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Voice Engineering Revolutionizing Consumer Interactions

In the fast-paced world of technological advancements, one innovation has taken center stage – voice engineering. The transformative capabilities of voice recognition technology are reshaping how consumers interact with brands across various industries. From virtual assistants like Alexa to speech-to-text applications, the potential of voice engineering is undeniable. In this article, we will explore how this revolutionary technology is poised to become the future of consumer interactions, revolutionizing the way businesses connect with their customers.Voice Search: A Preferred ChoiceVoice search has become an integral part of our daily lives, with 71% of users preferring voice-based queries over typing, according to a recent PWC survey. The popularity of digital assistants like Alexa and Siri further emphasizes the growing preference for voice technology. In 2022, a staggering 135.6 million users in the US alone had used a digital assistant at least once a month. The convenience and efficiency of voice search make it the go-to option for users seeking quick and accurate information.Seamless Hands-Free ComputingVoice recognition enables seamless hands-free computing, transforming how we interact with our devices. Tasks like writing emails, composing documents on platforms like Google Docs, and sending texts are now effortless through voice commands. Additionally, automatic closed captioning with speech recognition on platforms like YouTube and automatic translation features have extended content accessibility to a global audience.Smart Home Devices: The Ultimate ConvenienceThe voice commands for smart home devices have ushered in a new era of convenience. From controlling lights and thermostats to boiling water and setting reminders, voice recognition has turned our homes into smart, interconnected spaces. The projected revenue of the global smart home device market reaching $163.7 billion by 2028 indicates the growing significance of voice technology in shaping our daily lives.Customer Service EvolvedVoice recognition technology has emerged as a game-changer in customer service. Interactive Voice Response (IVR) systems and analytics have revolutionized call center services, offering cost-effective solutions available 24/7. By transcribing thousands of phone calls between customers and agents, businesses can identify common call patterns and issues, providing a more personalized and seamless customer experience.Innovative Business ApplicationsThe realm of voice engineering offers exciting opportunities for businesses to enhance customer engagement. Sales Development Reps (SDRs) calls can be automated with voice bots, streamlining the evaluation and qualification process for potential customers. Additionally, voice biometrics provide secure and user-friendly alternatives to traditional password authentication methods.Industry-Specific ApplicationsVoice technology is making significant impacts across diverse industries, catering to unique needs and challenges. In the automotive sector, in-car speech recognition systems have become standard, allowing drivers to access various features while keeping their focus on the road. In academic settings, speech recognition creates equitable learning platforms for children with visual impairments, evaluating language pronunciation through language learning tools.Voice Technology in the Indian ContextIndia’s mobile-friendly ecosystem is uniquely poised to embrace voice technology, with 99% of people accessing the internet through their phones. Android devices come equipped with voice technology, instantly offering millions of users access to the benefits of voice recognition. Even users without smartphones can utilize voice technology through feature phones running KaiOS, expanding access to a world of products and services.The Power of Voice: Empowering Brands and Consumers AlikeAs the adoption of voice technologies continues to surge, businesses are recognizing the immense potential it holds for cultivating customer delight and loyalty. Brands can begin by sharing existing information in a conversational format, enabling customers to access account details or order statuses with ease. For those targeting millennials, creating podcasts, playlists, recipes, quizzes, and contests are excellent ways to engage consumers using voice.Voice engineering is undoubtedly shaping the future of consumer interactions with all brands. With its transformative capabilities, voice recognition technology offers businesses an opportunity to create seamless and personalized experiences for their customers. Embracing voice technology today will empower brands to stay ahead of the curve and forge lasting connections with their customers in the dynamic digital landscape of tomorrow. As this technology continues to evolve, the possibilities are endless, and the impact on consumer interactions will be profound. The era of voice engineering has arrived, and it’s time for brands to adapt and thrive.

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Data-Centric Culture: Navigating Business Transformation

