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Create video subtitles with Amazon Transcribe using this no-code workflow

#artificialintelligence

Subtitle creation on video content poses challenges no matter how big or small the organization. To address those challenges, Amazon Transcribe has a helpful feature that enables subtitle creation directly within the service. There is no machine learning (ML) or code writing required to get started. This post walks you through setting up a no-code workflow for creating video subtitles using Amazon Transcribe within your Amazon Web Services account. The terms subtitles and closed captions are commonly used interchangeably, and both refer to spoken text displayed on the screen.


FrugalML: How to Use ML Prediction APIs More Accurately and Cheaply

arXiv.org Machine Learning

Prediction APIs offered for a fee are a fast-growing industry and an important part of machine learning as a service. While many such services are available, the heterogeneity in their price and performance makes it challenging for users to decide which API or combination of APIs to use for their own data and budget. We take a first step towards addressing this challenge by proposing FrugalML, a principled framework that jointly learns the strength and weakness of each API on different data, and performs an efficient optimization to automatically identify the best sequential strategy to adaptively use the available APIs within a budget constraint. Our theoretical analysis shows that natural sparsity in the formulation can be leveraged to make FrugalML efficient. We conduct systematic experiments using ML APIs from Google, Microsoft, Amazon, IBM, Baidu and other providers for tasks including facial emotion recognition, sentiment analysis and speech recognition. Across various tasks, FrugalML can achieve up to 90% cost reduction while matching the accuracy of the best single API, or up to 5% better accuracy while matching the best API's cost.


Google Brain Co-Founder Teams With Foxconn to Bring AI to Factories

#artificialintelligence

Consumers now experience AI mostly through image recognition to help categorize digital photographs and speech recognition that helps power digital voice assistants such as Apple Inc's Siri or Amazon.com But at a press briefing in San Francisco two days before Ng's Landing.ai In many factories, workers look over parts coming off an assembly line for defects. Ng showed a video in which a worker instead put a circuit board beneath a digital camera connected to a computer and the computer identified a defect in the part. Ng said that while typical computer vision systems might require thousands of sample images to become "trained," Landing.ai's


Google Brain co-founder teams with Foxconn to bring AI to factories

#artificialintelligence

Consumers now experience AI mostly through image recognition to help categorize digital photographs and speech recognition that helps power digital voice assistants such as Apple Inc's Siri or Amazon.com But at a press briefing in San Francisco two days before Ng's Landing.ai In many factories, workers look over parts coming off an assembly line for defects. Ng showed a video in which a worker instead put a circuit board beneath a digital camera connected to a computer and the computer identified a defect in the part. Ng said that while typical computer vision systems might require thousands of sample images to become "trained," Landing.ai's


How Echo Look could feed Amazon's big data fueled fashion ambitions

#artificialintelligence

This week Amazon took the wraps off a new incarnation of its Alexa voice assistant, giving the AI an eye so it can see as well as speak and hear. The Echo Look also contains a depth sensor that's being used, in the first instance, to create a bokeh effect for a hands-free style selfies feature that Amazon is hoping will sell the device to fashion lovers, by making their outfits pop out against the bedroom wallpaper, and making them more eager to socially share. The Echo Look app is where users can view the style selfies (and videos) they've asked Alexa to record for them (she indefinitely stores a copy for Amazon too). But the flagship feature of the app is a fashion feedback service, called Style Check, which Amazon says will utilize machine learning to rate fashion choices and help users choose between outfit pairs. And ultimately, presumably, give their entire wardrobe a score.


Build Your Own Text-to-Speech Applications with Amazon Polly

#artificialintelligence

You can't just assume that when an application reads each letter of a sentence that the output will make sense. Amazon Polly provides speech synthesis functionality that overcomes those challenges, allowing you to focus on building applications that use text-to-speech instead of addressing interpretation challenges. Amazon Polly turns text into lifelike speech. It lets you create applications that talk naturally, enabling you to build entirely new categories of speech-enabled products. Amazon Polly is an Amazon AI service that uses advanced deep learning technologies to synthesize speech that sounds like a human voice.


AI will help answer queries automatically: Amazon's Rajeev Rastogi - ETtech

#artificialintelligence

"We are applying AI to a number of problems such as speech recognition, natural language understanding, question answering, dialog systems," Rastogi said.Rajeev Rastogi, who heads the Machine Learning team at Amazon, explains how the global ecommerce giant employs Artificial Intelligence to improve the online shopping experience.Edited excerpts: In which areas does Amazon use AI? We are applying AI to a number of problems such as speech recognition, natural language understanding, question answering, dialog systems, product recommendations, product search, forecasting future product demand, among others. We have used Deep Learning to do better speech recognition. We use neural networks to convert speech (spoken by users) to text with very high accuracy. The speech recognition and understanding technology in Alexa (Amazon's voice-controlled virtual assistant) is powered by Deep Learning.


Welcome to the New AWS AI Blog!

#artificialintelligence

If you ask 100 people for the definition of "artificial intelligence," you'll get at least 100 answers, if not more. At AWS, we define it as a service or system which can perform tasks that usually require human-level intelligence such as visual perception, speech recognition, decision making, or translation. On this new AWS blog, we'll be covering these areas and more, with in-depth technical content, customer stories, and new feature announcements. The challenges related to building sophisticated AI systems center mostly around scale: the datasets are large, training is computationally hungry, and inferring predictions can be challenging to do at scale or on lower-power and mobile devices. Customers have been using AWS to solve these general problems for years, and the ability to be able to access storage, GPUs, CPUs, and IoT services on demand has emerged as a perfect fit for intelligent systems in production.