generative ai

Amazon Web Services (AWS) has expanded the language capabilities of its Amazon Transcribe product by introducing generative AI-based transcription for 100 languages. This enhancement, announced during the AWS re: Invent event, signifies a significant advancement in AI capabilities for AWS customers.

Amazon Transcribe can now recognize an increased number of spoken languages and facilitate call transcriptions, serving as a valuable tool for AWS customers integrating speech-to-text capabilities into their applications on the AWS Cloud.

In a detailed blog post, AWS highlighted that Amazon Transcribe underwent training on an extensive dataset comprising millions of hours of unlabeled audio data from over 100 languages. Leveraging self-supervised algorithms, Transcribe learned intricate patterns of human speech in various languages and accents. AWS emphasized the importance of avoiding the over-representation of certain languages in the training data, ensuring accuracy across both frequently spoken and lesser-used languages.

As of late 2024, Amazon Transcribe supported 79 languages, demonstrating AWS’s commitment to language diversity and inclusivity. The platform achieves an accuracy range of 20 percent to 50 percent across multiple languages. Notable features include automatic punctuation, custom vocabulary, automatic language identification, and custom vocabulary filters. Furthermore, Amazon Transcribe can effectively recognize speech in various formats, including audio, video, and noisy environments.

In addition to language recognition improvements, AWS highlighted the impact on the accuracy of its Call Analytics platform. This platform, frequently utilized by contact centers, is now powered by generative AI models. It efficiently summarizes interactions between agents and customers, reducing the need for extensive post-call report creation. Managers can swiftly access pertinent information without having to review entire transcripts.

While AWS stands out in the AI-powered transcription services landscape, other players like Otter have been providing AI transcriptions to consumers and enterprises, introducing a summarization tool in June. Additionally, Meta is actively working on a generative AI-powered translation model, recognizing nearly 100 spoken languages.

AWS also announced additional features for its Amazon Personalization product, enabling clients to provide personalized product recommendations based on user activity. The new Content Generation capability enhances the thematic alignment of recommendation lists by creating titles or email subject lines.

What is generative AI?

Generative Artificial Intelligence (generative AI) stands as a revolutionary subset of AI that holds the capability to innovate and create across various domains, encompassing conversations, stories, images, videos, and music. Unlike traditional AI, which specializes in tasks like image recognition and natural language processing, generative AI takes a leap forward by generating novel content and ideas. Its versatility allows it to learn and adapt to diverse subjects such as human language, programming languages, art, chemistry, biology, and beyond, utilizing training data to address new challenges.

Why is generative AI important?

Generative AI finds applications in diverse areas, making it a valuable asset for organizations. From powering chatbots to aiding in media creation, product development, and design, its adaptability positions it as a transformative force in the AI landscape.

Applications Across Industries

Generative AI finds applications in diverse areas, making it a valuable asset for organizations. From powering chatbots to aiding in media creation, product development, and design, its adaptability positions it as a transformative force in the AI landscape.

Significance and Growth Potential

Generative AI, exemplified by applications like ChatGPT, has garnered significant attention due to its potential to reshape customer experiences and drive innovation. Goldman Sachs predicts that generative AI could contribute to a remarkable 7 percent increase in global GDP, potentially translating to nearly $7 trillion. Furthermore, it is anticipated to elevate productivity growth by 1.5 percentage points over a decade.

Key Benefits of Generative AI

Accelerating Research and Innovation:

Generative AI algorithms offer a fresh perspective on complex data, enabling researchers to uncover trends and patterns that might elude traditional methods. In fields like pharmaceuticals, it accelerates drug discovery by generating and optimizing protein sequences.

Enhancing Customer Experience:

Generative AI facilitates natural interactions in customer service, employing tools like AI-powered chatbots and virtual assistants for personalized customer workflows. This can lead to improved first-contact resolution and increased engagement through personalized offers.

Optimizing Business Processes:

Businesses can leverage generative AI across various departments, applying machine learning and AI applications to enhance processes in engineering, marketing, customer service, finance, and sales. It can extract and summarize data, optimize scenarios for cost reduction, and generate synthetic data for machine learning processes.

Boosting Employee Productivity:

Generative AI models serve as efficient assistants across organizational workflows, supporting creative tasks, generating software code suggestions, assisting management with reports and projections, and providing content creation support for marketing teams. This not only saves time but also enhances efficiency and reduces costs.

How does generative AI work?

Understanding the intricacies of generative AI unveils the essence of its functionality and the transformative power it holds. At its core, generative AI operates on the foundation of machine learning models, specifically large-scale models pre-trained on extensive datasets.

Foundation Models (FMs): The Building Blocks

Foundation models serve as the backbone of generative AI, encompassing machine learning models trained on diverse, generalized, and unlabeled data. These models exhibit a versatile capability, allowing them to perform a wide array of general tasks. Leveraging learned patterns and relationships, FMs predict the next item in a sequence, showcasing their prowess across various domains.

For instance, in image generation, an FM analyzes an image, enhancing its clarity and definition. Similarly, in text-based tasks, the model predicts the next word based on contextual cues, utilizing probability distribution techniques for precise selection.

