In just a few years, all copiers (a.k.a. multi-functional devices or MFDs) will be rocking AI tech. Whether it's embedded in the copier or available through apps that connect to AI in the cloud, this is the future we're heading towards.
AI-powered MFDs are going to be the next big thing, making it possible to upgrade almost every copier that doesn’t have AI yet.
Just like we had to get used to digital copiers in the 90s, we now need to level up our skills and knowledge about AI. To kick things off, here’s a glossary to help us all get up to speed in this ever-evolving field.
Each month I'll try to post something in reference to how AI and copiers can help our clients workflows.
For Starters I thought it would be good to review
Artificial Intelligence Terms
Artificial Intelligence (AI) refers to the simulation of human intelligence in machines. These machines are programmed to think, learn, and problem-solve in ways that mimic human cognitive functions. AI applications include things like speech recognition, decision-making, language translation, and autonomous driving.
AI ethics refers to the principles and guidelines that govern the development and use of artificial intelligence to ensure it is fair, transparent, and beneficial. It addresses concerns like bias, privacy, accountability, and the impact of AI on society. The goal is to create AI systems that are ethical, responsible, and align with human values.
AI algorithm is a set of rules or instructions designed for machines to follow in order to solve problems, make decisions, or perform tasks. These algorithms enable computers to learn from data, recognize patterns, and improve their performance over time without being explicitly programmed for specific tasks. Examples include decision trees, neural networks, and genetic algorithms.
Application Programming Interface (API) is a set of rules and protocols that allows different software applications to communicate with each other. APIs enable developers to access the functionality of other software or services, such as databases, web services, or operating systems, without having to understand the internal workings of those systems. This facilitates the integration and interaction of different software components, making it easier to build and connect applications.
Big data refers to extremely large and complex datasets that are difficult to process and analyze using traditional data-processing tools. These datasets come from various sources like social media, sensors, and transactions, and they require advanced technologies and techniques, such as machine learning and data mining, to extract valuable insights and make informed decisions.
More to come next week
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