Ford Business Solutions Expands in India with New Bengaluru Office - Related to new, built, billion, india, expands
Ford Business Solutions Expands in India with New Bengaluru Office
Ford Business Solutions (FBS), the technology and business services hub of Ford, presented on Thursday that it has expanded its presence in India with the launch of a new office in Bengaluru.
The new facility, an extension of its Global Capability Center (GCC) in Chennai, has a seating capacity of 350 employees and currently houses about 75 professionals. Over the next four years, FBS plans to hire 2,000 additional employees across India, further strengthening its workforce of 12,000 in the country.
“We predominantly opened the new office for high-tech, niche, and high-demand skills. We’re building more platforms that can support various digital capabilities – stuff around cybersecurity, DevSecOps, DevOps, and AI, all of which are in high demand,” noted Mike Amend, chief enterprise technology officer at Ford.
FBS has been operating in India for over 25 years, initially starting with accounting services before expanding into technology, product development, manufacturing engineering, supply chain management, finance, HR, and marketing. The team in India supports Ford’s global operations across North America, Europe, and other geographies.
The Chennai GCC remains Ford’s largest tech hub in India, with nearly 50% of the enterprise’s global enterprise tech team based there. Last year, FBS hired 1,050 employees in Chennai, primarily for enterprise technology roles. Currently, 40% of its workforce is engaged in tech-related functions, while another 30% focuses on product development and engineering, including manufacturing engineering.
With its latest expansion in Bengaluru, Ford Business Solutions aims to strengthen its digital and AI capabilities, further reinforcing India’s strategic role in the firm’s global growth.
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How Krutrim Built Chitrarth for a Billion Indians
India has been aiming to develop its frontier AI model to serve the country’s vast population in their native language. However, this approach has many problems, including the lack of digitised data in Indian languages and also the unavailability of the images on which the models need to be trained.
To further the effort of building AI for Bharat, Ola’s Krutrim AI Lab has introduced Chitrarth, a multimodal Vision-Language Model (VLM). By combining multilingual text in ten predominant Indian languages with visual data, Chitrarth aims to democratise AI accessibility for over a billion Indians.
Most AI-powered VLMs struggle with linguistic inclusivity, as they are predominantly built on English datasets. This is also why BharatGen, the multimodal AI initiative supported by the Department of Science and Technology (DST), in recent times launched its e-vikrAI VLM for the Indian e-commerce ecosystem.
Similarly, Chitrarth is designed to close this language gap by supporting Hindi, Bengali, Telugu, Tamil, Marathi, Gujarati, Kannada, Malayalam, Odia, and Assamese. The model was built using Krutrim’s multilingual LLM as its backbone, ensuring it understands and generates content in these languages with high accuracy.
, Chitrarth is built on Krutrim-7B and incorporates SIGLIP (siglip-so400m-patch14-384) as its vision encoder. Its architecture follows a two-stage training process: Adapter Pre-Training (PT) and Instruction Tuning (IT).
Pre-training is conducted using a dataset chosen for superior performance in initial experiments. The dataset is translated into multiple Indic languages using an open-source model, ensuring a balanced split between English and Indic languages.
This approach maintains linguistic diversity, computational efficiency, and fairness in performance across languages. Fine-tuning is performed on an instruction dataset, enhancing the model’s ability to handle multimodal reasoning tasks.
The dataset includes a vision-language component containing academic tasks, in-house multilingual translations, and culturally significant images. The training data includes images representing prominent personalities, monuments, artwork, and cuisine, ensuring the model understands India’s diverse cultural heritage.
Chitrarth excels in tasks such as image captioning, visual question answering (VQA), and text-based image retrieval. The model is trained on multilingual image-text pairs, allowing it to interpret and describe images in multiple Indian languages.
This makes Chitrarth a game-changer for applications in education, accessibility, and digital content creation, enabling people to interact with AI in their native language without relying on English as an intermediary.
Like BharatGen, Chitrarth’s capabilities enable it to support various real-world applications, including e-commerce, UI/UX analysis, monitoring systems, and creative writing.
For example, automating product descriptions and attribute extraction for online retailers like Myntra, AJIO, and Nykaa is what the team is targeting as presented in the blog.
To evaluate Chitrarth’s performance across Indian languages, Krutrim developed BharatBench, a comprehensive benchmark suite designed for low-resource languages. BharatBench assesses VLMs on tasks such as VQA and image-text alignment, setting a new standard for multimodal AI in India.
Besides, Chitrarth has been evaluated against VLMs on academic multimodal tasks, consistently outperforming models like IDEFICS 2 (7B) and PALO 7B while maintaining competitive performance on TextVQA and VizWiz benchmarks.
Despite its advancements, Chitrarth faces challenges such as biases in automated translations and the availability of high-quality training data for Indic languages.
