#FoundationModels

[govindhtech]

Amazon Bedrock Studio: Accelerate generative AI development

AWS is pleased to present to the public today Amazon Bedrock Studio, a brand-new web-based generative artificial intelligence (generative AI) development environment. By offering a fast prototyping environment with essential Amazon Bedrock technologies like Knowledge Bases, Agents, and Guardrails, Amazon Bedrock Studio speeds up the creation of generative AI applications.

Summary

A brand-new SSO-enabled web interface called Amazon Bedrock Studio offers developers from all over an organisation the simplest way to work together on projects, experiment with large language models (LLMs) and other foundation models (FMs), and refine generative AI applications. It simplifies access to various Foundation Models (FMs) and developer tools in Bedrock and provides a fast prototyping environment. AWS administrators can set up one or more workspaces for their company in the AWS Management Console for Bedrock and allow individuals or groups to utilise the workspace in order to enable Bedrock Studio.

In only a few minutes, begin developing applications

Using their company credentials (SSO), developers at your firm can easily log in to the Amazon Bedrock Studio online experience and begin experimenting with Bedrock FMs and application development tools right away. Bedrock Studio provides developers with a safe haven away from the AWS Management Console in which to utilise Bedrock features like Knowledge Bases, Amazon Guardrails, and Agents.

Create flexible generative AI applications

With Amazon Bedrock Studio, developers can gradually improve the accuracy and relevance of their generative AI applications. To acquire more accurate responses from their app, developers can begin by choosing an FM that is appropriate for their use case and then iteratively enhance the prompts. Then, they can add APIs to obtain the most recent results and use their own data to ground the app to receive more pertinent responses. Bedrock Studio streamlines and reduces the complexity of app development by automatically deploying pertinent AWS services (such Knowledge Bases and Agents). Additionally, enterprise use cases benefit from a secure environment because data and apps are never removed from the assigned AWS account.

Work together on projects with ease

Teams may brainstorm, test, and improve their generative AI applications together in Amazon Bedrock Studio‘s collaborative development environment. In addition to creating projects and inviting peers, developers may also share apps and insights and receive immediate feedback on their prototypes. Access control is a feature of Bedrock Studio projects that guarantees that only members with permission can use the apps and resources inside of a project.

Encourage creativity without worrying about infrastructure management

Knowledge bases, agents, and guardrails are examples of managed resources that are automatically installed in an AWS account when developers construct applications in Amazon Bedrock Studio. Because these Bedrock resources are always available and scaleable as needed, they don’t need to worry about the underlying compute and storage infrastructure. Furthermore, the Bedrock API makes it simple to access these resources. This means that by utilising the Bedrock API, you can easily combine the generative AI apps created in Bedrock Studio with their workflows and processes.

Take precautions to ensure the finest answers

To make sure their programme doesn’t provide incorrect output, developers can install content filters and create guardrails for both user input and model replies. To acquire the desired results from their apps, they can add prohibited topics and configure filtering levels across different categories to customise the behaviour of Guardrail.

As a developer, you can now log into Bedrock Studio and begin experimenting with your company’s single sign-on credentials. Within Bedrock Studio, you may create apps with a variety of high-performing models, assess them, and distribute your generative AI creations. You can enhance a model’s replies by following the stages that the user interface walks you through. You can play around with the model’s settings, set limits, and safely integrate tools, APIs, and data sources used by your business. Working in teams, you can brainstorm, test, and improve your generative AI apps without needing access to the AWS Management Console or sophisticated machine learning (ML) knowledge.

You can be sure that developers will only be able to utilise the functionality offered by Bedrock Studio and won’t have wider access to AWS infrastructure and services as an Amazon Web Services (AWS) administrator.

Let me now walk you through the process of installing Amazon Bedrock Studio.

Use Amazon Bedrock Studio to get started

You must first create an Amazon Bedrock Studio workspace as an AWS administrator, after which you must choose and add the users you wish to grant access to the workspace. You can provide the relevant individuals with the workspace URL once it has been built. Users with the necessary permissions can start developing generative AI apps, create projects inside their workspace, and log in using single sign-on.

Establish a workstation in Amazon Bedrock Studio

Select Bedrock Studio from the bottom left pane of the Amazon Bedrock dashboard.

You must use the AWS IAM Identity Centre to set up and secure the single sign-on integration with your identity provider (IdP) before you can create a workspace. See the AWS IAM Identity Centre User Guide for comprehensive instructions on configuring other IdPs, such as Okta, Microsoft Entra ID, and AWS Directory Service for Microsoft Active Directory. You set up user access using the IAM Identity Centre default directory for this demo.

Next, select Create workspace, fill in the specifics of your workspace, and create any AWS Identity and Access Management (IAM) roles that are needed.

