Element-wise operations are a crucial part of data preprocessing in Pandas. Learn how to perform them with practical examples using the DataFrame.map() function.
Год: 2025
Autonomous businesses will be powered by AI agents
Research from 2025 finds that 77% of executives believe unlocking the true benefits of AI will only be possible when it is built on a foundation of trust, according to the Accenture Technology Vision 2025 report.
The Accenture Technology Vision 2025 explores how the future is being shaped by AI-powered autonomy. As adoption of AI accelerates across all businesses and society at a rate faster than any prior technology, 69% of executives believe AI brings new urgency to reinvention and how technology systems and the processes it enables are designed, built, and operated. The ripple effect of AI and its impact will be felt across multiple dimensions, including technology development, customer experience, the physical world, and the workforce.
Also: AI agents might be the new workforce, but they still need a manager
Accenture identifies four key emerging trends in AI, autonomy, and trust, focused on the following: what happens when AI acts autonomously at the center of enterprise technology, speaks on behalf of your brand, inhabits robotic bodies, and collaborates on behalf of employees.
- The Binary Big Bang: When AI expands exponentially, systems are upended — As generative AI becomes central to enterprise tech, development costs plummet, new systems abound, and digital agents gain autonomy — transforming applications as we know them.
- Your Face, in the Future: Differentiating when every interface looks the same — AI agents can personalize customer interactions at scale, but brands must protect their unique voice to avoid becoming generic.
- When LLMs Get Their Bodies: How foundation models reinvent robotics — Robots with embedded LLMs have generalist versatility, enabling them to take on new tasks in human spaces beyond today's highly programmed use cases.
- The New Learning Loop: How people and AI are defining a virtuous cycle of learning, leading, and creating — When generative AI is diffused through an organization, every employee has the full power of their organization behind them, which expands the autonomy of both people and AI over time.
AI: A declaration of autonomy
AI: A Declaration of Autonomy
The focus of this article will be on the binary big bang and the agentic AI revolution in the enterprise. Accenture notes that AI will drive new levels of autonomy throughout business, evolving the ability to reinvent with tech, data, and AI — a limitless opportunity for innovation and growth. The key disruption here is "AI cognitive digital brains."
AI "cognitive digital brains" will completely reshape the role technology plays across the enterprise and in people's lives. Accenture defines cognitive digital brains at multiple levels: For individuals, the cognitive digital brain will operate as a co-pilot or sidekick, something that will understand their job, learn their preferences, and get to know them through its interactions, in service of helping them become an enhanced version of themselves. For businesses, it might seem more like a central nervous system — an evolution of the enterprise architecture into something that can capture the collective knowledge of the business, its unique differentiators, and its culture and persona, and become a key orchestrator (and even autonomous operator) for parts of it.
Also: Agents are the 'third wave' of the AI revolution
What makes a Cognitive Digital Brain? The cognitive digital brain will become the central nervous system for enterprise decision-making and continuous learning. Used to power enterprises' future ambitions, like intention-based architectures, it is comprised of four interconnected layers that together organize, process, and act on information.
- Knowledge: Technologies like knowledge graphs and vector databases gather, organize, and structure data from across the enterprise and beyond.
- Models: Large-scale generative AI models, as well as classical machine learning and deep learning models, perform critical thinking and reasoning functions to turn data into actionable outcomes.
- Agents: Designed to be problem-solvers, tackle tasks with minimal human input, and learn and grow over time, AI agents bring planning, reflection, and adaptability to the mix.
- Architecture: A comprehensive backbone is what turns AI experiments into enterprise-grade solutions. It scales intelligence across the organization and into existing workflows, enabling repeatability so solutions can be made once and reused.
Accenture emphasizes the importance of trust, which may be the most important limiting factor for businesses to achieve autonomy with AI. First, enterprises need to bolster the cybersecurity and trust of their digital systems. The second dimension of the roadmap is thinking about building trust in AI itself. Lastly, the third and uncharted part of the trust roadmap is finding a new path to people-driven trust.
The Binary Big Bang
According to Accenture, the Binary Big Bang tracks the emergence of language models coupled with agentic systems and how they challenge conventions around building software and crafting new digital ecosystems. The trend dives into a generational transition, as leaders rethink how digital systems are designed — building the foundation for the cognitive digital brains that will become an essential part of enterprise DNA. The result will be a dramatic increase in technology diffusion touching every aspect of business, consumer, and societal interactions. It sets the stage for the emerging AI era, where we will rapidly expand digital ecosystems and increasingly trust autonomous systems to find new ways to innovate with us.
Accenture highlights Salesforce as the moment we entered the Binary Big Bang:
"In September 2024, Marc Benioff announced that Salesforce would 'hard pivot' to Agentforce, a platform for building and deploying autonomous AI agents. It's rare for a company of such scale to pivot like this. But Salesforce realized something groundbreaking and formidable that every company needs to recognize too: We have just entered the Binary Big Bang." — Accenture Technology Vision 2025.
