#add your data to azure openai service

[mobileappdevelopercompany]

Introducing an easy way to add your data to Azure OpenAI Service for enhanced, personalized applications. Explore the possibilities today!

[softssolutionservice]

How to Integrate AI Chatbots in Flutter Apps

In today's digital landscape, AI chatbots have become essential for delivering exceptional user experiences. As a Flutter developer, you're probably wondering how to seamlessly integrate these intelligent conversational interfaces into your mobile applications. This comprehensive guide will walk you through everything you need to know about implementing AI chatbots in Flutter apps.

Why AI Chatbots Are Game-Changers for Flutter Apps

Flutter's cross-platform capabilities combined with AI chatbots create powerful applications that can engage users around the clock. Whether you're building an e-commerce app, customer service platform, or educational tool, AI chatbots can significantly enhance user interaction and satisfaction.

The benefits are compelling and measurable. Businesses report reduced support costs by up to sixty percent, improved user engagement rates, instant response capabilities, and the ability to handle multiple conversations simultaneously. For Flutter development, this means creating more valuable applications that users actually want to keep using.

Popular AI Chatbot Services for Flutter Integration

The AI chatbot ecosystem offers numerous options, each with distinct advantages. OpenAI's GPT models have gained significant popularity due to their sophisticated conversational abilities and straightforward integration process. These models excel at generating human-like responses and can handle complex queries across various topics.

Google's Dialogflow stands out for its natural language understanding capabilities and seamless integration with other Google services. This platform particularly shines when building apps that need to handle complex conversation flows and structured data extraction from user messages.

Microsoft Azure Bot Service provides enterprise-grade chatbot capabilities with robust security features and excellent scalability options. This service is ideal for applications that require strict compliance standards or need to handle high volumes of conversations.

Planning Your Implementation Strategy

Creating an effective chat interface requires careful consideration of user experience principles and mobile design best practices. Your chat interface should feel familiar to users while providing clear visual distinction between user messages and AI responses.

Modern chat interfaces typically feature bubble-style messages with different colors or alignments for users and AI responses. Consider implementing timestamps, read receipts, and typing indicators to create a more engaging and informative experience.

Setting Up API Communication

Establishing reliable communication with AI services forms the backbone of your chatbot implementation. This involves creating service classes that handle HTTP requests, manage authentication, and process responses appropriately.

Most AI services require API keys for authentication, which should never be exposed in client-side code. Instead, consider implementing environment variables or, for production applications, a backend proxy that handles sensitive API communications.

Error handling becomes crucial when dealing with external services. Network interruptions, API rate limits, and service outages can all impact your chatbot's functionality. Implementing graceful degradation and appropriate user feedback ensures your app remains usable even when AI services are temporarily unavailable.

Managing Chat State and Data Flow

Effective state management is essential for creating responsive and reliable chat experiences. Your application needs to track conversation history, manage loading states, and handle real-time updates efficiently.

Message persistence adds significant value to your chatbot implementation. Users expect their conversation history to remain available when they return to your app. This can be achieved through local storage solutions or cloud-based synchronization, depending on your app's requirements and privacy considerations.

Enhancing User Experience with Advanced Features

Beyond basic message exchange, several features can significantly improve your chatbot's user experience. Typing indicators provide immediate feedback when the AI is processing requests, reducing user uncertainty during response delays.

Rich media support allows your chatbot to share images, links, formatted text, and interactive elements. This capability transforms simple text conversations into engaging, multimedia experiences that can provide more comprehensive assistance to users.

Performance and Security Considerations

Mobile applications face unique performance challenges, and AI chatbots can impact app responsiveness if not implemented carefully. Always handle API calls asynchronously to prevent interface blocking, and implement appropriate loading states to keep users informed during processing.

Security and privacy considerations are paramount when integrating AI chatbots. User conversations often contain sensitive information, making data protection a top priority. Implement proper data encryption for stored conversations and ensure secure transmission of messages to AI services.

Testing and Deployment Best Practices

Comprehensive testing ensures your AI chatbot provides reliable service across various scenarios and edge cases. Test with different message types, lengths, and formats to verify your implementation handles diverse user inputs appropriately.

Network connectivity testing is particularly important for chatbot applications. Verify your app's behavior during network interruptions, slow connections, and complete offline scenarios. Users should receive clear feedback about connectivity issues and potential resolution steps.

Common Challenges and Solutions

API rate limiting represents one of the most common challenges when integrating third-party AI services. Implement request queuing and throttling mechanisms to handle rate limits gracefully, providing users with appropriate feedback when delays occur.

Context management becomes complex as conversations grow longer or span multiple sessions. Design your conversation flow to handle context appropriately while avoiding overwhelming the AI service with excessive historical data.

User expectation management is crucial for successful chatbot deployment. Clearly communicate your chatbot's capabilities and limitations, providing easy escalation paths to human support when needed.

Future Trends and Enhancement Opportunities

The AI chatbot landscape continues evolving rapidly, presenting numerous opportunities for enhancement and innovation. Voice integration represents a significant trend, allowing users to interact with your chatbot through speech rather than text input.

Multi-language support opens your application to global audiences, though it requires careful consideration of cultural differences and region-specific AI service capabilities. Personalization features that adapt to individual user preferences and behavior patterns can significantly improve user engagement and satisfaction.

Conclusion

Integrating AI chatbots into Flutter apps represents more than just adding a new feature – it's about fundamentally enhancing how users interact with your application. The combination of Flutter development efficiency and AI's conversational capabilities creates opportunities for building truly innovative mobile experiences.

[transcriptionservicephiladelphia]

The Pitfalls of AI: Common Errors in Automated Transcription (and How Physicians Can Avoid Them)

Is AI transcription safe for doctors and clinicians to use? If you thought that AI transcription was “good enough” for your patients’ EMRs, it might be time to think again. Common errors in AI transcription can lead to a myriad of problems, from small errors in notes to potentially life-threatening alterations to the patient record. Then, of course, is the question: Who’s responsible when AI gets it wrong?

Here are some of the common errors in AI transcription, along with two ideas on how to avoid them.

4 Ways AI Transcriptions Jeopardize Patient Care

1. AI Transcription Bots Can’t Accurately Recognize Accents

Many of the common errors in AI transcription can be traced back to accents alone. A recent Guide2Fluency survey found that voice recognition software like ChatGPT has problems with almost all accents in the United States. It doesn’t seem to matter where you live: Boston, New York, the South, Philadelphia, Minnesota, the LA Valley, Hawaii, or Alaska – they’re all in the Top 30 regions. And that just covers speakers born within the U.S.… You and I may ask for clarification when confused by an accent, but AI can’t (or won’t).

2. Technical Jargon – Like Medical Terms – Are Confounding for AI

If AI can’t recognize “y’all” with a southern accent, how is it expected to recognize “amniocentesis”? In turn, the words AI tries to spell get confusing for clinicians going back to the patient record. De-coding “Am neo-scent thesis” isn’t always as easy as it looks, especially if similar errors happen a dozen times in a given transcript. AI transcription software just isn’t built to recognize medical terms well enough.

3. AI Hallucinations

Then there’s the problem of AI hallucinations. A recent Associated Press article points out that OpenAI’s Whisper transcription software can invent new medications and even add commentary that the physician never said. These common AI errors can have “really grave consequences” according to the article – as we can all well imagine. In one example, AI changed “He, the boy, was going to, I’m not sure exactly, take the umbrella.” to “He took a big piece of a cross, a teeny, small piece … I’m sure he didn’t have a terror knife so he killed a number of people.” Clearly, there can be medical and legal implications with these changes to the patient record. Who’s responsible when AI gets it wrong?

4. AI Transcription Does Not Ensure Privacy

You can almost guarantee that AI transcription software is storing that data somewhere in its system – or a third party’s. OpenAI uses Microsoft Azure as its server, which prompted one California lawyer to refuse signing a consent form to share medical information with Microsoft. OpenAI says it follows all laws (which presumably includes HIPAA), but that assertion would likely need to be tested in court before anyone knows for sure. Either way, the horse might already be out of the barn regardless of what a court finds…

How to Avoid Common AI Transcription Errors Corrupting Patient Records

There are two main ways physicians and clinicians can reduce the risk of these and other common errors in AI transcription from corrupting your patients’ EMRs.

1. Use Human Transcription instead of AI Transcription

This is the most straightforward approach. According to that same Associated Press report, “OpenAI recommended in its online disclosures against using Whisper in ‘decision-making contexts, where flaws in accuracy can lead to pronounced flaws in outcomes.’” If there is any situation where flaws in accuracy lead to flaws in outcomes, patient EMRs is certainly one of them. Preferred Transcriptions’ medical transcription services provide greater accuracy for better outcomes.

2. Use AI Transcription Editing Services

Busy doctors are more likely to miss errors because they are already burned out with administrative tasks. Plus, you may think AI transcription is helping relieve this documentation burden, but it may be adding to it instead when the doctor has to fix so many errors. Some companies now offer AI transcription editing services that can help clean up these errors, making the final review faster and easier for the busy clinician.

