Рубрика: AI

The Karnataka government has approved the establishment of an Artificial Intelligence Centre of Excellence (AI-CoE) to accelerate Industry 4.0 adoption and deep-tech innovation across sectors.

The centre, named CATS, or Centre for Applied AI for Tech Solutions, has been cleared by the Department of IT, and will be set up at the KEONICS facility in HSR Layout, in partnership with Nasscom, according to an official release.

The initiative will have a total outlay of ₹20 crore spread over four years, funded through a 40:40:20 model, with contributions from the Ministry of Electronics and Information Technology (MeitY), the Government of Karnataka, and industry partners.

The CATS AI CoE will focus on priority areas such as AI, robotics and automation, supply chain optimisation, and digital transformation.
It aims to strengthen Karnataka’s innovation ecosystem by supporting start-ups, MSMEs, industry players, research institutions, and academia.

Over the next four years, the centre is expected to establish advanced laboratories and testing facilities, support deep-tech start-ups, and facilitate proof-of-concepts, industry collaborations, and technology commercialisation, with collaboration envisaged with global technology and industry leaders.

Minister for electronics, IT Priyank Kharge said, “This is Karnataka’s deep tech decade, and the CATS AI Centre of Excellence will be a launchpad for deep-tech start-ups and industry collaborations.”
He added that by bringing government, academia, and industry together, we are building a pipeline from research to market-ready innovation.

The centre will track key performance indicators including the number of start-ups incubated, prototypes developed, intellectual property filings, industry collaborations, and professionals trained, with year-wise growth targets to ensure measurable outcomes.

The post HSR Layout to Get Karnataka’s ₹20 Crore AI Centre of Excellence appeared first on Analytics India Magazine.

Mphasis Limited reported a 2.6% quarter-on-quarter and 12.4% year-on-year increase in revenue in the third quarter of FY26, but a change in labour laws led to a charge of ₹35.5 crore, denting profitability during the period.

For the quarter ended December, the IT services company said revenue rose to ₹4,002.6 crore in reported terms.
In constant currency terms, revenue grew 1.5% sequentially and 7.4% from a year earlier.
Direct revenue increased 3.1% QoQ and 15.9% YoY on a reported basis, and 1.9% QoQ and 9.6% YoY in constant currency.

The company said it is pleased with the continued progress on all metrics around growth across the business.
CEO and MD Nitin Rakesh said that Mphasis NeoIP is supersizing the company’s pipeline and deals, paving the way for faster revenue growth and continued gains in wallet share driven wins.
Net profit after the exceptional item related to the labour law change declined 5.7% quarter-on-quarter and rose 3.4% year-on-year to ₹442.2 crore.

Operating margin for the quarter came in at 15.2%, down 10 basis points quarter-on-quarter and year-on-year.
The company reported new total contract value (TCV) wins of $428 million during the quarter, with 64% of the deal wins coming from new-generation services.
In Q2, the company reported TCV wins of $528 million, with the CEO calling it a testimony to the company’s AI first approach.
During Q3, Mphasis announced several large deal wins, including a multi-year engagement with a large US bank for financial crimes and anti-money laundering transformation,

It also signed a core administration modernisation mandate from a top healthcare company using the Mphasis Javelina platform, a comprehensive mortgage fulfilment partnership with a global bank, and a global remittance programme for another large bank.

The company recently told AIM that its Mphasis’ Sparkle Innovation Program has emerged as a significant channel for enterprise-focused innovation, helping it accelerate solution development, strengthen its AI-led portfolio, and support clients across banking, insurance, healthcare, logistics, and other sectors.

Srikumar Ramanathan, chief solutions officer, said the programme is delivering measurable business outcomes and is set to expand further over the next two years.

The post Labour Code Change Dent Mphasis Q3 Profits, Revenue Rises 2.6% QoQ appeared first on Analytics India Magazine.

A research group at TCG CREST (The Chatterjee Group Centres for Research and Education in Science and Technology) has found a way to make a key quantum algorithm run faster by doing something counterintuitive: keeping the wrong answer.