Accenture defines three forces — abundance, abstraction, and autonomy — emerging as the pillars of tomorrow's technology.
- Abundance: With coding agents, the creation of digital systems is getting much cheaper and faster.
- Abstraction: If speed and efficiency will expand the proliferation of technology, abstraction will expand who and how we use it, meaning modern AI systems will serve as a new bridge between people and machines.
- Autonomy: Today's architectures are designed to execute a singular and rigidly defined purpose. Autonomy breaks us out of that, as systems that can build and execute code on their own are poised to become powerful orchestrators and operators of business. Accenture notes that autonomy means we might not know how or why a system makes particular decisions — but we have to trust them to make the right ones. This is why trust is so important.
Also: We're not ready to support autonomous AI agents, survey suggests
A warning: The Binary Big Bang will feel hectic. There will no longer be a reference point for how an architecture should look. Accenture's guidance: Every company must be prepared to forge a new technological footprint, founded on AI — a unique DNA identifying and differentiating them as they launch into tomorrow's technology landscape. The Binary Big Bang is the moment to get your footing — but it won't last long. How can you take advantage of it today?
The technology that will define the Binary Big Bang is Agentic Systems, Digital Core, and, Generative UI.
- Agentic Systems — What is it? AI agents and agentic systems offer a powerful way to leverage LLMs and other foundation models to complete complicated and compound tasks. Agentic systems take the power of language models and extend them by integrating methods for reflection, tool use, planning, and collaboration. These methods transform the models from simple prompt-and-generate functions into reasoning engines that can tackle a wide variety of challenges.
- Digital Core — What is it? The digital core is the critical technological capability that can create and empower an organization's unique reinvention ambitions. Key to this is a composable architecture that emphasizes modularity and interoperability. Composability relies on independent, self-contained components that can be connected to build high-level functions and applications. They can come from internal systems, PaaS and SaaS providers, and other external parties. But in all cases, these components need to operate independently, be trusted and verifiable, and be discoverable and usable by composers. Being prepared for reinvention is crucial in an AI-first future. I recently shared my thoughts on Accenture's research on architecture and the mindset needed to adapt a strong digital core.
- Generative UI — What is it? Generative UI is the concept of leveraging AI techniques to generate user interfaces, commonly with the goal of offering a personalized experience. The long-term vision is a system that dynamically generates interfaces in real time, based on an individual's context and needs. This is not just about populating a predefined layout with user-specific content but completely changing the structure, flow, and interaction methods. These capabilities can be applied to websites, applications, or even agentic systems, and could rely on text, voice, or other intuitive interactions.
Accenture notes that agentic AI systems will be most effective when connecting components across the enterprise, turning them into new sources of customer value, automated workflows, or more. The Binary Big Bang is still just beginning, and we don't know the exact end state of all that will change — but the disruption is upon us, and enterprises need to start building tomorrow's strategy. But there is no denying the agentic AI revolution is here now.
Also: One third of consumers would prefer working with AI agents for faster service
In a machine-led economy, relational intelligence is key to success. AI agents will change work forever. To embrace that change, business leaders must focus on what matters most — designing and cultivating healthy and sustainable relationships.
How to prepare for the new technology paradigm
Accenture recommends the following for businesses to prepare for the new technology paradigm of abundance, abstraction, and autonomy:
- Define your new digital ecosystem (early adopters)
- Identify the highest value opportunities in tomorrow's technology landscape (early adopters)
- Map out ecosystem partners' agentic offerings (preparing to start)
- Start experimenting with agents internally (preparing to start)
- Prepare your digital core for agents (a slower approach)
- Watch signals to predict upcoming industry impacts (a slower approach)
- To preserve trust: Monitor autonomous systems' activity and train autonomous systems to make good decisions
Also: 25% of enterprises using AI will deploy AI agents by 2025
In summary, Accenture emphasizes the following key takeaways:
- Leaders must prepare today for an imminent world in which AI is everywhere and acting autonomously on behalf of people.
- New autonomy for AI also means new autonomy for systems and people, along with a refined relationship with trust.
- Opportunities will be lost unless business leaders secure enough trust from employees and consumers to engage with AI's unprecedented capabilities.
To learn more about the Accenture Technology Vision 2025, visit here.
Quantum computing’s status and near-term prospects (Part II)
Image by Gerd Altmann from Pixabay
Why today’s pre-quantum encryption now faces a Y2K scenario
I had the opportunity to attend the Quantum to Business (Q2B) 2024 Silicon Valley event in December 2024, courtesy of Allie Kuopus, one of the organizers.This is the second post in a series on my main takeaways from the event.
Time to start migrating to standard post-quantum cryptography
One of the talks I didn’t have a chance to attend at Q2B 2024 was Konstantinos Karagiannis’s presentation “NIST PQC is Here: Why It’s Secure and What Comes Next.” Karagiannis is Director, Quantum Computing Services at Protiviti, a global consultancy headquartered here in the San Francisco Bay Area. He was on the Q2B agenda to provide an overview of post-quantum cryptography (PQC) and related strategy.