Contact Preferred Transcriptions to Reduce Common Errors in AI Transcription

Why leave your patient records to chance? Preferred Transcriptions provides fast, accurate transcription services that eliminates all common AI transcription errors. Not only will our trained medical transcriptionists reduce your documentation burden, they can help preserve better patient outcomes, too. Call today at 888-779-5888 or contact us via our email form. We can get started on your transcription as early as today.

Blog is originally published at: https://www.preferredtranscriptions.com/ai-transcription-common-errors/

It is republished with the permission from the author.

[mtsuhail]

The Future of Java Full-Stack Development

As technology evolves, so does the role of Java full-stack developers. Staying ahead in this field requires understanding the trends shaping the industry and adapting to emerging tools and practices. From cloud-native development to artificial intelligence integration, the future of Java full-stack development looks promising and dynamic.

In this blog, we’ll explore five trends that are redefining Java full-stack development and how developers can prepare for the future.

1. Rise of Microservices Architecture

The Trend

Monolithic applications are giving way to microservices, where applications are broken into smaller, independent modules. Java frameworks like Spring Boot are ideal for building microservices, allowing developers to create scalable, modular applications.

What This Means for Developers

Master tools like Docker and Kubernetes for containerizing and managing microservices.

Gain expertise in API development to enable seamless communication between services.

Explore cloud platforms like AWS, Azure, or Google Cloud for deploying microservices-based applications.

2. Cloud-Native Development

The Trend

Cloud-native applications, designed to run efficiently on cloud platforms, are becoming the norm. Java’s compatibility with cloud technologies and tools like Spring Cloud positions it as a key player in this trend.

What This Means for Developers

Learn to integrate Java applications with cloud services.

Familiarize yourself with cloud-native tools like AWS Lambda, Azure Functions, and Google Cloud Functions.

Embrace DevOps practices like CI/CD pipelines to streamline cloud deployments.

3. Integration of Artificial Intelligence and Machine Learning

The Trend

AI and ML are transforming how applications operate, providing personalized user experiences and intelligent automation. Full-stack developers are increasingly required to integrate AI/ML capabilities into their applications.

What This Means for Developers

Explore Java libraries like Deeplearning4j and Weka for machine learning.

Work with APIs from platforms like TensorFlow and OpenAI for AI integration.

Develop a foundational understanding of data science to process and analyze data effectively.

4. Increased Emphasis on Security

The Trend

With rising cyber threats, building secure applications is a top priority. Java full-stack developers are expected to implement robust security measures at every layer of the stack.

What This Means for Developers

Learn secure coding practices to prevent vulnerabilities like SQL injection and XSS attacks.

Use Java’s Spring Security framework to add authentication and authorization.

Stay updated on regulations like GDPR and HIPAA to ensure compliance.

5. Growth of Serverless Architectures

The Trend

Serverless computing is gaining traction, allowing developers to build and run applications without managing servers. Java’s compatibility with serverless platforms like AWS Lambda makes it a strong contender in this space.

What This Means for Developers

Master serverless frameworks like AWS SAM and Serverless Framework.

Understand the pricing and scaling models of serverless platforms to optimize application costs.

Build lightweight, event-driven applications using Java.

How to Stay Ahead in Java Full-Stack Development

To thrive in this ever-evolving field:

Stay Updated: Follow industry blogs, attend webinars, and join developer communities.

Upskill Continuously: Invest in advanced certifications and explore emerging technologies.

Build Future-Ready Projects: Experiment with microservices, AI integration, and cloud deployments in your projects.

Collaborate: Engage with teams working on diverse technologies to broaden your expertise.

Conclusion

The future of Java full-stack development is exciting, filled with opportunities for innovation and growth. By staying informed about these trends and adapting to new technologies, you can position yourself as a sought-after professional in the tech industry.

Are you ready to embrace the future of Java full-stack development? Start exploring these trends today and prepare to build cutting-edge applications!

[drmikewatts]

Weekly Review 8 November 2024

Some interesting links that I Tweeted about in the last week (I also post these on Mastodon, Threads, Newsmast, and Bluesky):

AI that build better AI, without human involvement or intervention, is something we need to be very careful about: https://arstechnica.com/ai/2024/10/the-quest-to-use-ai-to-build-better-ai/

Honestly, he's not wrong about AI being hyped. And I agree that in time it will become useful, once the hype has died down: https://www.tomshardware.com/tech-industry/artificial-intelligence/linus-torvalds-reckons-ai-is-90-percent-marketing-and-10-percent-reality

Web search is another area where AI is taking over: https://www.bigdatawire.com/2024/11/01/openai-and-google-clash-in-the-evolution-of-ai-powered-search/

AI services is having a small but measurable impact on Microsoft's profitability: https://arstechnica.com/gadgets/2024/10/microsoft-reports-big-profits-amid-massive-ai-investments/

You don't need GPU to run AI, it can be done in CPU: https://www.theregister.com/2024/10/29/cpu_gen_ai_gpu/

How AI is affecting jobs and the workplace: https://www.datasciencecentral.com/the-impact-of-ai-powered-automation-on-workforce-dynamics-and-job-roles/

If the training data isn't open, then the AI isn't open: https://www.bigdatawire.com/2024/10/28/osi-open-ai-definition-stops-short-of-requiring-open-data/

Another way AI is affecting the climate-AI run in data centers, which use a lot of concrete in their construction, and concrete production releases carbon: https://spectrum.ieee.org/green-concrete

A point-by-point overview of ChatGPT: https://www.techrepublic.com/article/gpt-4-cheat-sheet/

Generative AI is now being rolled-out to Gmail: https://www.theverge.com/2024/10/28/24282103/gmail-help-me-write-email-web-ai-gemini

Here the AI is helping programmers be more productive, rather than replacing them. But given the known security issues with AI-generated code, is it too much to have 25% generated by AI? https://arstechnica.com/ai/2024/10/google-ceo-says-over-25-of-new-google-code-is-generated-by-ai/

Generative AI comes with a lot of legal risks: https://www.informationweek.com/machine-learning-ai/the-intellectual-property-risks-of-genai

Five things that Generative AI is expected to impact in 2025: https://www.techrepublic.com/article/generative-ai-trends-2025/

Microsoft is focusing on running AI inferencing in Azure rather than training: https://www.theregister.com/2024/10/31/microsoft_q1_fy_2025/

A swarm of cooperating agents might be the way to truly powerful AI: https://www.computerworld.com/article/3594235/agentic-ai-swarms-are-headed-your-way.html

An overview of AI in healthcare: https://www.datasciencecentral.com/how-ai-is-shaping-the-future-of-the-healthcare-industry/

You could achieve general AI with a billion people using abacuses. That doesn't mean it's feasible: https://futurism.com/sam-altman-agi-achievable-current-hardware

Am I being cynical in thinking that an AI powered web search engine is going to hallucinate web sites? https://www.stuff.co.nz/world-news/360472566/openai-adds-search-chatgpt-challenging-google

The current tools an AI developer needs to be familiar with: https://www.informationweek.com/machine-learning-ai/the-essential-tools-every-ai-developer-needs

Good clean data is essential for training AI. Here are ten Python commands that help clean data: https://www.kdnuggets.com/10-useful-python-one-liners-for-data-cleaning

Combining AI with Google maps: https://www.theverge.com/2024/10/31/24283970/google-maps-gemini-ai-answer-questions

This is the best use of AI in journalism-using it to support their work by transcribing recordings, rather than trying to replace the reporters entirely: https://arstechnica.com/ai/2024/10/the-new-york-times-shows-how-ai-can-aid-reporters-without-replacing-them/

If you're training your AI with other people's work, you really should know what plagiarism is: https://techcrunch.com/2024/10/30/perplexitys-ceo-punts-on-defining-plagiarism/

Giving instructions in hexadecimal can defeat AI guardrails, in this case tricking ChatGPT into writing exploit code: https://www.theregister.com/2024/10/29/chatgpt_hex_encoded_jailbreak/

[govindhtech]

Azure Database for PostgreSQL AI-powered app development

PostgreSQL is a popular open-source database framework with many features and a reputation for reliability and adaptability. Microsoft Azure Database for PostgreSQL is a highly scalable and convenient cloud-based solution that uses PostgreSQL. You can concentrate on creating incredible, AI-powered applications instead of worrying about maintaining your PostgreSQL instances thanks to Azure’s fully managed service.

In order to provide you with more information about how Azure Database for PostgreSQL enables customers to migrate their PostgreSQL databases and create intelligent applications, this blog will expose you to a variety of new learning opportunities, including two Cloud Skills Challenges. As if that weren’t thrilling enough, finishing a challenge entitles you to a drawing for a fantastic prize. Let’s move forward now!

Azure Database for PostgreSQL backup

Seamless development of apps and database migration

Embrace smooth deployments, automated patching, and built-in high availability in place of laborious maintenance chores. One completely managed service that makes moving current PostgreSQL databases to the cloud easier is Azure Database for PostgreSQL. You can concentrate on your apps while we take care of the scaling, backups, and patching.

