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Ola Partners with Lenovo to Build India’s Largest Supercomputer for AI, Develops 700B Krutrim 3 - Related to krutrim, 3, india’s, ai,, ola

Goodbye Gemini, hello Pixel Sense? What we know about Google's AI assistant for Pixel 10

Goodbye Gemini, hello Pixel Sense? What we know about Google's AI assistant for Pixel 10

As far back as 2023, Google was reportedly working on an AI assistant for Pixel phones called "Pixie." Many people expected to see that assistant debut with the Pixel 9, but we haven't really heard anything about that project since.

, Google is dropping a new context-aware assistant with the Pixel 10 -- Pixel Sense.

Also: Gemini Live just got much easier to talk to - here's how.

Android Authority says Pixel Sense will use information on your phone to provide a much more personal assistant experience. It will be able to pull information from a number of other apps, including Calendar, Chrome, Contacts, Docs, Files, Gmail, Keep Notes, Maps, Messages, Phone, Photos, Recorder, Screenshots, Wallet, YouTube Music, and YouTube.

The AI-powered assistant will run fully on-device, meaning you'll be able to use it offline and "not even Google can see" your data. It will be able to process media files, including text and images, and process screenshots (sounds like Pixel Screenshots). Pixel Sense will provide personal predictive suggestions, like suggesting places and names you use often, adapt to your interests, and learn how you use your phone to complete tasks faster.

At first, this sounds an awful lot like what Google seemingly wanted to do with Gemini (and what Google Now was supposed to do years ago). Google replaced Assistant with Gemini last fall, but now it seems it's already looking to replace it -- or at least some combination of Assistant, Gemini, and now Pixel Sense.

Also: 4 Pixel phone tricks every user should know - including my favorite.

I've been a Google Pixel devotee since the beginning, and I can't see myself switching up at this point, so I'm especially intrigued with what Google is doing here.

Everything running on-device is definitely interesting, and a big plus if you're concerned about privacy and security. It's something no other AI assistant is doing, and Google will stand out if they're able to pull it off.

The ability to work seamlessly across different apps is also a potentially huge addition. Most Google apps have Gemini integration, but Gemini can't pull information from one app to another yet. The ability to carry out complex, multimodal tasks -- say, "Give me directions to where I can buy those green shoes I took a screenshot of" -- is also a big plus, and something that will push Pixel Sense past other assistants.

Also: The best Google Pixel phones you can buy.

I feel like if Google can finally settle on an assistant, Pixel Sense has the opportunity to be a big selling point for Pixel phones (that is, if Google doesn't do what it did with Gemini and make it available to other phones).

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Opera unveils impressive preview of AI agentic browsing - see it in action

Opera unveils impressive preview of AI agentic browsing - see it in action

The team behind the Opera web browser has introduced a new AI agent called Browser Operator, capable of performing browsing tasks for customers. This new agentic browsing marks a paradigm shift that could mark the next evolution of web browsers.

Also: Which AI agent is the best? This new leaderboard can tell you.

, EVP at Opera, "For more than 30 years, the browser gave you access to the web, but it has never been able to get stuff done for you. Now it can. This is different from anything we've seen or shipped so far."

Kolondra continues, "The Browser Operator we're presenting today marks the first step toward shifting the role of the browser from a display engine to an application that is agentic and performs tasks for its clients."

The Browser Operator is designed to boost the user's efficiency. It accomplishes this by allowing the user to explain what they need to do in natural language, and the browser will then perform the necessary tasks.

Also: Crawl, then walk, before you run with AI agents, experts recommend.

For example, you could ask Browser Operator to buy you a pair of pink running shoes from Nike in a size [website] The Browser Operator will then perform the task. As Browser Operator performs the task, the user can see what's happening at any point in the process, so they are in control the whole time.

Essentially, Opera is turning the browser into more of a user-focused ecosystem that uses native client-side solutions to complete tasks while protecting user privacy.

Also: How businesses are accelerating time to agentic AI value.

The Browser Operator runs natively inside the browser and uses the DOM Tree and browser layout data to get context. , that makes the solution faster because the browser doesn't need to "see" and understand what's on the screen from its pixels or navigate with a mouse pointer. Browser Operator can access an entire page at once, without the need to scroll, which means it reduces overhead and time required. And because it all happens natively within the browser, the Browser Operator doesn't require a virtual machine or cloud server.

Browser Operator is currently in feature preview status and should become available soon as part of the Opera Feature Drop program. I checked my up-to-date version of Opera Developer, and it has yet to hit, but I'll keep my eyes open and write up a how-to as soon as it's available.

You can watch Browser Operator in action on the official Opera YouTube channel. Expect Browser Operator to appear in a Feature Drop in the near future.

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Ola Partners with Lenovo to Build India’s Largest Supercomputer for AI, Develops 700B Krutrim 3

Ola Partners with Lenovo to Build India’s Largest Supercomputer for AI, Develops 700B Krutrim 3

Ola is making a significant push into AI by developing Krutrim 3, a 700-billion parameter AI model, in partnership with Lenovo, the organization unveiled at Lenovo Tech World’25 India Edition in Mumbai.

