From business applications to IT support, Artificial Intelligence (AI) is going to impact almost all industries. Companies like Google, Amazon, Apple, Facebook, and IBM, are investing heavily in AI to bring the technology closer to customers. Let us try to understand current artificial intelligence trends that are disrupting businesses.
Automating redundant tasks that require little or no effort can free humans for handling sophisticated work. With automation, industries can improve productivity along with reducing errors.
The next level automation will witness the migration of DevOps to AIOps. Also, Machine Learning models will evolve to learn training (AutoML).
By collecting user data, businesses can understand their preferences and accordingly suggest offers and products. AI is making it possible to derive meaningful information from vast data sets.
For example, Thread, UK’s leading fashion retailer uses AI to provide personal style recommendation to its over 650,000 customers.
#3 Cognitive Services
Cognitive services are a set of machine learning algorithms to build intelligent applications that enables natural and contextual interactions between man and machine. Vision, speech, language, and data insights are the core of cognitive services.
International Data Corporation (IDC) states, Cognitive applications will yield productivity improvements over $60B annually for U.S. enterprises by 2020.
#4 Natural Language Processing (NLP)
NLP enables machines to extract information from the human language and take an appropriate decision. Language modelling, document intelligence, understanding intents and contexts, sentiment analysis, and chatbots are the emerging artificial intelligence trends facilitated by NLP. Following are the features of NLP.
- Web Scraping: Also known as web harvesting, web scraping is extracting data from websites.
- Text wrangling: It involves gathering text from many sources, and consolidating them into a unified document instead of handling multiple documents.
- Parts of Speech Tagging: POS Tagging (or POST) is the process of marking up a word in a text corresponding to a particular part of speech. It helps the machine to decipher the natural human language.
- Shallow Parsing: It is also known as chunking. Shallow parsing just analyses the parts of sentences and passes the text for higher-level semantic analysis.
- Dependency Parsing: Dependency parsing is connecting the words according to their relationships.
- Named Entity Recognition: It is classifying the extracted information into predefined categories.
- Emotion and Sentiment Analysis: Emotion Analysis recognizes feelings through the expression of texts, such as anger, disgust, fear, happiness, sadness, and surprise. Sentiment Analysis detects positive, neutral, or negative feelings from the text.
Representation learning and deep neural-network style machine learning methods are used to achieve state-of-the-art results in Natural Language Processing. Here’s how computers are trained to detect objects.
#5 Internet of Things (IoT)
IoT involves transferring data over a network without human-to-human or human-to-computer interaction. It uses internet-connected appliances with sensors, control systems, and automation to transfer real-time data from the consumer, commercial, industrial, and infrastructure spaces. The recent artificial intelligence trend that IoT witnesses is voice control for devices. For example, Amazon Echo and Google Home use voice-interface for controlling machines in IoT.
What would be the Future Artificial Intelligence Trends?
The very idea of AI is to make the lives of humans easier. We will continue to see the advancement of artificial intelligence in the coming years.
Elon Musk says, AI is much smarter than the smartest human on earth and the digital super-intelligence would be like an Alien!
Excited to read about the connection between AI and Aliens?
Contributing Authors: Nidhi Agrawal (Content Writer @Mantra Labs