Adaptability to different needs and budgets is ensured by seamless integration with PostgreSQL, which reduces code modifications during the transition. Data and schema transfers to the cloud are made simple with Azure Database for PostgreSQL’s migration capabilities.

Beyond migration, Azure Database for PostgreSQL facilitates the creation of applications driven by artificial intelligence. Effective storage and querying of vector embeddings is made possible by its native support for the pgvector extension, which is crucial for AI and machine learning applications. The service offers developers a comprehensive toolkit for creating intelligent applications by integrating seamlessly with existing Azure AI services, including Microsoft Azure AI Language, Microsoft Azure AI Translator, Azure Machine Learning, and Azure OpenAI Service.

Furthermore, the scalability of the service guarantees optimal performance as AI workloads increase, preserving cost effectiveness throughout the development process. All in all, Azure Database for PostgreSQL offers a complete solution for cloud migration as well as for developing potent artificial intelligence applications.

Here are a few salient characteristics:

High availability: Automatic patching, updates, zone-redundant high availability, and up to 99.99% uptime are all guaranteed.

Automating performance: Find ways to enhance query performance with suggestions for query stores and indexes by getting an analysis of your database workloads.

Security: consists of Azure IP Advantage, which shields companies and developers using Azure from intellectual property risks, and Microsoft Defender, which protects your data using open-source relational databases.

Azure AI add-on: Create and save vector embeddings, use Azure AI services, and develop AI-enabled applications straight from the database.

Support for migration: To facilitate the switch from Oracle to Azure databases for PostgreSQL, tools are offered.

Budget-friendly: offers complete database optimization and monitoring solutions that can result in operational savings of up to 62% when compared to on-premises, thus lowering the total cost of ownership.

Create AI Applications using Azure PostgreSQL Databases

This learning path, which is intended for developers who are interested in integrating AI into their PostgreSQL applications on Azure, explains how to do so by utilizing the Azure AI extension for Azure Database for PostgreSQL.

You will obtain a thorough understanding of the Azure AI extension and its numerous functions by finishing this learning route. Learn the differences between extractive, abstractive, and query-focused summarization, assess several summarization methods made available by Azure AI services and the azure_ai extension, and apply generative AI summarization methods to data in a PostgreSQL database. During this practical training, you will get the ability to use Azure AI services and the azure_ai extension to create intelligent apps that can condense and enlighten complex content into manageable summaries.

Set up PostgreSQL to be migrated to Azure Database

The knowledge and abilities you’ll need to operate with Azure Database for PostgreSQL efficiently are provided via this study route. Before getting into more practical topics like connecting to databases, running queries, and making sure security is tight, it starts with a basic overview of PostgreSQL architecture and fundamental ideas.

Additionally, you will learn how to use stored procedures and functions to repurpose code, as well as how to design and manage databases, schemas, and tables. You’ll feel more confident setting up, maintaining, and moving current PostgreSQL databases to Azure once you understand how Azure Database for PostgreSQL handles ACID transactions and write-ahead logging for data integrity and durability.

To win Azure prizes, finish timed challenges

Two matching Azure Database for PostgreSQL Cloud Skills Challenges have also been put together to go along with these learning courses. Cloud Skills Challenges combine an interactive learning sprint with a friendly competition between you and thousands of your friends worldwide, in contrast to the self-paced, isolated activities that characterize learning paths. In order to provide students with a comprehensive education, these immersive, gamified learning environments combine lessons, exams, and practical tasks.

If you finish at least one of these tasks before the timer goes off, you’ll instantly be added to a drawing to win one of 20 fantastic Azure prizes. Join the competition when these challenges begin on June 11, 2024!

Attend POSETTE 2024 to network with PostgreSQL specialists

POSETTE 2024, originally known as Citus Con and hosted by Microsoft, is an exciting development conference devoted to anything PostgreSQL. This is a special chance to hear from professionals, connect with other Postgres fans, and discover the newest developments in database technology.

Microsoft will be demonstrating their dedication to the open-source database system as a major participant in the PostgreSQL community. During a session titled “Azure Database for PostgreSQL: A Look Ahead,” attendees may anticipate hearing from Azure’s experts about their plans for the service and how it will work with other Azure products.

POSETTE, or Postgres Open Source Ecosystem Talks, Training, and Education, is a free online event that takes place from June 11 to June 13, 2024. It features four distinct livestreams. All scheduled talks will be available for viewing online right away following the event; registration is not required. Seize the opportunity to engage with the Microsoft team and discover how we are propelling PostgreSQL’s advancement in the cloud.

Take your Azure Database for PostgreSQL adventure to the next level

PostgreSQL and Azure Database for PostgreSQL are an ideal combination for creating cutting-edge, scalable, and AI-powered applications, regardless of your level of development experience. Azure Database for PostgreSQL enables customers to create complex AI applications and migrate to the cloud with ease. It does this by providing strong migration tools and a smooth connection with AI and machine learning services.

Check out the POSETTE 2024 livestreams to discover more about what you can achieve with the most sophisticated open-source database in the world, and get started with Azure’s two learning tracks and their corresponding Cloud Skills Challenges to enter a drawing for awesome Azure prizes.

Read more on Govindhtech.com

[migrateshop21]

Build Your Own AI Companion: A ChatGPT Clone for Everyone

The rapid advancements in artificial intelligence (AI) have ushered in a new era of possibilities, revolutionizing the way we interact with technology. At the forefront of this transformation lies the development of conversational agents, or chatbots, capable of engaging users in natural and intuitive dialogues.

Among these innovative solutions, OpenAI's ChatGPT has captured the imagination of businesses and individuals alike, showcasing the remarkable ability to generate human-like text with astonishing fluency.

Understanding ChatGPT

Let's take a moment to define ChatGPT and discuss its rising popularity before getting into the technical specifics. The Generative Pre-trained Transformer (GPT) architecture serves as the foundation for the language model ChatGPT.

 Its purpose is to use the input it gets to produce writing that is both logical and appropriate for the situation. This makes it perfect for use in customer service, virtual assistants, content creation, and more.

Why Build a ChatGPT Clone?

Creating your own ChatGPT clone has a number of benefits.

Customization: Make the AI buddy fit your unique requirements and tastes.

Cost-effectiveness: Take complete control of your AI infrastructure and do not pay membership fees.

Data privacy: Host the AI model on your own servers to guarantee that private information is kept safe.

Innovation: To produce a distinctive user experience, try out new features and functionalities.

Getting Started

To construct a ChatGPT clone, adhere to these guidelines:

Select a Platform: Make the decision to host the model on your own servers or to use a cloud-based platform such as AWS, Google Cloud, or Azure.  So Migrateshop’s ChatGPT clone script is the perfect choice for that.

Get the Model: OpenAI or other sites may provide the pre-trained GPT model. Verify that you are authorized to use the model and that you have the required licenses.

Install the necessary software and libraries to set up the environment. Usually, this involves PyTorch, TensorFlow, Python, and other dependencies.

Fine-Tune the Model: Adjust the model to your unique dataset in order to make it more unique. By doing this, the AI companion will be better able to comprehend the situation and give more precise answers.

Develop the Interface: Provide an intuitive user interface so that users may communicate with the AI companion. This might be a desktop program, a Smartphone application, or even an online application.

Put Features in Place: To improve your AI companion's functionality, add capabilities like speech recognition, real-time messaging, and service integration.

Test and Deploy: Make sure the AI companion performs as anticipated by giving it a thorough test. Once content, publish it on the platform of your choice.

Key Features to Include

The following features should be taken into consideration while developing an engaging AI companion:

Natural Language Understanding: Verify that the AI can appropriately interpret and process user input.

Context Awareness: To ensure a flawless user experience, empower the AI to preserve context over a variety of interactions.

Personalization: Give people the option to alter the demeanor and answers of their AI friend.

Security and privacy: To safeguard user data, put strong security measures in place.

Top Techniques

Put the User Experience First: Create a user-friendly interface that is entertaining to use so that communicating with the AI is pleasurable.

Constant Improvement: Based on user input and developments in AI technology, update and enhance the AI companion on a regular basis.

Ethical Considerations: Make sure your AI friend follows moral principles and refrains from acting in an unethical manner.

In summary

Constructing a ChatGPT clone appof your own is a fun and fulfilling endeavor that can lead to new developments in AI personalization and creativity. This blog post will walk you through the process of creating a powerful AI companion that is customized to your requirements.

This initiative presents a unique chance for developers, company owners, and AI enthusiasts to fully utilize conversational AI.

Launch your adventure now and create your very own AI travel buddy!

[hajsadhku]

New easy way to add your data to Azure OpenAI Service

Say goodbye to limitations and hello to limitless possibilities! Azure OpenAI Service lets you run cutting-edge models on your own data, creating personalized applications and mind-blowing experiences.