Fortunately, Protiviti had posted an interview in mid-October 2024 with Karagiannis that also touched on the PQC topic. The interview, conducted by editor-in-chief Joe Kornick of VISION by Protiviti, focused on a recent claim that the Chinese had successfully cracked military-grade encryption. Kornick asked bluntly, “So is the end of encryption here early? Has Q-Day arrived?”
“The short answer is no, which is good,” Karagiannis answered. As he described it, a good translation of the Chinese paper was not available. Only the abstract was in English. Machine translations that were available had “holes in them”, inhibiting the ability to reproduce the results of the paper.
Karagiannis didn’t accept the claims in the paper at face value, but he didn’t dismiss them entirely either. Interestingly, his privacy protection through renewed encryption advice remains the same regardless. In either case, it’s time to act to ensure the continuation of effective encryption by starting to adopt appropriate post-quantum cryptography. “Let’s say the claims are true. Even if the claims are true, it doesn’t spell the end of encryption.”
The fact he pointed out is that the US National Institute for Standards and Technology (NIST) published new post-quantum encryption Federal Information Processing Standards (FIPS) 203, 204, and 205 in August 2024. Every device that currently uses public key cryptography will eventually need to discard the older, pre-quantum encryption in pervasive use that’s vulnerable to factoring attacks.
Adam Zewe in MIT News in August 2024 described the challenge organizations face if they stay with the older encryption methods this way: “Quantum computers promise to rapidly crack complex cryptographic systems that a classical computer might never be able to unravel. This promise is based on a quantum factoring algorithm proposed in 1994 by Peter Shor, who is now a professor at MIT.
“But while researchers have taken great strides in the last 30 years, scientists have yet to build a quantum computer powerful enough to run Shor’s algorithm.”
Events such as Q2B 2024 persuade me that the day scientists do manage to run Shor’s algorithm -– the “Q-Day” that Kornick mentioned -– is getting closer. Maybe it’ll be in five years, maybe less. Organizations will have to move to the new standard before Q-day, just as they had to make sure to be Y2K-compliant (by upgrading older systems that couldn’t manage dates with more than two digits in the year field) before January 1, 2000 arrived.
Those attacks aren’t yet imminent, says Karagiannis, which means organizations have the time now to make the shift methodically.
Karagiannis provided some welcome reassurances about the new NIST standards. “This approach that was published in the Chinese paper can’t touch the new NIST post-quantum cryptographic standards that were released on August 13, 2024. The lattice-based approach in there is safe from this type of attack and safe from Shor’s algorithm–the quantum attack we were all worried about. So really the best thing you could be doing right now is starting the migration plans to PQC.
“It’s time to start taking inventory, start looking at what cryptographies you have in place, start looking at which critical assets you might want to protect first,” Karagiannis concluded. “Because migrating to new cryptography takes time, and it’s tricky. This paper will not threaten PQC, so why not start now?”
Ways AI and trusted knowledge graph technology can help with post-quantum encryption migration
One of the good things about the era of hybrid or neurosymbolic AI (NSAI)-enabled automation (statistical neural net plus symbolic AI plus a fresh crop of agents and an expanded range of interaction capability) is that it promises to lighten the burden of major migration challenges such as those involving post-quantum encryption.
Software development firm RTS Labs in a November 2024 post at the firm’s website, for example, points out that AI can automate key generation, distribution, and monitoring processes, reducing the risk of human error.
More broadly, the use of NSAI rather than statistical and agentic AI methods alone implies a much bigger opportunity for scaling and boosting the uniformity of encryption management across supply chains.
I have more questions than answers here. For instance, what’s the inventory management and documentation and key management needed to prove and verify compliance? Should there be a meta-layer of agent-oriented compliance management for reasons of scale economies, visibility and assurance? How can we ensure that hostile actors won’t invade systems in the middle of upgrades?
Just more things to ponder. Every challenge here becomes an opportunity for hybrid AI-enabled systems that are only getting started. We do want these systems to be trustworthy, a challenge requiring lots of human oversight, vision and proactive leadership ability.
Bengaluru-based BrainSightAI Secures $5 Million to Transform Brain Care
Bengaluru-based BrainSightAI, a deep-tech neuroscience startup, has raised $5 million in a pre-Series A funding round. The funding was led by IAN Alpha Fund, with participation from IvyCap Ventures, Silver Needle, and existing investors.
Through its neuroinformatics platform, VoxelBox, BrainSightAI is working to transform the diagnosis and treatment of brain disorders. Currently working with over 40 leading hospitals in India, BrainSightAI focuses on neurosurgery and radiology, particularly brain tumour cases.
The startup is now expanding its scope to include neurological and psychiatric disorders, aiming for a comprehensive approach to brain care. The new funding will support its growth across tier 1 and tier 2 cities in India. It will also boost its efforts to secure FDA certification for entry into the US and allied markets and exploratory opportunities in Africa and Southeast Asia.
Solving Brain Disorders
Additionally, BrainSightAI plans to invest in the research and development of a caregiver-focused app designed to empower families of patients with brain disorders.