In the business landscape, considerations include the business itself, customers, vendors, regulations, and the economy. Despite their significance, technological disruption stands paramount. Amid trendsetters’ fleeting success and transformative disruptions, early adopters thrive. Survival hinges on adaptation in this digital age. As titled, this blog explores data’s reinvention of business. Embracing a data-centric culture empowers rule rewriting, innovation, and success in a tech-driven future.Industry Transformation:It was identified that 90% of data that has ever been generated was generated in the last decade. As surprising as it sounds are you really shocked?What brought us here?The availability of data, willingness to gather and share data, and the awareness of what value this data holds in decision-making.This collection of data has resulted in cheaper storage costs and higher accessibility to businesses. We are witnessing a transformation with the adoption of data-centricity — moving away from traditional application architectures and becoming more agile, data-centric architectures that can handle vast amounts of data in real time.The focus is shifting towards a data-driven culture, where data is utilized to generate value, support decision-making, and drive business growth.With data-centricity at its core, businesses are leveraging real customer data to create personalized experiences, optimize decision-making processes, and innovate new business models. This transformation leads to improved efficiency and enhanced cybersecurity, making businesses more resilient in the digital landscape.What all entails data centricity:Data-Driven Decision Making: Integrating data into all aspects of the organization’s processes, using real-time processing, flexible data storage, and automated data management for privacy and security.Data-Centric Architecture: Involves a central data repository, data accessor for big data communication, and components for efficient data management and integrity.Data-Centric Culture: Cultivated through employee data literacy training, improved data communication, and organization-wide data access to empower informed strategic decisions.How to stay competent with this you ask?The only correct answer is strategy!A strategy encompassing a 360 vision to calibrate the employees, the internal operations, and external factors.Here is what the industry leaders are doing:– Cloud Computing: EY employs cloud tech for agile, data-rich digital services. Cloud platforms with containers offer cost-effective agility.– Building leadership roles with vertices that solely focus on data.– AI/ML Insights: AI extracts insights, enabling intelligent, risk-aware decisions for innovation and advantage.– Value-Driven Data Governance: Strategic data governance breaks silos, fosters collaboration, supports decisions, ensuring integrity and understanding.What this transformation is looking for:Data Professionals: Businesses require skilled data professionals, such as data scientists, data analysts, and data engineers, who can handle large datasets, perform advanced analytics, and develop data-centric architectures. The data analytics market is expected to reach $329.8 billion by 2030, growing at a CAGR of 29.9%. (Source: Globenewswire.com)Chief Data Officer (CDO): CDOs play a crucial role in fostering a data-centric culture, setting the data agenda, and ensuring data use aligns with the business strategy.A data-centric culture presents immense opportunities for organizations to grow, innovate, and stay competitive in the ever-changing tech industry. By prioritizing data-driven strategies and involving the entire organization, you can harness the power of data to create personalized experiences, drive efficiency, and achieve your ambitious goals.To embark upon your journey to becoming a data-centric organization reach out to us at https://www.nineleaps.com/contact/

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Understanding LLMs: Unveiling the Language Revolution

When we trace back the timeline of advancements in artificial intelligence, LLM or “Large Language Models” hold a pivotal place. Over the past few months, I’ve been immersed in studying, working on, and analyzing LLMs. This article marks the beginning of my series on LLMs, where I will be recounting my experiences, discoveries, and learnings. Will dig deeper and divulge into understanding LLMs further in upcoming articlesUnderstanding LLMs: The Foundation of Modern AIAt its core, AI aims to replicate human abilities in machines, and one of the most big human abilities is language. LLMs (Large Language Models) play a significant role in this mission.What are LLMs?LLMs are like big knowledge sponges. They’ve read and learned from tons of text — from books, websites, and other sources. By studying all this information, they learn how language works, how people talk, and about a wide range of topics.Why are they called “Large”?It’s not just about the size of the