The work, led by Professor Srinivasa Prasannaa, improved the performance of the Harrow–Hassidim–Lloyd (HHL) algorithm, a foundational method for solving systems of linear equations on quantum computers. The approach, called Psi(ψ)HHL, reduces a major runtime bottleneck without adding extra quantum hardware or circuit depth.

“At the end of the day, what you’re doing is solving problems,” Prof Prasannaa tells AIM. “A quantum algorithm is what tells you how to change those qubit strings towards solving a specific problem, just as a classical algorithm tells you how to change bit strings.”

The breakthrough matters because HHL sits at the heart of many proposed quantum applications, from chemistry simulations to optimisation.

While HHL, proposed in 2009 by Aram W. Harrow (University of Bristol), Avinatan Hassidim (Cambridge), and Seth Lloyd (Cambridge), promised exponential speedups in theory, one practical factor kept holding it back.

It was the condition number, denoted by kappa (κ), which is a measure of the ease or hardness of inverting the matrix that occurs in the system of linear equations. When large, it can dramatically increase the number of times the algorithm is executed.

“The runtime goes as kappa cubed in HHL,” Prof Prasannaa explains. “While it is polynomial, in practical calculations, it is going to be very difficult.”

Why HHL Struggles in Practice

HHL solves an equation form that appears across science and engineering. The difficulty lies in how quantum computers produce results. Unlike classical machines, quantum systems rely on probabilities and repeated measurements.

In all quantum algorithms, you have an extra step called measurement,” he notes. “Because the outcome of a measure is going to be random, you will have to repeat any quantum circuit execution several times before you build enough statistics.”

When kappa is large, the probability of measuring the correct result drops sharply. That forces researchers to repeatedly run the same quantum circuit. This creates a practical problem. Researchers do not know the condition number in advance, and calculating it classically is hard.

“You’re not supposed to know kappa before you start executing an HHL algorithm,” Prof Prasannaa clarifies. “That’s why you went to a quantum computer in the first place.”

Over the years, other researchers tried to fix this by adding complex modules to HHL. These reduced the dependence on kappa but dramatically increased circuit size. “These methods came with having many, many more gates in your circuits,” he adds.

For today’s noisy quantum hardware, as well as those to come over the next several years, that trade-off makes many solutions impractical.

The Accident That Led to PsiHHL

PsiHHL emerged while the team worked on a different problem. The original goal was to speed up the calculation of molecular properties using HHL, not to redesign the algorithm itself. “A PhD student had joined us, and I thought he could just do a low-hanging fruit kind of a problem,” Prof Prasannaa recalls.

That student, Peniel Bertrand Tsemo, kept getting inconsistent results. “Then we found out he’s getting bad answers because he’s not repeating the experiment enough number of times.”

As the team investigated, they realised that kappa grew, although it did so slowly, for larger molecular systems. That made HHL increasingly inefficient. The group explored standard fixes, including amplitude amplification for boosting the probability of measuring desired states, but hit a wall.

The turning point came unexpectedly.

A post-doctoral fellow, Akshaya Jayashankar, “had this very interesting idea of subtracting two different signals,” Prof Prasannaa recalls. “It was just an intuition.” Instead of focusing only on the correct measurement outcome, the team tried something unusual. They deliberately selected the wrong outcome first.

“What we do is in our first HHL execution, we completely ignore the correct answer and instead deliberately select the wrong answer,” he explains.

In a second run, they introduced a small tweak that produced a mixed signal of both wrong and right answers. Subtracting the two cancelled the unwanted result and isolated the correct one.

This simple change had a major effect. By running HHL twice, PsiHHL reduced the runtime scaling from kappa cubed to kappa for ill-conditioned systems.

What This Means for Quantum Advantage

The result does not mean quantum computers will suddenly outperform classical machines across the board. Prof Prasannaa remains cautious. “Just because an algorithm promises an exponential advantage in principle doesn’t mean you can pick any real-world application.”