[waqasblog2]

40 Must-Read Machine Learning, Artificial Intelligence, and Deep Learning Blogs

To keep up to date on the latest industry news, blogs are a valuable, yet sometimes overlooked, resource. They educate. They more narrowly focus on niche areas that textbooks and news publications may gloss over. And they provide fascinating insight into how some of the top minds in a particular industry work.

In the world of artificial intelligence, there is an abundance of blogs that offer illuminating perspectives on recent trends, new products, and industry news. For any budding machine learning engineer, spending time in the AI blogosphere will not only help your prospects for career advancement but also keep you connected to the broader AI community.

However, there are tons of machine learning, artificial intelligence, and deep learning blogs out there. So, to save you some time, we compiled a list of blogs that you should keep on your radar.

Some of these are affiliated with companies and academic institutions, while others are personal endeavors by industry professionals. We’ve only featured AI blogs that have been updated in the last two years.

Here are 40 machine learning, artificial intelligence, and deep learning blogs you should add to your reading lists:

Machine Learning Blogs

A great introduction to machine learning and AI, Machine Learnings features helpful articles on how this technology may affect your work and life. The site’s non-technical guide to artificial intelligence is also particularly useful for those who are interested in jumping into the world of machine learning but don’t have the technical background.

If you only choose to follow a few select blogs, this should be one of them. Updated frequently, the content on this site is curated and created by employees of Microsoft. It encompasses topics like AI adoption, machine learning, and deep learning, and features posts on Microsoft’s Azure platform and Cortana Intelligence Suite.

Professional developer and machine learning practitioner Jason Brownlee started this blog years ago as a resource to help other developers become well-versed in ML. Today, it remains a top-referenced blog for industry professionals looking to broaden their knowledge of ML concepts. Brownlee’s blog is updated frequently and is a treasure chest of educational information on AI.

Microsoft principal researcher John Langford is the architect and author behind this blog, which is a favorite in the ML community. Langford provides insightful commentary on machine learning theory, posts about potential job opportunities, and contributes updates about the International Conference on Machine Learning.

In this company blog, Algorithmia, a Seattle-based DevOps firm, provides tech rundowns, deep dives, and introductory posts on how AI technology works. Some of its most popular posts of 2018 included “Hardware for Machine Learning,” “Facial Recognition Through OpenFace,” and “Introduction to Machine Learning.”

Branded as a “machine learning craftsmanship blog,” i am trask is a blog from AI researcher Andrew Trask and features tutorials along with other helpful AI guides.

Shakir Mohamed, a research scientist at DeepMind, writes about machine learning tips and tricks on his blog, named after an English publication that circulated in the early 18th century. Many of Mohamed’s articles, including a recent post on making artificial intelligence truly global, are thought-provoking and consider the broader implications of AI on the world around us.

This blog, run by economist Zygmunt Zając, seeks to be a go-to platform for entertaining and easy-to-read articles on machine learning. FastML’s blog posts tackle topics like overfitting, pointer networks, chatbots, and more.

Data science company Domino offers up a bounty of AI-related content on its ML blog, covering industry best practices and subjects like machine learning models and captive learning.

Distill is technically not a blog—it’s an online research journal dedicated to machine learning. However, the scientific journal’s content and fresh presentation style warrant it a mention on this list. Distill aims to present AI research in a more user-friendly way, incorporating reactive diagrams and compelling graphics that help the reader to more easily understand the research.

Artificial Intelligence Blogs

If you’re looking for first-hand insight and tips on breaking into the field of AI, you’re right where you belong! Springboard’s AI and machine learning blog posts include practical guides, tutorials and other bits of career advice on how totransition into a machine learning engineer role.

The news site AI Trends provides comprehensive coverage of the latest AI-related technology and business news. It features interviews with top industry executives, thought-leadership pieces, as well as in-depth articles on the business of AI.

Dive deep into the world of AI with this O’Reilly Media blog devoted to the subject. Drawing on the expertise of industry influencers, O’Reilly’s blog posts show how companies are utilizing AI technology, highlighting the latest ideas, tools, and solutions in the industry.

In this company blog, researchers and engineers at Google provide a riveting look at how the tech giant is incorporating AI and ML technology into its products, like its remarkable translation tools. Through this blog, you’ll get a glimpse into the Google AI team’s research and thought process.

This news stream provides the latest on what’s happening in AI at the Massachusetts Institute of Technology, which is considered one of the leading academic institutions in AI research. This news stream includes stories on AI research being conducted at the university, as well as briefs on AI-related campus events.

You’ll find BAIR research findings, AI perspectives, and other content on this blog written by students, post-docs, and faculty of this highly rated UC Berkeley program, which brings together researchers across the areas of computer vision, machine learning, natural language processing, planning, and robotics.

Geared toward business leaders seeking to leverage AI technology to their company’s advantage, Topbots provides useful information on how businesses can use AI to grow.

AI solutions provider Indico offers interesting insight into machine learning, deep learning, and other AI-related topics on this company blog.

DeepMind is a U.K.-based artificial intelligence company that was acquired in 2014 for more than $500 million by Google. On this company blog, DeepMind team members share information on their research, important milestones, and other company news in a clear and compelling fashion.

Google engineer and Jetpac founder Pete Warden explains AI concepts in a simple and clear manner on his blog.

This blog—which boasts more than 125,000 views per month—sources content from researchers, developers, entrepreneurs, and other individuals who have a passion for AI. Becoming Human articles touch on the future of this technology, explaining AI concepts and providing analysis on how AI can impact human lives.

AI is one of the central topics that 33rd Square includes in its coverage. On this site, you’ll find a mix of videos and articles that explore the applications and the implications of AI.

This blog features articles on businesses’ AI efforts, analysis on industry trends, and coverage of AI-related conferences and notable AI influencers. The site also features a helpful news stream that will keep you abreast of the latest AI developments.

A good resource for legal professionals, Artificial Lawyer is a blog focusing on how AI technology will drive the automation of legal work. Founded by Richard Tromans, the site accrues around 110,000 page views per month and has been recognized as a top legal and technology blog by various sources.

MarkTechPost publishes articles on a variety of technology topics, artificial intelligence among them. Scroll through this site and you’ll find a host of intriguing articles on how AI technology is being deployed, as well as educational posts explaining machine learning and other related topics.

A news outlet dedicated to covering data science and AI, insideBIGDATA features news stories, white papers, and reports, as well as interviews with data scientists and AI professionals.

Hal Daume III, presently a principal researcher at Microsoft, was inspired to create this blog in 2005 after noticing that, while there were machine learning research blogs at the time, he couldn’t find an open forum dedicated to natural language processing. On this blog, Daume writes about that topic, along with computational linguistics and other related subjects.

Analytics Vidhya’s site provides a vast toolbox of educational content for data science professionals, including lots of material focused on AI, machine learning, and deep learning. The organization’s blog provides guides on topics such as neural networks and overviews of how technology like facial recognition works.

There are tons of fascinating AI-related articles floating out there on Medium. Follow this news stream and it will ensure that you stay up to date on all of them.

Seattle-based Avanade Insights is a provider of digital and cloud services and IT consulting. Its blog offers tips and tricks on how AI technology can help simplify and enhance a business’ operations.

While it’s not technically a blog, Gengo’s resource page certainly acts like one. There, you can find AI predictions, interviews with notable industry experts, and educational articles like this one.

In this blog, Google data scientists publish articles they believe would be interesting to those data scientists employed outside of Google. As the name suggests, this blog isn’t officially affiliated with the tech giant, but that fact doesn’t make the site’s content any less interesting. Machine learning is among the topics the site’s authors explore.

OpenAI is a nonprofit dedicated to AI research that is sponsored by big names like Elon Musk and Peter Thiel. The focus of its blog is to communicate its research, which is absolutely worth exploring.

Deep Learning Blogs

California tech company Nvidia shares its corporate news and announcements, along with other deep learning-related stories on this company blog.

If you’re a beginner to deep learning and machine learning concepts, Adit Deshpande’s deep learning blog should be on your list of sites to peruse. Deshpande is currently a computer science undergraduate at the University of California, Los Angeles. His posts are engaging, easy to understand, and he sometimes incorporates real-life happenings into his blog posts—like applying machine learning to March Madness, for instance—to help explain complex ideas.

The director of AI at Tesla, Andrej Karpathy is considered a top expert in the field of deep learning. He used to post on a GitHub site, but is now publishing articles on Medium, where you can find his latest takes.

Former Google Brain team member Denny Britz focuses on deep learning concepts in this blog, providing tutorials and other in-depth posts. WildML also features a useful deep learning glossary for those who want to brush up on their vocabulary.

With a tagline of “making deep learning accessible,” the blog of Tim Dettmers, a Ph.D. student at the University of Washington, includes long-form articles and constructive guides on deep learning concepts.

Chatbot Blogs

Chatbots Life is a prime resource for information on chatbots and AI. This blog is beginner-friendly, offering a host of tutorials, how-to articles, and other educational tools to those who are interested in learning more about chatbots.

