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AI terms you need to know


According to PwC, AI could contribute up to $15.7 trillion to the global economy by 2030. Company leaders know this and are weighing their options. Some 60% of C-suite execs said they were planning to step up AI investment by more than half in 2018. Right now, AI is changing the way people do business, making teams more efficient, productive and profitable.

We’ve searched through the best case studies, ebooks and guides to bring you the most crucial need-to-know terminology to get you started on your automation and AI journey.

1. Algorithm: In math and computer science, an algorithm is the process or equation that a machine goes through to solve a problem, complete a task or perform a certain computation.

2. Artificial intelligence (AI): Machines that learn from data and can perform tasks that normally require human intelligence. These include tasks like visual perception, speech recognition, decision-making and language translations.

3. Augmented intelligence: Tools and technology designed to elevate human workers and aid them in working smarter. This is seen as a complement to humans, rather than a replacement. Often referred to as intelligence augmentation (IA).

4. Automation: Having a machine or tool that can perform a function with minimal human involvement.

5. Autonomous business processes: When a series of business tasks can all be fully automated with little human interaction or interference.

6. Business Process Automation (BPA): Automation of business processes and workflows as a whole, rather than one step or process, with the goal of making the organization as efficient and productive as possible.

7. Chatbot: A software tool designed to replicate human conversations.

8. Crowdsourcing: Getting input from a large number of people about a topic or problem, usually via the internet. In the context of AI, it’s often used to create datasets that are then used to train an AI model.

9. Decision intelligence: Decision intelligence is a practical discipline framing a wide range of decision-making techniques; it encompasses practical applications in the field of complex adaptive systems. Decision intelligence provides a framework that brings multiple traditional and advanced techniques together to design, model, align, execute, monitor and tune decision models.

10. Deep learning: An area of machine learning that essentially stacks neural networks on top of each other to achieve much higher accuracy than any other ML algorithm has before.

11. Hyperautomation: Defined by Gartner, hyperautomation means rapidly identifying and automating as many business processes as possible, using software, robotic process automation and machine learning.

12. Information extraction: When a machine mines for interesting or relevant pieces of data found in natural language text (for example, names, companies, telephone numbers, etc.).

13. Ingestion engine: Any technology that grabs data from one place (or several places) and ingests it, or takes it in, so that it can be further analyzed.

14. Intent detection: When a system uses NLP to predict the intention inside a human message. This can be used to assist in getting the message to the right department or helping respond to it.

15. Knowledge-based AI: Humans assemble a handcrafted set of rules that are then used to make decision graphs. These graphs often take a very long time to manually create by subject matter experts.

16. Machine learning: A subset of artificial intelligence in which a machine uses an algorithm to solve a problem or do a certain task. Machine learning tools learn by finding patterns in datasets that they can then use to create bigger-picture outcomes over time. This is also called data mining.

17. Natural Language Processing (NLP): Using software or other technology to understand, interpret and manipulate human language. This is also called text mining.

18. Neural network: This is an artificial network that essentially simulates how the human brain works. A network of firing “neurons” interprets data, making decisions and learning from the input over time.

19. Predictive analytics: When a machine can make predictions about the future using current and historical data.

20. Process mining: Software that helps a company understand their current business processes, find any variations or problems across the organization, and gauge whether it’s worth investing in improvements.

21. Reinforcement learning: Systems that learn based on a reward. They create outcomes that are then rewarded or punished, based on whether the outcome is correct or not. Once the correct output is achieved, it will optimize for maximum reward.

22. Robotic Process Automation (RPA): Software that automates tasks and processes usually done by humans. This can be tasks like processing information, manipulating data, and triggering responses. Essentially this is software automating the existing tools in your tech stack.

23. Sales AI: A tool that utilizes artificial intelligence to improve the sales process. This can be in the form of automation, in which a simple sales task is completed autonomously, or through augmentation, which assists in making predictions.

24Sales automation: Using technology to automate sales processes through static roles. For example, converting leads into the next stage in the CRM based on triggers that occur elsewhere like sending out certain documents through email.

25. Supervised learning: Machine learning models that learn by comparing their output to the “correct” output. If the system is incorrect, it adjusts the algorithm accordingly.

26. Unsupervised learning: Machine learning models that are trained without receiving the correct “answer” to the problem they might be solving, meaning they learn through a process of trial and error.

By Jessica Munday


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