Quantum advantage depends on many factors, including algorithm design, hardware maturity, and the problem structure. He presents a three-piece solution.

“There has to be maturity on the algorithm side, on the software side and on the hardware side.” He also warns against underestimating classical systems, calling them incredibly mature.

Still, PsiHHL addresses a real bottleneck in quantum computing research. By improving algorithm efficiency without adding hardware cost, it aligns better with the early fault-tolerant era quantum machines.

“If this goes on, I would be more conservative and say you have to wait at least another 10 years before you actually start seeing [quantum advantage],” Prof Prasannaa notes.

For now, the work highlights how progress in quantum computing does not always come from bigger machines. Sometimes, it comes from rethinking what to do with a wrong answer.

The post These Indian Professors Fixed a 16-Year-Old Quantum Algorithm With the Wrong Answer appeared first on Analytics India Magazine.

Eightfold AI, an AI recruitment platform based in the US and used by companies such as Microsoft and PayPal, as well as various Fortune 500 firms, is being sued in California for reportedly compiling applicant screening reports without their consent.

The lawsuit, filed on January 20, marks the first case in the US to accuse an AI recruitment firm of breaching the Fair Credit Reporting Act, according to the legal firms that initiated the suit. It also highlights how consumer advocates are seeking to enforce existing laws on AI systems that can infer information about individuals through extensive data analysis.

“In order to protect against the harms of such reports, the FCRA requires consumer reporting agencies like Eightfold to make certain disclosures, obtain certain certifications, and ensure that consumers (here, job applicants) have a mechanism to review and correct reports that are provided to prospective employers for purposes of determining eligibility for employment,” the suit said.

The startup offers tools to speed up hiring by assessing job applicants and predicting their fit for positions using data from online resumes and job listings.

“There is no AI-exemption to these laws, which, for decades, have been an essential tool in protecting job applicants from abuses by third parties, like background check companies, that profit by collecting information about and evaluating job applicants,” they said in the lawsuit.

However, individuals seeking employment at firms that use these technologies are not informed or given an opportunity to contest inaccuracies, as alleged by Erin Kistler and Sruti Bhaumik in their proposed class-action lawsuit.

As a result, they assert that Eightfold breached the FCRA and a California statute that grants consumers the right to access and dispute credit reports utilised in hiring and lending.

According to Eightfold representative Kurt Foeller, the platform operates on data provided by candidates or clients, as reported by Reuters.

“We do not scrape social media and the like. We are deeply committed to responsible AI, transparency, and compliance with applicable data protection and employment laws,” Foeller said.

According to the lawsuit, Eightfold generates consumer reports for potential employers using its Evaluation Tools. They evaluate job candidates not just as individuals by claiming to pinpoint their likely skills, experiences, and traits, but also in relation to each other, ranking applicants on a scale from 0 to 5 based on the findings, conclusions, and assumptions derived from Eightfold’s proprietary AI regarding their “likelihood of success.”

Eightfold creates talent profiles of job seekers that include personality descriptions such as ‘team player’ and ‘introvert’, ranks their ‘quality of education’, and predicts their future titles and companies, according to the lawsuit.

“Employers use these reports to sift through applications, typically only reviewing highly ranked candidates. Lower-ranked candidates are often discarded before a human being ever looks at their application,” the lawsuit said.

Kistler and Bhaumik filed a lawsuit in California state court on behalf of all job applicants in the US who were assessed using the company’s tools. The proposed class is represented by the labour law firm Outten & Golden and the nonprofit advocacy organisation Towards Justice.

Kistler sought positions at various companies that use Eightfold, including PayPal, while Bhaumik pursued opportunities at firms like Microsoft, as stated in the complaint. Both individuals have degrees in science or technology and over a decade of experience. They were not selected for employment, and each believes that Eightfold’s tools contributed to this outcome.

Microsoft and PayPal are not named as defendants in the lawsuit.

The post AI Recruitment Platform Eightfold Sued for Screening Job Applicants Without Consent appeared first on Analytics India Magazine.