You’ll be able to find a variety of educational posts and news articles on chatbots and how they’re used on this site. Chatbots Magazine—which touts itself as the No. 1 place to learn about chatbots—also delves into topics like AI, machine learning, and natural language processing.

If you’re looking for mentor-guided, career-focused training in this space, check out Springboard’s AI / Machine Learning Career Track, the first of its kind to come with a job guarantee.

The post 40 Must-Read Machine Learning, Artificial Intelligence, and Deep Learning Blogs appeared first on Springboard Blog.

This content was originally published here.

[thelmasirby32]

What the commoditization of search engine technology with GPT-3 means for Google and SEO

30-second summary:

Google’s technological edge has always come from its computational power.

This edge is no longer special. AWS, Microsoft, and other cloud services now give us access to essentially unlimited computing power on demand.

Generative Pre-trained Transformer 3 (GPT-3) technology is the largest most advanced text predictor ever. It will eventually be available as a commercial product.

We may see big players like Apple enter the search engine market.

Founder and CTO of LinkGraph gives you foresight into the sea of opportunities ahead.

The tech-world recently geeked out after its first glimpse into OpenAI’s GPT-3 technology. Despite some kinks, the text predictor is already really good. From generating code to writing GoogleAds copy, to UI/UX design, the applications of GPT-3 have sparked the imaginations of web developers everywhere. 

This changes everything.

With GPT-3, I built a Figma plugin to design for you.

I call it "Designer" pic.twitter.com/OzW1sKNLEC

— Jordan Singer (@jsngr) July 18, 2020

But they should also be sparking the imaginations of search engine optimizers. In its debut week, we already witnessed a developer build a search engine on top of GPT-3. It can hardly rival Google’s product, but the potential is clearly there. OpenAI plans to turn GPT-3 into a commercial product next year, meaning any brand could use the technology to create their own search platform. 

The implications would be significant for the SEO landscape. More brands innovating their own search engines would create new opportunities for digital marketers and the brands we help build. For Google, though, the potential is far more nerve-wracking. GPT-3 has shown that Google’s core technological advantages – natural language processing (NLP) and massive computing – are no longer unique and are essentially being commoditized.

GPT-3 challenges Google’s technological edge from all directions

Few have monetized NLP and machine learning as well as Google, but their technological edge has always been computational power. Google’s ability to crawl billions of pages a day, its massive data centers, and its extensive computing across that data, have cemented their status as the dominant search engine and digital advertising market leader.

But AWS, Microsoft Azure, and other cloud services now give us access to essentially unlimited computing power on demand. A decade of Moore’s Law has also reduced the cost of this computing power by one-to-three orders of magnitude. 

Additionally, open-source software and advances in research have made it easier for developers to access the latest breakthroughs in NLP and machine learning technology. Python, Natural Language Toolkit (NLTK), Pytorch, and Tensorflow are just a few that have granted developers access to their programming and software innovations. 

Yes, Google still has BERT, which shares similar architecture with GPT-3. But GPT-3 is slightly larger (by about 175-billion parameters). GPT-3 also doesn’t need nearly as large of a training data set as Google’s BERT.

Not only will GPT-3 add value to those businesses and applications that are already using AI and machine learning with a newer, larger, and significantly improved NLP model, it would also equip Google’s biggest Cloud competitors with the ability to pair that technology with their own computing power. 

Other players will soon be able to build massive search engines like Google did

In order to build a search engine, you need to be able to retrieve many different types of information: Web results for one, but also maps, data, images, and videos. Google’s indexing power is what catapulted them to be the primary retriever of all web knowledge. 

In addition to building that massive information retrieval system, Google monetized on those economic searches through advertising. But the majority of searches Google doesn’t actually earn any money on. 

OpenAI built its own information retrieval system GPT-3 so that it could create superintelligence. If OpenAI wanted to, they could build a competitor to Google. But the hardest part would be bringing this to a massive audience. Bing’s market share is only 6.5% while Yahoo’s is 3.5%. 

It’s been a long time since the search engine market was a realistic place to compete. But what if a GPT-3 commercial product equipped a new competitor with an equally-matched technological edge, market share, cloud service, and devoted customer-base to enter the search market? 

Let’s say a competitor like Apple. They launched a newly redesigned Apple Maps earlier this year. They already announced they are indexing the web through Applebot. When it comes to launching the next best search engine, Apple is well-positioned.

How could Apple change the SEO landscape with its own search engine?

Most likely, an Apple search engine would use ranking factors similar to Google. The app store ecosystem would equip Apple with greater use of in-app engagement data. We could also see a greater reliance on social signals from Facebook and Twitter.

All android devices currently ship with Chrome + Google Search as the default search OS. Apple’s devices ship with Safari and you can select your own preferred search OS. It could easily do what Google has done and default to its own search engine. With just one iPhone model launch, Apple could transition its massive customer-base away from Google through its technological edge and dominance with devices. 

But what would be most troublesome for Google is how Apple could disrupt Google’s massive ads ecosystem. 71% of Google’s revenue comes from advertising. With Google Ads now being the most expensive (and competitive) place to advertise on the internet, many advertisers would welcome a disruption. It’s possible we could see billions of dollars of advertising revenue shift to Apple.

For SEOs and digital marketers, it’s fun to imagine. We could see entirely new markets for search, creating more need for our expertise and additional platforms our customers can use to grow. We’re not quite there yet, but SEOs and digital marketers should be prepared for what advancements like GPT-3 could potentially mean for our industry.

Manick Bhan is the founder and CTO of LinkGraph, an award-winning digital marketing and SEO agency that provides SEO, paid media, and content marketing services. He is also the founder and CEO of SearchAtlas, a software suite of free SEO tools. He is the former CEO of the ticket reselling app Rukkus.

The post What the commoditization of search engine technology with GPT-3 means for Google and SEO appeared first on Search Engine Watch.

from Digital Marketing News https://www.searchenginewatch.com/2020/08/21/what-the-commoditization-of-search-engine-technology-with-gpt-3-means-for-google-and-seo/

[govindhtech]

AI Collaboration Between Microsoft Office and SAP Production

Microsoft Office and SAP and have worked together for over 30 years to develop breakthrough business solutions and accelerate business transformation for thousands of clients. Microsoft Cloud leads the market for SAP workloads on the cloud, including RISE with SAP. At SAP Sapphire 2024, Microsoft are pleased to present more incredible innovation to their collaborative customers.

SAP and Microsoft

This blog discusses Microsoft Office and SAP latest announcements and how they assist customers accelerate business transformation.

Microsoft 365 Copilot and SAP Joule announce AI integration.

Microsoft Office and SAP are expanding our partnership at SAP Sapphire by combining Joule and Microsoft Copilot into a unified experience that lets employees get more done in the flow of their work by seamlessly accessing SAP and Microsoft 365 business applications.

Customers want AI assistants to complete queries regardless of data location or system. The generative AI solutions Joule and Copilot for Microsoft 365 will integrate intuitively to let users locate information faster and complete activities without leaving the site.

Copilot for Microsoft 365 users can use SAP Joule to access SAP S/4HANA Cloud, Success Factors, and Concur data. Someone utilizing SAP Joule in a SAP application can use Copilot for Microsoft 365 without context switching.

Microsoft AI and SAP RISE enable business change

SAP systems store mission-critical data that powers fundamental business processes and can benefit from AI insights, automation, and efficiencies. Microsoft Cloud, the most reliable, complete, and integrated cloud, may help you attain these benefits. SAP may improve RISE with a variety of Microsoft AI services to maximize business results tailored to your needs:Credit to Microsoft Azure

Collaboration with SAP and Microsoft Azure on AI innovation: SAP’s BTP platform enhances RISE with SAP. We announced SAP AI Core, an essential aspect of BTP, including Generative AI Hub and Joule on Microsoft Azure in West Europe, US East, and Sydney, alongside SAP.

Azure makes it easy for BTP users to add insight to their financial, supply chain, and order-to-cash processes. SAP users can use the most popular large language models like GPT-4o in SAP Generative AI Hub, available only on Azure through Azure OpenAI Service.

Microsoft Copilot unlocks end-user productivity: SAP clients may utilize Copilot for Microsoft 365 in Microsoft Word, PowerPoint, Excel, and PowerBI to unlock insights and complete tasks faster. The SAP plugin in Microsoft Copilot Studio lets you customize Copilot with your data, processes, and policies. You can customize Copilot to link to SAP systems and retrieve expenditure, inventory, etc. data. New Copilot connectors and agent features unveiled at Microsoft Build last week enable this.

Microsoft azure and SAP

Enabling AI transformation with Azure AI services: SAP customers utilizing Azure may quickly construct generative AI applications using the Azure OpenAI Service and SAP data. Azure AI offers over 1,600 frontier and open models, including the latest from OpenAI, Meta, and others, giving you the flexibility to choose the best model for your use case. Today, over 50,000 customers use Azure AI, demonstrating its rapid growth.

