#generativeaimodels

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Generative AI Development for Business Continuity in Uncertain Times

Market volatility, supply chain instability, regulatory shifts, and rapid digital transformation have created an environment where uncertainty is constant rather than temporary. Traditional contingency plans built around static risk models are no longer sufficient. Businesses must now operate in dynamic ecosystems where disruption can emerge from economic downturns, geopolitical shifts, technological change, or competitive pressure.

In this environment, resilience is no longer about survival alone; it is about adaptability, speed, and intelligent decision-making. This is where Generative AI Development becomes a strategic enabler of business continuity and long-term growth.

What is Generative AI Development?

Generative AI Development refers to the design and deployment of AI systems capable of generating content, insights, and solutions based on large datasets and contextual inputs. Unlike traditional automation tools that follow predefined rules, generative AI models learn patterns and create new outputs, including text, reports, code, designs, forecasts, and simulations.

Over time, these systems have evolved from simple task automation to intelligent decision-support engines. Today, generative AI supports marketing content creation, financial forecasting, customer engagement, compliance documentation, and strategic analysis. Its adaptability makes it particularly valuable during periods of instability.

The Role of Generative AI in Business Continuity

Maintaining Operations During Disruption

When disruptions occur, operational bottlenecks often emerge in communication, documentation, and workflow management. Generative AI ensures business continuity by automating reporting, generating crisis communication materials, and supporting operational coordination in real time. It acts as a stabilizing layer that keeps critical functions moving.

Reducing Dependency on Manual Processes

Manual processes increase vulnerability during uncertainty. Generative AI reduces reliance on repetitive tasks by automating documentation, summarizing data, generating proposals, and supporting customer interactions. This not only improves speed but also reduces the risk of operational breakdowns.

Enabling Remote and Distributed Workforce Productivity

In uncertain environments, distributed teams become the norm. Generative AI supports collaboration through intelligent knowledge retrieval, automated meeting summaries, contextual assistance, and AI-powered internal support systems. This ensures productivity remains consistent regardless of physical location.

Strengthening Operational Resilience with Generative AI

Intelligent Document Generation and Reporting

From compliance reports to financial summaries, generative AI accelerates document creation while maintaining accuracy and consistency. This reduces turnaround time and ensures leadership has timely insights during critical decision-making moments.

AI-Powered Customer Communication

Customer confidence is crucial during market instability. Generative AI enables personalized, responsive, and consistent communication across channels. Automated yet intelligent messaging enhances customer retention and brand trust.

Knowledge Management and Internal Support Systems

Operational resilience depends on access to institutional knowledge. Generative AI organizes, retrieves, and synthesizes internal data, helping teams access the right information quickly. This minimizes knowledge silos and supports faster problem-solving.

Cost Optimization and Efficiency During Market Instability

During uncertain times, cost control becomes a priority. Generative AI drives efficiency by automating repetitive workflows, reducing administrative overhead, and optimizing resource allocation. It improves process speed while lowering operational expenses.

By shortening production cycles and enhancing output quality, businesses can maintain profitability even when market conditions are volatile. The ability to do more with fewer resources becomes a significant competitive advantage.

Enhancing Decision-Making with AI-Driven Insights

Predictive Analysis and Scenario Modeling

Generative AI can simulate multiple business scenarios, enabling leaders to evaluate potential outcomes before making strategic decisions. Scenario modeling enhances preparedness and reduces uncertainty in planning.

Real-Time Data Interpretation

Markets move quickly. Generative AI processes large volumes of data in real time, transforming raw information into actionable insights. This allows organizations to respond rapidly to emerging risks and opportunities.

Risk Mitigation Through AI Intelligence

By identifying patterns and anomalies, generative AI helps detect operational vulnerabilities early. Proactive risk identification strengthens business continuity planning and reduces long-term exposure.

Implementation Framework: Adopting Generative AI Safely and Strategically

Organizational Readiness Assessment

Before implementation, businesses must evaluate digital maturity, data infrastructure, and internal capabilities. A readiness assessment ensures AI adoption aligns with strategic objectives.

Build vs. Buy Decision Model

Organizations must determine whether to develop custom AI systems or adopt third-party solutions. This decision impacts cost, scalability, and long-term control.