The health-tech startup was founded in 2019 by CEO Laina Emmanuel and CTO Rimjhim Agrawal. Emmanuel, an ISB graduate with two decades of experience in healthcare and technology, and Agrawal, a NIMHANS PhD specialising in machine learning for neurological disorders, envisioned BrainSightAI as a platform to democratise advanced brain care.
“We are excited about the potential of connectomics to advance personalised brain care for patients and, in the long run, for everybody,” said Emmanuel.
In a previous interview with AIM, Emmanuel said that VoxelBox has already found many applications. “Today, we work with hospitals for brain tumour surgeries. Before conducting surgery, doctors want to know where the different functions of the brain are located. If doctors know, with relation to the tumour, where these networks are, they can actually do a better job of figuring out how to cut the brain,” said Emmanuel.
BrainSightAI combines AI and neuroscience to map neural connections, enabling precise diagnosis and prognosis of neuro-oncological and neuropsychiatric disorders. Traditionally exclusive to institutions like Harvard and Stanford, this technology is now accessible to hospitals and patients worldwide, reinforcing BrainSightAI’s commitment to delivering impactful healthcare solutions.
At AIM’s MLDS conference last year, Agrawal presented a paper on ML-powered Neurotech in Healthcare. You may watch it here.
The post Bengaluru-based BrainSightAI Secures $5 Million to Transform Brain Care appeared first on Analytics India Magazine.
India Launches Consultation to Regulate AI
India has launched a public consultation to undertake the development of an AI for India-Specific Regulatory Framework to advance its efforts to regulate AI. A multi-stakeholder Advisory Group led by India’s principal scientific advisor (PSA) has been tasked with shaping the framework.
A subcommittee of the group on AI governance and guidelines development, constituted to provide actionable recommendations for AI governance in India, focuses on examining key issues related to AI governance in India, conducting a gap analysis of existing frameworks, and proposing recommendations for a comprehensive approach to build a trustworthy and accountable AI ecosystem.
The report highlights gaps in existing governance frameworks and proposes a coordinated, government-wide approach to enforce compliance while fostering innovation. “The aim is to ensure that governance mechanisms reflect India’s aspirations,” the central government’s ministry of electronics and IT (MeitY) said.
Copyright Issues with AI-Generated Works
One key issue highlighted in the AI governance discussions is whether AI-generated works qualify for copyright protection. Current laws require “human authorship”, which leaves uncertainty about whether human input, such as writing prompts or algorithms, is enough to establish copyright ownership.
Big tech companies like Microsoft and GitHub have faced copyright challenges related to the datasets used to train AI models. Lawsuits worldwide are questioning whether generative AI tools, including ChatGPT, have violated copyright laws. A US court recently ruled that AI-generated artwork cannot be copyrighted.
“At present, works exclusively created by AI, even if they stem from a human-written text prompt, are not copyright protected,” the report noted. These AI systems are not legally considered authors, and their outputs are based on human-created material.
Experts are urging authorities, including the copyright office and the ministry of commerce and industry, to issue clear guidelines on whether and to what extent AI-generated works can be copyrighted. They propose technical and legal measures, such as tracing the use of copyrighted data in training AI models to ensure fair practices.
Real-World Cases and Public Concerns
The lack of clarity is already leading to disputes. For instance, a law student recently filed a petition against Haryana’s OP Jindal Global University after accusing the institute of penalising him for allegedly using AI to answer an exam.
This underscores the urgent need for a strong legal framework. Establishing clear policies could help India balance innovation with accountability.
The post India Launches Consultation to Regulate AI appeared first on Analytics India Magazine.
AI transformation is a double-edged sword. Here’s how to avoid the risks
A new year means a fresh focus on IT investments. Tech analyst Gartner predicts worldwide IT spending will hit $5.74 trillion in 2025, an increase of 9.3% from 2024. The analyst says explorations into generative AI (Gen AI) will help drive this rise.
How to use ChatGPT to write: Resumes | Excel formulas | Essays | Cover letters
Most of us will have dabbled with Gen AI by now. Whether polishing text, creating photos, or generating code, the technology's capabilities can feel like magic.
You got to prove your hypothesis
However, James Fleming, CIO at the Francis Crick Institute, is one digital leader who isn't letting his organization get carried away by the hype.
He told ZDNET that using emerging technologies to power revolutionary scientific discoveries isn't straightforward. This challenge means the rise of Gen AI has not led to a major shift in working methods at his world-leading research organization.
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"Doing scientific AI, as opposed to creating publicly available large language models, is quite a different discipline. You're operating in a tightly bounded scientific world where you've got to prove your hypothesis," he said.
"It's not good enough to say that you've thrown a model out there that's almost right. Most of the time, it's got to be exactly right under certain conditions. And you've got to demonstrate that you're basing your work on a fundamental understanding."
Fleming described the use of AI in science as a "double-edged sword." While emerging technology can help speed up the research process, any new conclusions must be generated and presented with a high degree of certainty.