The initiative includes building India’s largest supercomputer to support Ola’s AI ecosystem and advance sovereign cloud and hyperscale infrastructure.

“We are very proud to say that we are working on a bigger model, Krutrim 3, which will be a 700 billion parameter model, and will be our answer from India to show that we can build the best,” noted Navendu Agarwal, CIO of Ola Electric, in a conversation with Mathew Zielinski, President, International Markets, Lenovo. “We are very proud to partner with Lenovo in this endeavour.”.

Ola’s AI ambitions stem from its success in electric vehicles and mobility, with the firm now viewing AI as a critical pillar of India’s digital future. The firm has over 700 AI professionals working on a full-stack approach, positioning itself as one of the few Indian firms to build foundation models from scratch.

The business began its AI journey in late 2023 with Krutrim 1, a 7-billion parameter model, followed by Krutrim 2, a 12-billion parameter model, both of which were open-sourced. With Krutrim 3, Ola aims to compete on a global scale.

A key component of Ola’s AI strategy is robust infrastructure, which is driving its collaboration with Lenovo. “We are building the infrastructure, and we will again speak more about it,” Agarwal stated, adding that Ola’s foundation model layer will support a wide range of AI applications, including vision models, language models, and indigenous LLMs.

He pointed out that AI adoption in India remains in its early stages, with only [website] of the country’s GDP directed toward the software industry, compared to 1% in the [website] “That is inhibiting us from achieving the vision of a $21 trillion economy,” he noted.

Sovereign Cloud and AI-First Infrastructure.

Beyond AI model development, Ola is working toward establishing India’s own sovereign cloud and hyperscale AI infrastructure, reducing dependency on Western cloud providers. “India doesn’t have its own hyperscale. Everything is Western. We don’t have our own sovereign cloud, and that’s where Ola is working,” Agarwal stated.

The supercomputer, developed with Lenovo, will power Ola’s AI-first cloud, serving not only Ola’s AI ecosystem but also startups and enterprises looking to build AI-powered applications.

“We will make it state-of-the-art. We will make it green. We will make it liquid cooling-based,” Agarwal emphasised, highlighting the focus on cost-effective, sustainable technology.

Agarwal stressed that achieving India’s AI potential requires strong collaboration. “This is a dream that not one business can achieve alone. Ola is doing its bit, stretching ourselves, and building the infrastructure, but we need strong partners like Lenovo,” he mentioned.

With Krutrim 3, Ola is making a bold move in AI innovation, positioning itself at the forefront of India’s AI revolution.

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Market Impact Analysis

Market Growth Trend

2018201920202021202220232024
23.1%27.8%29.2%32.4%34.2%35.2%35.6%
23.1%27.8%29.2%32.4%34.2%35.2%35.6% 2018201920202021202220232024

Quarterly Growth Rate

Q1 2024 Q2 2024 Q3 2024 Q4 2024
32.5% 34.8% 36.2% 35.6%
32.5% Q1 34.8% Q2 36.2% Q3 35.6% Q4

Market Segments and Growth Drivers

Segment Market Share Growth Rate
Machine Learning29%38.4%
Computer Vision18%35.7%
Natural Language Processing24%41.5%
Robotics15%22.3%
Other AI Technologies14%31.8%
Machine Learning29.0%Computer Vision18.0%Natural Language Processing24.0%Robotics15.0%Other AI Technologies14.0%

Technology Maturity Curve

Different technologies within the ecosystem are at varying stages of maturity:

Innovation Trigger Peak of Inflated Expectations Trough of Disillusionment Slope of Enlightenment Plateau of Productivity AI/ML Blockchain VR/AR Cloud Mobile

Competitive Landscape Analysis

Company Market Share
Google AI18.3%
Microsoft AI15.7%
IBM Watson11.2%
Amazon AI9.8%
OpenAI8.4%

Future Outlook and Predictions

The Pixel Goodbye Gemini landscape is evolving rapidly, driven by technological advancements, changing threat vectors, and shifting business requirements. Based on current trends and expert analyses, we can anticipate several significant developments across different time horizons:

Year-by-Year Technology Evolution

Based on current trajectory and expert analyses, we can project the following development timeline:

2024Early adopters begin implementing specialized solutions with measurable results
2025Industry standards emerging to facilitate broader adoption and integration
2026Mainstream adoption begins as technical barriers are addressed
2027Integration with adjacent technologies creates new capabilities
2028Business models transform as capabilities mature
2029Technology becomes embedded in core infrastructure and processes
2030New paradigms emerge as the technology reaches full maturity

Technology Maturity Curve

Different technologies within the ecosystem are at varying stages of maturity, influencing adoption timelines and investment priorities:

Time / Development Stage Adoption / Maturity Innovation Early Adoption Growth Maturity Decline/Legacy Emerging Tech Current Focus Established Tech Mature Solutions (Interactive diagram available in full report)

Innovation Trigger

  • Generative AI for specialized domains
  • Blockchain for supply chain verification

Peak of Inflated Expectations

  • Digital twins for business processes
  • Quantum-resistant cryptography

Trough of Disillusionment

  • Consumer AR/VR applications
  • General-purpose blockchain

Slope of Enlightenment

  • AI-driven analytics
  • Edge computing

Plateau of Productivity

  • Cloud infrastructure
  • Mobile applications

Technology Evolution Timeline

1-2 Years
  • Improved generative models
  • specialized AI applications
3-5 Years
  • AI-human collaboration systems
  • multimodal AI platforms
5+ Years
  • General AI capabilities
  • AI-driven scientific breakthroughs

Expert Perspectives

Leading experts in the ai tech sector provide diverse perspectives on how the landscape will evolve over the coming years:

"The next frontier is AI systems that can reason across modalities and domains with minimal human guidance."