data they learn from; it’s also about how complex their inner workings are. Imagine a massive maze where every turn is a tiny piece of language knowledge. LLMs have millions, sometimes billions, of these turns. This vast structure helps them remember and use what they’ve learned.How Do They Work?Think of LLMs as very advanced guessers. If you ask a question or start a sentence, they’ll guess the next word based on everything they’ve seen before. And they do this super quickly, which is why they can chat with us in real-time.Learning Like Humans?In some ways, yes. Just like a child learns language by listening and reading, LLMs learn from the text. The more they see, the better they get. But remember, they don’t “understand” like humans. They’re smart pattern finders.Why LLM’s are important: A Deeper Dive into Human Language UnderstandingHuman language isn’t just about words; it’s about meaning, intention, context, and emotion. Traditional models, though effective to an extent, often grapple with the many sides of language. They could interpret words but often missed the layered context that we, as humans, naturally infer. Here’s where LLMs make a significant difference:Grasping Nuances: Language is filled with subtleties like idioms, metaphors, and cultural references. LLMs, with their extensive training on vast textual datasets, have an edge in understanding these nuances. So, whether you’re using a local saying or a special term, LLMs can usually understand what you mean.Contextual Understanding: Imagine typing a query into a search engine like, “Best practices for following design patterns in your system architecture”. While a traditional model might offer generic results on design patterns, an LLM delves deeper. Recognizing the intricate connection between “design pattern” and “system architecture”, it could suggest specific design patterns ideal for robust system architecture.Enhanced Customer Interactions: Think of the last time you interacted with a customer support chatbot. The robotic, templated responses often lacked empathy and contextual understanding. LLMs are changing this way of doing things. By comprehending the sentiment and context behind a user’s query, they can craft responses that not only solve the problem but also resonate with the user’s emotions.Meaning-related Depth: Words often have multiple meanings based on context. For instance, the word “bank” can refer to a financial institution or the side of a river. LLMs are adept at determining the correct interpretation based on surrounding information, ensuring more accurate responses.Fluid Conversations: For applications like voice assistants, where conversations can meander and topics can shift rapidly, LLMs provide the fluidity needed. They can keep track of conversation threads and offer relevant inputs, much like conversing with another human.Multilingual Mastery: The global digital space is multilingual. LLMs can understand, generate, and translate across a multitude of languages, ensuring content and platforms are accessible to a broader audience.Error Mitigation: In areas like technical support or specialized queries, a slight misinterpretation can lead to drastically incorrect results. LLMs reduce such risks by cross-referencing vast knowledge bases and ensuring responses align with the user’s intent.In essence, while traditional models viewed language as a combination of words and syntax, LLMs perceive it as a dynamic interplay of meaning, context, and emotion. This shift in understanding, powered by expansive training and sophisticated algorithms, makes LLMs invaluable in our increasingly digital world.Challenges and Considerations:While LLMs are groundbreaking, they are not without challenges:Bias and Ethical Concerns: Given that LLMs are trained on vast amounts of data, they may inadvertently learn and propagate biases present in these datasets. Thus, deploying LLMs without scrutinizing their outputs can lead to unintentional and sometimes harmful misrepresentations.Resource Intensiveness: Training an LLM demands significant computational resources, which can be both expensive and environmentally taxing.Over-reliance: Depending too much on LLMs can lead to reduced human oversight and interaction, potentially missing out on the intuition and emotional intelligence that humans bring to the table.Looking Forward:The journey of LLMs, from their start to their present state, is nothing short of amazing. With the pace of technological advancements, the potential applications of LLMs seem boundless. However, as with any technological marvel, careful application and consistent evaluation are vital.In the upcoming articles in this series, we will delve deeper into the platforms and models like OpenAI, Azure, and Hugging Face, which have been instrumental in the rise of LLMs.Furthermore, LLMs are a big deal in the computer world. But like all cool stuff, we gotta use them right. Can’t wait to chat more about them in my next articles!Writer: Sriram C