"Provability is critical, particularly if you're thinking about something with a pathway to real-world impact in a clinic or as a medical device," said Fleming.
"If you're putting an innovation in front of a clinician and saying, 'I think this tool can predict the evolution of cancer,' for example, they're going to say, 'Can it? Show me why.'"
Also: 5 ways AI is changing baseball — and big data is up at bat
However, while explainability is critical to scientific research, it's a focus at odds with the black-box working methods and hallucinations of many popular AI models.
Use an iterative approach
So, to demystify emerging technology processes, Fleming said the Crick uses an iterative approach to help its researchers embrace AI models confidently.
"You've got to work slowly and incrementally towards the goal," he said. "We take a much more focused approach that builds provenance and trust from day one."
The Crick's incremental approach helps researchers deploy AI models in two ways.
First, enhancing existing scientific methodologies. Fleming said the institute started its work here five years ago in microscopy.
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The Crick's microscopy facility analyzes cryogenic electrons and produces incredibly dense and beautiful images of tissues, cells, and individual molecules.
However, producing a nice picture is just the starting point. The image must be turned into data, such as details that can help show the differences between a cancerous cell and one that isn't.
Fleming's team has worked iteratively to prove that the right models can produce game-changing research results faster.
"AI models can do a lot of the grunt work for you, such as feature analysis and extraction and turning an image into data that you can work with to derive understanding," he said.
Also: How your business can best exploit AI: Tell your board these 4 things
The second area where Crick uses AI is in a discovery context, but a tightly bounded one.
Fleming gave the example of one lab's work on Parkinson's. The research team created a classifier that could identify which patients had the disease in a population of stem cells. However, the researchers couldn't explain why — so they worked backward, iteratively.
"Having trained the model, there was then a process of reverse interrogating that model with various statistical methods to say, 'Actually, the dominant thing is the ellipticity of the cell. They're more oval. And there was also a whole set of other features that the model then extracted.'"
He said these results in isolation aren't the answer, but they do prompt the next line of inquiry: "'OK, cell morphology is different. Why is that? What's our next round of experimentation?' And that's where the iterative piece starts to come in."
Also: 4 ways to turn generative AI experiments into real business value
Test and hone
Fleming said the Crick's careful process of incrementally testing and honing AI technologies is now leading to a more sophisticated approach where multiple AIs are brought together to power trusted research programs.
The biggest project is led by Samra Turaljic, whose Cancer Dynamics Laboratory focuses on understanding how kidney cancer evolves.
The team uses AI to predict the genomic evolution of a tumor from pathology images. Fleming said that effort has involved training multiple AIs and cross-training models with genomic databases that span 10 years of research.
"The result is you create something that can clinically predict the evolution of the kidney," he said.
"But in each of those processes, you're both meticulously building up a sub-component to the point that you can trust it, and you're also working through layers and layers of data and getting closer and closer to real-world population scale as you work."
Also: The future of computing must be more sustainable, even as AI demand fuels energy use
Fleming said this super-detailed approach is important because results can be affected by biological and acquisitional variations, such as different intensities of a stain on a pathology slide.
This stage-by-stage process proves that the key to unlocking faster results from AI in the long term is working slowly and methodically in the shorter term.
That's a perspective we can all learn from in an age of AI, where vendor hyperbole suggests brilliant solutions to intractable challenges are just a click away.
"We start with a small data set, understand it, get it predictive and working, and bring in more data," said Fleming.
"Then, when we add more data, we hone the model again. This iterative process is critical because if you don't do it, you don't build the understanding and the provenance."
Artificial Intelligence
OpenAI CEO Sam Altman Accused of Sexual Abuse by Sister Ann
OpenAI CEO Sam Altman has been accused of sexual abuse by his sister Ann Altman. Ann has filed a lawsuit alleging that she has faced abuse for nearly a decade, between 1997 and 2006. According to reports, the lawsuit is requesting a jury trial and damages in excess of $75,000.
Ann claimed the abuse, which allegedly includes rape, began when she was three, and Sam was 12 and that the last instance allegedly took place when Sam was an adult and she was still a minor.
The lawsuit also claimed that “as a direct and proximate result of the foregoing acts of sexual assault”, the plaintiff has experienced “severe emotional distress, mental anguish, and depression, which is expected to continue into the future”.
Sam, along with his brothers Max and Jack and his mother Connie, took to X to issue a statement. “All of these claims are utterly untrue. This situation causes immense pain to our entire family,” it stated. “It is especially gut-wrenching when she refuses conventional treatment and lashes out at family members who are genuinely trying to help.”
The family also claimed that they were “concerned about Ann’s mental health”, and despite providing the support, she has continued to demand more money. “In this vein, Annie [Ann] has made deeply hurtful and entirely untrue claims about our family, and especially Sam,” the statement read.
Meanwhile, as revealed by Sam, Ann’s lawyer wrote to his counsel, “Given our client’s tort claims involve sexual assault and sexual battery, we are allowed to seek punitive damages in this case under Missouri law. This means we will be allowed to seek discovery on Sam Altman’s net worth and present Sam Altman’s net worth to the jury for consideration on a punitive damages award.”