Pilot Project Strategy

Launching controlled pilot initiatives allows companies to test use cases, measure ROI, and refine deployment strategies before scaling AI across departments.

AI Governance Framework

Clear governance policies ensure ethical usage, accountability, and compliance. Establishing oversight structures reduces operational and reputational risk.

Risk Management Protocols

Risk mitigation plans must address model bias, data integrity, and cybersecurity threats. Proactive governance ensures sustainable AI integration.

Workforce Training & AI Adoption Roadmap

Successful implementation depends on people. Training programs and change management initiatives help teams adapt to AI-driven workflows confidently.

Why Choose Osiz for Generative AI Development?

Osiz is a leading Generative AI Development Company helping businesses navigate uncertainty with intelligent, scalable solutions. We combine strategic expertise and advanced engineering to build AI systems aligned with your business continuity goals.

Our approach focuses on practical implementation, seamless integration, and performance-driven results. We prioritize security, scalability, and long-term value, ensuring your AI investments deliver measurable impact.

With deep industry experience and a forward-thinking mindset, Osiz empowers organizations to strengthen operations, enhance decision-making, and transform disruption into sustainable growth.

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Generative AI Development Explained: Models, Training, and Deployment

Artificial intelligence has moved beyond rule-based automation into systems that can create, reason, and adapt. This shift is driven by models capable of generating text, images, code, and even decision logic with minimal human input. As organizations push for smarter digital products and faster innovation cycles, Generative AI Development has become a core technology powering modern software ecosystems. Its ability to learn patterns from massive datasets and produce original outputs is redefining how applications are built and scaled across industries.

What Is Generative AI Development?

Generative AI Development refers to the process of designing, training, optimizing, and deploying AI systems that can generate new content rather than simply analyze existing data. Unlike traditional AI models that focus on classification or prediction, generative models learn underlying data distributions to produce meaningful outputs such as natural language responses, images, audio, or synthetic data. This approach enables applications like AI copilots, content generation tools, intelligent assistants, and simulation systems that continuously evolve through learning and feedback.

Core Generative AI Models Powering Modern Applications

Large Language Models (LLMs)

Large Language Models are trained on vast text corpora to understand context, semantics, and intent. They power conversational AI, document summarization, code generation, and knowledge assistants. LLMs rely on transformer architectures that scale effectively with data and compute.

Diffusion Models

Diffusion models generate outputs by gradually transforming noise into structured data. They are widely used in image, video, and media generation, offering high-quality and controllable outputs compared to earlier generative techniques.

Generative Adversarial Networks (GANs)

GANs consist of two competing networks a generator and a discriminator, that improve through adversarial learning. They are effective for realistic image synthesis, data augmentation, and style transfer but require careful training to avoid instability.

Variational Autoencoders (VAEs)

VAEs encode data into a latent space and reconstruct it with controlled variation. They are useful for anomaly detection, representation learning, and scenarios where interpretability of generated outputs is important.

Data Collection and Training Process in Generative AI Development

Data Collection and Preparation

High-quality data is the foundation of any generative system. This stage involves sourcing relevant datasets, cleaning inconsistencies, removing duplicates, and ensuring data diversity. Proper preprocessing reduces bias and improves output reliability.

Model Training Approaches

Training generative models can involve supervised, unsupervised, or self-supervised learning depending on the use case. Pretraining on large datasets followed by domain-specific fine-tuning is a common approach to balance performance and cost.

Infrastructure and Compute Requirements

Generative models demand significant compute resources. GPUs, TPUs, distributed training frameworks, and optimized storage pipelines are essential to handle large-scale training while maintaining efficiency.

Fine-Tuning and Optimization of Generative AI Models

Fine-Tuning Techniques

Fine-tuning adapts pretrained models to specific domains or tasks using smaller, targeted datasets. This improves relevance, accuracy, and contextual understanding without retraining from scratch.

Prompt Engineering and Parameter Tuning

Prompt design plays a critical role in guiding model behavior, especially for LLM-based systems. Adjusting parameters such as temperature, token limits, and context windows helps balance creativity, accuracy, and consistency.

Performance Evaluation Metrics

Evaluation goes beyond accuracy. Metrics such as coherence, relevance, latency, cost per inference, and human feedback are used to measure real-world effectiveness and guide continuous improvement.