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Navigating LLM Platforms: Pre-Deployed vs. Customizable

If you’re new to the world of Large Language Models (LLMs), make sure to check out my previous article, “ChatGPT: Breathing New Life into LLMs(Large Language Models),” for a quick primer.Now, let’s dive deeper into the intriguing world of LLMs by exploring the platforms that grant access to these powerful language models. In this article, we’ll closely examine three leading platforms: OpenAI, Azure, and Hugging Face.The Two Faces of LLM Platforms:When it comes to LLM platforms, they typically fall into two categories:Pre Deployed ModelsCustomisable DeploymentPre-Deployed Models:These platforms offer access to LLMs that are already up and running, allowing users to leverage the models within certain usage limits. Think of it as renting a high-end tool for specific tasks.Examples:OpenAI models (i.e., gpt, davinci etc) : To Learn more about how to integrate openAI gpt model with streaming, refer this article, Accelerating GPT-4’s Response Time with Streaming: A Simple ExplanationAzure OpenAI models (i.e., gpt models): To Learn more about how to get access to the azure OpenAI models, Please refer this article, Harnessing the Power of Azure OpenAI: Your Guide to Access, Usage, GPT-4, and Quota IncreasePros:Availabilty: Pre-deployed models are readily available, making them accessible for a wide range of users, including developers, businesses, and researchers.Quick Implementation: You can start using these models almost immediately, saving time on model development and training (Can be fine-tuned if needed)Ease of Use: These platforms provides both API’s as well as the SDK’s which simplify integration for developers, making it easier to incorporate these powerful models into applications and services.Vast Services: Azure offers a wide range of AI services beyond LLMs, including computer vision and machine learning, providing a comprehensive AI ecosystem.Managed Infrastructure: Pre-deployed models come with managed infrastructure, reducing the need for users to handle server setup and maintenance.Cons:Latency: Latency is the biggest concern for Pre-deployment publically accessed models like GPT. As it is public the traffic will be from every one who is using this publically available model. But using enterprise account of the platform can help us avoid the latencyUsage Limit: Most pre-deployed models impose usage limits, which can restrict usage of the model according to the tokens(1token = 4 words approximately) per minute and Requests per minute. Increasing the limit would need the quota request process to be followed according to the platform we are using(Currently OpenAI is not increasing the quota for gpt-4 which will directly affect the gpt-4 integrated products if the user base increases)Usage Cost: Accessing top-tier pre-deployed models can be expensive, especially for extensive usage, which might not be feasible for smaller businesses and projects. But, Cost is generally more manageable compared to customizable deploymentsDependence on Third-Party Services: Relying on pre-deployed models means you are dependent on the service provider’s availability and reliability, which can impact your own service uptime.Customizable Deployment:On the other side, some platforms empower users to deploy LLMs according to their unique requirements. You have the freedom to select from a pool of available models and fine-tune them to respond precisely how you need. It’s like having the raw materials and the workshop to craft your own tools.Examples:Platform: Hugging Face, Azure ML, Amazon SagemakerModels: Falcon-40B, LLAMA-70BNote: Considering hugging face as representative for customisable deployment . So when the hugging face platform is used in this article, it means i am referring to customisable deploymentsPros:Less latency issues: As we have deployed the model ourself paying of the resources needed for the deployment, we will have the model instance which is being used only from our application. So, traffic will only be from our platform which resolve latency issues from the model sideIn-Depth Customization: With Hugging Face’s platform, You have the capability to tailor models (like Falcon-40B and LLAMA) to meet highly specific requirements, which means you can fine-tune the model’s behaviour to deliver precisely the results you need, whether it’s for language understanding, text generation, or other NLP tasks..Wide Model Selection: Hugging Face provides access to a diverse collection of pre-trained models. This extensive library allows you to choose a starting point that closely matches your project’s needs, saving you time and effort.Transparency and Responsibility: You have direct control over ethical considerations, including addressing biases and ensuring responsible use. The customization process provides transparency, enabling you to understand and influence the model’s behavior in line with ethical guidelinesCons:Huge Costing: Here we dont have anything called pay as you options for deploying your own model and to pay only as you use. Instead we have to pay for the resources according the number of hours it is being up. This might actually cost more even though you are not using the model which is not the case in pre-deployed models where you only pay for what you have usedMaintenance: The owner has to worry about the availability of the deployment all the time for the user which will add more pressure on the owner to keep it alive. Should also calculate and think about the configurations we need to put in for scaling the model for varying or increasing user countSteep Learning Curve: The steep learning curve associated with customizable deployment can be particularly challenging for users with limited machine learning expertise. It involves understanding model architectures, data preprocessing, and deployment infrastructure, which may not be feasible for newcomers.High Computational Demands: Developing and fine-tuning models like Falcon-40B and LLAMA can be resource-intensive. These models require powerful hardware and significant computational resources, which may pose challenges for individuals or small teams with limited access to such resources.In conclusion, as we navigate the intricate landscape of Large Language Models (LLMs), the choice between pre-deployed models and customizable deployments depends on your project’s unique needs, budget, resources, and ethical considerations. Both avenues offer distinct advantages and challenges, empowering you to harness the power of language models in different ways.Regardless of your chosen path, the ability to fine-tune these models with custom data remains a powerful tool to tailor their behaviour to your specific requirements.In our upcoming articles, we will delve deeper into the practical aspects, guiding you on how to integrate and utilize both pre-deployed models and custom deployments using Node.js. Stay tuned for more insights into the fascinating world of LLMs.Author: Sriram C

