Amazon Web Services release the new AI services during last re:Invent, here we see a little introduction to them and a simple integration with a Lego robot
An Overview of AI on the AWS Platform - February 2017 Online Tech TalksAmazon Web Services
AWS offers a family of intelligent services that provide cloud-native machine learning and deep learning technologies to address your different use cases and needs. For developers looking to add managed AI services to their applications, AWS brings natural language understanding (NLU) and automatic speech recognition (ASR) with Amazon Lex, visual search and image recognition with Amazon Rekognition, text-to-speech (TTS) with Amazon Polly, and developer-focused machine learning with Amazon Machine Learning.
For more in-depth deep learning applications, the AWS Deep Learning AMI lets you run deep learning in the cloud, at any scale. Launch instances of the AMI, pre-installed with open source deep learning engines (Apache MXNet, TensorFlow, Caffe, Theano, Torch and Keras), to train sophisticated, custom AI models, experiment with new algorithms, and learn new deep learning skills and techniques; all backed by auto-scaling clusters of GPU-based instances.
Whether you’re just getting started with AI or you’re a deep learning expert, this session will provide a meaningful overview of how to improve scale and efficiency with the AWS Cloud.
Learning Objectives
• Learn about the breadth of AI services available on the AWS Cloud
• Gain insight into practical use cases for Amazon Lex, Amazon Polly, and Amazon Rekognition
• Understand why Amazon has selected MXNet as its deep learning framework of choice due its programmability, portability, and performance
An Overview of AI at AWS: Amazon Lex, Amazon Polly, Amazon Rekognition, Apach...Amazon Web Services
by Keith Steward, Solutions Architect, AWS
AI services on the AWS cloud bring deep learning technologies like natural language understanding, automatic speech recognition, computer vision, text-to-speech, and machine learning within reach of every developer. For more in-depth deep learning applications, the Deep Learning AMIs let you create managed, auto-scaling clusters of GPUs for large scale training, or run inference on trained models with compute-optimized or general-purpose CPU instances. Whether you’re just getting started with AI or you’re a deep learning expert, this session will provide a meaningful overview of how to improve scale and efficiency with the AWS Cloud. Level 200
This document discusses Amazon's artificial intelligence and deep learning capabilities. It summarizes Amazon's AI services including Amazon Lex for building conversational bots, Amazon Polly for text-to-speech, and Amazon Rekognition for computer vision tasks like image moderation, facial analysis, and celebrity recognition. It also discusses Amazon's deep learning framework MXNet and partnerships with Intel for high performance and low cost AI and machine learning.
This document provides tips for effective keyword research to improve search engine optimization. It emphasizes selecting keywords that are highly relevant to your website's content and goals, have sufficient search volume, and low competition from other websites. Specific tools like Google Keyword Planner, Trends, and SEMRush are recommended for analyzing keyword difficulty, volume, and what competitors are targeting. The overall message is that keyword research is important for getting the right visitors to your site.
Introduction to k-Nearest Neighbors and Amazon SageMaker Suman Debnath
The document provides an overview of machine learning concepts including deep learning, artificial intelligence, and machine learning. It then discusses different types of machine learning algorithms like classification, regression, and k-nearest neighbors (KNN). For KNN, it covers geometric intuition, distance measures, choosing the parameter k, and code implementation. It also includes diagrams of Amazon SageMaker's machine learning capabilities and the AWS machine learning stack.
AWS re:Invent 2016: Machine Learning State of the Union Mini Con (MAC206)Amazon Web Services
With the growing number of business cases for artificial intelligence (AI), machine learning (ML) and deep learning (DL) continue to drive the development of cutting edge technology solutions. We see this manifested in computer vision, predictive modeling, natural language understanding, and recommendation engines. During this full afternoon of sessions and workshops, learn how you can develop your own applications to leverage the benefits of these services. Join this State of the Union presentation to hear more about ML and DL at AWS and see how Motorola Solutions is leveraging these state-of-the-art technologies to solve public safety challenges, and how Ohio Health intends to inject AI into the medical system.
