The services are fully managed so there are no deep learning algorithms to build, no machine learning models to train, and no up-front commitments or infrastructure investments required.
Building, deploying and scaling apps with AI capabilities has been difficult for many developers because of the heavy lifting involved, according to, Raju Gulabani, VP, Databases, Analytics, and AI, AWS.
AI involves extensive manual effort to develop and tune many different types of machine learning and deep learning algorithms for automatic speech recognition, natural language understanding, image classification, collecting and cleaning the training data, and to train and tune the machine learning models.
The process must also be repeated for every object, face, voice, and language feature in an application.
“Thousands of machine learning and deep learning experts across Amazon have been developing AI technologies for years to predict what customers might like to read, to drive efficiencies in our fulfillment centers through robotics and computer vision technologies, and to give customers our AI-powered virtual assistant, Alexa,” said Gulabani. “Now, we are making the technology underlying these innovations available to any developer in the form of three fully managed Amazon AI services that are easy to use, powerful, and cost-effective.”
The services announced yesterday were: Amazon Lex, Amazon Polly, and Amazon Rekognition
Amazon Lex is a new service for building conversational interfaces using voice and text that is built on the same automatic speech recognition (ASR) technology and natural language understanding (NLU) that powers Amazon Alexa.
It makes it easy to bring natural language capabilities to virtually any app, said Dr. Matt Wood, general manager for product strategy at AWS. Developers can build and test bots directly from the AWS Management Console by typing in a few sample phrases along with instructions for getting the required parameters to complete the task and the corresponding clarifying questions to ask the user.
“Amazon Lex takes care of the rest, building the language model and asking the follow-up questions needed to complete the task so that you can have a fluid natural conversation with Lex,” he said.
I his presentation, Wood demonstrated how Lex can facilitate a smoother conversation with a digital assistant in booking a flight. With the use of deep learning capabilities, Lex is not only able to book a flight for its user, the service is able to make intelligent suggestions based on price, destination, schedules, and other factors.
He said Lex employs deep learning “to create text, to understand meaning, and intent” and “graph its answer based on 15 different ways to respond” to the user’s question.
Because Lex is integrated with AWS Lambda, developers can configure Amazon Lex to invoke the appropriate backend service (e.g., the flight booking service) through an AWS Lambda function. Developers can also use pre-built enterprise connectors that execute AWS Lambda functions to answer questions like “what are my top 10 accounts in Salesforce.com,” by fetching data from enterprise systems like Salesforce, Microsoft Dynamics, Marketo, Zendesk, QuickBooks, and HubSpot.
Amazon Polly – This service makes it easy for developers to add natural-sounding speech capabilities to existing applications like newsreaders and e-learning platforms, or create entirely new categories of speech-enabled products – from mobile apps to devices and appliances.
With Polly, developers can send text to Amazon Polly using the SDK or from within the AWS Management Console and Polly immediately returns an audio stream that can be played directly or stored in a standard audio file format. With 47 lifelike voices and support for 24 languages, developers can choose from both male and female voices with a variety of accents to make applications for users around the globe.
Polly’s fluid pronunciation of text content means applications deliver high-quality voice output across a wide variety of text formats. Amazon Polly is scalable, returning high-quality speech fast, even when converting large volumes of text to speech. With Amazon Polly, developers pay only for the text they convert, and they can cache generated speech and replay it as many times as they like with no restrictions.
Amazon Rekognition – Amazon Rekognition enables developers to quickly and easily build applications that analyze images, and recognize faces, objects, and scenes. Amazon Rekognition uses deep learning technologies to automatically identify objects and scenes, such as vehicles, pets, or furniture and provides a confidence score that lets developers tag images so that application users can search for specific images using keywords.
Rekognition can locate faces within images and detect attributes, such as whether or not the face is smiling or the eyes are open. Amazon Rekognition also supports advanced facial analysis functionalities such as face comparison and facial search.
Using Rekognition, developers can build an application that measures the likelihood that faces in two images are of the same person, thereby being able to verify a user against a reference photo in near real-time. Similarly, developers can create collections of millions of faces (detected in images) and can search for a face similar to their reference image in the collection.
Rekognition removes the complexity and overhead required to develop and manage expensive image processing pipelines by making comprehensive image classification, detection, and management capabilities available in a simple, cost-effective, and reliable AWS service. There are no upfront costs for Amazon Rekognition, developers pay only for the images they analyze and the facial feature vectors they store.
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