11 new AI terms you need to understand

18 September 2024
Written by Danielle Larocca

With over 25 years in SAP Human Capital Management, Danielle is a recognized leader in HR technology. She holds the distinguished roles of SAP Mentor and SuccessFactors Confidant, and serves as HXM Chairperson for ASUG (America's SAP User Group). Danielle is a sought-after speaker at international conferences, sharing insights on HR tech trends. She has authored four best-selling books on SAP and holds certifications in both SAP and SuccessFactors technologies. Danielle is Global Vice President Strategic Alliances at EPI-USE Labs, where she continues to drive innovation in HR solutions.

Want to get some simple definitions of new AI terms? Everyone I talk to inside and outside the IT community is curious about how they can stay in the know on all that is happening in Artificial Intelligence (AI). As we have seen, new technology arises every day, and with that technology comes a learning curve, and new terminology.

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Everyone I talk to inside and outside the IT community is curious about how they can stay in the know on all that is happening in Artificial Intelligence (AI). As we have seen, new technology arises every day, and with that technology comes a learning curve and new terminology.

 

For example, you may have heard the expression ‘hallucinations’ in the context of AI. This is a great example of how a word we know – and have used previously in a different context – is now used to describe a technical phenomenon.

 

In this blog, I’m sharing simple definitions of some of this new terminology in an attempt to make it easier for you to understand what role AI plays in our day-to-day lives.

 

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Prompt Engineering

The process of crafting inputs or prompts to get the best possible response from an AI system.

Asking ChatGPT to write an email, by giving it specific instructions like tone and subject.

Large Language Model

A type of AI trained on vast amounts of text to understand and generate human-like language.

ChatGPT is a large language model capable of answering questions and holding conversations.

Natural Language Processing (NLP)

The technology that enables computers to understand and work with human language.

Using voice assistants like Siri or Alexa to answer everyday questions.

Machine Learning

A method where computers learn patterns from data to make decisions or predictions without being explicitly programmed.

Netflix recommending shows you may like, based on your viewing history.

Neural Network

A type of machine learning inspired by the human brain, consisting of layers of nodes that process data.

Facial recognition systems that identify people from photos.

Generative AI

AI that creates new content, such as text, images, or music, based on the data it was trained on.

AI tools like DALL-E generating artwork or images from text descriptions.

Reinforcement Learning

A method where AI learns by trial and error, getting rewards or penalties to improve its decisions.

AI in self-driving cars learning how to navigate roads by receiving feedback from its performance.

Supervised Learning

Training an AI model using labeled data, where the correct output is provided for each input during training.

Teaching an AI to recognize cats by showing it labeled images of cats and non-cats.

Unsupervised Learning

Training an AI model with unlabeled data, where the AI looks for patterns or groupings on its own.

AI clustering customer behavior data to identify new segments without knowing what they are in advance.

Deep Learning

A subset of machine learning using neural networks with many layers to process large and complex data.

AI that powers speech recognition in smartphones or automatic translation tools.

Hallucinations

Refer to instances where an AI system generates incorrect, misleading, or entirely fabricated information that is not based on the data it was trained on or the input it received. This often happens with language models like ChatGPT, which may confidently provide false facts, create non-existent references, or invent responses that sound plausible but are not accurate.

These errors occur because the AI generates content by predicting the next word in a sequence based on patterns from its training data, but it doesn’t actually ‘know’ facts in the way humans do.

Input: "Who won the 2023 Nobel Prize in Physics?"

AI Hallucination: "The 2023 Nobel Prize in Physics was awarded to Dr. Elena Johnson for her ground-breaking work on dark matter teleportation."

In this case, the AI might invent a person, a discovery, or an award that never actually happened, confidently presenting the fabricated information as if it were true. This happens because the AI generates responses by predicting patterns rather than verifying facts.

 

To keep an eye on the SAP SuccessFactors Roadmap for Artificial Intelligence, visit https://roadmaps.sap.com/ (SAP log-in required) and select the option for Artificial Intelligence from the Focus Topic menu. One of the exciting items planned is a detailed Explanation of pay statement using Joule, scheduled for Q2 2025.

 

For anyone interested in learning more about Artificial Intelligence and how it is used in the SAP SuccessFactors world, read my blog titled Five things to know about Artificial Intelligence in SAP SuccessFactors.

 

 

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