An Overview of AI on the AWS Platform - February 2017 Online Tech TalksAmazon Web Services
AWS offers a family of intelligent services that provide cloud-native machine learning and deep learning technologies to address your different use cases and needs. For developers looking to add managed AI services to their applications, AWS brings natural language understanding (NLU) and automatic speech recognition (ASR) with Amazon Lex, visual search and image recognition with Amazon Rekognition, text-to-speech (TTS) with Amazon Polly, and developer-focused machine learning with Amazon Machine Learning.
For more in-depth deep learning applications, the AWS Deep Learning AMI lets you run deep learning in the cloud, at any scale. Launch instances of the AMI, pre-installed with open source deep learning engines (Apache MXNet, TensorFlow, Caffe, Theano, Torch and Keras), to train sophisticated, custom AI models, experiment with new algorithms, and learn new deep learning skills and techniques; all backed by auto-scaling clusters of GPU-based instances.
Whether you’re just getting started with AI or you’re a deep learning expert, this session will provide a meaningful overview of how to improve scale and efficiency with the AWS Cloud.
Learning Objectives
• Learn about the breadth of AI services available on the AWS Cloud
• Gain insight into practical use cases for Amazon Lex, Amazon Polly, and Amazon Rekognition
• Understand why Amazon has selected MXNet as its deep learning framework of choice due its programmability, portability, and performance
An Overview of AI at AWS: Amazon Lex, Amazon Polly, Amazon Rekognition, Apach...Amazon Web Services
by Keith Steward, Solutions Architect, AWS
AI services on the AWS cloud bring deep learning technologies like natural language understanding, automatic speech recognition, computer vision, text-to-speech, and machine learning within reach of every developer. For more in-depth deep learning applications, the Deep Learning AMIs let you create managed, auto-scaling clusters of GPUs for large scale training, or run inference on trained models with compute-optimized or general-purpose CPU instances. Whether you’re just getting started with AI or you’re a deep learning expert, this session will provide a meaningful overview of how to improve scale and efficiency with the AWS Cloud. Level 200
This document discusses Amazon's artificial intelligence and deep learning capabilities. It summarizes Amazon's AI services including Amazon Lex for building conversational bots, Amazon Polly for text-to-speech, and Amazon Rekognition for computer vision tasks like image moderation, facial analysis, and celebrity recognition. It also discusses Amazon's deep learning framework MXNet and partnerships with Intel for high performance and low cost AI and machine learning.
This document provides tips for effective keyword research to improve search engine optimization. It emphasizes selecting keywords that are highly relevant to your website's content and goals, have sufficient search volume, and low competition from other websites. Specific tools like Google Keyword Planner, Trends, and SEMRush are recommended for analyzing keyword difficulty, volume, and what competitors are targeting. The overall message is that keyword research is important for getting the right visitors to your site.
Introduction to k-Nearest Neighbors and Amazon SageMaker Suman Debnath
The document provides an overview of machine learning concepts including deep learning, artificial intelligence, and machine learning. It then discusses different types of machine learning algorithms like classification, regression, and k-nearest neighbors (KNN). For KNN, it covers geometric intuition, distance measures, choosing the parameter k, and code implementation. It also includes diagrams of Amazon SageMaker's machine learning capabilities and the AWS machine learning stack.
AWS re:Invent 2016: Machine Learning State of the Union Mini Con (MAC206)Amazon Web Services
With the growing number of business cases for artificial intelligence (AI), machine learning (ML) and deep learning (DL) continue to drive the development of cutting edge technology solutions. We see this manifested in computer vision, predictive modeling, natural language understanding, and recommendation engines. During this full afternoon of sessions and workshops, learn how you can develop your own applications to leverage the benefits of these services. Join this State of the Union presentation to hear more about ML and DL at AWS and see how Motorola Solutions is leveraging these state-of-the-art technologies to solve public safety challenges, and how Ohio Health intends to inject AI into the medical system.
