Рубрика: AI

HCLTech and NVIDIA have announced the launch of a new Physical AI Innovation Lab in Santa Clara, California, aimed at accelerating enterprise adoption of physical AI and cognitive robotics.

The facility is integrated into HCLTech’s global AI Lab network and is designed to help global enterprises experiment with, incubate and scale advanced physical AI applications.

The lab will leverage the full NVIDIA technology stack, including NVIDIA Omniverse, NVIDIA Metropolis, NVIDIA Isaac Sim, NVIDIA Jetson and NVIDIA Holoscan, in combination with HCLTech’s portfolio of physical AI solutions such as VisionX, Kinetic AI, IEdgeX and SmartTwin.

The goal is to enable Global 2000 organisations to simulate, test and deploy robotics, automation and edge intelligence systems that improve competitiveness and real-world operational efficiency.

“Generative physical AI is set to revolutionize industrial automation, but bridging the gap from digital simulation to real-world deployment remains a critical challenge,” said Deepu Talla, VP of robotics and rdge AI at NVIDIA.

“HCLTech’s innovation lab will help enterprises develop, test and validate the complex autonomous systems needed to turn AI ambitions into operational reality.”

Vijay Guntur, CTO and head of ecosystems at HCLTech, said the collaboration strengthens the companies’ joint focus on industrial AI.

“By combining HCLTech’s global AI Engineering capabilities with NVIDIA’s cutting-edge platforms, this new collaboration marks a pivotal step in strengthening our synergy in the Physical AI space,” Guntur said.

“It empowers enterprises to reimagine physical operations, driving breakthroughs in robotics, automation, safety and operational intelligence, reinforcing our commitment to scaling AI-led transformation across industries and further deepening our strategic collaboration with NVIDIA.”

HCLTech’s physical AI solutions integrate robotics, autonomous systems, intelligent edge technologies, simulation and digital twins to help enterprises enhance productivity, resilience and sustainability in live operational environments.

The company said it is already working with several Global 2000 clients in the physical AI domain, including a major port operator, a global hi-tech leader and a Europe-based mining company.

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Years ago, a teenager preparing for the IIT entrance exam moved into co-founder Anand Prakash’s home for a few months.
What happened next became folklore inside Vedantu. The child’s rank shot up dramatically, not because the syllabus changed, but because someone watched his journey every day, caught his dips in confidence, and helped him course-correct in real time.

But this story goes even deeper, it’s the founding spark that built Vedantu itself.
Founded in 2014 by Vamsi Krishna, Prakash and Pulkit Jain, Vedantu was born from a simple but radical belief: great teaching is deeply personal, not industrial.
Their first mission wasn’t to scale content, it was to scale true mentorship, the kind Anand provided to that one student.
This founding philosophy eventually shaped every product decision, every model, and finally, Ved, India’s first AI mentor, designed to behave like a real human guide.

“If one Anand can change one life so profoundly… what would it take to give that level of mentorship to every child in India?” Jain told AIM, emotionally sharing the origin of Ved, India’s first AI mentor designed to deliver human-like academic guidance to every student, which the company launched a few days ago.

The timing couldn’t have been better. Vedantu recently turned profitable in Q4 FY25, raised new capital, expanded its hybrid learning footprint, and is now preparing for an IPO in 2027.

The launch signals a major shift for India’s edtech sector, from distributing content to scaling mentorship, from generic doubt-solving to understanding each student’s complete learning journey.

“Content was never the problem in Indian education. The real gap was knowing exactly what a student needs next, not once a week, but every single day,” shared Jain, in an exclusive interview on Front Page by AIM Network.

Why AI Mentorship

For over a decade, Vedantu solved the access problem: millions of students could finally attend high-quality Master Teacher classes from anywhere in the country. But one element remained stubbornly unscalable—continuous and deeply personalised mentorship.

Jain told AIM that this became obvious early on. “Students don’t drop out because they can’t understand a topic. They drop out because somewhere in the middle, they lose confidence and no one catches it in time.”

In high-pressure exams like JEE and NEET, this “confidence collapse” is where ranks fall off a cliff. Beyond the first few lakh ranks, marks often drop to near zero, not for lack of knowledge, but because students mentally disengage midway.