Deployment Strategies for Generative AI Applications

Cloud-Based Deployment

Cloud environments offer scalability, flexibility, and managed AI services. They are ideal for applications with fluctuating workloads and global user bases.

On-Premise and Hybrid Deployment

For industries with strict data governance requirements, on-premise or hybrid setups provide greater control over data security and compliance while still leveraging cloud scalability when needed.

Scalability and Cost Optimization

Efficient deployment involves model compression, caching, batch inference, and autoscaling mechanisms to reduce operational costs while maintaining performance.

Challenges and Ethics in Generative AI Development

Data Privacy and Security Risks

Generative models often process sensitive information, making data protection and secure access controls essential. Improper handling can lead to data leakage or misuse.

Bias, Hallucinations, and Model Accuracy

Models may produce biased or incorrect outputs due to limitations in training data. Continuous monitoring and corrective feedback loops are required to maintain trust and reliability.

Compliance and Responsible AI Practices

Adhering to regulatory standards, ensuring transparency, and implementing responsible AI frameworks help organizations deploy generative systems ethically and sustainably.

Why Choose Osiz for Generative AI Development

Osiz is a leading Generative AI Development Company specializing in building intelligent, scalable, and business-ready AI solutions. With deep expertise in large language models, custom generative architectures, and enterprise-grade deployment, Osiz helps organizations transform ideas into production-ready AI systems. The team follows a structured development approach that prioritizes data security, model accuracy, and performance optimization. By combining advanced AI engineering with real-world industry knowledge, Osiz delivers generative AI solutions that are reliable, compliant, and built for long-term growth.

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The Evolution Of Large Language Models: From GPT To GPT-4 And Beyond

The Evolution Of Large Language Models: From GPT To GPT-4 And Beyond - EnFuse Solutions

Discover how GPT-1 evolved into GPT-4 and beyond, transforming AI from a research concept into powerful tools reshaping work, automation, an

AI’s evolution from GPT to GPT-4 shows its shift from experimentation to everyday impact. With the rise of agentic and personalized AI, the focus is now on responsible and aligned use. Partners like EnFuse Solutions are helping make this progress smarter, safer, and more accessible.

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Generative AI in 2025: Exploration of All Gen-AI Models

Generative AI in 2025! Learn about generative AI models, their functions, and their application in different industries.

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Updates to Azure AI, Phi 3 Fine tuning, And gen AI models

Introducing new generative AI models, Phi 3 fine tuning, and other Azure AI enhancements to enable businesses to scale and personalise AI applications.

All sectors are being transformed by artificial intelligence, which also creates fresh growth and innovation opportunities. But developing and deploying artificial intelligence applications at scale requires a reliable and flexible platform capable of handling the complex and varied needs of modern companies and allowing them to construct solutions grounded on their organisational data. They are happy to share the following enhancements to enable developers to use the Azure AI toolchain to swiftly and more freely construct customised AI solutions:

Developers can rapidly and simply customise the Phi-3-mini and Phi-3-medium models for cloud and edge scenarios with serverless fine-tuning, eliminating the need to schedule computing.

Updates to Phi-3-mini allow developers to create with a more performant model without incurring additional costs. These updates include a considerable improvement in core quality, instruction-following, and organised output.

This month, OpenAI (GPT-4o small), Meta (Llama 3.1 405B), and Mistral (Large 2) shipped their newest models to Azure AI on the same day, giving clients more options and flexibility.

Value unlocking via customised and innovative models

Microsoft unveiled the Microsoft Phi-3 line of compact, open models in April. Compared to models of the same size and the next level up, Phi-3 models are their most powerful and economical small language models (SLMs). Phi 3 Fine tuning a tiny model is a wonderful alternative without losing efficiency, as developers attempt to customise AI systems to match unique business objectives and increase the quality of responses. Developers may now use their data to fine-tune Phi-3-mini and Phi-3-medium, enabling them to create AI experiences that are more affordable, safe, and relevant to their users.

Phi-3 models are well suited for fine-tuning to improve base model performance across a variety of scenarios, such as learning a new skill or task (e.g., tutoring) or improving consistency and quality of the response (e.g., tone or style of responses in chat/Q&A). This is because of their small compute footprint and compatibility with clouds and edges. Phi-3 is already being modified for new use cases.

