If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
The first step to creating a voice assistant is to decide what it should do. The Speech service provides multiple, complementary solutions for crafting your assistant interactions. You can add voice in and voice out capabilities to your flexible and versatile bot built using Azure Bot Service with the Direct Line Speech channel, or leverage the simplicity of authoring a Custom Commands app for straightforward voice commanding scenarios.
Google today announced that it has signed up Verizon as the newest customer of its Google Cloud Contact Center AI service, which aims to bring natural language recognition to the often inscrutable phone menus that many companies still use today (disclaimer: TechCrunch is part of the Verizon Media Group). For Google, that's a major win, but it's also a chance for the Google Cloud team to highlight some of the work it has done in this area. It's also worth noting that the Contact Center AI product is a good example of Google Cloud's strategy of packaging up many of its disparate technologies into products that solve specific problems. "A big part of our approach is that machine learning has enormous power but it's hard for people," Google Cloud CEO Thomas Kurian told me in an interview ahead of today's announcement. "Instead of telling people, 'well, here's our natural language processing tools, here is speech recognition, here is text-to-speech and speech-to-text -- and why don't you just write a big neural network of your own to process all that?' Very few companies can do that well. We thought that we can take the collection of these things and bring that as a solution to people to solve a business problem. And it's much easier for them when we do that and […] that it's a big part of our strategy to take our expertise in machine intelligence and artificial intelligence and build domain-specific solutions for a number of customers."
Natural language processing, or NLP, is a type of artificial intelligence (AI) that specializes in analyzing human language. Have you ever used Apple's Siri and wondered how it understands (most of) what you're saying? This is an example of NLP in practice. NLP is becoming an essential part of our lives, and together with machine learning and deep learning, produces results that are far superior to what could be achieved just a few years ago. In this article we'll take a closer look at NLP, see how it's applied and learn how it works.
As we said, TensorFlow.js is a powerful library, and we can work on a lot of different things like image classification, video manipulation, and speech recognition among others. For today I decided to work on a basic speech recognition example. Our code will be able to listen through the microphone and identify what the user is saying, at least up to a few words as we have some limitations on the sample model I'm using. But rather than explaining, I think it's cool if we see it first in action: I know it can be a bit erratic, and it's limited to a few words, but if you use the right model, the possibilities are endless. Enough talking, let's start coding.
The ongoing success of deep learning techniques depends on the quality of the representations automatically discovered from data 1. These representations must capture important underlying structures from the raw input, e.g., intermediate concepts, features, or latent variables that are useful for the downstream task. While supervised learning using large annotated corpora can leverage useful representations, collecting large amounts of annotated examples is costly, time-consuming, and not always feasible. This is particularly problematic for a large variety of applications. In the speech domain, for instance, there are many low-resource languages, where the progress is dramatically slower than in high-resource languages such as English.
UNIGE scientists developed a neuro-computer model which helps explain how the brain identifies syllables in natural speech. The model uses the equivalent of neuronal oscillations produced by brain activity to process the continuous sound flow of connected speech. The model functions according to a theory known as predictive coding, whereby the brain optimizes perception by constantly trying to predict the sensory signals based on candidate hypotheses (syllables in this model).
You may have realized something now. The overwhelming success of speech-enabled products like Amazon Alexa has proven that some degree of speech support will be an essential aspect of household technology for the foreseeable future. In other words, speech-enabled products would be a game changer as that offer a level of interactivity and accessibility that few technologies can match. Check out what books helped 20 successful data scientists grow in their career. Speed is a big reason voice is poised to become the next major user interface.
In a world where new technology emerges at exponential rates, and our daily lives are increasingly mediated by speakers and sound waves, text to speech technology is the latest force evolving the way we communicate. Text to speech technology refers to a field of computer science that enables the conversion of language text into audible speech. Also known as voice computing, text to speech (TTS) often involves building a database of recorded human speech to train a computer to produce sound waves that resemble the natural sound of a human speaking. This process is called speech synthesis. The technology is trailblazing and major breakthroughs in the field occur regularly.
Natural language processing (NLP) is a branch of artificial intelligence that helps computers understand, interpret and manipulate human language. NLP draws from many disciplines, including computer science and computational linguistics, in its pursuit to fill the gap between human communication and computer understanding. While natural language processing isn't a new science, the technology is rapidly advancing thanks to an increased interest in human-to-machine communications, plus an availability of big data, powerful computing and enhanced algorithms. As a human, you may speak and write in English, Spanish or Chinese. But a computer's native language -- known as machine code or machine language -- is largely incomprehensible to most people.
Ever since Android first came into existence in 2008, it has become the world's biggest mobile platform in terms of popularity and number of users. Over the years, Android developers have built advances in machine learning, features like on-device speech recognition, real-time video interactiveness, and real-time enhancements when taking a photo/selfie. In addition, image recognition with machine learning can enable users to point their smartphone camera at text and have it live-translated into 88 different languages with the help of Google Translate. Android users can even point your camera at a beautiful flower, use Google Lens to identify what type of flower that is, and then set a reminder to order a bouquet for someone. Google Lens is able to use computer vision models to expand and speed up web search and mobile experience.