Apple may be developing its own AI wearable, according to a report published by The Information. The device is described as a pin worn on clothing, equipped with two cameras and three microphones.

If the rumoured product reaches the market, it would be another clear sign that competition in the Physical AI space is accelerating. The report follows comments made in Davos by OpenAI’s chief global affairs officer, Chris Lehane, who said the company is likely to announce its first AI hardware device in the second half of this year.

According to The Information, Apple’s device is a “thin, flat, circular disc with an aluminium-and-glass shell”, which engineers hope to make roughly the size of an AirTag, albeit slightly thicker. The pin is expected to include two cameras—one with a standard lens and another with a wide-angle lens—enabling both photography and video capture, according to the report. It would also feature a physical button, an in-built speaker and a Fitbit-style charging strip on the back.

The report suggests Apple may be accelerating development to compete more directly with OpenAI’s upcoming hardware. The device could potentially launch in 2027, with Apple reportedly considering production volumes of up to 20 million units at release.

However, it remains unclear whether consumers actually want this category of AI device, considering previous attempts have struggled to gain traction. Two former Apple employees founded Humane AI, which launched an AI pin featuring microphones and a camera designed to act as an always-on assistant. The product failed to resonate with users, and the company shut down operations and sold its assets to HP within two years of launch.

Apple’s entry into the space would nevertheless be significant, given its track record of turning niche hardware into mainstream categories through tight integration with its software and services ecosystem.

The post Apple Plans AI Wearable Pin as Competition Intensifies in AI Hardware: Report appeared first on Analytics India Magazine.

India’s global capability centre (GCC) story has become one of its strongest global differentiators, fueling innovation, R&D, IP creation, and strategic decision-making for some of the world’s largest multinational corporations. But a growing concern is emerging in the industry: what really qualifies to be a ‘GCC’?

Recently, State Bank of India (SBI) chairperson Challa Sreenivasulu Setty inaugurated the bank’s GCC in Bengaluru. Conceived as a centre of excellence, the Bengaluru facility will focus on building advanced capabilities across digital banking platforms, data analytics, artificial intelligence, cybersecurity, and enterprise technology support.

However, the announcement also brings into focus a broader and increasingly important question: what does the term ‘global capability centre’ actually mean?

Traditionally, GCCs refer to offshore or nearshore entities that are fully owned and operated by multinational corporations, established outside the headquarters geography to centralise and deliver critical business functions for global operations. The “global” aspect of a GCC is structural, not aspirational—it reflects a model designed to serve a foreign parent’s worldwide business mandates.

In this context, any domestic use of the GCC label raises questions around interpretation and scope. When applied to domestically headquartered institutions, the term appears to be either broadened or redefined beyond its conventional meaning, potentially blurring important distinctions in operating models, policy frameworks, and industry reporting.

In this regard, Alouk Kumar, CEO of Inductus Group, tells AIM, “We’re witnessing a concerning dilution of what ‘GCC’ actually means, and not just in semantics. It has real implications for policy effectiveness, talent positioning, and India’s competitive differentiation in the global services landscape.”

AIM reached out to SBI to seek clarity on how the bank defines its Bengaluru facility within this framework; however, the queries didn’t elicit a response.

Why ‘Global’ Matters in GCC

At its core, a GCC is not just a large technology hub or shared services operation. It has a specific strategic architecture. Kumar explains, “The ‘global’ in the ‘global capability centre’ isn’t aspirational; it’s structural.”

These centres are designed to build intellectual property, drive R&D and advanced engineering, run enterprise-wide technology platforms, and influence global business strategy from India.

This definition is echoed in state policies as well.

Karnataka’s GCC policy, for instance, explicitly defines GCCs as “Fully owned hubs by foreign-headquartered MNCs leveraging India’s talent to enhance their worldwide operational efficiency.”

The emphasis is clear: foreign parentage, global mandate, and integrated ownership.