Building Speech Enabled Products with Amazon Polly & Amazon LexAmazon Web Services
This document provides an introduction to Amazon Polly and Amazon Lex. It discusses the features and functionality of Polly, including its wide selection of voices and languages available as well as its quality, pricing and use cases. It then introduces Amazon Lex, discussing its text and speech language understanding capabilities powered by the same technology as Alexa. It covers Lex's features such as enterprise connectors, deployment to chat services, versioning and aliases. The document concludes with examples of Lex bots and a demo of a "DevOps" chatbot integrated with Slack using Lex and AWS Lambda.
Building Speech Enabled Products with Amazon Polly & Amazon LexAmazon Web Services
This session will introduce you to Amazon Polly, a deep learning service that turns text into lifelike speech. Polly enables existing applications to speak as a first class feature and creates the opportunity for entirely new categories of speech-enabled products – from mobile apps and cars, to devices and appliances. Polly includes 47 lifelike voices and support for 24 languages, so you can select the ideal voice and distribute your speech-enabled applications in many geographies. Polly is easy to use – you just send the text you want converted into speech to the Polly API, and Polly immediately returns the audio stream to your application so you can play it directly or store it in a standard audio file format, such as MP3. Polly supports Speech Synthesis Markup Language (SSML) tags like prosody so you can adjust the speech rate, pitch, or volume. Polly is a secure service that delivers all of these benefits at high scale and at low latency. You can cache and replay Polly’s generated speech at no additional cost. Polly lets you convert 5M characters per month for free during the first year. Polly’s pay-as-you-go pricing, low cost per request, and lack of restrictions on storage and reuse of voice output make it a cost-effective way to enable speech synthesis everywhere.
The document describes the design and development of a Speech to Sign Language Interpreter System (SSLIS). The system uses Sphinx 3.5 for automatic speech recognition (ASR) to convert speech to text. It then maps the recognized text to American Sign Language (ASL) videos stored in a database for translation and live signing. The system aims to improve accessibility for the deaf by providing free, open-source sign language interpretation software.
Building Serverless AI-powered Apps on AWSAdrian Hornsby
This document summarizes a presentation about building serverless AI applications using Amazon Web Services (AWS). It discusses Amazon Polly for text-to-speech, Amazon Rekognition for image analysis, and provides examples of how companies like Duolingo and Bynder use these services. It also demonstrates a sample app called Poliko that uses Polly and Rekognition to take an image, detect faces and labels, and synthesize speech describing the image.
[REPEAT 1] How to Get the Most out of TTS for Your Alexa Skill (ALX342-R1) - ...Amazon Web Services
In this session, we walk through the complete set of the Amazon Alexa text-to-speech (TTS) features. Through this, we teach you how to apply the TTS features in order to modify and enhance your speech responses. Learn how to make Alexa whisper or breathe and use other voices with Amazon Polly. Leave this session with insider tips on how to optimize TTS speech using SSML. Come prepared for an interactive conversation. You have the opportunity to ask questions and discuss ideas with fellow skill developers.
Building speech enabled products with Amazon Polly & Amazon LexAmazon Web Services
Amazon Lex and Amazon Polly are services for building conversational interfaces and converting text to speech. Lex allows developers to build bots that understand natural language and integrate with back-end systems. It features tools for building conversations using text or speech and deploying bots to messaging platforms. Polly is a text-to-speech service that converts text into high-quality speech for 47 voices and 24 languages. It offers features like SSML and lexicons to customize output. Both services aim to make building conversational applications easier and more cost-effective for developers.
The document describes the Speech to Sign Language Interpreter System (SSLIS), including its main capabilities of translating speech to text and sign language in real-time. It provides an overview of the system's components and architecture, including the speech recognition engine used. It also discusses the research goals, some capabilities and limitations of the current system, and ideas for further improvements.
