Artificial intelligence (AI), natural language processing (NLP), machine learning (ML), deep learning and neural networks represent powerful software-based techniques used to solve many problems.
Artificial intelligence (AI), natural language processing (NLP), machine learning (ML), deep learning and neural networks represent powerful software-based techniques used to solve many problems. However, recently it seems like everywhere you look there is a new tech company that’s using AI. It’s almost as if AI is just hype or some sort of fad. But it isn’t. I believe AI is the future. The overuse of the term made me wonder why so many companies reference the technology even though their products or services don’t even use AI. A few years ago, the big buzzword was cloud. Then it was big data. Then blockchain. And now it’s AI.
What Is AI?
According to Techopedia , it is “an area of computer science that emphasizes the creation of intelligent machines that work and react like humans.” AI technologies include entity recognition, entity linking, speech recognition, pattern recognition, complex problem-solving and more. The knowledge base that AI learns from often includes corpora of data, such as articles, books, patents and dissertations. As a result of this learning, computers are able to gain an understanding of written text, making common sense out of it to perform problem-solving, entity cross-linking and statistical analysis — all tasks that are inefficient and tedious for humans. With there being an almost infinite amount of information in the world, AI is becoming an invaluable tool for interpreting and understanding the relationships between data entities (objects) across hundreds of industries.
The Major Parts That Form The Core Of AI
NLP is the interaction between computers and the human language. More specifically, NLP refers to the programming of computers to understand and process large amounts of text and natural language data.
ML refers to a machine’s ability to learn models and patterns so it can progressively improve its performance on a specific task with or without any supervision. This requires the identification of patterns in streams of inputs and learning with classification and numerical regressions. ML uses mathematical, algorithmic analysis and statistical models (also known as computational learning theory).
Robotics is the programming of robots with the required level of intelligence to perform simple tasks that require problem-solving, though its execution is complicated. Such tasks can involve navigation, localization, motion-planning and mapping.
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While human-like decision making by a computer isn’t here yet, remarkable gains have been made in the application of AI technologies and associated algorithms. However, when companies proclaim themselves to be AI-centric but in actuality are performing mere entity indexing within a corpus of data, they are misusing the term AI. […]