Additionally, you have the option to choose the workspace’s embedding models and default generative AI models. Select Create once you’re finished.

Choose the newly formed workspace next.

Next, pick the users you wish to grant access to this workspace by choosing User management and then Add users or groups.

You can now copy the Bedrock Studio URL and share it with your users from the Overview tab.

Create apps for generative AI using Amazon Bedrock Studio

Now that the Bedrock Studio URL has been provided, builders can access it and log in using their single sign-on login credentials. Here at Amazon Bedrock Studio, welcome! Allow me to demonstrate how to select among top-tier FMs, import your own data, use functions to call APIs, and use guardrails to secure your apps.

Select from a number of FMs that lead the industry

By selecting examine, you may begin choosing from among the FMs that are offered and use natural language prompts to examine the models.

If you select Build, you may begin developing generative AI applications in playground mode, play around with model settings, refine your application’s behaviour through iterative system prompts, and create new feature prototypes.

Bring your personal data

Using Bedrock Studio, you can choose from a knowledge base built in Amazon Bedrock or securely bring your own data to customise your application by supplying a single file.

Make API calls using functions to increase the relevancy of model responses

When replying to a prompt, the FM can dynamically access and incorporate external data or capabilities by using a function. The model uses an OpenAPI schema you supply to decide which function it needs to call.

A model can include data into its response through functions that it is not directly aware of or has access to beforehand. For instance, even though the model doesn’t save the current weather information, a function may enable the model to acquire it and incorporate it into its response.

Using Guardrails for Amazon Bedrock, secure your apps

By putting in place safeguards tailored to your use cases and responsible AI rules, you may build boundaries to encourage safe interactions between users and your generative AI apps.

The relevant managed resources, including knowledge bases, agents, and guardrails, are automatically deployed in your AWS account when you construct apps in Amazon Bedrock Studio. To access those resources in downstream applications, use the Amazon Bedrock API.

Amazon Bedrock Studio availability

The public preview of Amazon Bedrock Studio is now accessible in the AWS Regions US West (Oregon) and US East (Northern Virginia).

Read more on govindhtech.com

[shireen46]

Difference between Generative AI, LLMs, and Foundation Models

In recent years, the field of artificial intelligence has witnessed remarkable advancements, particularly in the development of sophisticated language models that have transformed the way we interact with machines. Among the key players in this AI revolution are Generative AI, Large Language Models, and Foundation Models. While these terms are often used interchangeably, they have distinct characteristics and serve different purposes. In this article, we will delve into the differences between these three categories of AI models to provide a better understanding of their respective roles and capabilities.

Generative AI:

Generative AI refers to a class of artificial intelligence models that are capable of generating creative content, often in the form of text, images, or even audio. These models are designed to produce novel output that is not directly copied from the input data. Generative AI systems, like GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders), work by learning patterns and generating content from scratch based on those patterns. Some popular examples of Generative AI include:

GANs (Generative Adversarial Networks): GANs consist of two neural networks, a generator, and a discriminator, which work in opposition to produce realistic outputs. GANs have been used to create realistic images, deepfakes, and more.

VAEs (Variational Autoencoders): VAEs are used to generate data by learning the underlying structure and variability within a dataset. They have applications in image generation and text generation.

Recurrent Neural Networks (RNNs): RNNs are used for sequence-to-sequence tasks, making them suitable for text generation, language translation, and more.Generative AI is a broad field with applications across various domains, including art, entertainment, content generation, and even scientific research.

Key characteristics of Generative AI:

a. Creativity: Generative AI systems are designed to be creative and produce content that is not found in their training data. They can generate new ideas, artworks, or text that is unique and often unpredictable.

b. Variability: These models can produce a wide range of outputs, making them useful in creative tasks such as art, music, and storytelling.

c. Not language-focused: While Generative AI can work with text, it is not limited to language generation and can be used for various creative applications.

Large Language Models:

Large Language Models

GPT (Generative Pre-trained Transformer): The GPT series, such as GPT-2 and GPT-3, are renowned for their text generation capabilities. They are often used for tasks like language translation, content generation, and chatbots.

BERT (Bidirectional Encoder Representations from Transformers): BERT models are designed for natural language understanding and perform exceptionally well on tasks like sentiment analysis, question-answering, and more.

Large Language Models are versatile and can be fine-tuned for specific tasks, making them valuable tools in various applications, from chatbots and virtual assistants to content generation and text classification.

Key characteristics of Large Language Models:

a. Language-centric: Large Language Models are primarily designed for natural language processing tasks, making them exceptionally good at tasks like text completion, conversation, and text summarization.

b. Transfer learning: They leverage pre-training on massive text corpora to generalize to various language-related tasks, making them versatile and adaptable.

c. Not necessarily creative: While Large Language Models can generate text, their output is often limited to producing coherent and contextually relevant content. They are less focused on creative, novel generation compared to other Generative AI models.