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Revolutionizing GPT-4: Enhancing Responsiveness with Streaming

In the realm of cutting-edge technology, GPT-4 represents a remarkable milestone in the field of natural language processing. However, like any advanced tool, it faces certain challenges, one of which is response time. Fortunately, we’ve harnessed the power of streaming to elegantly address this challenge, significantly enhancing GT-4’s responsiveness. In this post, we will delve into how we’ve leveraged streaming to overcome slow response times and provide users with a smoother and more interactive experience.The Challenge: GPT-4’s Response TimeWhile GPT-4 is highly advanced, it can sometimes take a noticeable amount of time to generate responses. This delay can pose issues for applications that rely on real-time interactions. Imagine waiting several seconds for a chatbot to respond — clearly not an ideal user experience. So, the question arises: How can we make GPT-4 faster without compromising its quality? The solution lies in the concept of streaming.Introducing StreamingStreaming can be likened to watching a video while it’s still downloading — you don’t need to wait for the entire content to load before you start enjoying it. Similarly, with GPT-4, instead of waiting for the entire response to be generated, we begin sending chunks of the response as soon as they become available. This approach allows users to see meaningful content on their screens while the remaining response is being processed in the background.Why Streaming MattersBy implementing streaming, we’ve made substantial improvements to the user experience when interacting with GPT APIs. Users no longer have to endure the frustration of waiting for a complete response. They can engage with the content as it’s being generated. Whether it’s a chatbot, content recommendation system, or any other application powered by GPT-4, this approach ensures that interactions feel seamless, responsive, and engaging.Setting Up GPT-4 and StreamingBefore we delve into the code, let’s cover the prerequisites:1. Install Dependencies: Ensure that you have all the necessary dependencies installed. You can utilize libraries like OpenAI’s npm package (openai) for integrating GPT-4.2. GPT-4 API Key: Obtain an API key from OpenAI to authenticate your requests.Here, we are using a pre-deployed model. To learn more about this, refer this article, Exploring LLM Platforms and Models: Unpacking OpenAI, Azure, and Hugging FaceWith these prerequisites in place, you’re ready to proceed.Below is a code example illustrating how the streaming approach is implemented to handle real-time responses from GPT-4. This mechanism enables the generation of dynamic content, making applications like on-the-fly question generation and instant response evaluation possible without keeping the user waiting:// Import necessary dependencies // Define a function to handle AI response streaming public getAIStreamResponse = async (messages, res: Response, payload) => { try { let stream; // Create a chat completion request with streaming enabled const completion: any = await openai.createChatCompletion({ model: 'gpt-4', // Specify the GPT-4 model messages, // Pass in the conversation messages stream: true, // Enable streaming for real-time responses temperature: +process.env.INTERVIEW_PREP_OPEN_AI_MODEL_TEMPERATURE, // Set the model temperature }, { responseType: 'stream', // Specify the response type as 'stream' }); stream = completion.data; // Get the streaming data // Resolve and handle response chunks using the 'resolveResponseChunks' function const data = await this.resolveResponseChunks(stream, res, payload); return data; } catch (error) { // Handle any errors and log them this.logger.error(error); throw error; } } public resolveReponseChunks = (stream, res: Response, payload) => { let tableData = ''; return new Promise<string>((resolve, reject) => { let completeResponse = ''; res.setHeader('Content-Type', 'text/html; charset=UTF-8'); res.setHeader('Transfer-Encoding', 'chunked'); stream.on('data', (chunk) => { // Decoding and parsing data const decodedChunk = new TextDecoder().decode(chunk); const lines = decodedChunk.split('\n'); const parsedLines = lines .map((line) => line.replace(/^data: /, '').trim()) .filter((line) => line !== '' && line !== '[DONE]') .map((line) => JSON.parse(line)); for (const parsedLine of parsedLines) { // Processing parsed data const { choices } = parsedLine; const { delta } = choices[0]; const { content } = delta; if (content) { // Displaying dynamic content tableData += content; completeResponse += content; res.write(content); } } }); stream.on('end', () => { // Completing the response res.end(); resolve(completeResponse); }); }); }Initialization and Response Headers:This function is defined as resolveReponseChunks, and it takes three arguments: stream, res, and payload. It returns a promise that resolves to a string. Inside the function, variables tableData and completeResponse are initialized to empty strings. The res object represents the HTTP response that will be sent to the client. Response headers are set, specifying the content type and transfer encoding for the streaming response.Streaming Data Event Handling:The stream object, responsible for streaming data from GPT-4, is configured to listen for the ‘data’ event. As data chunks arrive, each chunk is decoded using TextDecoder, converted into lines, and then processed. The function establishes event handlers to process data as it arrives in chunks from the stream. It uses the on method to listen for the ‘data’ and ‘end’ events of the stream.‘data’ Event Handling:When data chunks arrive (stream.on('data', (chunk) => { ... })), the function performs the following tasks:Decodes the received chunk using TextDecoder().decode(chunk) to convert it into a readable string.Splits the decoded chunk into lines using split('\n'), assuming that each line corresponds to a message from the GPT-4 model.Processes each line by:Removing the ‘data: ‘ prefix and trimming any leading or trailing whitespace using line.replace(/^data: /, '').trim().Filtering out lines that are empty or contain ‘[DONE]’, as these lines typically indicate the end of the response.Parsing each remaining line as a JSON object using JSON.parse(line).Decoding and Parsing Data:Each line is first processed to remove any ‘data:’ prefix and unnecessary whitespace using replace and trim functions. Lines containing the text ‘[DONE]’ are filtered out since they signal the end of the response. The remaining lines are parsed as JSON objects using JSON.parse.Processing Parsed Data:The parsed data is an array of objects, and within these objects, the relevant content generated by GPT-4 is extracted. This content typically includes textual responses.Dynamic UI Update:If there is content present, it is added to both the tableData and completeResponse strings. Additionally, the content is written to the response object using res.write(content). This step ensures that the content is sent to the client as soon as it becomes available, creating a real-time and dynamic user experience.Streaming End Event Handling:When the ‘end’ event is triggered by the stream, it signifies that all data chunks have been received. At this point, the response is finalized by ending the response object using res.end(). The completeResponse is then resolved as part of the promise, making the accumulated content available for further use.Example of streaming response, integrated in one of our projects.ConclusionUsing streaming technology has completely changed how GPT-4 responds to users, making it faster and more interactive. Instead of waiting for the entire answer, users now get a quick and continuous flow of information. This makes using GPT-4 much smoother and engaging, as you don’t have to wait for everything to be finished before getting a response. It’s like having a conversation that flows naturally and quickly, thanks to streaming technology. This is a big leap forward in how we use and interact with GPT.Author: Ankit Kumar Jha

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Unlocking GPT-4: Azure OpenAI Integration Guide