Building Speech Enabled Products with Amazon Polly & Amazon LexAmazon Web Services
by Dario Rivera, Solutions Architect, AWS
This session will introduce you to Amazon Polly, a deep learning service that turns text into lifelike speech. Polly enables existing applications to speak as a first class feature and creates the opportunity for entirely new categories of speech-enabled products – from mobile apps and cars, to devices and appliances. Polly includes 47 lifelike voices and support for 24 languages, so you can select the ideal voice and distribute your speech-enabled applications in many geographies. Polly is easy to use – you just send the text you want converted into speech to the Polly API, and Polly immediately returns the audio stream to your application so you can play it directly or store it in a standard audio file format, such as MP3. Polly supports Speech Synthesis Markup Language (SSML) tags like prosody so you can adjust the speech rate, pitch, or volume. Polly is a secure service that delivers all of these benefits at high scale and at low latency. You can cache and replay Polly’s generated speech at no additional cost. Polly lets you convert 5M characters per month for free during the first year. Polly’s pay-as-you-go pricing, low cost per request, and lack of restrictions on storage and reuse of voice output make it a cost-effective way to enable speech synthesis everywhere. Join this session to learn more and find out how you get can started with Amazon Polly, today!
AWS offers a suite of AI and machine learning services including:
- Rekognition for image and video analysis including object detection, facial recognition and analysis, and image moderation.
- Polly for text-to-speech conversion with many voices and languages.
- Lex for building conversational bots using voice and text across channels like Alexa, Slack, and Facebook Messenger.
- Comprehend for natural language processing including keyword extraction, sentiment analysis, and topic modeling from text.
- SageMaker as a fully managed platform for building, training, and deploying machine learning models at scale.
re:Invent re:Cap - An overview of Artificial Intelligence and Machine Learnin...Adrian Hornsby
In this session, you will learn about our strategy for driving machine learning innovation for our customers and learn what’s new from AWS in the machine learning space. We will discuss and demonstrate the latest new services for ML on AWS: Amazon SageMaker, AWS DeepLens, Amazon Rekogntion Video, Amazon Translate, Amazon Transcribe, and Amazon Comprehend. Attend this session to understand how to make the most of machine learning in the cloud.
The document discusses Amazon Web Services (AWS) machine learning and artificial intelligence tools including Amazon Polly for text-to-speech, Amazon Lex for building conversational interfaces, and Amazon Rekognition for image and video analysis; it provides examples of how these tools work and can be used to build applications for tasks like flight booking, facial recognition, and building chatbots.
Better Accessibility with Lex, Polly, and Alexa | AWS Public Sector Summit 2017Amazon Web Services
Most AWS services can be applied at scale. We'll provide a demo of what and how these services help modernize interactions with IT systems even those with government regulations and requirements. We will also demonstrate ways the overall solution helps meet those requirements. GeorgiaGov Interactive will share their story on how they are using Alexa to reach more disabled residents, extending its informational and transactional services onto Amazon's voice-driven platform. Learn More: https://aws.amazon.com/government-education/
Hands-on with Rekognition, Polly & Lex - Pop-up Loft TLV 2017Amazon Web Services
This document discusses Amazon's artificial intelligence services, including Amazon Polly for text-to-speech, Amazon Lex for conversational interfaces, and Amazon Rekognition for image and video analysis. It provides overviews of the capabilities and features of each service, such as Polly's 47 text-to-speech voices across 24 languages, Lex's tools for building conversational bots, and Rekognition's face detection, analysis, and recognition tools. Examples and demos of each service are presented to illustrate their functionality.