Earlier this month, Ola chief Bhavish Aggarwal presented Krutrim AI Lab and the launch of several open source AI models tailored to India’s unique linguistic and cultural landscape. In addition to Chitrarth, these include the launch of Dhwani, Vyakhyarth, and Krutrim Translate.
In partnership with NVIDIA, the lab will also deploy India’s first GB200 supercomputer by March, and plans to scale it into the nation’s largest supercomputer by the end of the year.
This infrastructure will support the training and deployment of AI models, addressing challenges related to data scarcity and cultural context. The lab has committed to investing ₹2,000 crore into Krutrim, with a pledge to increase this to ₹10,000 crore by next year.
In an interview to Outlook Business, an Ola executive stated they plan to release Krutrim’s third model on August 15. It is likely to be a Mixture of Experts model consisting of 700 billion parameters. The team also has ambitious plans to develop its own AI chip, Bodhi, by 2028.
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Netflix is Hiring for ML Scientist and ML Engineer
Netflix, one of the world’s leading entertainment services, is offering new remote job openings for a machine learning scientist and a machine learning engineer. These roles, part of the Content & Media ML Foundations team, aim to enhance content intelligence, personalisation, and advertising through machine learning.
The team builds foundational ML solutions embracing Netflix’s vast media data, driving advancements in multi-modal content understanding. They also explore generative AI for filmmaking and media intelligence to position Netflix at the forefront of AI-driven content creation and distribution.
Netflix is on the hunt for an ML scientist to pioneer innovations in multimodal representation learning. The role involves building state-of-the-art ML models for visual, audio, and textual data, optimising performance and scalability using PyTorch and Netflix’s ML infrastructure, engaging with the ML research community, and influencing strategic decisions.
Meanwhile, the enterprise is also hiring an ML engineer to develop scalable ML pipelines powering content intelligence. Key responsibilities include optimising large-scale ML models for media understanding, automating ML workflows for faster experimentation and deployment, and enhancing observability and monitoring to ensure model reliability.
Netflix seeks engineers with expertise in deep learning architectures, embedding methods, and distributed ML training. Candidates should have 5+ years of industry experience, particularly in NLP, audio, and video understanding.
During the Q3 2024 earnings interview, Netflix co-CEO Ted Sarandos, noted, “AI needs to pass a crucial test. Actually, can it help make improved presents and improved films? That is the test and that’s what they have to figure out.” He emphasised that for AI to be truly impactful, it must contribute to the quality of storytelling rather than simply reducing production costs.
Sarandos’ statement reinforces Netflix’s commitment to enhancing viewer experience and industry standards through technology.
“Netflix is the best platform for premium stories because we’re the home to the best storytellers. We have an enormous reach–600 million watchers. We assume the financial risk when we’re making your content,” stated Sarandos.
That’s not all. Netflix is doubling down on AI, not just in film and TV but also in gaming. The corporation has onboarded Mike Verdu as the VP of GenAI for Games at Netflix.
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Market Impact Analysis
Market Growth Trend
| 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 |
|---|---|---|---|---|---|---|
| 23.1% | 27.8% | 29.2% | 32.4% | 34.2% | 35.2% | 35.6% |
Quarterly Growth Rate
| Q1 2024 | Q2 2024 | Q3 2024 | Q4 2024 |
|---|---|---|---|
| 32.5% | 34.8% | 36.2% | 35.6% |
Market Segments and Growth Drivers
| Segment | Market Share | Growth Rate |
|---|---|---|
| Machine Learning | 29% | 38.4% |
| Computer Vision | 18% | 35.7% |
| Natural Language Processing | 24% | 41.5% |
| Robotics | 15% | 22.3% |
| Other AI Technologies | 14% | 31.8% |
Technology Maturity Curve
Different technologies within the ecosystem are at varying stages of maturity:
Competitive Landscape Analysis
| Company | Market Share |
|---|---|
| Google AI | 18.3% |
| Microsoft AI | 15.7% |
| IBM Watson | 11.2% |
| Amazon AI | 9.8% |
| OpenAI | 8.4% |
Future Outlook and Predictions
The Ford Business Solutions 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:
Technology Maturity Curve
Different technologies within the ecosystem are at varying stages of maturity, influencing adoption timelines and investment priorities:
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
- Improved generative models
- specialized AI applications
- AI-human collaboration systems
- multimodal AI platforms
- 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:
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
| Factor | Optimistic | Base Case | Conservative |
|---|---|---|---|
| Implementation Timeline | Accelerated | Steady | Delayed |
| Market Adoption | Widespread | Selective | Limited |
| Technology Evolution | Rapid | Progressive | Incremental |
| Regulatory Environment | Supportive | Balanced | Restrictive |
| Business Impact | Transformative | Significant | Modest |
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.
machine learning intermediate
platform intermediate
generative AI intermediate
deep learning intermediate
NLP intermediate
DevOps intermediate
scalability intermediate
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