Ved is built exactly for this forgotten zone. It tracks how a student learns, where they drift, what patterns signal fatigue, and when they need a nudge. Instead of simply answering questions, it anticipates gaps and guides students back onto a structured path.

“A great teacher changes what you understand. A great mentor changes your journey. Ved is our attempt to give every child that journey, consistently, patiently, every single day,” Jain said.

Inside Ved

Ved is a multi-layered mentoring intelligence built on top of millions of hours of Vedantu’s proprietary learning data.

Years of Master Teacher interactions, classroom dialogues, student attempt patterns, lecture transcripts, handwritten solutions, behavioural signals, and test histories form the backbone of the system. This gives Ved what Jain calls a “context memory that finally lets AI behave like a mentor, not a machine.”

When a student asks a doubt, Ved identifies the root concept behind the confusion and instantly generates a LearnList with videos, SmartNotes and practice problems. During test analysis, it breaks down time usage, confidence levels and attempt style, translating it into an actionable improvement plan rather than just a correctness report.

“Different large models give us language understanding. But the mentoring behaviour, like the empathy, the direction, and the clarity, comes from years of how our teachers have guided students. That’s our real moat,” Jain said.

“The goal,” he added, “is that a child never feels alone in their learning. Ved should be the senior who’s always sitting next to you, quietly guiding you through the tough parts.”

The AI Layer Powering Vedantu’s 2027 IPO Ambition

The launch of Ved also marks a strategic milestone in Vedantu’s comeback story after the pandemic boom and subsequent edtech winter. Instead of scaling blindly offline like many competitors, Vedantu paused, experimented, and then built a hybrid model where technology standardises quality across its 65+ centres.

Regional content strategies lowered acquisition costs; local YouTube channels now reach nearly two crore students monthly. Most importantly, profitability returned in FY25, giving Vedantu the operating discipline it needed to prepare for public markets.

Ved is now the centrepiece of that vision. “AI will not only improve outcomes,” Jain said, “it will make our entire service delivery more predictable. When you want to build a public company, predictability matters more than hyper growth.”

For paid users, Ved deepens the personalisation layer using class history and test data. For free users, Ved democratises high-quality guidance through conversational mentoring and structured content paths. This dual structure expands reach while keeping premium offerings outcome-driven, a key advantage as Vedantu positions itself for a listing in 2027.

“Our job over the next two years is simple: build trust, build outcomes, and build systems that scale. Ved is at the heart of all three,” Jain said, sharing Vedantu’s roadmap for the next phase of growth.

The post What If Every Kid in India Had an Anand? appeared first on Analytics India Magazine.

Disseqt AI has teamed up with HCLTech and Microsoft to push agentic AI deeper into the banking stack. The Ireland-based startup is positioning its lean agent tools as a simpler way for banks to handle critical IT and DevOps work. The pitch is to bring faster testing, cleaner monitoring, tighter governance.

“Our partnership with HCLTech and Microsoft validates our mission of bringing intelligent, agent-based automation to the heart of enterprise IT,” Apoorva Kumar, founder of Ireland-based Disseqt, said. “By embedding trustworthy AI into core ITOps and FinOps processes, we’re enabling organisations to break free from tedious cycles of testing and monitoring, and instead embrace secure, scalable AI transformation.”

At a roundtable in Ireland, the three companies walked financial institutions through live demos that showed how agentic tools can strengthen processes when paired with proper oversight. Leaders from banks, tech firms, and regulatory circles discussed what real adoption looks like inside large systems.

Disseqt says its platform uses simulations, red teaming, and live observability to flag risks early, stop system drift, and stay aligned with Responsible AI rules. The firm claims up to 80% cost cuts, up to 70% faster production cycles, and a 40% jump in testing output.

HCLTech has been expanding its agentic AI work this year. In March, it rolled out a suite of tools built with Google Cloud’s Agentspace to give enterprises a common layer where agents can work directly with data and applications.