Amazon Alexa Skills Empower my Business with Voice - AWS Summit Sydney 2018Amazon Web Services
Amazon Alexa Skills, Empower my Business with Voice
Voice experiences have transformed the way that customers interact with the world around them. Alexa is Amazon’s voice service and the brain behind millions of devices including Amazon Echo. Alexa provides capabilities, or skills, that enable customers to create a more personalised experience and makes it possible for businesses to create Alexa Skills to engage with employees or customers through voice.
With the Alexa Skills Kit (ASK), designers, developers, and brands can build engaging skills to reach millions of customers. In this session, we’ll provide an overview of Amazon Alexa and Alexa Skills to show you how to create business value and reach more customers.
Azi Farjad, Senior Developer Evangelist, Alexa Skills
Enhance customer experience with conversational interfacesAmazon Web Services
This document discusses conversational interfaces and Amazon's services that can help enable them. It summarizes that conversational interfaces are the third generation of user interfaces after punch cards/memory registers and pointers/sliders. It then discusses challenges in building conversational interfaces and outlines Amazon services like Transcribe, Translate, Polly, Comprehend, and Lex that provide capabilities for speech recognition, language translation and understanding, text-to-speech, and building conversational bots and interfaces. Use cases for these services include information bots, customer service, digital assistants, and more.
This presentation is focused on building solutions and strategy to solve business or customer engagement challenges. It tells the Amazon Machine Learning story and describes core AWS Artificial Intelligence services such as Polly, Lex and Rekognition can be applied to business problems.
This document provides an overview of Amazon's AI and machine learning strategy and current offerings. It begins with an introduction from Guy Ernest of Amazon AI and discusses why companies talk about AI and machine learning. It then provides an overview of Amazon's machine learning services and platforms, including Amazon Machine Learning, Amazon AI, Deep Learning AMI, and services like EC2, ECS, EMR, Redshift, and Athena that can be used for machine learning workloads. It discusses the machine learning workflow and how different Amazon services fit into different parts of that workflow. It also discusses Amazon's approach to pricing for machine learning services.
Well not quite, but they can detect them. This talk will explore Microsoft Cognitive Services in Azure. We'll take a look at the offerings overall, and then take a deeper look into specifics such as Sentiment analysis, Computer vision (image recognition) and Emotion detection.
Automate for Efficiency with Amazon Transcribe & Amazon TranslateAmazon Web Services
by Mike Gillespie, Solutions Architect, AWS
Teaching a computer how to understand human language is one of the most challenging problems in computer science. However, significant progress has been made in automatic speech recognition (ASR) and machine translation (MT) to create highly accurate and fluent transcriptions and translations. Amazon Transcribe is an ASR service that makes it easy for developers to add speech to text capability to their applications, and Amazon Translate is a MT service that delivers fast, high-quality, and affordable language translation. In this session, you’ll learn how to weave machine translation and transcription into your workflows, to increase the efficiency and reach of your operations.
Building Speech Enabled Products with Amazon Polly & Amazon LexAmazon Web Services
This document provides an introduction to Amazon Polly and Amazon Lex. It discusses the features and functionality of Polly, including its wide selection of voices and languages available as well as its quality, pricing and use cases. It then introduces Amazon Lex, discussing its text and speech language understanding capabilities powered by the same technology as Alexa. It covers Lex's features such as enterprise connectors, deployment to chat services, versioning and aliases. The document concludes with examples of Lex bots and a demo of a "DevOps" chatbot integrated with Slack using Lex and AWS Lambda.
Building Speech Enabled Products with Amazon Polly & Amazon LexAmazon Web Services
This session will introduce you to Amazon Polly, a deep learning service that turns text into lifelike speech. Polly enables existing applications to speak as a first class feature and creates the opportunity for entirely new categories of speech-enabled products – from mobile apps and cars, to devices and appliances. Polly includes 47 lifelike voices and support for 24 languages, so you can select the ideal voice and distribute your speech-enabled applications in many geographies. Polly is easy to use – you just send the text you want converted into speech to the Polly API, and Polly immediately returns the audio stream to your application so you can play it directly or store it in a standard audio file format, such as MP3. Polly supports Speech Synthesis Markup Language (SSML) tags like prosody so you can adjust the speech rate, pitch, or volume. Polly is a secure service that delivers all of these benefits at high scale and at low latency. You can cache and replay Polly’s generated speech at no additional cost. Polly lets you convert 5M characters per month for free during the first year. Polly’s pay-as-you-go pricing, low cost per request, and lack of restrictions on storage and reuse of voice output make it a cost-effective way to enable speech synthesis everywhere.
