Relative Algorithms Behind Smart Home
Artificial Intelligence and Smart Home — Part2
Deep learning was first proposed by Hinton et al. in 2006. The essence of deep learning is an artificial neural network, which refers to a collection of algorithms, solving various problems such as images and texts on multi-layer neural networks. The core of deep learning is feature learning, which aims to obtain feature information through a hierarchical network, so as to solve the important problem of manually designing features in the past.
Deep learning is a framework that contains several important algorithms:
- Convolutional Neural Networks (CNN)
- Sparse Coding
- Restricted Boltzmann Machine(RBM)
- Deep Belief Networks(DBN)
- Recurrent Neural Network (RNN)
Deep learning has been applied for 14 years since the concept was put forward. For different types (image, voice, text), different network models need to be selected to achieve the best results. In addition, the combination of reinforcement learning and deep learning has also created many great results in recent years, for example, AlphaGo is one of them.
(1) Face recognition
Image recognition is the most popular application in the field of deep learning. Whether it is deep learning or other machine learning methods (for example vector machine), image recognition is popular for research. And that’s why the ImageNet competition came into being. ImageNet invited major AI researchers to test the effects of the algorithms by establishing a powerful image library, which promoted vigorous development and led to today’s AI field.
(2) Voice recognition
Since deep learning was introduced into the field of speech recognition in 2009, in just a few years, the error rate based on the traditional Gaussian Mixture Model (GMM) in the TIMIT dataset has dropped from 21.7% to 17.9%, which has attracted widespread attention in the industry. Google’s GoogleNow, Apple’s Siri, Microsoft’s Skype Translator, and iFlytek are all algorithms based on deep learning. Speech recognition is currently well developed, and some dialects in China can be recognized accurately now.
(3) Natural language processing
Natural language processing (NLP) is a subject area that automatically analyzes and characterizes human natural language by constructing computational algorithms. Natural language processing has five main tasks, namely classification, matching, translation, structure prediction, and sequential decision-making process. In these tasks, words, phrases, sentences, paragraphs, and even documents are usually treated as a series of tags (strings), although the complexity is not the same.
In fact, sentences are the most widely used processing unit. Recent studies have shown that deep learning can improve the performance of the first four tasks and has become the most advanced technology to solve these tasks.
Today, NLP-based systems have been widely used, such as Google’s powerful search engine and Amazon’s voice assistant Alexa. In addition, NLP can also help machines perform tasks related to complex natural languages, such as machine translation and dialogue generation.
Why the High-Quality Training Data is so Important to AI Machine Learning?
The current artificial intelligence is also called data intelligence. At this stage of development, the more layers of the neural network, the larger amount of labeled data is needed. Indeed, data has an important role. Thus, all developers from Google and Microsoft to ordinary individual developers are paying a lot of attention to the high-quality labeled data.
In the current practice of AI applications, different level of data quality demonstrates the value of AI solutions with a very obvious gap. If the general datasets used by the previous algorithm model are coarse grains, what the algorithm model needs at present is a customized nutritious meal. If companies want to further improve certain models’ commercialization, they must gradually move forward from the general dataset to create the unique one.
We provide different types of NLP in E-commerce, Retail, Search engines, Social Media, etc. Our service includes Voice Classification, Sentiment Analysis, Text Recognition and Text Classification(Chatbot Relevance).
Partnered with over 30 different language-speaking communities across the globe, ByteBridge now provides data collection and text annotation services covering languages such as English, Chinese, Spanish, Korean, Bengali, Vietnamese, Indonesian, Turkish, Arabic, Russian and more.
Outsource your data labeling tasks to ByteBridge, you can get the high-quality ML training datasets cheaper and faster!
- Free Trial Without Credit Card: you can get your sample result in a fast turnaround, check the output, and give feedback directly to our project manager.
- 100% Human Validated
- Transparent & Standard Pricing: clear pricing is available(labor cost included)
Why not have a try?