Foundation Models:

Foundation Models are at the forefront of AI research and technology. These models are designed to serve as the basis for a wide range of AI applications, including both language-related tasks and other domains. They are often large-scale models, like GPT-3 or its successors, and serve as the foundation upon which specialized models can be built.

Some examples of Foundation Models include:

OpenAI's GPT-3: While GPT-3 is a Large Language Model, it can also be viewed as a Foundation Model because of its extensive knowledge base and versatility in various applications, beyond just text generation.

Google's T5 (Text-To-Text Transfer Transformer): T5 is a model that frames all NLP tasks as a text-to-text problem, making it highly adaptable to a wide range of tasks.

Foundation Models serve as a base upon which developers and researchers can build specialized AI applications. They provide a strong starting point for various domains, such as natural language processing, computer vision, and more.

Key characteristics of Foundation Models:

a. Versatility: Foundation Models are general-purpose and versatile, capable of being fine-tuned for specific applications. They can serve as the starting point for various AI tasks beyond language processing, such as image recognition and even medical diagnosis.

b. Scalability: These models are often extremely large, with millions or even billions of parameters, allowing them to capture vast amounts of knowledge and nuances.

c. Potential for creative tasks: While not their primary focus, Foundation Models can be used for creative tasks when fine-tuned and adapted, but their primary strength lies in their versatility and adaptability.

Conclusion

In summary, Generative AI, Large Language Models, and Foundation Models are distinct categories of artificial intelligence models, each with its own set of characteristics and applications. Generative AI is known for its creativity and versatility in generating content, while Large Language Models, like GPT-3, excel in natural language processing tasks. Foundation Models serve as the cornerstone for a wide array of AI applications, providing a versatile starting point for specialized models. Understanding the differences between these categories is crucial for leveraging their capabilities effectively and choosing the right model for specific tasks in the evolving landscape of artificial intelligence.

[sonalikrishnan]

Unlocking the Future of AI and Data with Pragma Edge's Watson X Platform 

In a rapidly evolving digital landscape, enterprises are constantly seeking innovative solutions to harness the power of artificial intelligence (AI) and data to gain a competitive edge. Enter Pragma Edge's Watson X, a groundbreaking AI and Data Platform designed to empower enterprises with scalability and accelerate the impact of AI capabilities using trusted data. This comprehensive platform offers a holistic solution, encompassing data storage, hardware, and foundational models for AI and Machine Learning (ML). 

The All-in-One Solution for AI Advancement 

At the heart of Watson X is its commitment to providing an open ecosystem, allowing enterprises to design and fine-tune large language models (LLMs) to meet their operational and business requirements. This platform is not just about AI; it's about transforming your business through automation, streamlining workflows, enhancing security, and driving sustainability goals. 

Key Components of Watson X 

Watsonx.ai: The AI Builder's Playground 

Watsonx.ai is an enterprise development studio where AI builders can train, test, tune, and deploy both traditional machine learning and cutting-edge generative AI capabilities. 

It offers a diverse array of foundation models, training and tuning tools, and cost-effective infrastructure to facilitate the entire data and AI lifecycle. 

Watsonx.data: Fueling AI Initiatives 

Watsonx.data is a specialized data store built on the open lakehouse architecture, tailored for analytics and AI workloads. 

This agile and open data repository empowers enterprises to efficiently manage and access vast amounts of data, driving quick decision-making processes. 

Watsonx.governance: Building Responsible AI 

Watsonx.governance lays the foundation for an AI workforce that operates responsibly and transparently. 

It establishes guidelines for explainable AI, ensuring businesses can understand AI model decisions, fostering trust with clients and partners. 

Benefits of WatsonX 

Unified Data Access: Gain access to information data across both on-premises and cloud environments, streamlining data management. 

Enhanced Governance: Apply robust governance measures, reduce costs, and accelerate model deployment, ensuring high-quality outcomes. 

End-to-End AI Lifecycle: Accelerate the entire AI model lifecycle with comprehensive tools and runtimes for training, validation, tuning, and deployment—all in one location. 

In a world driven by data and AI, Pragma Edge's Watson X Platform empowers enterprises to harness the full potential of these technologies. Whether you're looking to streamline processes, enhance security, or unlock new business opportunities, Watson X is your partner in navigating the future of AI and data. Don't miss out on the transformative possibilities—explore Watson X today at watsonx.ai and embark on your journey towards AI excellence. 

Learn more: https://pragmaedge.com/watsonx/ 

[hitechnectar-htn]

Hello #TechGeeks, 

Did you know #FoundationModels in #AI are #algorithms that train and develop with broader datasets to execute various functions? 