Microsoft Office and SAP

OpenAI combined with business process automation solutions like Microsoft Power Platform, which offers SAP connectors, can boost productivity by making SAP data interaction easier and task completion faster.

Cloud Application programming model (CAP) and SAP BTP, AI Core allow RISE with SAP clients to use OpenAI services.

Automated SAP attack disruption with AI: Microsoft Security Copilot helps security and IT professionals discover and manage SAP-related cyberthreats quickly.

Microsoft Sentinel for SAP, certified for RISE with SAP, automatically detects financial fraud and disables the native SAP and Microsoft Extra account to prevent the cyberattacked from transferring funds without additional intervention.

Greater SAP BTP on Azure availability increases choice and flexibility.

SAP and Microsoft recently announced the expansion of SAP BTP to six new Azure regions (Brazil, Canada, India, the UK, Germany, and one to be announced) and additional BTP services in seven public Microsoft Azure regions. After completion, SAP Business Technology Platform will run in 13 Microsoft Azure regions and offer all the most requested BTP services.

The expanded service availability, based on customer demand, allows customers to integrate SAP Cloud ERP and SAP BTP services with Microsoft Azure services, including AI services from both companies, in the same data Centre region, making AI innovation and business transformation easier.

New and powerful Azure SAP HANA infrastructure choices

The Azure M-Series Mv3 family of memory-optimized virtual machines, announced last year, provides faster insights, more uptime, a lower total cost of ownership, and better price-performance for SAP HANA workloads with Azure IaaS and SAP RISE. These VMs, which enable up to 32 TB of memory, use 4th-generation Intel Xeon Scalable CPUs and Azure Boost, Azure’s latest infrastructure innovation.

They are glad to build on this investment and announce that the Mv3 Very High Memory (up to 32TB) offering is generally available and the Mv3 High Memory offering (up to 16TB) is under preview.

SAP Signavio and Microsoft collaborate to accelerate transformation

Microsoft Office and SAP collaborate to help customers adopt S/4HANA and RISE on Azure. SAP Signavio Process Insights, part of SAP’s cloud-based process transformation suite, helps firms quickly identify SAP business process improvements and automation opportunities. SAP Signavio Process Insights on Microsoft Azure accelerates S/4HANA for ECC customers, unlocking innovation with Microsoft.

New Microsoft Teams connectors simplify business collaboration. For years, Microsoft Office and SAP have collaborated to boost productivity by combining SAP mission-critical data with Microsoft Teams and Microsoft 365.

Upcoming Microsoft Teams S/4HANA app features

S/4HANA Copilot plugin: Using natural language, users may retrieve S/4HANA sales information, order status, sales quotes, and more using Copilot for Microsoft 365.

Card Loop Modifiable Components: Exchange intelligent cards with Outlook and Teams (restricted pre-release).Credit to Microsoft Azure

Message extension for Teams based on search: Users can quickly look up and add S/4HANA data without ever leaving Teams.

Search SAP Community and share content : Search SAP Community and share content with coworkers in Microsoft Teams without leaving the app. Click the three dots below the text box, open the SAP S/4HANA app, and input your search phrase in the popup window while speaking with a colleague or group. Select a topic from the results list and email your peers.

Share Card to MS Teams: Microsoft Teams provides a collaborative view that opens application content in a new window for meaningful conversations with coworkers. S/4HANA is available in Outlook and Microsoft 365 Home.

New release: SuccessFactors for Microsoft Teams Chatbot-based HR task reminders A private message from the SuccessFactors Teams chatbot can assist you execute HR activities in Teams or direct you to SuccessFactors online for more sophisticated tasks.

Command-Based Quick Actions: Request and give feedback to coworkers, manage time entries, view learning assignments and approvals, access employee and management self-services, and more!

Microsoft and SAP partnership clients modernize identification

They stated earlier this year that they are working with SAP to develop a solution to convert identity management scenarios from SAP Identity Management (IDM) to Microsoft Entrap ID. Very thrilled to share that migration guidance will be available soon.

Microsoft Office and SAP partnership for customer success

Product innovation that drives customer success and business outcomes is thrilling. Microsoft was an early adopter of RISE with SAP internally and is happy to have helped thousands of clients speed their business transformation using the Microsoft Cloud.

To spur continuous innovation, Hilti Group, a construction provider, transformed its massive SAP landscape to RISE with SAP on Microsoft Azure. It also changed its SAP S/4HANA ERP application from 12 to 24 terabytes, closing its on-premises datacenter to utilize Azure only. Using RISE with SAP, which combines project management, technical migration, and premium engagement services, Hilti aimed to be among the first.

The company’s massive Microsoft Office and SAPlandscape, which underpins its transactional operations, proved to be the perfect platform for evolving its business transformation solution an expedited, on-demand solution.

Read more on Govindhtech.com

[govindhtech]

Microsoft and MongoDB Cloud Services

Microsoft and MongoDB Alliance: Key Customer Benefits

Microsoft must remember that data fuels AI as Microsoft enter the AI era. This explains why Microsoft want Azure to be the best data destination. Customers can choose from relational, non-relational, open source, and caching databases in Azure. Microsoft also have strong partnerships with MongoDB Inc. to allow digital transformation leveraging their databases as Azure managed services.

MongoDB, a renowned data platform, makes data modeling easy for developers. Microsoft connection with MongoDB has grown over the years, culminating in a multiyear strategic partnership deal this year. Microsoft are proud of their collaboration to offer Azure a wonderful place to run MongoDB Atlas. The popularity of the MongoDB Atlas on Azure pay-as-you-go self-service has made MongoDB one of our top Azure Marketplace partners in the previous six months.

Microsoft wants to empower everyone to succeed, and their customers prefer using MongoDB to build apps. In year one of Microsoft’s strategic collaboration, Microsoft worked with MongoDB to help their customers do more with Microsoft services and MongoDB Atlas on Azure. Developers now use MongoDB Atlas in 40+ Azure areas worldwide, including Doha, Qatar, which Microsoft announced last month at Ignite. It’s not just about the data center developers need an easy way to start with MongoDB Atlas on Azure. With its code suggestions, GitHub Copilot makes it easy to build MongoDB applications on Azure. They are working together to optimize its performance utilizing MongoDB schema, among other things.

Customers are already benefiting from their strategic partnership. Their collaboration with Temenos helped their banking customers rise to historic levels. Another collaborative intelligence business, Mural, highlighted their MongoDB Atlas and Microsoft Azure experience to help clients communicate smarter.

Microsoft Ignite 2023: MongoDB

MongoDB Atlas on Azure client experience is improved by ongoing efforts. Microsoft and MongoDB announced three major integrations at Microsoft Ignite 2023 in November: Microsoft Semantic Kernel, Microsoft Fabric, and EF Core. How can customers profit from each?

Semantic Kernel, an open-source SDK, integrates OpenAI, Azure OpenAI, and Hugging Face with C# and Python. At Ignite, MongoDB revealed Semantic Kernel native support for Atlas Vector Search. Customers may integrate operational data and vectors in a single, managed platform with MongoDB Atlas Vector Search. Semantic Kernel lets customers add Atlas Vector Search to apps. This allows Atlas Vector Search to engage with retrieval-augmented generation (RAG) in large language models (LLMs), minimizing AI hallucinations and other benefits.

By uniting your teams on an AI-powered platform optimized for AI, Microsoft Fabric can transform how they work with data. Many applications use MongoDB Atlas as their operational data layer to store data from internal enterprise apps, customer-facing services, and third-party APIs across numerous channels. With interfaces for Microsoft Fabric pipelines and Dataflow Gen2, Microsoft customers can mix MongoDB Atlas data with relational data from traditional applications and unstructured data from logs, clickstreams, and more.

At Microsoft Ignite, innovative announcements made this integration seamless and straightforward for MongoDB clients. Microsoft revealed that Fabric is now broadly accessible and introduced Mirroring, a frictionless approach to integrate and manage cloud data warehouses and databases like MongoDB, in Fabric, during the first presentation. Now MongoDB customers can replicate a snapshot of their database to One Lake, which will automatically sync it in near real-time. Learn how Microsoft Fabric intelligent analytics can uncover MongoDB Atlas data value here.

Many of the millions of C# developers use Entity Framework (EF) Core, a lightweight, extensible, open source, and cross-platform data access technology. MongoDB revealed MongoDB Provider for EF Core in Public Preview. This lets EF Core developers construct C#/.NET apps with MongoDB using their preferred APIs and design patterns.

Each time, Microsoft worked with MongoDB to make it easy for developers, data engineers, and data scientists to connect MongoDB data to Microsoft services.

A year of collaboration improvement

These new integrations follow a successful year of Microsoft-MongoDB collaboration. Microsoft highlighted great developer news outside Microsoft Ignite:

August 2023 saw the general release of MongoDB for VS Code Extension. During its public preview, developers downloaded the MongoDB extension over 1 million times in VS Code, the world’s most popular IDE. This free, downloadable add on lets developers build apps and manage MongoDB data from VS Code.