Rebranding Without Structural Change

Despite this clarity, the GCC label is increasingly being applied far more loosely. Domestic banks like Bank of Baroda, Indian conglomerates like Tata Motors, Adani Enterprises, and ITC, and even IT services firms are rebranding delivery or captive units as GCCs largely because the term carries policy weight and talent appeal.

Kumar notes, “A state bank opening branches in India to serve Indian customers is domestic expansion. It’s not tapping into global talent pools to serve a foreign parent’s worldwide operations. The structural DNA is fundamentally different.”

This blurring of definitions is not merely cosmetic—it has tangible consequences.

One immediate impact is on talent markets.“Engineers evaluating opportunities need to understand whether they’re joining a centre that’s genuinely shaping global products and strategy, or a rebranded delivery operation with a more appealing nameplate,” Kumar points out.

Equally concerning is the effect on policymaking. The incentives, infrastructure support, and regulatory frameworks are being designed for GCC and calibrated for a specific economic activity to attract foreign multinationals to anchor high-value capabilities in India.

For instance, Telangana offers single-window clearances for all required state-level licences and approvals for GCCs, alongside targeted initiatives focused on skilling and developing local talent, while Karnataka provides financial support for workforce development by reimbursing up to 20% of skilling expenses, capped at ₹36,000 per graduate and ₹18,000 per diploma holder.

When domestic entities adopt the GCC label indiscriminately, it muddies data, dilutes policy intent, and risks misallocation of resources.

Legal Reality

Meanwhile, Salman Waris, a technology lawyer, adds an important legal perspective, clarifying that ‘GCC’ itself is not a statutory designation.

“The term ‘global capability centre’ does not have a distinct, overarching legal definition under Indian law. It is primarily a commercial or operational term used by multinational corporations,” Waris clarifies.

He further explains that while Indian companies may use the term internally, the term ‘GCC’ itself does not automatically grant benefits. In fact, the benefits are tied to the specific legal structure and location (SEZ, IFSC).

The risk of misrepresentation arises only when the label is used to imply eligibility for incentives without meeting the required legal frameworks.

Why Definitions Matter More Than Ever

However, Karthik Padmanabhan, managing partner at consulting firm Zinnov, brings the debate back to first principles. “A global capability centre is not a catch-all term for large or sophisticated tech hubs. It has a very specific meaning.”

He underscores that the single most important distinction is the relationship of the GCC to headquarters—not scale, talent quality, or global ambition. In simple terms, “You cannot be a GCC in your own headquarters country,” Padmanabhan clarifies.

Just as Google’s Mountain View campus is its headquarters—not a GCC—an Indian enterprise’s centre in India remains a corporate or R&D centre. By contrast, “Google Bangalore is a GCC because it supports a global business from outside the HQ geography, ” he explains.

India’s GCC narrative is powerful precisely because of its clarity. India is where Fortune 500 companies establish strategic capability hubs, not just offshore delivery centres. They are innovation engines, IP generators, and increasingly, strategic decision-makers for their global parents.

Kumar warns, “Singapore doesn’t call every office a ‘regional headquarter’. Ireland doesn’t label every subsidiary a ‘European centre of excellence’. The discipline in terminology reflects strategic clarity.”

Blurring definitions weakens India’s competitive position and risks turning a strategic advantage into generic jargon.

However, there is room for nuance and evolution. Kumar acknowledges that there are grey areas—such as Indian multinationals setting up regional capability hubs. But the ‘global’ qualifier must retain meaning: foreign-headquartered parent, multinational scope, and capability building beyond India’s borders.

Growing Need for Definition Discipline

Rohan Lobo, partner and GCC industry leader, Deloitte South Asia, says that as models expand and mature, so does the confusion around terminology. Multiple labels—GCC, GIC (global innovation centre), GVC (global value ventre), captive, shared services—are often used interchangeably, even though the work and intent behind them differ.

Lobo acknowledges this ambiguity, “maybe there is a little bit of definition that’s required in terms of what is a GCC, what is not a GCC, what is value-adding work, what is not value-adding work.”

Lobo believes that it is now upon the government, working with industry, to establish clarity.

