Google asserts that the patent industry stands to benefit from AI and machine learning models like BERT, a natural language processing algorithm that attained state-of-the-art results when it was released in 2018. In a whitepaper published today, the tech giant outlines a methodology to train a BERT model on over 100 million patent publications from the U.S. and other countries using open-source tooling, which can then be used to determine the novelty of patents and generate classifications to assist with categorization. The global patent corpus is large, with millions of new patents issued every year. Patent applications average around 10,000 words and are meticulously wordsmithed by inventors, lawyers, and patent examiners. Patent filings are also written with language that can be unintelligible to lay readers and highly context-dependent; many terms are used to mean completely different things in different patents.

CAPE TOWN – One of the major drivers of the Fourth Industrial Revolution (4IR) or the age of intelligentisation is the major advances in Artificial Intelligence (AI) that are supporting and even taking over from humans in many situations. AI is increasingly replacing humans where knowledge could be learned or the decision-making formula is known. It is in particular the AI abilities of machine learning and deep learning that makes AI so powerful in numerous fields. Machine learning refers to the ability of computer systems to learn by itself and to adapt accordingly, allowing them to perform a specific task without explicit instructions. In the Business Report of last Friday I illustrated that AI even transforms the disciplines based on "human touch" such as social work and is used to predict successful youth influencers in an HIV campaign; match homeless people with the best-suited housing and most effective social interventions; and select vulnerable families and children in need of intervention.

By nature, human language is complex. To understand human speech, a technology must also understand the grammatical rules, meaning and context, but also colloquialisms, slang and acronyms used in a language. Natural language processing algorithms support computers by simulating the human ability to understand language. Many NLP algorithms are based on statistics and may be combined with deep learning. This approach is superficial in its analysis of language, however, because it isn't able to understand the meaning of words.

Natural language processing (NLP) refers to the capacity of a computer or machine to accept, analyze and generate human speech. The ultimate aim is to make the interaction between humans and computers and human to human possible. Although in our modern world we are equipped with artificial intelligence ( A.I) and machine learning devices, but lack of proper communication has always been a problem among people. We do not understand each other although we speak the same language. I have been working on a project so that we will be able to understand what we really mean when we interact with each other.