MongoDB integrates with many Microsoft Intelligent Data Platform (MIDP) services, such as Azure Synapse Analytics for operational data analysis, Microsoft Purview for data security, and Power BI for data analysts to create and share dashboards using live MongoDB Atlas data.

Data Federation: Atlas Data Federation supports Azure Blob Storage in private preview on Microsoft Azure.

Microsoft have published tutorials on building server less functions with MongoDB Atlas and Azure Functions, creating MongoDB applications with Azure App Service, building Flask and MongoDB applications with Azure App Container Apps, developing IoT data hubs for smart manufacturing, and connecting MongoDB Atlas and Azure Data Studio for Azure customers.

The year has been terrific for Microsoft and MongoDB, helping enterprises of all sizes do more with their data.

Read more on Govindhtech.com

[govindhtech]

Transform LLMOps with AI Mastery!

LLMOps should employ acceptable AI tools and processes LLMOps must address the problems and risks of generative AI as improve it. Data security and privacy, low-quality or ungrounded outputs, misuse and overreliance on AI, hazardous content, and AI systems vulnerable to adversarial attacks like jailbreaks are common problems. Building a generative AI application requires identifying, measuring, mitigating, and monitoring these risks.

Some of the obstacles of constructing generative AI applications are typical software challenges that apply to many applications. Role-based access (RBAC), network isolation and monitoring, data encryption, and application monitoring and logging are security best practices.

Microsoft provides many tools and controls to help IT and development teams solve these predictable concerns. This blog will discuss the probabilistic AI issues of constructing generative AI applications.

First, implementing responsible AI principles like transparency and safety in a production application is difficult. Without pre-built tools and controls, few firms have the research, policy, and engineering resources to operationalize responsible AI. Microsoft takes the greatest cutting-edge research ideas, considers policy and consumer feedback, and produces and integrates practical, responsible AI tools and techniques directly into AI portfolio.

This post covers Azure AI Studio’s model catalog, quick flow, and Azure AI Content Safety. To help developers deploy responsible AI in their businesses, document and share her learnings and best practices.

Mitigation and evaluation mapping to LLMOps livecycle

Generative AI models have risks that must be mitigated by iterative, layered testing and monitoring. Typically, production applications have four technological mitigation layers: LLMOps,model, safety system, metaprompt and grounding, and user experience. Platform layers like the model and safety system include built-in mitigations for many applications.

Application purpose and design determine the next two layers, therefore mitigations might differ greatly. They will compare these mitigation layers to massive language model operations below.

Loop of ideas and exploration: Add model layer and safety system safeguards

One developer explores and evaluates models in a model library to determine whether they meet their use case in the first iteration loop of LLMOps. Responsible AI requires understanding each model’s harm-related capabilities and limitations. Developers can stress-test the model using model cards from the model developer and work data and prompts.

Model

The Azure AI model library includes models from OpenAI, Meta, Hugging Face, Cohere, NVIDIA, and Azure OpenAI Service, classified by collection and task. Model cards describe in depth and allow sample inferences or custom data testing. Some model suppliers fine-tune safety mitigations within their models.

Model cards describe these mitigations and allow sample inferences or custom data testing. Microsoft Ignite 2023 also saw the launch of Azure AI Studio’s model benchmark function, which helps compare library models’ performance.

A safety system

Most applications require more than model-based safety fine-tuning. Big language models can make mistakes and be jailbroken. Azure AI Content Safety, another AI-based safety technology, blocks hazardous content in many Microsoft applications. LLMOps,Customers like South Australia’s Department of Education and Shell show how Azure AI Content Safety protects classroom and chatroom users.

This safety runs your model’s prompt and completion through categorization models that detect and prohibit harmful content across hate, sexual, violent, and self-harm categories and adjustable severity levels (safe, low, medium, and high).

Azure AI Content Safety jailbreak risk and protected material detection public previews were announced at Ignite. Azure AI Content Safety can be used to deploy your model using the Azure AI Studio model catalog or big language model apps to an endpoint.

Expand loop with metaprompt and grounding mitigations

After identifying and evaluating their desired large language model’s essential features, developers go on to guiding and improving it to fit their needs. This is where companies can differentiate their apps.

Metaprompt and foundation

Every generative AI application needs grounding and metaprompt design. Rooting your model in relevant context, or retrieval augmented generation (RAG), can greatly increase model accuracy and relevance. Azure AI Studio lets you easily and securely ground models on structured, unstructured, and real-time data, including Microsoft Fabric data.

Building a metaprompt follows getting the correct data into your application. An AI system follows natural language metaprompts (do this, not that). A metaprompt should let a model use grounded data and enforce rules to prevent dangerous content production or user manipulations like jailbreaks or prompt injections.

They prompt engineering guidance and metaprompt templates are updated with industry and Microsoft research best practices to help you get started. Siemens, Gunnebo, and PwC make custom Azure experiences utilizing generative AI and their own data.

Assess mitigations

Best-practice mitigations aren’t enough. Testing them before releasing your application in production will ensure they perform properly. Pre-built or custom evaluation processes allow developers to evaluate their apps using performance measures like accuracy and safety metrics like groundedness. A developer can even design and evaluate metaprompt alternatives to see which produces higher-quality results aligned with corporate goals and ethical AI standards.

Operating loop: Add monitoring and UX design safeguards

The third loop depicts development-to-production. This loop focuses on deployment, monitoring, and CI/CD integration. It also demands UX design team collaboration to enable safe and responsible human-AI interactions.

The user experience

This layer focuses on end-user interaction with massive language model applications. Your interface should help consumers comprehend and apply AI technologies while avoiding dangers. The HAX Toolkit and Azure AI documents include best practices for reinforcing user responsibility, highlighting AI’s limitations to avoid overreliance, and ensuring users are using AI appropriately.

Watch your app

Continuous model monitoring is a key LLMOps step to keep AI systems current with changing social behaviors and data. Azure AI provides strong capabilities to monitor production application safety and quality. Build your own metrics or monitor groundedness, relevance, coherence, fluency, and similarity rapidly.

Outlook for Azure AI

Microsoft’s inclusion of responsible AI tools and practices in LLMOps proves that technology innovation and governance are mutually reinforcing.

Azure AI leverages Microsoft’s years of AI policy, research, and engineering knowledge to help your teams build safe, secure, and reliable AI solutions from the start and apply corporate controls for data privacy, compliance, and security on AI-scale infrastructure. Look forward to developing for her customers to assist every organization experience the short- and long-term benefits of trust-based applications.

Read more on Govindhtech.com

[govindhtech]

LLMOps Success Stories: Real-World Impact

AI has repeatedly accelerated business growth by improving operations, personalising customer interactions, and launching new goods and services. Generative AI and foundation model shifts in the last year are accelerating AI adoption in organisations as they see Azure OpenAI Service’s potential. They also recommend new tools, methods, and a fundamental change in how technical and non-technical teams work to grow AI practises. 

Large language model operations (LLMOps) describe this change. Azure AI has several features to support healthy LLMOps before the name was coined, drawing on its MLOps platform roots. But at our Build event last spring, Microsoft introduced prompt flow, a new capability in Azure AI that raises the bar for LLMOps. Last month, Microsoft released the public preview of prompt flow’s code-first experience in the Azure AI Software Development Kit, Command Line Interface, and VS Code extension. 

LLMOps and Azure AI in particular will be discussed in greater detail today. Microsoft launched this new blog series on LLMOps for foundation models to share our learnings with the industry and explore its implications for organisations worldwide. The series will explore what makes generative AI distinctive, how it may solve business problems, and how it encourages new types of teamwork to build the next generation of apps and services. The series will also teach organisations safe AI practises and data governance as they develop today and in the future. 

From MLOps to LLMOps

The latest foundation model is often the focus, but building systems that use LLMs requires selecting the right models, designing architecture, orchestrating prompts, embedding them into applications, checking them for groundedness, and monitoring them with responsible AI toolchains. Customers who started with MLOps will realise that MLOps practises prepare them for LLMOps. 

LLMs are non-deterministic, so Microsoft must work with them differently than typical ML models. A data scientist today may define weights, regulate training and testing data, spot biases using the Azure Machine Learning responsible AI dashboard, and monitor the model in production. 

The best practises for modern LLM-based systems include quick engineering, evaluation, data grounding, vector search configuration, chunking, embedding, safety mechanisms, and testing/evaluation. 

Like MLOps, LLMOps goes beyond technology and product adoption. It’s a mix of problem-solvers, processes, and products. Compliance, legal, and subject matter experts commonly work with data science, user experience design, and engineering teams to put LLMs to production. As the system grows, the team needs to be ready to think through often complex questions about topics such as how to deal with the variance you might see in model output, or how best to tackle a safety issue.

Overcoming LLM-Powered app development issues

An LLM-based application system has three phases:

Startup or initialization: You choose your business use case and quickly launch a proof of concept. This step includes choosing the user experience, data to pull into it (e.g., retrieval enhanced generation), and business questions concerning impact. To start, create an Azure AI Search index on data and utilise the user interface to add data to a model like GPT 4 to construct an endpoint.