Large Language Models (LLMs): Masters of Language-Based Tasks

A subset of Foundation Models, Large Language Models (LLMs) focus specifically on language-oriented tasks. Noteworthy examples include OpenAI’s Generative Pre-trained Transformer (GPT) models. LLMs excel in tasks such as summarization, text generation, classification, open-ended conversation, and information extraction.

Read about GPT »

What sets LLMs apart is their multifunctional prowess. With an extensive number of parameters, these models can delve into advanced concepts and perform a spectrum of tasks. Take GPT-3, for instance, capable of considering billions of parameters and generating content with minimal input. The pretraining exposure to vast internet-scale data equips LLMs like GPT-3 to apply their knowledge across diverse contexts, showcasing their adaptability and proficiency in various applications.

How will generative AI affect industries?

Generative AI, with its transformative potential, is set to revolutionize various industries, offering rapid benefits and innovative solutions.

Financial Services

Generative AI is reshaping the financial landscape, enabling companies to enhance customer service and streamline operations:

  • Chatbots provide personalized product recommendations and respond to customer inquiries, elevating customer service.
  • Lending institutions accelerate loan approvals in underserved markets, particularly in developing nations.
  • Banks swiftly detect fraud in claims, credit cards, and loans.
  • Investment firms offer safe, personalized financial advice to clients at a low cost.

Healthcare and Life Sciences:

Generative AI emerges as a catalyst for accelerated drug discovery and research, transforming healthcare and life sciences:

  • Models create novel protein sequences for designing antibodies, enzymes, vaccines, and gene therapy.
  • Generative models design synthetic gene sequences for synthetic biology and metabolic engineering applications.
  • Synthetic patient and healthcare data are generated for AI model training, clinical trial simulation, and rare disease studies.

Automotive and Manufacturing:

The automotive industry leverages generative AI for engineering, in-vehicle experiences, and customer service:

  • Optimization of mechanical part design to reduce drag and adapt the personal assistant design.
  • Improved customer service with quick responses to common queries.
  • Creation of new material, chip, and part designs to optimize manufacturing processes.
  • Synthetic data generation for comprehensive application testing, including defects and edge cases.

Media and Entertainment:

Generative AI revolutionizes content creation, offering cost-effective solutions across various domains:

  • Production of animations, scripts, and full-length movies at a fraction of traditional costs.
  • AI-generated music enhances artist albums for unique auditory experiences.
  • Personalized content and ads improve audience experiences and boost revenues.
  • Gaming companies utilize generative AI for creating new games and customizable avatars.


Generative AI transforms the telecommunication sector, enhancing customer experiences and optimizing network performance:

  • Live human-like conversational agents improve customer service.
  • Network data analysis recommends fixes for optimized performance.
  • Personalized one-to-one sales assistants redefine customer relationships.


Generative AI proves invaluable in the energy sector, addressing complex tasks and optimizing operations:

  • Analysis of enterprise data identifies usage patterns for targeted product offerings and efficiency programs.
  • Grid management, operational site safety, and energy production optimization are enhanced through reservoir simulation.

How can AWS help Generative AI?

Amazon Web Services (AWS) is at the forefront of simplifying the development and scaling of generative AI applications, offering a robust ecosystem tailored for diverse data, use cases, and customers. By choosing generative AI on AWS, users benefit from enterprise-grade security, privacy features, access to industry-leading Foundation Models (FMs), and applications powered by cutting-edge generative AI technology, all underpinned by a data-centric approach.

Explore a range of generative AI technologies catering to organizations at various stages of generative AI adoption and maturity:

  • 1. Code Generation Excellence with Amazon CodeWhisperer: Amazon introduces CodeWhisperer, an AI coding companion that promises significant improvements in developer productivity. During the preview phase, a productivity challenge showcased that participants using CodeWhisperer were 27 percent more likely to successfully complete tasks, accomplishing them 57 percent faster on average compared to those not utilizing CodeWhisperer.
  • 2. Amazon Bedrock – A Comprehensive Managed Service: Amazon Bedrock stands as a fully managed service offering a selection of high-performing Foundation Models (FMs) and a versatile range of capabilities. Experiment freely with top FMs, personalize them with private data, and craft managed agents capable of executing intricate business tasks.
  • 3. SageMaker JumpStart for Seamless AI Adoption: Leverage SageMaker JumpStart to seamlessly discover, explore, and deploy open-source Foundation Models (FMs) or create bespoke ones. SageMaker JumpStart provides managed infrastructure and tools to expedite the scalable, reliable, and secure building, training, and deployment of models.
  • 4. AWS HealthScribe – Transforming Healthcare Documentation: AWS HealthScribe, a HIPAA-eligible service, empowers healthcare software vendors to develop clinical applications automating the generation of clinical notes. By analyzing patient-clinician conversations, HealthScribe utilizes speech recognition and generative AI to transcribe discussions and generate easily reviewable clinical notes, alleviating the burden of clinical documentation.
  • 5. Generative BI Authoring with Amazon QuickSight: Amazon QuickSight introduces Generative BI authoring capabilities, empowering business analysts to effortlessly create and customize visuals using natural-language commands. Extending beyond structured queries, these capabilities enable analysts to quickly generate customizable visuals from question fragments, clarify query intent with follow-up questions, refine visualizations, and perform complex calculations. QuickSight’s Generative BI authoring capabilities redefine the landscape of business intelligence, making it more intuitive and powerful.

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