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Artificial intelligence is further defined as “narrow AI” or “general AI”. Narrow AI, which we interact with today, is designed to perform specific tasks within a domain (e.g. language translation). General AI is hypothetical and not domain specific, but can learn and perform tasks anywhere. This is outside the scope of this paper. This paper focuses on advances in narrow AI, particularly on the development of new algorithms and models in a field of computer science referred to as machine learning.

Machine-learning algorithms made possible by a combination of deep learning and artificial intelligence has dominated 2017. The dramatic rises in consumer expectation levels are also forcing businesses to simplify and personalize everything in a bid to remain relevant to their tech savvy customers.

We now have access to unlimited music to accompany us on our travels thanks to all-you-can-eat packages offered by the likes of Apple Music and Spotify. However, discovery algorithms on these digital services could never replace the art of manually creating your playlist, or could they?

In the global Internet environment processing of information in multiple languages has a great importance. Intellexer Language Recognizer identifies the language and character encoding of incoming documents. It supports more than 30 languages, covering major European and Asian languages. Intellexer Language Recognizer can be successfully used: - as a pre-filtering step to improve the quality of input text data (because of most natural processing algorithms deal with monolingual texts and inclusion of other languages can decrease the performance of document management systems); - in mining bilingual texts for machine translation from online resources; - for retrieval, grouping and understanding relevant information (user’s texts, emails and etc.) in multilingual environment.Language Recognizer

Generative AI starts with a prompt that could be in the form of a text, an image, a video, a design, musical notes, or any input that the AI system can process. Various AI algorithms then return new content in response to the prompt. Content can include essays, solutions to problems, or realistic fakes created from pictures or audio of a person.

Early versions of generative AI required submitting data via an API or an otherwise complicated process. Developers had to familiarize themselves with special tools and write applications using languages such as Python.

Now, pioneers in generative AI are developing better user experiences that let you describe a request in plain language. After an initial response, you can also customize the results with feedback about the style, tone and other elements you want the generated content to reflect.