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Azimuth AI and Cyient Semiconductors launched India’s first-generation IP-powered silicon chip, ARKA GKT-1, in New Delhi on November 17. The companies introduced the chip to support edge AI, smart energy, and industrial systems.

The launch took place at an event where Ashwini Vaishnaw, union minister for electronics and IT, unveiled the chip. The firms developed the platform to address high-efficiency computing needs and real-time intelligence at the edge.

Cyient said the collaboration supports India’s semiconductor mission. Krishna Bodanapu, executive vice-chairman and MD, said that the programme shows “true custom ASIC turnkey solution” and covers work from design to fab, OSAT management, and final chip delivery.

ARKA GKT-1 will serve sectors such as smart utilities, battery systems, industrial automation, and smart city infrastructure. The chip also integrates multi-core computing, analogue sensing, memory, and power management.

The companies built the device using Azimuth AI’s Software-Defined Silicon framework and Cyient’s expertise in mixed-signal and low-energy ASIC design. The goal is to enable domestic and global clients to deploy intelligent edge hardware.

“The ARKA GKT-1 showcases India’s capability to design and develop world-class semiconductor technologies for critical global industries. This milestone reflects our nation’s accelerating progress toward self-reliance, advanced manufacturing, and leadership in next-generation chip innovation,” Vaishnaw said during the launch.

Praveen Yasarapu, founder and CEO of Azimuth AI, stated that ARKA GKT-1 meets the growing demand in fast-expanding sectors. He said it is the “realisation of our vision to bring intelligent, low-power silicon to the edge. This milestone validates our technology roadmap and sets the stage for large-scale deployment and commercialisation.”

This year, Cyient has also partnered with MIPS and Vodafone. The partnership with MIPS focused on developing specialised chips for power management, industrial robotics, and automotive applications. Meanwhile, with Vodafone, Cyient launched VISMON, a new AI-driven network configuration management platform designed to streamline network operations.

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Sakana AI has announced on November 17 that it has raised $135 million (20 billion Yen) for its Series B round to accelerate its mission to develop AI sustainably and implement AI that benefits Japan.

The funding was received from new and existing investors, including Mitsubishi UFJ Financial Group (MUFG), Khosla Ventures, Factorial, Macquarie Capital, Fundomo, Mouro Capital, New Enterprise Associates, Geodesic Capital, Lux Capital, Ora Global, MPower Partners, Shikoku Electric Power, and In-Q-Tel (IQT).
The Series B funding will be allocated to accelerate R&D, with a focus on advancements in artificial intelligence through collective intelligence and self-evolution, particularly for the optimisation of Sovereign AI.

The company will strengthen its Applied Team to deepen finance partnerships and explore opportunities in defence and manufacturing. Additionally, it aims to build a scalable ecosystem by pursuing strategic investments, partnerships, and mergers and acquisitions for long-term global growth.

“Sakana AI’s unique determination to develop frontier AI technology sustainably, through innovation, aligns with our core values as Japan’s first ESG-integrated global VC fund investing in technologies that transform industries and society,” Kathy Matsui, general partner at MPower Partners, said.

Furthermore, this funding round has inflated Sakana AI’s valuation to around $2.635 billion (400 billion yen), setting a new record for unlisted startups in Japan.

The amount raised is approximately double that of the Series A funding acquired in September of the previous year. The new resources will be allocated toward the development of Sakana AI’s proprietary large language model (LLM).

Sakana AI’s LLM is designed to reflect the nuances of the Japanese language and culture, making it suitable for local industries. Until now, the startup has collaborated with entities such as MUFG and Daiwa Securities Group.

“For Japan, with a declining workforce and an ageing population, the benefits of AI technology are clear. Sakana AI is now entering its most exciting phase: deploying R&D into the business and public sectors,” the company said in a statement.

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The generative AI boom has unleashed a new wave of legal battles, and 2025 may go down as a watershed year. From entertainment giants to regulators and even rival tech founders, several lawsuits are crystallising how copyright, safety, and competition rules apply in the age of artificial intelligence (AI).

To understand how these issues are unfolding, here are seven examples of legal actions shaping the development and oversight of AI.