The document describes the design and development of a Speech to Sign Language Interpreter System (SSLIS). The system uses Sphinx 3.5 for automatic speech recognition (ASR) to convert speech to text. It then maps the recognized text to American Sign Language (ASL) videos stored in a database for translation and live signing. The system aims to improve accessibility for the deaf by providing free, open-source sign language interpretation software.
Building Serverless AI-powered Apps on AWSAdrian Hornsby
This document summarizes a presentation about building serverless AI applications using Amazon Web Services (AWS). It discusses Amazon Polly for text-to-speech, Amazon Rekognition for image analysis, and provides examples of how companies like Duolingo and Bynder use these services. It also demonstrates a sample app called Poliko that uses Polly and Rekognition to take an image, detect faces and labels, and synthesize speech describing the image.
[REPEAT 1] How to Get the Most out of TTS for Your Alexa Skill (ALX342-R1) - ...Amazon Web Services
In this session, we walk through the complete set of the Amazon Alexa text-to-speech (TTS) features. Through this, we teach you how to apply the TTS features in order to modify and enhance your speech responses. Learn how to make Alexa whisper or breathe and use other voices with Amazon Polly. Leave this session with insider tips on how to optimize TTS speech using SSML. Come prepared for an interactive conversation. You have the opportunity to ask questions and discuss ideas with fellow skill developers.
Building speech enabled products with Amazon Polly & Amazon LexAmazon Web Services
Amazon Lex and Amazon Polly are services for building conversational interfaces and converting text to speech. Lex allows developers to build bots that understand natural language and integrate with back-end systems. It features tools for building conversations using text or speech and deploying bots to messaging platforms. Polly is a text-to-speech service that converts text into high-quality speech for 47 voices and 24 languages. It offers features like SSML and lexicons to customize output. Both services aim to make building conversational applications easier and more cost-effective for developers.
The document describes the Speech to Sign Language Interpreter System (SSLIS), including its main capabilities of translating speech to text and sign language in real-time. It provides an overview of the system's components and architecture, including the speech recognition engine used. It also discusses the research goals, some capabilities and limitations of the current system, and ideas for further improvements.
Building Speech Enabled Products with Amazon Polly & Amazon LexAmazon Web Services
by Dario Rivera, Solutions Architect, AWS
This session will introduce you to Amazon Polly, a deep learning service that turns text into lifelike speech. Polly enables existing applications to speak as a first class feature and creates the opportunity for entirely new categories of speech-enabled products – from mobile apps and cars, to devices and appliances. Polly includes 47 lifelike voices and support for 24 languages, so you can select the ideal voice and distribute your speech-enabled applications in many geographies. Polly is easy to use – you just send the text you want converted into speech to the Polly API, and Polly immediately returns the audio stream to your application so you can play it directly or store it in a standard audio file format, such as MP3. Polly supports Speech Synthesis Markup Language (SSML) tags like prosody so you can adjust the speech rate, pitch, or volume. Polly is a secure service that delivers all of these benefits at high scale and at low latency. You can cache and replay Polly’s generated speech at no additional cost. Polly lets you convert 5M characters per month for free during the first year. Polly’s pay-as-you-go pricing, low cost per request, and lack of restrictions on storage and reuse of voice output make it a cost-effective way to enable speech synthesis everywhere. Join this session to learn more and find out how you get can started with Amazon Polly, today!
