The term “embedding” has become quite common in the descriptions of AI systems only during the last few years. It first appeared in the works of specialists in Natural Language Processing (NLP). It means a process or, more often, the result of a process of transforming a language entity (a word, sentence, paragraph, or the whole text) into a set of numbers — a numerical vector. In the Russian-language literature, embeddings are numerical vectors that are derived from words or other language entities.
Studying the ready models, I found an article with an overview of the six most famous options. These are, of course, digital music formats. According to the article, there are two main approaches to music generation: based on the digitized audio stream (the sound we hear from the speakers – raw audio, wav files) and based on working with MIDI (musical notation). I didn’t take into account the raw audio options, and here’s why:
Artezio will provide expert support for AdTech, a prestigious forum in New York. Together with top brands and tech leaders, Artezio will discuss the key aspects of digital transformation of the world and communication at the forum.
Artezio is among the exhibitors at GITEX (Gulf Information Technology Exhibition), a prestigious trade show that takes place in Dubai, United Arab Emirates, on 8-12 October 2017. It is an annual event held with the support of the UAE government that features the best developments in the field of IoT, smart cities, telecommunication, AR and VR.
“In many cases a diagnosis made by Watson, IBM’s supercomputer used for diagnosis of oncological diseases, was first treated with much skepticism but during additional examination it was generally confirmed. Today AI is mostly implemented in screening research, and it has already showed excellent results. Watson achieved considerable progress in assisting with the development of new drugs and predicting their effect. It is only one most known AI that has initially started its training at a game show,” says the scientist.
Today adoption of machine learning technologies has become the key development trend for software companies. The use of AI determines product competitiveness on the global IT market. However, engineers who are just at the start of AI developments come across a great variety of questions concerning the choice of basic algorithmic approaches to problem solving. In this article, I’d like to describe the main features and use cases of AI in software development. Problems and Solutions
Publications on developments in machine learning usually describe not only operating processes of a neural network or an algorithm but also the basis of AI training. Generally, publicly available datasets recognized by the community are used for training purposes. However, it would be interesting to assess how effectively a system can be trained to the required quality level in another target system that a reader, for example, would like to develop.