Microsoft and Khan Academy are collaborating to enhance resources for educators and learners worldwide. As part of the partnership, Khan Academy is experimenting with Phi-3 to enhance math tutoring and leverages Azure OpenAI Service to power Khanmigo for Teachers, a pilot AI-powered teaching assistant for educators in 44 countries. A study from Khan Academy, which includes benchmarks from an improved version of Phi-3, shows how various AI models perform when assessing mathematical accuracy in tutoring scenarios.

According to preliminary data, Phi-3 fared better than the majority of other top generative AI models at identifying and fixing mathematical errors made by students.

Additionally, they have optimised Phi-3 for the gadget. To provide developers with a strong, reliable foundation for creating apps with safe, secure AI experiences, they launched Phi Silica in June. Built specifically for the NPUs in Copilot+ PCs, Phi Silica expands upon the Phi family of models. The state-of-the-art short language model (SLM) for the Neural Processing Unit (NPU) and shipping inbox is exclusive to Microsoft Windows.

Today, you may test Phi 3 fine tuning in Azure AI

Azure AI’s Models-as-a-Service (serverless endpoint) feature is now widely accessible. Additionally, developers can now rapidly and simply begin developing AI applications without having to worry about managing underlying infrastructure thanks to the availability of Phi-3-small via a serverless endpoint.

The multi-modal Phi-3 model, Phi-3-vision, was unveiled at Microsoft Build and may be accessed via the Azure AI model catalogue. It will also soon be accessible through a serverless endpoint. While Phi-3-vision (4.2B parameter) has also been optimised for chart and diagram interpretation and may be used to produce insights and answer queries, Phi-3-small (7B parameter) is offered in two context lengths, 128K and 8K.

The community’s response to Phi-3 is excellent. Last month, they launched an update for Phi-3-mini that significantly enhances the core quality and training after. After the model was retrained, support for structured output and instruction following significantly improved.They also added support for |system|> prompts, enhanced reasoning capability, and enhanced the quality of multi-turn conversations.

They also keep enhancing the safety of Phi-3. In order to increase the safety of the Phi-3 models, Microsoft used an iterative “break-fix” strategy that included vulnerability identification, red teaming, and several iterations of testing and improvement. This approach was recently highlighted in a research study. By using this strategy, harmful content was reduced by 75% and the models performed better on responsible AI benchmarks.

Increasing model selection; around 1600 models are already accessible in Azure AI They’re dedicated to providing the widest range of open and frontier models together with cutting-edge tooling through Azure AI in order to assist clients in meeting their specific cost, latency, and design requirements. Since the debut of the Azure AI model catalogue last year, over 1,600 models from providers such as AI21, Cohere, Databricks, Hugging Face, Meta, Mistral, Microsoft Research, OpenAI, Snowflake, Stability AI, and others have been added, giving us the widest collection to date. This month, they added Mistral Large 2, Meta Llama 3.1 405B, and OpenAI’s GPT-4o small via Azure OpenAI Service.

Keeping up the good work, they are happy to announce that Cohere Rerank is now accessible on Azure. Using Azure to access Cohere’s enterprise-ready language models Businesses can easily, consistently, and securely integrate state-of-the-art semantic search technology into their applications because to AI’s strong infrastructure. With the help of this integration, users may provide better search results in production by utilising the scalability and flexibility of Azure in conjunction with the highly effective and performant language models from Cohere.

With Cohere Rerank, Atomicwork, a digital workplace experience platform and a seasoned Azure user, has greatly improved its IT service management platform. Atomicwork has enhanced search relevancy and accuracy by incorporating the model into Atom AI, their AI digital assistant, hence offering quicker, more accurate responses to intricate IT help enquiries. Enterprise-wide productivity has increased as a result of this integration, which has simplified IT processes.

Read more on govindhtech.com

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Meta Unveils Llama 3.1: A Challenger in the AI Arena

Meta launches new Llama 3.1 models, including anticipated 405B parameter version.

Meta released Llama 3.1, a multilingual LLM collection. Llama 3.1 includes pretrained and instruction-tuned text in/text out open source generative AI models with 8B, 70B, and 405B parameters.