Moreover, foundation models are built on conventional deep learning and transfer learning algorithms. 

Hence, read more about the Foundation Models in AI, A new Trend and the Future. 

https://bit.ly/3x50V8Z

#ArtificialIntelligence #mlops #datascientist #machinelearning #DeepLearning

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[govindhtech]

Innovations in Generative AI and Foundation Models

Generative AI in Therapeutic Antibody Development: With the cooperation announced today, Boehringer Ingelheim and IBM will be able to employ IBM’s foundation model technology to find new candidate antibodies for the development of effective treatments.

Boehringer Ingelheim’s Andrew Nixon, Global Head of Biotherapeutics Discovery, said, “We are very excited to collaborate with the research team at IBM, who share our vision of making in silico biologic drug discovery a reality.” “We will create an unparalleled platform for expedited antibody discovery by collaborating with IBM scientists, and I am sure that this will allow Boehringer to create and provide novel treatments for patients with significant unmet needs.”

Boehringer plans to use a pre-trained AI model created by IBM, which will be further refined using additional proprietary data owned by Boehringer. Vice President of Accelerated Discovery at IBM Research Alessandro Curioni stated, “IBM has been at the forefront of creating generative AI models that extend AI’s impact beyond the domain of language.” “We are excited to now enable Boehringer, a pioneer in the creation and production of antibody-based treatments, to leverage IBM’s multimodal foundation model technologies to help quicken Boehringer’s ability to develop new therapeutics.”

Foundational models for the finding of antibodies

Therapeutic antibodies play a key role in the management of numerous illnesses, such as infectious, autoimmune, and cancerous conditions. The identification and creation of therapeutic antibodies encompassing a variety of epitopes continues to be an extremely difficult and time-consuming procedure, even with significant technological advancements.

Researchers from IBM and Boehringer will work together to use in-silico techniques to speed up the antibody discovery process.  New human antibody sequences will be generated in silico using the sequence, structure, and molecular profile data of disease-relevant targets as well as success criteria for therapeutically relevant antibody molecules, such as developability, specificity, and affinity. The efficacy and speed of antibody discovery, as well as the quality of anticipated antibody candidates, are intended to be enhanced by these techniques, which are based on new IBM foundation model technology.

The defined targets are designed with antibody candidates using IBM’s foundation model technologies, which have proven effective in producing biologics and small molecules with relevant target affinities. AI-enhanced simulation is then used to screen the antibody candidates and select and refine the best binders for the target. The antibody candidates will be produced at mini-scales and evaluated experimentally by Boehringer Ingelheim as part of a validation process. Subsequently, the outcomes of the lab trials will be applied to enhance the in-silico techniques through feedback loops.

Boehringer is creating a cutting-edge digital ecosystem to facilitate the acceleration of medication discovery and development and to generate new breakthrough prospects to improve the lives of patients by working with top academic and industry partners.

Generative AI in Therapeutic Antibody Development

Additionally, IBM is using foundation models and Generative AI to speed up the discovery and development of new biologics and small chemicals, and this study is the latest in this endeavor. Earlier in the year, the business’s Generative AI model accurately predicted the physico-chemical characteristics of tiny compounds that resembled drugs. 

Pre-trained models for drug-target interactions and protein-protein interactions are developed using a variety of heterogeneous, publically available data sets by the IBM Biomedical Foundation Model Technologies.  In order to provide newly created proteins and small molecules with the required qualities, the pre-trained models are subsequently refined using particular confidential data belonging to IBM’s partner.

Concerning Boehringer Ingelheim

Innovative treatments that change lives now and for future generations are being developed by Boehringer Ingelheim. As a top biopharmaceutical business focused on research, it adds value through innovation in highly unmet medical needs. Having been family-owned since its founding in 1885, Boehringer Ingelheim adopts a long-term, sustainable viewpoint. The two business groups, Human Pharma and Animal Health, employ more than 53,000 people to service more than 130 markets. Go to www.boehringer-ingelheim.com to learn more.

Regarding IBM

IBM is a top global supplier of Generative AI, hybrid cloud, and consulting services. They assist clients in over 175 countries to acquire a competitive advantage in their respective industries, optimize business processes, cut expenses, and capitalize on insights from their data. IBM’s hybrid cloud platform and Red Hat OpenShift are used by over 4,000 government and business institutions in critical infrastructure domains including financial services, telecommunications, and healthcare to facilitate their digital transformations in a timely, secure, and effective manner.

IBM clients are given open and flexible alternatives by IBM’s ground-breaking advances in artificial intelligence (AI), quantum computing, industry-specific cloud solutions, and consultancy. IBM has a strong history of upholding integrity, openness, accountability, diversity, and customer service.

Read more on Govindhtech.com