While Ann has accused Sam and her brother Jack over the years, this is the first time she seems to have taken the legal route. A few years ago, she said on X, “I experienced sexual, physical, emotional, verbal, financial, and technological abuse from my biological siblings, mostly Sam Altman and some from Jack Altman.”
“I feel strongly that others have also been abused by these perpetrators. I’m seeking people to join me in pursuing legal justice, safety for others in the future, and group healing,” Ann had written in an X post in 2021.
She also alleged numerous forms of abuse, including sexual, physical, emotional, verbal, financial, pharmacological, technological, and psychological.
According to the family statement, Ann previously accused them of “improperly withholding her father’s 401k funds, hacking her wifi, and ‘shadowbanning’ her from various websites, including ChatGPT and X”.
In June last year, a leading media publication reached out to the family in a podcast series. “Navigating the balance between providing support without enabling self-destructive behaviour for a family member with mental health struggles is extraordinarily difficult. We only want the best for Annie and hope everyone will treat her with compassion,” Connie had said.
The post OpenAI CEO Sam Altman Accused of Sexual Abuse by Sister Ann appeared first on Analytics India Magazine.
India’s Thriving GCCs Drive Innovation and Growth for MNCs
What began as a cost-cutting initiative in the early 90s for India’s Global Capability Centers (GCCs), has now evolved into a thriving business that drives innovation and growth for multinational corporations (MNCs). Once focused on routine tasks like business process outsourcing (BPO) and IT support, India’s GCCs have evolved into innovation hubs that influence strategy and fuel business growth for global corporations.
India’s flourishing GCC sector currently employs over 1.6 million professionals, and the growth is projected to continue. Ernst & Young predicts that by 2030, there will be over 2,400 GCCs generating jobs for about 4.5 million Indians.
The market size is expected to surge to $110 billion by 2030, up from the current $45 billion. These GCCs will not only thrive in established centers like Bengaluru and
Hyderabad but are likely to spread to Tier-II & Tier-III cities, driving innovation from every corner of the country.
The Evolution of GCCs in India
The story of GCCs in India began with MNCs recognizing the talent pool and cost advantages of offshore operations in the 1990s and mid-2000s. These early centers were focused on back-office work and IT services, laying the foundation for global businesses to tap into India’s skilled workforce while saving on operational expenses. As India’s tech landscape matured, so did the GCCs. By the late 2010s, these centers had ventured into higher-value services, such as advanced IT support, process optimization and more, no longer content with basic support.
This shift marked the dawn of full-scale GCCs, which were now seen as crucial to driving strategic business growth. India’s booming IT sector became its backbone, transforming these centers from quiet back offices to vital extensions of global companies.
The past decade and a half has seen GCCs successfully take over an uncharted territory. Today, they function as ‘Centers of Excellence’ (COEs), integrating the latest technologies, such as artificial intelligence (AI), machine learning and cloud computing. These centers aren’t just supporting anymore—they’re innovating.
For example, Carrier Digital Hub India, based in the tech hubs of Bengaluru and Hyderabad, is leading innovation in deep tech. The hub leverages AI, machine learning, big data, cybersecurity, and IoT to create intelligent climate solutions.
Kamal Sharma, Senior Director of Global Digital Connected Hubs at Carrier, emphasized India’s growing prominence, stating, “As the GCC landscape continues to evolve and expand, India’s position as the leading digital enabler of world business will only grow stronger.” Sharma was recently recognized by AIM Research as one of the Top 25 GCC Heads, India 2024.
Carrier Digital Hub India, renowned for its technological excellence, attracts top diverse talent from premier institutes across India. In 2024, the hub was recognized as a Best Place to Work for Data Scientists by AIM and acknowledged as one of the most impactful Global Capability Centers in India by Deloitte and Economic Times. The examples of Indian GCCs adding value are countless, spanning sectors like healthcare, finance and manufacturing. GCCs promote a ‘2-in-a-box’ leadership model, where leaders from India collaborate closely with colleagues across the business. This blend of expertise empowers GCCs to deliver advanced digital capabilities that operations.
In the healthcare sector, Indian GCCs are currently leading efforts in drug discovery and development by harnessing computational chemistry, bioinformatics and AI-driven platforms. These centers are accelerating the R&D of new drugs and bringing them to market faster than ever before.
Balasubramanian Sankaranarayanan, CEO and President of Thryve Digital LLP, explains that establishing a GCC in a country like India presents an immediate 20% cost differential compared to working with service providers.
Beyond healthcare, GCCs are also bringing valuable advancements to the insurance industry, with data-driven models, personalized products, and AI-powered solutions.
“More than 50% of our time is saved using some of the advanced predictive models developed by our global capability team,” said Prawal Kalita, Managing Director at Marsh.