The post When Everyone is a GCC, No One is appeared first on Analytics India Magazine.

TPC26 Set for Early June in Baltimore

The Trillion Parameter Consortium (TPC), the global initiative driving the future of AI for scientific…

Real estate major RMZ Group has announced a $10 billion investment to develop a large-scale global capability centre (GCC) Park and a 1-gigawatt hyperscale data centre in Andhra Pradesh over the next five to six years.

The announcement was made jointly by the company and the Andhra Pradesh government on the sidelines of the World Economic Forum Annual Meeting in Davos.

The developer plans to build a GCC Park at Kapuluppada Phase-1 IT Park in Visakhapatnam, spread across nearly 50 acres with a potential built-up area of up to 10 million square feet. The project is aimed at attracting global enterprises looking to establish large GCC operations in India.

In parallel, RMZ Group also proposes to develop a hyperscale data centre cluster in the Visakhapatnam region, with a planned capacity of up to 1 gigawatt, to be rolled out in phases.

The data centre project will require approximately 500–700 acres of land and is designed to support next-generation digital and AI-driven workloads, with a strong focus on sustainability and green energy integration, according to a state government statement.

Beyond Visakhapatnam, RMZ Group is planning an Industrial and Logistics Park at Tekulodu in the Rayalaseema region, spanning about 1,000 acres, to strengthen manufacturing, warehousing and logistics capabilities.

Collectively, these projects are expected to create around 100,000 jobs across IT services, data centres, industrial and logistics sectors.

The post RMZ Group to Invest $10 Billion in GCC Park, Data Centre in Andhra Pradesh appeared first on Analytics India Magazine.

TPC26 Set for Early June in Baltimore

The Trillion Parameter Consortium (TPC), the global initiative driving the future of AI for scientific…

Adobe has revealed that it is enhancing video creators’ capabilities through new AI advancements and investments in professional development. Ahead of the Sundance Film Festival, where 85% of all films utilised Adobe products, the company introduced innovations to streamline post-production.

These upgrades include AI-enhanced masking in Premiere and new features in After Effects, such as updated typography, materials, and 3D options, that expand motion design and storytelling. Premiere now integrates with Firefly Boards, Adobe’s AI-powered brainstorming platform, enabling teams to generate concepts using Adobe’s, Google’s, and OpenAI’s leading AI models.

Additionally, Adobe is investing $10 million this year to support video professionals from underrepresented communities through the Adobe Film & TV Fund, bringing the total to $20 million since its launch in 2024. The fund offers grants and training opportunities and will showcase films at Sundance while fostering industry partnerships.

New integrations between Adobe Firefly and Premiere enhance Adobe Firefly’s video AI capabilities, an all-in-one creative AI studio that combines leading AI models with top creative tools.

These innovations include precision controls for prompt-based edits, camera motion refinement, and the public beta of the Firefly video editor. This lightweight tool allows creators to combine generative clips, footage, graphics, and audio into polished stories directly in the browser. Adobe has also partnered with Runway to deliver next-generation AI video models across its workflows.

These advancements enable video professionals to move quickly from concept to edit, providing full creative control and exceptional flexibility.

According to the company, recent updates in Adobe Premiere and After Effects are transforming workflows for video professionals, enabling quicker execution of previously time-intensive tasks.

The new Object Selection and Mask tool simplifies selection, enabling faster tracking of complex subjects, while redesigned Shape Masks provide greater creative control for effects like blurring faces or relighting areas of a frame.

In After Effects, enhancements such as Native 3D Parametric Meshes allow for the creation of intricate 3D shapes with realistic shadows. Additionally, access to over 1,300 free Substance 3D Materials enhances motion graphics quality, and the Variable Font Animation feature adds dynamism to text elements.

“We’re thrilled to see so many filmmakers creating their stories with Adobe’s industry-leading tools,” Deepa Subramaniam, vice president of product marketing and creative professionals at Adobe, said in a statement. “The creative community inspires everything we do, and we’re committed to advancing AI video tools with new innovations and investments for the next generation of storytellers.”

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