In the ever-evolving landscape of AI and machine learning, OpenAI’s models have emerged as powerful tools for various applications. If you’re looking to leverage the capabilities of GPT-4 and integrating it into your Azure-based projects, you’re in the right place. This article will guide you through the steps to access OpenAI models from Azure and guide you on the procedure for requesting GPT-4 access and quota increases.Overview of Azure OpenAI and Its CollaborationOpenAI, an artificial intelligence research lab, and Microsoft Azure, a leading cloud service provider, have partnered to bring the next generation of AI models to the cloud. This collaboration allows developers to access OpenAI’s powerful models on Azure, transforming how AI can be integrated into applications and services.Advantages of Azure OpenAI Over Normal OpenAIAzure OpenAI provides several benefits over the standalone OpenAI. Firstly, Azure offers robust and scalable cloud-based infrastructure, which ensures reliable access and performance. Secondly, Azure adheres to stringent security and compliance standards, ensuring data privacy. Lastly, Azure OpenAI seamlessly integrates with other Azure services, providing a comprehensive AI development ecosystem.Accessing OpenAI Models from AzureCreate an Azure AccountIf you don`t have an Azure account, please create a new one following the steps mentioned in the azure documentation.OpenAI Resource on AzureOnce your azure account is created, please search for Azure OpenAI in the azure resources.If you dont have access to Azure OpenAI, you need to submit a request using this form from azure https://aka.ms/oai/access.Once your request is approved from Azure, you will get a confirmation email from Azure Cognitive Services, which looks like belowRequest for GPT-4 Access(Ignore this step if you want to continue without GPT-4 Model)By default, azure wont give you access for GPT-4, you have to fill this form https://aka.ms/oai/get-gpt4 to request for GPT-4 access.Once you submit above form, you will get some email confirmation from azure and it may take some days for azure to process your request. (I got GPT-4 access in 5 working days post request).Once you got access to GPT-4, you will receive some confirmation email as below from azure.Create Azure OpenAI deploymentSelect Azure OpenAI resource from Azure, then click on create button, it will open up a form where you have to provide your azure subscription, pricing and region where you want to host azure openAI details.Deploy OpenAI Model on AzureOnce you create deployment on azure openAI, you need to deploy a model on azure openAI to start using it.Click on the deployment you created, in the overview page, you will see an option to deploy a model. click on it and it will redirect you to azure openAI Deployments page.Click on create new deployment, it will open a popup where you need to select openAI model which you want to deploy and a name for your model deployment.Hurray you have successfully created a model deployment.Please note that currently GPT-4 model deployments are available in some regions only.Accessing Azure OpenAI APIs:Once your model deployment is success, on Azure AI Studio, you can go to playground to test your deployment.There you can also view the code which you can copy paste to your code based on the programming language you use.Please note that you need to use different endpoints and corresponding access keys for different resource and model deployments.QUOTA Increase RequestAzure OpenAI comes with default rate limits on each model deployments based on region.You can find the azure default rate limits here: https://learn.microsoft.com/en-us/azure/ai-services/openai/quotas-limitsAs developers start using Azure OpenAI, they may find that their usage exceeds the allocated quota especially TPM (Transactions Per Minute). In such cases, developers can request a quota increase from the Azure OpenAI team. To do this, developers need to fill out a quota increase request form on the Azure OpenAI website. The Azure OpenAI team will review the request and provide developers with additional quota if deemed necessary.You can request quota increase here: https://aka.ms/oai/quotaincreaseOnce you submit the form, you will get a confirmation email.ConclusionAzure OpenAI provides developers with a robust platform to develop and deploy AI models. With access to OpenAI’s cutting-edge models and tools, developers can create innovative AI-based applications. Developers can request access to Azure OpenAI by submitting an application form on the Azure OpenAI website. They can also request quota increases and gain access to the GPT-4 model. Azure OpenAI is a game-changer for AI development, and developers should leverage this powerful platform.Author: Vivek Bhupathi

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Deploying Custom LLMs: A Hugging Face End to End Guide

