientists trust AI with their work? Google DeepMind's Pushmeet Kohli answers

At the HTLS 2025, Pushmeet Kohli highlighted the nature of AI, which is powerful yet prone to errors. He stressed the need for responsible AI deployment.

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AI for Education: Niveda Foundation and Udhyam Learning Foundation win top place at All About AI Tech4Good Awards

AI for Education: Niveda Foundation and Udhyam Learning Foundation win top place at All About AI Tech4Good Awards

Education is vital for India's progress, yet access remains limited. AI can bridge this gap, as demonstrated by Niveda Foundation and Udhyam Learning Foundation, winners of the Mint All About AI...

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Rezo.ai and Nivinsure honoured for financial inclusion at the Mint All About AI Tech4Good Awards

Rezo.ai and Nivinsure honoured for financial inclusion at the Mint All About AI Tech4Good Awards

Despite a rise in the Financial Inclusion Index to 64.2, many in India remain excluded from credit and insurance. AI tools like Rezo.ai and Nivinsure are addressing these gaps, streamlining processes...

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India proposes sweeping AI–copyright overhaul with ‘one nation, one licence, one payment’ model

India proposes sweeping AI–copyright overhaul with ‘one nation, one licence, one payment’ model

The proposal is the government’s first formal policy outline in an area that has sparked intense global debate over the future of intellectual property. It comes in the wake of soaring AI adoption,...

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Govt rolls out 12 new standards across AI, payments security, audit data and industrial systems

Govt rolls out 12 new standards across AI, payments security, audit data and industrial systems

The new AI data-quality standards are particularly significant, aiming to improve accountability and reduce bias in regulated sectors.

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Legal tech is flush with capital—but drowning in data cleanup

Legal tech is flush with capital—but drowning in data cleanup

The promise of automation in the legal sector faces a major structural hurdle: the tedious process of cleaning and annotating messy, unstructured legal data to make it machine-readable.

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