1. Disney & Universal vs Midjourney

On June 11, Disney and Universal filed a landmark suit in a US district court for Central California against Midjourney, Inc., accusing the AI company of infringing their copyrighted characters. According to the studios, Midjourney trained its image-generation model on their IP, including characters like Darth Vader, Minions, Elsa, Shrek and Buzz Lightyear without permission, and is now producing unauthorised copies for paying users. The complaint characterises Midjourney as a “bottomless pit of plagiarism” and seeks a preliminary injunction, damages, and a ban on further infringing content.

The case marks a critical moment: two of Hollywood’s biggest content owners argue that without limits, generative models could undercut the very business models they built.

2. Getty Images vs Stability AI

Getty Images sued Stability AI, maker of Stable Diffusion, in the UK, alleging that millions of its copyrighted photographs were scraped and used without authorisation to train the model. Stability, for its part, argued that its models do not store or reproduce the original images. The court agreed, ruling that there are “no copies in the model.” Getty also raised trademark claims, saying some AI-generated images still bore Getty’s watermark; the court found limited trademark infringement. Crucially, Getty dropped its key copyright-infringement claims tied to where training happened, which underscores how territorial issues—where training or scraping occurs—may shape future AI copyright litigation.

3. Raine vs OpenAI

The family of 16-year-old Adam Raine filed a wrongful-death lawsuit against OpenAI in August, alleging that the company weakened self-harm guardrails in ChatGPT before launching GPT-4o. The amended complaint argues OpenAI prioritised user engagement over safety, claiming that ChatGPT responded improperly to Raine’s suicidal ideation, and that the company ignored clear risks linked to its AI behaviour.

If the court finds OpenAI liable, it could force a reevaluation of how AI platforms incorporate psychological safety by design and whether they owe a duty of care to vulnerable users, especially minors.

4. State of Utah vs Snap Inc

On June 30, Utah’s attorney general and commerce department sued Snap Inc., accusing Snapchat of designing addictive features, like streaks and ephemeral messages, and misrepresenting the safety of its AI chatbot, My AI. According to Utah, Snap withheld critical data collection disclosures, including geolocation and biometric data, and profited by exploiting teenage users’ vulnerabilities.

The complaint asserts that the company’s design and engagement algorithms effectively “caught” minors in addictive loops, raising novel arguments about duty of care, algorithmic supervision, and child-safety regulation in digital products.

5. xAI / X Corp vs Apple & OpenAI

On August 25, Elon Musk’s xAI and X Corp filed a 61-page lawsuit in Texas against Apple and OpenAI, accusing both companies of anti-competitive behaviour. The complaint alleges Apple gave ChatGPT preferential treatment in the App Store and deeply integrated it into iOS, via Siri or Apple Intelligence, making it harder for rivals like xAI’s Grok to compete. xAI claims this creates a “moat” protecting OpenAI’s dominance and seeks injunctive relief, as well as billions in damages. A US district judge denied Apple and OpenAI’s request to have the lawsuit dismissed, meaning the case will continue, and xAI’s antitrust claims will now undergo more thorough legal scrutiny.

6) Anthropic’s $1.5 Billion Copyright Settlement

In a separate but equally consequential case, this year, Andrea Bartz, Charles Graeber, Kirk Wallace Johnson, et al. vs Anthropic PBC,Anthropic—maker of the Claude chatbot—agreed to pay $1.5 billion to settle a class-action lawsuit by authors and publishers. The plaintiffs had alleged that Anthropic downloaded millions of copyrighted books, including from pirate “shadow libraries”, without permission. Earlier, in June, a US judge ruled that Anthropic’s use of these books to train Claude was “exceedingly transformative” and thus constituted fair use, but the court also found that the way it stored pirated books in a central library was infringing.

7) Robby Starbuck vs Google

On October 22, activist and filmmaker Robby Starbuck filed a defamation lawsuit against Google, alleging that its AI models Bard, Gemini and Gemma generated fabricated statements falsely linking him to murder and child abuse. Starbucks claims that Google failed to prevent the spread of these statements and did not take timely corrective action even after receiving notice. His legal team argues that the incident caused severe personal and professional harm. The case is being closely watched because it could establish new rules on defamation, platform responsibility and AI hallucination liability.