Today, IBM watsonx.ai will offer the instruction-tuned Llama 3.1-405B, the largest and most powerful open source language model available and competitive with the best proprietary models.It can be set up on-site, in a hybrid cloud environment, or on the IBM cloud.

Llama 3.1 follows the April 18 debut of Llama 3 models. Meta stated in the launch release that “[their] goal in the near future is to make Llama 3 multilingual and multimodal, have longer context, and continue to improve overall performance across LLM capabilities such as reasoning and coding.”

Llama 3.1’s debut today shows tremendous progress towards that goal, from dramatically enhanced context length to tool use and multilingual features.

An significant step towards open, responsible, accessible AI innovation

Meta and IBM launched the  AI Alliance in December 2023 with over 50 global initial members and collaborators. The AI Alliance unites leading business, startup, university, research, and government organisations to guide AI’s evolution to meet society’s requirements and complexities. Since its formation, the Alliance has over 100 members.

Additionally, the AI Alliance promotes an open community that helps developers and researchers accelerate responsible innovation while maintaining trust, safety, security, diversity, scientific rigour, and economic competitiveness. To that aim, the Alliance supports initiatives that develop and deploy benchmarks and evaluation standards, address society-wide issues, enhance global AI capabilities, and promote safe and useful AI development.

Llama 3.1 gives the global  AI community an open, state-of-the-art model family and development ecosystem to explore, experiment, and responsibly scale new ideas and techniques. The release features strong new models, system-level safety safeguards, cyber security evaluation methods, and improved inference-time guardrails. These resources promote generative AI trust and safety tool standardisation.

How Llama 3.1-405B compares to top models

The April release of Llama 3 highlighted upcoming Llama models with “over 400B parameters” and some early model performance evaluation, but their exact size and details were not made public until today’s debut. Llama 3.1 improves all model sizes, but the 405B open source model matches leading proprietary, closed source LLMs for the first time.

Looking beyond numbers

Performance benchmarks are not the only factor when comparing the 405B to other cutting-edge models. Llama 3.1-405B may be built upon, modified, and run on-premises, unlike its closed source contemporaries, which can change their model without notice. That level of control and predictability benefits researchers, businesses, and other entities that seek consistency and repeatability.

Effective Llama-3.1-405B usage

IBM, like Meta, believes open models improve product safety, innovation, and the  AI market. An advanced 405B-parameter open source model offers unique potential and use cases for organisations of all sizes.

Aside from inference and text creation, which may require quantisation or other optimisation approaches to execute locally on most hardware systems, the 405B can be used for:

Synthetic data can fill the gap in pre-training, fine-tuning, and instruction tuning when data is limited or expensive. The 405B generates high-quality task- and domain-specific synthetic data for LLM training. IBM’s Large-scale Alignment for chatBots (LAB) phased-training approach quickly updates LLMs with synthetic data while conserving model knowledge.

The 405B model’s knowledge and emergent abilities can be reduced into a smaller model, combining the capabilities of a big “teacher” model with the quick and cost-effective inference of a “student” model (such an 8B or 70B Llama 3.1). Effective Llama-based models like Alpaca and Vicuna need knowledge distillation, particularly instruction tailoring on synthetic data provided by bigger GPT models.

LLM-as-a-judge: The subjectivity of human preferences and the inability of standards to approximate them make LLM evaluation difficult. The Llama 2 research report showed that larger models can impartially measure response quality in other models. Learn more about LLM-as-a-judge’s efficacy in this 2023 article.

A powerful domain-specific fine-tune: Many leading closed models allow fine-tuning only on a case-by-case basis, for older or smaller model versions, or not at all. Meta has made Llama 3.1-405B accessible for pre-training (to update the model’s general knowledge) or domain-specific fine-tuning coming soon to the watsonx Tuning Studio.

Meta  AI “strongly recommends” using a platform like IBM watsonx for model evaluation, safety guardrails, and retrieval augmented generation to deploy Llama 3.1 models.

Every llama 3.1 size gets upgrades

The long-awaited 405B model may be the most notable component of Llama 3.1, but it’s hardly the only one. Llama 3.1 models share the dense transformer design of Llama 3, but they are much improved at all model sizes.