If you are new to LLM world, Then I would suggest you to go through previous articles to have the LLM understanding before going further with the articles Exploring LLM Platforms and Models: Unpacking OpenAI, Azure, and Hugging FaceCustom LLMs (Large Language Models) have become indispensable tools in the field of Natural Language Processing (NLP). They empower developers and researchers to tailor language models to their specific needs. In this comprehensive guide, we’ll explore three popular methods for deploying custom LLMs and delve into the detailed process of deploying models as Hugging Face Inference Endpoints, Other options we will go and explore in our upcoming articlesOverview of Deployment OptionsBefore we embark on our journey to deploy custom LLMs, it’s essential to understand the options available:Hugging Face Inference Endpoints: Hugging Face, known for its vast model repository, offers not only pre-trained models but also a user-friendly platform for deployment. It simplifies the deployment process, making it an excellent choice for experimentation and small-scale deployment.Amazon Sage Maker: Amazon Sage Maker, a part of AWS, provides a comprehensive machine-learning platform. It’s ideal for larger-scale deployments, offering additional capabilities for data preprocessing, training, and monitoring.Azure Machine Learning: Microsoft’s Azure ML platform is another robust option for deploying custom LLMs. It provides a cloud-based environment for building, training, and deploying machine learning models, including LLMs.The Role of Hugging FaceHugging Face is a central hub for all things related to NLP and language models. It plays a pivotal role in both sourcing models and facilitating their deployment.Sourcing Models from Hugging FaceBefore delving into deployment specifics, let’s discuss Hugging Face’s significance as a model source:Pre-Trained Models: Hugging Face’s Model Hub boasts an extensive collection of pre-trained models. These models, such as FALCON-40B, LLAMA-7B, LLAMA-40B, and more, serve as foundational building blocks for custom LLM development.Custom Fine-Tuning: Researchers and developers can leverage pre-trained models from Hugging Face and fine-tune them on domain-specific data. This process allows for the creation of custom LLMs tailored to unique NLP tasks.Deploying the LLM as a hugging face Inference Endpoint:Here, we are trying to deploy falcon-40B-instruct which is the hugging face modelTo start with, we need to have an account on Hugging Face (HF), which can be created at the following link: https://huggingface.co/join. The signup process is straightforward; you need to provide the email for which the account has to be created and a password of your choice.Hugging face sign-up and login screen2. Once the account is created, you can log in with the credentials you provided during registration. On the homepage, you can search for the models you need and select to view the details of the specific model you’ve chosen.Hugging face home screen for searching required modelsHugging face selected models details page3. There is an option called Deploy, under which you will be able to see the deployment options. Since we are focusing on the Hugging Face inference endpoints, you can choose the same and proceed4. When you select the Inference Endpoint, it will take you to the page where you can create an inference endpoint for the selected model. The “Create Inference Endpoint” page looks as shown below:Create a new Inference Endpoint pageThere are several fields and options to be filled up and selected accordingly. This guide will go through the steps to deploy tiiuae/falcon-40b-instruct for text classification.Enter the Hugging Face Repository ID and your desired endpoint name:Select your Cloud Provider and regionDefine the [Security Level](security) for the Endpoint:Create your Endpoint by clicking **Create Endpoint**. By default, your Endpoint is created with a large GPU (4x Nvidia Tesla T4). The cost estimate assumes the Endpoint will be up for an entire month, and does not take autoscaling into account.Wait for the Endpoint to build, initialize, and run which can take between 5 to 10 minutesTest your endpoint with the API endpoint generated from the deployment using the Hugging Face access tokenIn conclusion, this guide provides an overview of deploying Hugging Face models, specifically focusing on creating inference endpoints for text classification. However, for more in-depth insights into deploying Hugging Face models on cloud platforms like Azure and AWS, stay tuned for future articles where we will explore these topics in greater detail.Author: Sriram C

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Trends Shaping the future of Conversational AI

In today’s rapidly evolving digital landscape, businesses are in constant pursuit of innovative ways to enhance customer experiences, streamline operations, and maintain a competitive edge. As leaders and decision-makers in the business world, it’s crucial to stay ahead of the curve and grasp the trends and insights that are shaping the future of Conversational AI.Embracing the Conversational RevolutionConversational AI is ushering in a new era in how businesses interact with their customers, automate processes, and drive operational efficiency. Gone are the days of rigid chatbots with limited capabilities. The new breed of Conversational AI is marked by its ability to understand and respond to human intent, thanks to the remarkable advancements in Natural Language Processing (NLP) and Machine Learning (ML).The Era of Personalization and Contextual UnderstandingOne of the most notable trends in Conversational AI is its capacity to deliver highly personalized experiences. By harnessing NLP, these systems can decipher not just user queries but also their preferences and even emotions. This level of contextual understanding empowers businesses to tailor responses and recommendations, forging deeper and more meaningful connections with their customers.Beyond Text: Multimodal ExperiencesThe future of Conversational AI transcends text-based exchanges. Voice, images, and videos are becoming integral parts of these conversational interfaces. By integrating multimodal capabilities, businesses can cater to a broader audience and offer a seamless experience across various communication channels.Integration for Holistic Business SolutionsConversational AI isn’t an isolated tool; it’s an integral part of the broader business ecosystem. When integrated with Customer Relationship Management (CRM) systems, Enterprise Resource Planning (ERP) platforms, and other mission-critical applications, it facilitates smooth data flow, enabling Conversational AI to provide more informed and comprehensive responses.Unleashing Business Value with Conversational AI1. Elevating Customer Engagement and SatisfactionThe adoption of Conversational AI empowers businesses to provide round-the-clock support, ensuring customers receive prompt responses to their inquiries. These personalized interactions foster stronger relationships and elevate customer satisfaction.2. Enhancing Operational Efficiency and Cost SavingsConversational AI streamlines operations across various departments, from customer service to HR. By automating routine tasks and delivering instant solutions, it enables businesses to significantly reduce operational costs and allocate resources to more strategic endeavors.3. Gaining a Competitive EdgeBeing an early adopter of Conversational AI offers businesses a distinct competitive advantage. It showcases a commitment to innovation and a customer-centric approach, positioning your organization as a trailblazer in the industry.As we forge ahead, Conversational AI will continue to evolve, ushering in fresh opportunities and challenges. Embracing this technology now empowers businesses to stay at the forefront of innovation and provide unparalleled value to their customers.In conclusion, the future of Conversational AI holds immense promise. By understanding the trends and insights, businesses can make informed decisions about integrating this transformative technology into their operations. As leaders, it’s our responsibility to seize this potential and guide our organizations toward a more conversational and human-centric future.