AWS offers a suite of AI and machine learning services including:
- Rekognition for image and video analysis including object detection, facial recognition and analysis, and image moderation.
- Polly for text-to-speech conversion with many voices and languages.
- Lex for building conversational bots using voice and text across channels like Alexa, Slack, and Facebook Messenger.
- Comprehend for natural language processing including keyword extraction, sentiment analysis, and topic modeling from text.
- SageMaker as a fully managed platform for building, training, and deploying machine learning models at scale.
re:Invent re:Cap - An overview of Artificial Intelligence and Machine Learnin...Adrian Hornsby
In this session, you will learn about our strategy for driving machine learning innovation for our customers and learn what’s new from AWS in the machine learning space. We will discuss and demonstrate the latest new services for ML on AWS: Amazon SageMaker, AWS DeepLens, Amazon Rekogntion Video, Amazon Translate, Amazon Transcribe, and Amazon Comprehend. Attend this session to understand how to make the most of machine learning in the cloud.
The document discusses Amazon Web Services (AWS) machine learning and artificial intelligence tools including Amazon Polly for text-to-speech, Amazon Lex for building conversational interfaces, and Amazon Rekognition for image and video analysis; it provides examples of how these tools work and can be used to build applications for tasks like flight booking, facial recognition, and building chatbots.
Better Accessibility with Lex, Polly, and Alexa | AWS Public Sector Summit 2017Amazon Web Services
Most AWS services can be applied at scale. We'll provide a demo of what and how these services help modernize interactions with IT systems even those with government regulations and requirements. We will also demonstrate ways the overall solution helps meet those requirements. GeorgiaGov Interactive will share their story on how they are using Alexa to reach more disabled residents, extending its informational and transactional services onto Amazon's voice-driven platform. Learn More: https://aws.amazon.com/government-education/
Hands-on with Rekognition, Polly & Lex - Pop-up Loft TLV 2017Amazon Web Services
This document discusses Amazon's artificial intelligence services, including Amazon Polly for text-to-speech, Amazon Lex for conversational interfaces, and Amazon Rekognition for image and video analysis. It provides overviews of the capabilities and features of each service, such as Polly's 47 text-to-speech voices across 24 languages, Lex's tools for building conversational bots, and Rekognition's face detection, analysis, and recognition tools. Examples and demos of each service are presented to illustrate their functionality.
Amazon Alexa Skills Empower my Business with Voice - AWS Summit Sydney 2018Amazon Web Services
Amazon Alexa Skills, Empower my Business with Voice
Voice experiences have transformed the way that customers interact with the world around them. Alexa is Amazon’s voice service and the brain behind millions of devices including Amazon Echo. Alexa provides capabilities, or skills, that enable customers to create a more personalised experience and makes it possible for businesses to create Alexa Skills to engage with employees or customers through voice.
With the Alexa Skills Kit (ASK), designers, developers, and brands can build engaging skills to reach millions of customers. In this session, we’ll provide an overview of Amazon Alexa and Alexa Skills to show you how to create business value and reach more customers.
Azi Farjad, Senior Developer Evangelist, Alexa Skills
Enhance customer experience with conversational interfacesAmazon Web Services
This document discusses conversational interfaces and Amazon's services that can help enable them. It summarizes that conversational interfaces are the third generation of user interfaces after punch cards/memory registers and pointers/sliders. It then discusses challenges in building conversational interfaces and outlines Amazon services like Transcribe, Translate, Polly, Comprehend, and Lex that provide capabilities for speech recognition, language translation and understanding, text-to-speech, and building conversational bots and interfaces. Use cases for these services include information bots, customer service, digital assistants, and more.
This presentation is focused on building solutions and strategy to solve business or customer engagement challenges. It tells the Amazon Machine Learning story and describes core AWS Artificial Intelligence services such as Polly, Lex and Rekognition can be applied to business problems.
