Generative AI, a subfield of Machine Learning (ML)

The introduction of advanced GPU as a service (GPUaaS) by ESDS Software Solution is a massive advancement in the domain of AI services in India. This offering is anchored by cutting-edge Nvidia and AMD GPUs.

ESDS innovatively created a new specialized sovereign GPU cloud that empowers commercial enterprises, BFSI sector institutions, research organizations, and government agencies within the data-residency rules in India the ability to manage ultra-computing capabilities in order to facilitate large-scale deployment of AI, ML, and Generative AI.

ESDS has a vision to make enterprise-level GPU computing more widely available and is committed to accelerating AI development by implementing a unique strategy of deploying integrated systems in the DGX and HGX classes and combining them with various systems.

What is Generative AI?    

Generative AI, a subfield of Machine Learning (ML)    can generate new data, resembling a given training set. The underlying patterns and distributions in the data are learned, and the information is used to produce fresh, diverse data.

While there are many kinds of Generative AI models, the commonly used ones include the following:

Generative Adversarial Networks: These are Deep Learning models, comprising two neural networks, namely generator and discriminator, that compete with one another. The generator creates synthetic data that can misguide the discriminator, while the discriminator tries to identify the data as fake or real. This continues until the samples produced by the generator are indistinguishable from the real data. Generative Adversarial Networks have applications in image generation, natural language processing, video generation, and other areas. TensorFlow, an open-source software library for artificial intelligence, provides support for implementing Generative Adversarial Networks.

New Insights from Data Structuring × Advanced Analysis

For generative AI to deliver real business value, it must make full use of corporate data. Yet 90% of corporate data is unstructured, making it difficult to extract actionable insights without systematic organization. Contents include:

Market Size and Growth Projections (2025-2035): Detailed forecasts for AI chips, GPUs, CPUs, AI ASICs, DPUs, network ASICs, and crypto ASICs, covering both shipments and revenues

Key Technology Inflection Points: Analysis of next-generation node transitions, advanced packaging technologies, and memory system innovations

Strategic Market Drivers and Challenges: Deep dive into generative AI computing requirements, energy efficiency imperatives, and supply chain vulnerabilities with mitigation strategies

Investment Outlook and Opportunities: Examination of high-growth market segments and emerging technology areas

Introduction to HPC and AI: Evolution from historical HPC systems to the exascale computing era, supercomputer vs. hyperscale data center comparisons, and AI computing fundamentals

Processor Technologies and Architectures: Comprehensive analysis of CPUs (x86, ARM, RISC-V), GPUs, AI ASICs, FPGAs, DPUs, and cryptocurrency computing hardware

Fujitsu’s “Knowledge Graph Enhanced RAG” technology leverages Takane to automatically structure corporate data into knowledge graphs, ensuring generative AI has access to accurate, organized information.

Once data structuring is complete, advanced data analysis becomes possible. Fujitsu possesses extensive intellectual property in the field of "Causal AI," which performs sophisticated analysis of causal relationships between business events to support management decision-making. The combination of data structuring technology and causal AI makes new discoveries and insights possible. For example in retail, identifying the relationship between frequency of visits to stores that customers make and their average spend. Or in medicine, understanding the relationship between genes and lifestyle habits. [1]

By - Aaradhay Sharma