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Modern Turing Test Unlocking AI Potential

The Turing test, developed by Alan Turing in 1950, has left an indelible mark on the history of Artificial Intelligence (AI). Serving as a benchmark for determining whether a machine can exhibit human-like intelligence, the test has long been regarded as a significant milestone. However, as AI technology has advanced significantly in recent years, questions have arisen about the relevance and limitations of the traditional Turing test. In this article, we will explore the shortcomings of the traditional approach and delve into the proposal of a modern Turing test that aims to measure AI’s practical capabilities in achieving complex goals.The Limitations of the Traditional Turing Test:DeepMind’s co-founder, Mustafa Suleyman exclaimed that “It is unclear whether AI passing Turing Test is a meaningful milestone or not, it doesn’t tell us anything about what the system can do or understand, anything about whether it has established complex inner monologues or can engage in planning over abstract time horizons, which is key to human intelligence”While it gauges an AI’s ability to generate human-like responses, it fails to encompass critical aspects of human intelligence, such as complex inner monologues and long-term planning abilities. This criticism highlights the need for a new test that expands the evaluation criteria beyond conversational capabilities.Human or Not by A121 Labs — The Obsolescence:The world’s largest Turing test, conducted by AI21 Labs through the “Human or Not?” game, revealed that AI algorithms have reached a point where they can successfully deceive humans. While Turing predicted that an average interrogator would have a 70% chance of correctly identifying AI responses, the experiment showed that users could only guess correctly 60% of the time when interacting with AI bots. These findings indicate that AI can now effectively fool a significant portion of the population.Introducing the Modern Turing Test: Artificial Capable Intelligence (ACI):As disruptive technologies like ChatGPT, Google Bard, and Bing Chat emerge in the market, passing the Turing test is no longer perceived as a groundbreaking achievement. AI chatbots equipped with sophisticated NLP algorithms and neural networks can engage in human-like conversations effortlessly. Recognizing this shift, Mustafa Suleyman proposes the “Modern Turing Test” as a more comprehensive measure of AI capabilities.Unlike its predecessor, the Modern Turing Test does not limit itself to conversational prowess alone. Instead, it challenges AI chatbots to utilize a seed investment of $100,000 and transform it into $1 million through various e-commerce tasks. By assessing an AI’s ability to research, plan, find manufacturers, and sell products with minimal human intervention, this test seeks to evaluate the AI’s practical capabilities in achieving complex goals.One Side Of The CoinMeasures practical capabilities: By focusing on e-commerce tasks, the modern Turing test reflects real-world applications, moving beyond purely conversational abilities.Promotes problem-solving skills: Challenging AI chatbots to research, plan, and execute tasks encourages the development of problem-solving and decision-making abilities.Evaluates autonomous performance: The test assesses an AI’s ability to perform tasks independently, advancing the autonomy of AI systems.Encourages innovation: Providing a financial incentive and investment opportunity fosters innovation and entrepreneurial thinking among AI chatbots.Other Side Of The Coin:Narrow focus: The test’s emphasis on e-commerce tasks may overlook other essential aspects of AI capabilities, such as creativity, emotional intelligence, or critical thinking.Lack of generalization: The specific focus on e-commerce tasks may not fully capture an AI’s overall intelligence and adaptability in diverse real-world scenarios.Financial bias: The primary measure of AI capability being a financial success may prioritize profit-driven strategies over ethical considerations or other societal values.Limited human interaction: The importance of human-like conversation and social skills may be neglected, hindering AI systems’ ability to effectively engage and interact with humans in various contexts.While the traditional Turing test has played a crucial role in the development of AI, its limitations have become increasingly apparent in light of recent technological advancements. The proposal of the modern Turing test, with its emphasis on practical capabilities and goal achievement, marks a significant step forward in assessing AI’s potential.However, it is essential to consider the broader spectrum of AI intelligence and the potential ethical implications associated with financial success as the sole measure of capability. By continuously rethinking and evolving these tests, we can gain a deeper understanding of AI’s capabilities and unlock new frontiers in artificial capable intelligence.

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