The post 7 Times AI Went to Court in 2025 appeared first on Analytics India Magazine.

Even before regulators could decide how to govern AI, India’s IT services companies have started rewriting their own rulebooks.

At Mphasis, that rewrite concretised with the recent ISO/IEC 42001:2023 certification it was presented with, a first-of-its-kind international standard for AI management systems (AIMS) designed to ensure that organisations build, deploy, and monitor AI responsibly.

The standard provides an auditable framework for governing risks such as bias, data integrity, model drift, and ethical compliance, making it increasingly essential for enterprises in regulated or large-scale AI environments.

Mphasis told AIM the framework is being “embedded directly into our AI development and deployment processes within Mphasis.ai,” creating uniform governance across the full lifecycle.

Every AI project now incorporates “mandatory steps for risk assessment, bias mitigation, transparency documentation, and compliance with ethical guidelines,” making responsible AI a non-negotiable part of delivery. The company has restructured governance accordingly, establishing an AI Risk Management Committee and formal accountability for AI system owners.

Documentation requirements have been tightened; fairness checks, data validation, and systematic monitoring are now required at each stage. This aligns with the ISO’s continuous improvement loop, which Mphasis describes as embedding “the ISO’s ‘Plan-Do-Check-Act’ approach into our existing engineering culture.”

It expects measurable gains, saying the certification “strengthens our competitive positioning and helps reduce project risk”, while enabling faster time-to-value and smoother entry into regulated markets.

With 68% of new deals now AI-led, Mphasis says scalability is ensured by NeoIP, where Ontosphere applies governance automatically so responsible AI becomes “a scalable, self-sustaining capability.”

The Big Picture

The broader industry is moving in the same direction. Infosys, Cognizant, and other Indian IT providers have begun adopting ISO/IEC 42001 to meet rising expectations from global clients navigating tightening AI regulations.

NABCB, India’s national accreditation body, has played a pivotal role in operationalising ISO/IEC 42001 by becoming one of the earliest global bodies to approve accreditation schemes for the new AI management standard.

Its framework, launched in 2024, enables Indian certification bodies to conduct ISO 42001 audits under internationally recognised conformity norms.

This has effectively allowed Indian IT and enterprise organisations to be among the first globally to obtain certifiable, regulator-ready AI governance credentials.

Infosys’ executive vice-president Balakrishna DR said the certification not only distinguishes the company but “serves as a guiding beacon” for clients managing AI-related risks.

Cognizant CEO Ravi Kumar S said the standard reflects how businesses now expect partners who drive innovation while aligning with their values, adding that the certification “solidifies our role as a trusted leader in enabling ethical and sustainable digital transformation worldwide.”

As enterprises demand auditable controls around bias, transparency, and accountability, ISO 42001 is rapidly emerging as the AI-era equivalent of ISO 27001 for information security.

Mphasis reflects this sentiment, noting that the standard “is poised to become the global benchmark for AI governance,” evolving into a baseline qualification for IT service providers worldwide.

Reto P Grubenmann, director, head of certification & attestation, KPMG Switzerland, noted that ISO/IEC 42001:2023 verifies that a company’s AIMS meets international standards, offering long-term strategic and operational value.

EY similarly wrote that the standard “was crafted to tackle the concerns and obstacles associated with the conscientious deployment of AI technologies,” emphasising controls around security, fairness, transparency, safety, and data quality throughout the AI lifecycle.

EY also draws parallels with ISO/IEC 27001, urging organisations already compliant with it to integrate ISO 42001 to streamline governance and risk oversight.

What once served as a differentiator is now becoming a minimum qualification for companies handling mission-critical AI workloads, marking a decisive shift in how India’s IT sector builds, deploys, and assures AI systems.
As companies deepen their AI capabilities, many are expanding their broader compliance stack to reinforce trust. Certifications such as ISO 27001 for security, ISO 27701 for privacy, ISO 9001 for quality and SOC 2 for service reliability continue to anchor enterprise expectations, while sector-specific frameworks like PCI DSS and HIPAA apply where financial or health data is involved.

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