Longer context windows

All pre-trained and instruction-tuned Llama 3.1 models have context lengths of 128,000 tokens, a 1600% increase over 8,192 tokens in Llama 3. Llama 3.1’s context length is identical to the enterprise version of GPT-4o, substantially longer than GPT-4 (or ChatGPT Free), and comparable to Claude 3’s 200,000 token window. Llama 3.1’s context length is not constrained in situations of high demand because it can be installed on the user’s hardware or through a cloud provider.. Llama 3.1 has few usage restrictions.

An LLM can consider or “remember” a certain amount of tokenised text (called its context window) at any given moment. To continue, a model must trim or summarise a conversation, document, or code base that exceeds its context length. Llama 3.1’s extended context window lets models have longer discussions without forgetting details and ingest larger texts or code samples during training and inference.

Text-to-token conversion doesn’t have a defined “exchange rate,” but 1.5 tokens per word is a good estimate. Thus, Llama 3.1’s 128,000 token context window contains 85,000 words. The Hugging Face Tokeniser Playground lets you test multiple tokenisation models on text inputs.

Llama 3.1 models benefit from Llama 3’s new tokeniser, which encodes language more effectively than Llama 2.

Protecting safety

Meta has cautiously and thoroughly expanded context length in line with its responsible innovation approach. Previous experimental open source attempts produced Llama derivatives with 128,000 or 1M token windows. These projects demonstrate Meta’s open model commitment, however they should be approached with caution: Without strong countermeasures, lengthy context windows “present a rich new attack surface for LLMs” according to recent study.

Fortunately, Llama 3.1 adds inference guardrails. The release includes direct and indirect prompt injection filtering from Prompt Guard and updated Llama Guard and CyberSec Eval. CodeShield, a powerful inference time filtering technology from Meta, prevents LLM-generated unsafe code from entering production systems.

As with any generative  AI solution, models should be deployed on a secure, private, and safe platform.

Multilingual models

Pretrained and instruction tailored Llama 3.1 models of all sizes will be bilingual. In addition to English, Llama 3.1 models speak Spanish, Portuguese, Italian, German, and Thai. Meta said “a few other languages” are undergoing post-training validation and may be released.

Optimised for tools

Meta optimised the Llama 3.1 Instruct models for “tool use,” allowing them to interface with applications that enhance the LLM’s capabilities. Training comprises creating tool calls for specific search, picture production, code execution, and mathematical reasoning tools, as well as zero-shot tool use—the capacity to effortlessly integrate with tools not previously encountered in training.

Starting Llama 3.1

Meta’s latest version allows you to customise state-of-the-art generative  AI models for your use case.

IBM supports Llama 3.1 to promote open source AI innovation and give clients access to best-in-class open models in watsonx, including third-party models and the IBM Granite model family.

IBM Watsonx allows clients to deploy open source models like Llama 3.1 on-premises or in their preferred cloud environment and use intuitive workflows for fine-tuning, prompt engineering, and integration with enterprise applications. Build business-specific AI apps, manage data sources, and expedite safe AI workflows on one platform.

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With IBM Consulting, Casper Labs Builds Blockchain Solution

Blockchain-Powered Solutions by Casper Labs

Casper Labs, a provider of enterprise blockchain software and services, and IBM Consulting have announced their partnership to assist clients in utilizing blockchain technology to enhance the transparency and auditability of their artificial intelligence systems. In collaboration with IBM Consulting, Casper Labs and the latter will create a new Casper Labs solution that will be built on top of blockchain technology and utilize IBM Watsonx.governance to create an extra layer of analytics and policy enforcement for managing AI training data across enterprises.

From the original model creator to the end user organization, several organizations are involved in the training, development, and deployment of generative AI models. The outputs of various organizations change as a result of new data sets being integrated or models being altered, and numerous organizations must be able to monitor, audit, and correctly identify and fix problems in addition to tracking these changes. By minimizing the possibility of unneeded data sharing across organizational boundaries and facilitating the sharing of trusted context information through metadata in the ledger indicating model changes, blockchain technology can assist organizations in sharing their knowledge.

The goal of Casper Labs’ solution is to train generative AI systems throughout organizations by monitoring and measuring highly serialized input and output data. It will be built on Casper, a tamper-resistant and highly serialized ledger, and utilizing IBM Watsonx.governance and Watsonx.ai.

