There has been remarkable success of machine learning (ML) technologies in empowering practical artificial intelligence (AI) applications, such as automatic speech recognition and computer vision. However, we are facing two major challenges in adopting AI today. One is that data in most industries exist in the form of isolated islands. The other is the ever-increasing demand for privacy-preserving AI. Conventional AI approaches based on centralized data collection cannot meet these challenges.
Every day, Every Hour, Every Minute, we are saying few simple words like "Hey Google" or "Hey Alexa" or "Hey Siri" to know something or to get our works done. "Hey Google, How do you work?" or "Hey Alexa, Why are you so smart?" or "Hey Siri, What's behind your success?" It's simply because as a client we never bother about these things. In any application clients are the ones who needs to be satisfied and in today's world these assistants are so much smarter that there is no reason to think or ask these things as a client. But as a Tech Enthusiast, or as a guy from CS background it's always too much fascinating to know about behind the scene technologies, these popular companies are using to make these smart assistants capable of extraordinary performance.
And there's a pretty broad range of people that this will be helpful to." "It's definitely a great help for people with a hearing disability, but also for international, distributed workforces who don't speak English as their native language. And education as well: online classes could benefit from captions, on top of the Live Notes that they can go back to, to facilitate learning." The transcription is not exactly pitch perfect: some sentences don't make sense and words occasionally come up deformed.
Know about the scope, importance, features, types, and best examples of AI Software! The first industrial revolution was marked by the evolution of steam and water power, the second one was followed by electricity, the third one was marked by computing giving way to the fourth industrial revolution which will feature and enhance Artificial Intelligence and Big Data. We are now living in times where technology allows us to communicate and tell stories which otherwise would have never been possible to document. The inclusion of artificial intelligence in daily lives has helped humans to have a digital assistant who thinks in the same way and helps them with problem-solving, learning, planning, decision making via speech recognition sensors. AI Software is computer programs that possess and mimic near-human behaviour with the help of learning various data patterns and similar insights.
In today's world, it is nearly impossible to avoid voice-controlled digital assistants. From the interactive intelligent agents used by corporations, government agencies, and even personal devices, automated speech recognition (ASR) systems, combined with machine learning (ML) technology, increasingly are being used as an input modality that allows humans to interact with machines, ostensibly via the most common and simplest way possible: by speaking in a natural, conversational voice. Yet as a study published in May 2020 by researchers from Stanford University indicated, the accuracy level of ASR systems from Google, Facebook, Microsoft, and others vary widely depending on the speaker's race. While this study only focused on the differing accuracy levels for a small sample of African American and white speakers, it points to a larger concern about ASR accuracy and phonological awareness, including the ability to discern and understand accents, tonalities, rhythmic variations, and speech patterns that may differ from the voices used to initially train voice-activated chatbots, virtual assistants, and other voice-enabled systems. The Stanford study, which was published in the journal Proceedings of the National Academy of Sciences, measured the error rates of ASR technology from Amazon, Apple, Google, IBM, and Microsoft, by comparing the system's performance in understanding identical phrases (taken from pre-recorded interviews across two datasets) spoken by 73 black and 42 white speakers, then comparing the average word error rate (WER) for black and white speakers.
Uday Kamath has more than 20 years of experience architecting and building analytics-based commercial solutions. He currently works as the Chief Analytics Officer at Digital Reasoning, one of the leading companies in AI for NLP and Speech Recognition, heading the Applied Machine Learning research group. Most recently, Uday served as the Chief Data Scientist at BAE Systems Applied Intelligence, building machine learning products and solutions for the financial industry, focused on fraud, compliance, and cybersecurity. Uday has previously authored many books on machine learning such as Machine Learning: End-to-End guide for Java developers: Data Analysis, Machine Learning, and Neural Networks simplified and Mastering Java Machine Learning: A Java developer's guide to implementing machine learning and big data architectures. Uday has published many academic papers in different machine learning journals and conferences.
With the advent of new deep learning approaches based on transformer architecture, natural language processing (NLP) techniques have undergone a revolution in performance and capabilities. Cutting-edge NLP models are becoming the core of modern search engines, voice assistants, chatbots, and more. Modern NLP models can synthesize human-like text and answer questions posed in natural language. As DeepMind research scientist Sebastian Ruder says, NLP's ImageNet moment has arrived. While NLP use has grown in mainstream use cases, it still is not widely adopted in healthcare, clinical applications, and scientific research.
Researchers at Facebook AI recently introduced and open-sourced a new framework for self-supervised learning of representations from raw audio data known as wav2vec 2.0. The company claims that this framework can enable automatic speech recognition models with just 10 minutes of transcribed speech data. Neural network models have gained much traction over the last few years due to its applications across various sectors. The models work with the help of vast quantities of labelled training data. However, most of the time, it is challenging to gather labelled data than unlabelled data.
Speech Recognition is the ability of a machine or program to identify words and phrases in spoken language and convert them to textual information. You have probably seen it on Sci-fi, and personal assistants like Siri, Cortana, and Google Assistant, and other virtual assistants that interact with through voice. In order to understand your voice these virtual assistants need to do speech recognition. Speech Recognition is a complex process, so I'm not going to teach you how to train a Machine Learning/Deep Learning Model to do that. Instead, I will instruct you how to do it using google speech recognition API.
How does Amazon help Alexa understand what people mean and not just what they say? And, we couldn't be talking about Alexa, smart home tech, and AI at a better time. During this week's Amazon Devices event, the company made a host of smart home announcements, including a new batch of Echo smart speakers, which will include Amazon's new custom AZ1 Neural Edge processor. In August this year, I had a chance to speak with Evan Welbourne, senior manager of applied science for Alexa Smart Home at Amazon, about everything from how the company is using AI and ML to improve Alexa's understanding of what people say, Amazon's approach to data privacy, the unique ways people are interacting with Alexa around COVID-19, and where he sees the future of voice and smart tech going in the future. The following is an transcript of our conversation edited for readability. Bill Detwiler: So before we talk about maybe IoT, we talk about Alexa, and kind of what's happening with the COVID pandemic, as people are working more from home, and as they may have questions that they're asking about Alexa, about the pandemic, let's talk about kind of just your role there at Amazon, and what you're doing with Alexa, especially with AI and ML. So I lead machine learning for Alexa Smart Home. And what that sort of means generally is that we try to find ways to use machine learning to make Smart Home more useful and easier to use for everybody that uses smart home. It's always a challenge because we've got the early adopters who are tech savvy, they've been using smart home for years, and that's kind of one customer segment. But we've also got the people who are brand new to smart home these days, people who have no background in smart home, they're just unboxing their first light, they may not be that tech savvy.