Evaluation and Refinement: After the Proof of Concept, experiment with meta prompts, data indexing methods, and models. Prompt flow lets you construct flows and experiments, run them against sample data, evaluate their performance, and iterate if needed. Test the flow on a larger dataset, evaluate the results, and make any necessary changes. If the results meet expectations, continue.

Production: After testing, you deploy the system using DevOps and use Azure AI to monitor its performance in production and collect usage data and feedback. This data is used to improve flow and contribute to early stages for future iterations.

Microsoft strives to improve Azure’s reliability, privacy, security, inclusiveness, and correctness. Identifying, quantifying, and minimising generative AI harms is our top priority. With powerful natural language processing (NLP) content and code generating capabilities through (LLMs) like Llama 2 and GPT-4, Microsoft have created specific mitigations to assure responsible solutions. Microsoft streamline LLMOps and improve operational preparedness by preventing errors before application production.

Responsible AI requires monitoring findings for biases, misleading or inaccurate information, and addressing data groundedness concerns throughout the process. Prompt flow and Azure AI Content Safety help, but application developers and data scientists shoulder most of the responsibilities.

Design-test-revise during production can improve your application.

Azure accelerates innovation for companies

Microsoft has spent the last decade studying how organisations use developer and data scientist toolchains to build and expand applications and models. Recently, their work with customers and creating their Copilots has taught us a lot about the model lifecycle and helped them us streamline LLMOps’ workflow with Azure AI capabilities. 

LLMOps relies on an orchestration layer to connect user inputs to models for precise, context-aware answers. 

The quick flow feature of LLMOps on Azure is notable. This makes LLMs scalable and orchestrable, managing multiple prompt patterns precisely. Version control, flawless continuous integration, continuous delivery integration, and LLM asset monitoring are ensured. These traits improve LLM pipeline reproducibility and encourage machine learning engineers, app developers, and prompt engineers to collaborate. It helps developers produce consistent experiment results and performance.

Data processing is essential to LLMOps. Azure AI Integration is optimised to operate with Azure data sources such vector indices like Azure AI Search, databases like Microsoft Fabric, Azure Data Lake Storage Gen2, and Azure Blob Storage. This integration makes data access easy for developers, who can use it to improve LLMs or customise them.

Azure AI has a large model catalogue of foundation models, including Meta’s Llama 2, Falcon, and Stable Diffusion, in addition to OpenAI frontier models like GPT-4 and DALL-E. Customers can fast-start with little friction by employing pre-trained models from the model catalogue to reduce development time and computing costs. Developers can customise, evaluate, and deploy commercial apps confidently with Azure’s end-to-end security with unmatched scalability and comprehensive model selection.

Present and future LLMOps

Microsoft provides certification courses, tutorials, and training to help you succeed with Azure. Our application development, cloud migration, generative AI, and LLMOps courses are updated to reflect prompt engineering, fine-tuning, and LLM app development trends. 

However, invention continues. Vision Models were added to Azure AI model catalogue recently. Now, Azure’s vast catalogue offers a variety of curated models to the community. Vision provides image classification, object segmentation, and object detection models tested across architectures and delivered with default hyperparameters for reliable performance.

Microsoft will continue to enhance their product portfolio before their annual Microsoft Ignite Conference next month.

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Latest options for generative AI app creators

Generative AI is no longer a buzzword or “tech for tech’s sake.” Today, small and large companies across industries are using generative AI to create value for their employees and consumers. This has led to new methods including quick engineering, retrieval augmented generation, and fine-tuning to help enterprises use generative AI for their particular use cases and data. Innovation occurs along the value chain, from new foundation models and GPUs to unique applications of extant capabilities like vector similarity search and MLOps for generative AI. These fast expanding methods and technology will help enterprises improve generative AI application efficiency, accuracy, and safety. That means everyone can be more productive and creative!

Generative AI inspires new audiences to work on AI initiatives. Software developers who once thought AI and machine learning were for data scientists are selecting, customizing, evaluating, and deploying foundation models. Also, many company leaders feel a feeling of urgency to ramp up on AI technology to better grasp the opportunities, limitations, and hazards. This growth in addressable audiences excites Microsoft Azure and drives us to offer more integrated and personalized experiences that make responsible AI accessible to all skillsets. It also reminds us to invest in education so that all our clients can safely and ethically benefit from generative AI, regardless of their AI journey.

This month’s interesting news focuses on giving developers and data science teams more generative AI model options and application customization freedom. In the spirit of education, check out these core learning resources:

For Business leaders

AI Success Foundation: A Leader’s Guide: Learn from Microsoft, our customers and partners, industry analysts, and AI leaders to help your company succeed in AI transformation.

Change your business with Microsoft AI: This 1.5-hour learning route gives business leaders the skills and resources to implement AI. It examines responsible AI project planning, strategy, and scale.

Career Essentials in Generative AI: This 4-hour course covers AI fundamentals, generative AI capabilities, how to use it in your daily work, and responsible AI.

For builders

Introduction to generative AI: This 1-hour course explains LLMs, Azure OpenAI Service, and responsible AI solution planning.

Start Building AI Plugins With Semantic Kernel: Beginners will learn about Microsoft’s open-source orchestrator, Semantic Kernel, prompts, semantic functions, and vector databases in this 1-hour session.

Working with Azure Machine Learning generative AI models: In this 1-hour intermediate session, you’ll learn about the Transformer architecture and how to fine-tune a foundation model using Azure Machine Learning’s model catalog.

New, strong speech and vision foundation models in Azure AI

Azure are always searching for methods to help machine learning professionals and developers find, tweak, and integrate huge pre-trained AI models. A common hub for exploring Hugging Face, Meta, and Azure OpenAI Service foundation models was launched in May as a public preview. In another milestone, the Azure AI model library released a variety of new open-source vision models for image classification, object recognition, and picture segmentation this month. These models let developers simply add sophisticated, pre-trained vision models to their apps for predictive maintenance, smart retail store solutions, autonomous vehicles, and other computer vision situations.

Azure announced in July that Azure AI services would include OpenAI’s Whisper concept.Azure launched Whisper in Azure OpenAI Service and Azure AI Speech in public preview this month. Whisper transcribes audio in 57 languages. The foundation model can translate all those languages to English and provide transcripts with improved readability, complementing Azure AI strengths. Customers may quickly and accurately transcribe huge amounts of audio content using Whisper and the Azure AI Speech batch transcription API. We hope customers will use Whisper to make information more accessible.

Apply code-first experiences and model monitoring for generative AI to application development

MLOps for LLMs, or “LLMOps,” will help enterprises realize the full promise of generative AI as adoption accelerates and develops. At Microsoft Build 2023, we announced quick flow features in Azure Machine Learning to create, experiment, evaluate, and deploy LLM processes faster. This month, we previewed a code-first prompt flow experience in our SDK, CLI, and VS Code extension. Generative AI teams may now more easily use quick testing, optimization, and version control to move from ideation through experimentation to production-ready systems.

Deploying your LLM application in production isn’t the end. Data and user behavior can affect your application over time, resulting in obsolete AI systems that hurt business outcomes and put enterprises at regulatory and reputational risk. Azure Machine Learning previews model monitoring for generative AI applications this month. Users may now collect production data, analyze key safety, quality, and token usage metrics recurringly, receive crucial issue warnings, and view the results on a beautiful dashboard.

Enterprise search is changing with Azure Cognitive Search and Azure OpenAI Service

Microsoft Bing is changing how people find relevant web content. Bing will now intelligently evaluate your question and find the finest solutions from around the internet instead of displaying a long list of links. Additionally, the search engine provides clear, concise information with trustworthy data source connections. This search experience change simplifies and speeds up internet browsing.

If organizations could search, navigate, and analyze internal data as easily and efficiently, it would alter them. This new paradigm would let employees quickly access company knowledge and leverage enterprise data. This is Retrieval Augmented Generation architecture. With Azure Cognitive Search and Azure OpenAI Service, enterprises can streamline this experience.

Improve generative AI using Hybrid Retrieval and Semantic Ranking

Microsoft found that a combination of the following search techniques creates the most effective retrieval engine for most customer scenarios, especially in the context of generative AI, after extensive testing on representative customer indexes and popular academic benchmarks:

Chopping long content

Combining BM25 and vector search for hybrid retrieval

Activating semantic ranking

Developers of generative AI apps could try hybrid retrieval and reranking algorithms to improve results and please users.

Azure Cosmos DB vector search boosts Azure OpenAI Service application efficiency

Azure added sample code to their documentation and tutorials to help users understand Azure Cosmos DB and Azure OpenAI Service’s potential. Azure Cosmos DB vector search lets you maintain long-term memory and chat history in Azure OpenAI apps, enhancing user experience and LLM solution quality. Vector search lets you token-efficiently query the most appropriate context to personalize Azure OpenAI prompts. Storing vector embeddings with data in an integrated system reduces data synchronization and speeds AI app development.