India’s Secret Weapon: Talent Powerhouse for the Future
If there’s one thing that powers GCCs in India, it’s our talent pool. With over 2.15 million STEM graduates every year and a tech demand-supply gap of only 25% (one of the lowest globally), India is a tech leader in the making. Nearly half of these graduates are now primed for AI-ML roles, but demand for skilled professionals has never been higher, with GCCs often surpassing traditional IT firms when it comes to hiring.
The first quarter of FY25 alone saw a 46% increase in GCC hiring, underscoring continued growth. GCCs are not only hiring faster but also achieving greater gender diversity—women’s representation rose to 30% in 2023, up from 26.6% in 2020.
As new GCCs spring up in India, it’s essential that they understand the nation, its diverse cultural fabric, languages and customs. “Culture sensitization within GCCs should start from the top and become part of the strategy, not an afterthought,” said Avinash Samrit, country head, India, at Clean Harbors.
With a growing number of Indians employed at GCCs, flexible work hours and hybrid work models have become essential in today’s landscape.
“The first question we usually get asked is, ‘How many days are you expecting to be in the office?’” said Mansee Singhal, partner at Mercer. She added that flexibility is fundamental to the employment equation these days and its absence can significantly impact a company’s overall value proposition.
Indian GCCs place a great emphasis on developing AI capabilities and leading AI transformation within their organizations. Some of the most in-demand tech roles at GCCs include software engineers, developers, data specialists, AI and machine learning experts, cloud computing professionals and cybersecurity experts.
Once humble cost centers, Indian GCCs have now become vital players on the global stage. They are poised to continue fueling digital transformation, innovation and growth, carving a niche for India as a global tech leader. With each passing year, these centers strengthen their roles as strategic enablers, not just supporting but shaping the future of international business.
The post India’s Thriving GCCs Drive Innovation and Growth for MNCs appeared first on Analytics India Magazine.
Nvidia Project Digits: A Linux-powered desktop for AI developers
Believe it or not, that tiny little box below the display will be the world's most powerful desktop computer.
What's making the headlines at CES 2025 are AI-powered TVs, new smart home gadgets, and fresh laptop releases. For my money, though, the big news from CES is Nvidia Project Digits. This revolutionary desktop AI supercomputer is designed to bring unprecedented computing power to artificial intelligence (AI) developers, researchers, and students. And, by the way, it will be running Nvidia's DGX OS, a customized Ubuntu Linux 22.04 distro.
As you might guess, DGX OS is a Linux distro designed with system-specific optimizations and configurations, drivers, and diagnostic and monitoring tools to provide a fully supported version of Linux for running AI, machine learning, and analytics applications on Nvidia DGX Supercomputers.
Also: The best tech we've seen at CES so far
On top of that, Nvidia will also provide AI software development kits; orchestration tools; Nvidia NGC catalog frameworks and models; the Nvidia NeMo framework for fine-tuning models; and Nvidia Rapids libraries for data science acceleration. Of course, as an Ubuntu Linux system at heart, you can run Ubuntu and Linux software. So, if you want to, you can play Doom on it. That's probably the first I'll do if I get my hands on one of these devices.
Now, in a box that appears to be about the size of a Mac mini, Nvidia's Project Digits PC promises to bring a petaflop of AI performance to your desktop. A petaflop, for those of you who don't hang out in supercomputer land, is equal to 1,000,000,000,000,000 (one quadrillion) floating-point operations per second (flops).
Until 2008, when IBM rolled out its Roadrunner supercomputer, no one had ever hit that speed. Soon, you could have that level of computing performance in your home office. Even today, the latest Top 500 SuperComputer List has computers that squeak in with just over two petaflops. Mind you, these computers also need over a thousand cores to pull that trick off. Oh, and they all run Linux.
Project Digits will be almost Top 500-fast and is powered by Nvidia's band's new GB10 Grace Blackwell Superchip. With the Nvidia AI software stack preinstalled and 128GB of memory, developers can prototype, fine-tune, and infer large AI models of up to 200B parameters locally and then seamlessly deploy to the data center or cloud.
Also: The best robotics and AI tech of CES
The Grace Blackwell Superchip is named after Grace Hopper, the famed computer scientist, United States Navy rear admiral, and the "Mother of Cobol". In addition, the chip's name honors David Harold Blackwell, an American mathematician who made significant contributions to game theory, probability theory, information theory, and statistics, and was the first Black scholar to be inducted into the National Academy of Sciences.
In this superpowered chip, you'll find an ARM-based Grace CPU featuring 10 Cortex-X925 and 10 Cortex-A725 cores. That's 20 cores in all. The CPU is backed up by a Blackwell GPU equipped with Nvidia's latest CUDA and RT cores. The PC has 128 GBs of RAM and up to 4 TBs of flash storage to put all this chip firepower to good work.
Nvidia promises that AI developers will be able to work with models of up to 200 billion parameters on a single unit. For more demanding tasks, you can link two Project Digits machines together to handle models with up to 405 billion parameters.
Also: ZDNET joins CNET Group to award the Best of CES, and you can submit your entry now
Nvidia's CEO, Jensen Huang, emphasized Project Digits' transformative potential, stating: "Placing an AI supercomputer on the desks of every data scientist, AI researcher, and student empowers them to engage with and shape the age of AI."
