AI (Artificial Intelligence) comprises of two words “Artificial” and “Intelligence”. Artificial refers to something which is made by human or non-natural thing and Intelligence means ability to understand or think. There is a misconception that Artificial Intelligence is a system, but it is not a system. AI is implemented in the system.
There can be so many definition of AI, one definition can be “It is the study of how to train the computers so that computers can do things which at present human can do better.” Therefore, it is an intelligence where we want to add all the capabilities to machine that human contain. (ML) Machine Learning, (DL) Deep Learning are subsets of AI.
AI makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Most AI examples that you hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning and natural language processing. Using these technologies, computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in the data.
As it may sound, AI is just used to solve complex problems, but that’s not 100% true. Any repetitive work, or a work where you learn from working on it could be area where AI can be applied, being as smallest as filtering spam emails.
Here are some technologies of AI that we can help with:
Natural Language Generation (NLG)
Producing text from computer data. Currently used in customer service, report generation, and summarizing business intelligence insights.
While NLG is useful wherever there is a need to generate content from data, some of the most common implementations include:
- Written analysis for business intelligence dashboards.
- Reporting on business data/data analysis.
- Personalized customer communications via email and in-app messaging.
- IoT device status and maintenance reporting.
- Individual client financial portfolio summaries and updates.
- Ecommerce product descriptions and category landing page content
Speech Recognition
Transcribe and transform human speech into format useful for computer applications.
Virtual Agents
“The current darling of the media,” says Forrester (I believe they refer to my evolving relationships with Alexa), from simple chatbots to advanced systems that can network with humans. Currently used in customer service and support and as a smart home manager.
Machine Learning
Provides algorithms, APIs, development and training toolkits, data, as well as computing power to design, train, and deploy models into applications, processes, and other machines. Currently used in a wide range of enterprise applications, mostly `involving prediction or classification.
Deep Learning
A special type of machine learning consisting of artificial neural networks with multiple abstraction layers. Currently primarily used in pattern recognition and classification applications supported by very large data sets.
Decision Management
Engines that insert rules and logic into AI systems and used for initial setup/training and ongoing maintenance and tuning. A mature technology, it is used in a wide variety of enterprise applications, assisting in or performing automated decision-making.
AI-optimized Hardware
Graphics processing units (GPU) and appliances specifically designed and architected to efficiently run AI-oriented computational jobs. Currently primarily making a difference in deep learning applications.
Biometrics
Enable more natural interactions between humans and machines, including but not limited to image and touch recognition, speech, and body language. Currently used primarily in market research.
Robotic Process Automation
Using scripts and other methods to automate human action to support efficient business processes. Currently used where it’s too expensive or inefficient for humans to execute a task or a process.
Text Analytics and NLP
Natural language processing (NLP) uses and supports text analytics by facilitating the understanding of sentence structure and meaning, sentiment, and intent through statistical and machine learning methods. Currently used in fraud detection and security, a wide range of automated assistants, and applications for mining unstructured data.