This document provides an overview of Amazon's AI and machine learning strategy and current offerings. It begins with an introduction from Guy Ernest of Amazon AI and discusses why companies talk about AI and machine learning. It then provides an overview of Amazon's machine learning services and platforms, including Amazon Machine Learning, Amazon AI, Deep Learning AMI, and services like EC2, ECS, EMR, Redshift, and Athena that can be used for machine learning workloads. It discusses the machine learning workflow and how different Amazon services fit into different parts of that workflow. It also discusses Amazon's approach to pricing for machine learning services.
Well not quite, but they can detect them. This talk will explore Microsoft Cognitive Services in Azure. We'll take a look at the offerings overall, and then take a deeper look into specifics such as Sentiment analysis, Computer vision (image recognition) and Emotion detection.
Automate for Efficiency with Amazon Transcribe & Amazon TranslateAmazon Web Services
by Mike Gillespie, Solutions Architect, AWS
Teaching a computer how to understand human language is one of the most challenging problems in computer science. However, significant progress has been made in automatic speech recognition (ASR) and machine translation (MT) to create highly accurate and fluent transcriptions and translations. Amazon Transcribe is an ASR service that makes it easy for developers to add speech to text capability to their applications, and Amazon Translate is a MT service that delivers fast, high-quality, and affordable language translation. In this session, you’ll learn how to weave machine translation and transcription into your workflows, to increase the efficiency and reach of your operations.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
18. Polly
Lifelike Text To Speech
- 47 voices across 24
languages
- Low latency
- Free to reuse
19. Polly
Quality
- Natural sounding speech
A subjective measure of how close TTS output is to human speech.
- Accurate text processing
Ability of the system to interpret common text formats such as abbreviations, numerical sequences,
homographs etc.
- Today in Las Vegas, NV it's 54°F.
- "We live for the music", live from the Madison Square Garden.
- Highly intelligibile
A measure of how comprehensible speech is.
- ”Peter Piper picked a peck of pickled peppers.”
20. Polly
Quality
- Lexicons
Enables developers to customize the pronunciations of word or phrases
<lexeme>
<grapheme>Kaja</grapheme>
<grapheme>Kaja</grapheme>
<grapheme>Kaja</grapheme>
<phoneme>kaI.@</phoneme>
</lexeme>
- Homographs
Words written identically that have different pronunciation
- Proper Names
21. Polly
Quality
- Text Normalization
Disambiguation of abbreviations, acronyms, units (St. -> street/saint, KM -> kilometers)
- Foreign Words
Use of right pronunciation from a different language (C’est la vie, dèjà vu)
- Slang
Support for common used way of saying (ASAP, LOL, ROTFL)
- Prosody / Intonation contour
Prediction of changes in volume, rate, and pitch
22. Polly
SSML
Speech Synthesis Markup Language
is a W3C recommendation, an XML-based markup language for speech
synthesis applications
<speak>
My name is Kuklinski. It is spelled
<prosody rate=’x-slow’>
<say-as interpret-as=”characters”>Kuklinski</say-as>
</prosody>
</speak>
24. Raspberry PI 3
- AWS services with Java SDK
- Bluetooth speaker
- USB Webcam for image and audio input
- Send Python scripts to EV3Dev
- Send audio but receive text to handle different
cases
25. EV3Dev
- Receive and exec Python scripts
- Interact with motors and sensors
- It’s only a ARM9 300MHz 64MB
- EV3Dev Debian based distro with kernel
modules for Lego Mindstorm EV3 hardware
26. What was hard
- javax.sound API
- Choice of mic (success)
- Play wav file (fail)
- Bluetooth pair CLI
-
- Debugging Lex
27. TODO
- Use AWS IoT button to start/stop audio
recording (no gui needed)
- Alexa Voice Service integration
- Avoid SSH connection between RaspberryPI and
EV3Dev (what?)
- Replace EV3 with BrickPI?