Organizations can anticipate being able to better protect sensitive data stored in the solution from being accessible to external actors because of the hybrid nature of the Casper Blockchain and its permissioning system, which gives them control over who can access what data. Additionally, the solution will be designed to support version control by utilizing blockchain’s serialization capabilities. This will enable organizations to quickly and effectively roll back AI system iterations in the event that performance problems or biased outputs arise.

Casper Labs will build the solution with help from IBM Consulting’s AI governance and technology experts. The solution is anticipated to be made available to clients in beta in the first quarter of 2024, and then more widely through Casper Labs’ channels and the IBM Cloud Marketplace.

“An AI system’s efficacy is ultimately as good as an organization’s ability to govern it,” said Shyam Nagarajan, Global Partner, Blockchain and Responsible AI Leader at IBM Consulting. Businesses require solutions that reduce risk, improve explainability, and cultivate trust. IBM said that they are excited to work with Casper Labs to develop a new solution that adds a crucial layer of transparency and risk mitigation for businesses implementing AI at scale, by bringing IBM Consulting and technology to the table.

The new solution is intended to assist businesses in deploying AI responsibly at scale throughout their ecosystem of technology and service providers, including financial services, healthcare, and retail. In addition, the solution seeks to provide:   

Compliance Dashboard: A centralized dashboard designed to help organizations monitor and manage AI systems used throughout the organization in order to support their processes for adhering to ethical standards.

Tools to track the effectiveness and quality of AI systems, as well as an interface to improve the comprehensibility and transparency of AI outputs, comprise the Quality Control Toolkit.

Version control refers to the capability of “rolling back” to earlier versions of an AI system that did not exhibit problems in order to address performance or other issues.

A system for auditing AI procedures and producing thorough reports using context metadata gathered by Casper Labs’ ledger is called the Audit and Reporting System.

The CEO of Casper Labs, Mrinal Manohar, stated, “While generative AI has rightfully excited organizations for its transformative potential, its practical applications have been severely limited by an inability to monitor and react to the data feeding AI systems.” With IBM’s assistance, we’re dedicated to providing a clearer path for correcting behavior in the event of hallucinations or performance problems, as well as a better means of understanding why AI systems behave as they do. AI’s long-term potential will be determined by how well organizations comprehend, manage, and respond to ever-larger AI training data sets.

IBM Consulting and Casper Labs to Present AI Governance Solution in Davos

Mrinal Manohar and Shyam Nagarajan will present a live demonstration of this solution at The Hub in Davos on Tuesday, January 16.

Concerning Casper Labs

The industry leader in enterprise blockchain software is Casper Labs. In order to meet the scale and operational requirements of businesses, Casper Labs created the first layer-1 blockchain, which completely transparently recorded all business transactions. Casper Labs provides businesses and governments with software and services that increase revenue and drastically improve efficiency. Their goal is to construct the fundamental framework required for a completely new era of client value and commercial success.

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TELUS International Launches Experts Engine To Provide Highly Accurate, Predictably Faster And Cost-optimized Training Data For Generative AI Models

Leader in digital customer experience solutions TELUS International introduces Experts Engine, a managed sourcing solution that pairs generative AI (GenAI) tasks with human expertise. This breakthrough expedites the production of superior training datasets for sophisticated models, including large language models (LLMs). As a component of TELUS International's AI Data Platform, it integrates management and job execution capabilities and is supported by a machine learning model that profiles more than a million contributors worldwide.

Within TELUS International's AI Community, Experts Engine optimizes task allocation, ensuring that the appropriate experts undertake certain tasks based on their domain knowledge and task complexity. This approach preserves quality control workflows for various GenAI activities, eliminates over-allocation of expertise, and improves efficiency by tailoring the matching algorithm.

The importance of training data in enhancing model quality and safety is emphasized by Siddharth Mall, vice president of product at AI Data Solutions. The introduction of Experts Engine shows TELUS International's dedication to resolving customer issues by fusing human insight for dependability and automation for efficiency, promoting objective and inclusive content across markets and applications.

Read More - https://www.techdogs.com/tech-news/business-wire/telus-international-launches-experts-engine-to-provide-highly-accurate-predictably-faster-and-cost-optimized-training-data-for-generative-ai-models