— AI Researcher

"Organizations that develop effective AI governance frameworks will gain competitive advantage."

— Industry Analyst

"The AI talent gap remains a critical barrier to implementation for most enterprises."

— Chief AI Officer

Areas of Expert Consensus

  • Acceleration of Innovation: The pace of technological evolution will continue to increase
  • Practical Integration: Focus will shift from proof-of-concept to operational deployment
  • Human-Technology Partnership: Most effective implementations will optimize human-machine collaboration
  • Regulatory Influence: Regulatory frameworks will increasingly shape technology development

Short-Term Outlook (1-2 Years)

In the immediate future, organizations will focus on implementing and optimizing currently available technologies to address pressing ai tech challenges:

  • Improved generative models
  • specialized AI applications
  • enhanced AI ethics frameworks

These developments will be characterized by incremental improvements to existing frameworks rather than revolutionary changes, with emphasis on practical deployment and measurable outcomes.

Mid-Term Outlook (3-5 Years)

As technologies mature and organizations adapt, more substantial transformations will emerge in how security is approached and implemented:

  • AI-human collaboration systems
  • multimodal AI platforms
  • democratized AI development

This period will see significant changes in security architecture and operational models, with increasing automation and integration between previously siloed security functions. Organizations will shift from reactive to proactive security postures.

Long-Term Outlook (5+ Years)

Looking further ahead, more fundamental shifts will reshape how cybersecurity is conceptualized and implemented across digital ecosystems:

  • General AI capabilities
  • AI-driven scientific breakthroughs
  • new computing paradigms

These long-term developments will likely require significant technical breakthroughs, new regulatory frameworks, and evolution in how organizations approach security as a fundamental business function rather than a technical discipline.

Key Risk Factors and Uncertainties

Several critical factors could significantly impact the trajectory of ai tech evolution:

Ethical concerns about AI decision-making
Data privacy regulations
Algorithm bias

Organizations should monitor these factors closely and develop contingency strategies to mitigate potential negative impacts on technology implementation timelines.

Alternative Future Scenarios

The evolution of technology can follow different paths depending on various factors including regulatory developments, investment trends, technological breakthroughs, and market adoption. We analyze three potential scenarios:

Optimistic Scenario

Responsible AI driving innovation while minimizing societal disruption

Key Drivers: Supportive regulatory environment, significant research breakthroughs, strong market incentives, and rapid user adoption.

Probability: 25-30%

Base Case Scenario

Incremental adoption with mixed societal impacts and ongoing ethical challenges

Key Drivers: Balanced regulatory approach, steady technological progress, and selective implementation based on clear ROI.

Probability: 50-60%

Conservative Scenario

Technical and ethical barriers creating significant implementation challenges

Key Drivers: Restrictive regulations, technical limitations, implementation challenges, and risk-averse organizational cultures.

Probability: 15-20%

Scenario Comparison Matrix

FactorOptimisticBase CaseConservative
Implementation TimelineAcceleratedSteadyDelayed
Market AdoptionWidespreadSelectiveLimited
Technology EvolutionRapidProgressiveIncremental
Regulatory EnvironmentSupportiveBalancedRestrictive
Business ImpactTransformativeSignificantModest

Transformational Impact

Redefinition of knowledge work, automation of creative processes. This evolution will necessitate significant changes in organizational structures, talent development, and strategic planning processes.

The convergence of multiple technological trends—including artificial intelligence, quantum computing, and ubiquitous connectivity—will create both unprecedented security challenges and innovative defensive capabilities.

Implementation Challenges

Ethical concerns, computing resource limitations, talent shortages. Organizations will need to develop comprehensive change management strategies to successfully navigate these transitions.

Regulatory uncertainty, particularly around emerging technologies like AI in security applications, will require flexible security architectures that can adapt to evolving compliance requirements.

Key Innovations to Watch

Multimodal learning, resource-efficient AI, transparent decision systems. Organizations should monitor these developments closely to maintain competitive advantages and effective security postures.

Strategic investments in research partnerships, technology pilots, and talent development will position forward-thinking organizations to leverage these innovations early in their development cycle.

Technical Glossary

Key technical terms and definitions to help understand the technologies discussed in this article.

Understanding the following technical concepts is essential for grasping the full implications of the security threats and defensive measures discussed in this article. These definitions provide context for both technical and non-technical readers.

Filter by difficulty:

large language model intermediate

algorithm

platform intermediate

interface Platforms provide standardized environments that reduce development complexity and enable ecosystem growth through shared functionality and integration capabilities.