With a starting price of $3,000, Project Digits aims to make high-performance AI development more accessible to a broader range of users, from small enterprises to schools. At that price point, I expect to see Project Digits desktops in homes.
This Linux-powered system, set to launch in May 2025, promises to reshape the landscape of AI development by offering data center-level performance in a compact, desk-friendly form factor. I, for one, will be doing my darndest to get my hands on one.
CES 2025
‘Microsoft’s AI Tools Help Maha Farmers Increase Yield by 20%’
Microsoft’s $3 billion commitment to expand the Azure infrastructure in India may have grabbed the limelight at the Microsoft AI Tour in Bengaluru; however, the company’s push to revolutionise sectors such as healthcare and agriculture also led to some key announcements by CEO Satya Nadella.
An AI agritech startup that has partnered with Microsoft is working to eliminate guesswork in farming and empower farmers with science-backed insights to make effective decisions.
Meet AgriPilot.ai
Maharashtra-based AI startup AgriPilot.ai has collaborated with Microsoft Research for over five years, integrating AI, satellite imagery, and other tools to transform the farming sector.
Identifying critical factors such as soil nutrient levels, water availability, and suitable weather conditions are some of the areas AgriPilot.ai specialises in to optimise crop yield and resource usage.
“We have started seeing the results. Satya took that in his showcase because these are proven models now,” Prashant Mishra, founder of Click2cloud Inc., which hosts the platform AgriPilot.ai, told AIM in an exclusive interaction. Mishra confirmed that experimentation has been done for more than 2,50,000 hectares of land around the world.
“This [AgriPilot.ai] is precisely for the marginalised farmer because Microsoft wanted to work with those with less than two acres of land. So, about a thousand farmers, with less land and resources, are currently benefitting from it,” said Mishra, emphasising that their goal is to prevent farmer suicides and distress by making them self-sustainable.
The startup has conducted experiments, such as cultivating sugarcane thrice the size of conventional crops. It claims that the yield has doubled. Akin to conventional farming, the startup has also enabled the farming of exotic vegetables such as strawberries and dragon fruits on local farms.
“Normally, five-star hotels import strawberries and dragon fruits from other countries, but with AI, we are able to grow them on local farms. Thanks to these exotic vegetables, the poor farmers are able to multiply their earnings, probably by 10 times or more,” he said.
Agripilot.ai has collaborated with the Agriculture Development Trust, Baramati, which claims that these new tools have increased crop production by 20%, as presented in the Microsoft keynote session.
AgriPilot employs a ‘no-touch’ approach, utilising satellite and drone imagery to gather farm data remotely. This allows them to provide detailed crop management plans, from pre-planting to harvesting, customised for farmers. This has all been made possible with the help of AI.
The Microsoft Bond
AgriPilot has built a strong partnership with Microsoft Research, leveraging its advanced tools and platforms to transform farming practices. Though Microsoft has not directly invested in AgriPilot.ai, the latter depends on it for critical technological support and open-source tools.
Nadella also met with the team at ADT Baramati, which uses AI tools to help farmers achieve healthy and sustainable harvests.
The startup integrates Microsoft Azure Data Manager and FarmBeatsto analyse soil health, monitor water availability, and optimise fertiliser usage through precise, data-driven insights.
Empowering Women
Besides, AgriPilot partners with Pratham, a non-profit organisation, to train farmers in using these advanced technologies. This collaboration supports farmer education and provides employment opportunities for women, enabling them to conduct AI-powered soil testing independently using on-site machines.
To ensure accessibility, instructions are provided in local languages, such as Marathi, Kannada, Hindi, Telugu, Tamil, Farsi, and Hebrew, translated through Microsoft Copilot. The collected data is then fed into AgriPilot’s platform, where AI models analyse it to deliver actionable results and outcomes for the farmers.
AgriPilot is also conducting experiments in countries like Qatar, Dubai, Peru, USA, Malaysia and India. “Baramati [Maharashtra] in India was the first [place where we experimented], and now we are doing the same in Uttar Pradesh with Microsoft,” he said.
Big Tech’s Agri Mission
Meanwhile, other big tech companies have also been actively involved in the agri space.Last year, Google announced the availability of its Agricultural Landscape Understanding (ALU) Research API, which integrates satellite imagery with AI to deliver farm-level insights.
The API is designed to support India’s agricultural sector by enabling data-driven decision-making, optimising farm management, and addressing productivity challenges.
Google recently partnered with the UP government to launch a Gemini-powered open network for farmers. This DPI for agriculture utilises Google’s DPI-in-a-box solution and the Beckn protocol.
Big tech is also powering agritech startups such as Bengaluru-based Cropin, which helps farmers make informed decisions based on historical, present and future data.
When asked about future collaborations with other companies, Mishra was clear that their focus is currently on Microsoft alone. This year, they will work towards improving accuracy before proceeding to full-scale expansion.
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