Clarity breeds confidence.
You don’t have to an expert on AI for success . Demystifying the jargon around specific terms can help you understand AI functions more clearly, said Rebecca Simons, external communications manager at Cisco, during Ragan’s AI Horizons Virtual Conference.
“You can’t escape conversations around AI, and I know it might feel like you need a degree in computer science to understand half of what they’re talking about, but the whole idea is to work together to define what it actually is,” she said. “We’ve been using AI and machine learning for quite some time, but today, it’s just so much more powerful and essential to the way we work.”
By breaking down common AI terms and really getting to the root of what these things mean, communicators can integrate new tools with confidence, not only for themselves, but for their entire team.
So let’s do that. Here’s what Simons says you should know about AI definitions with examples of each.
- Artificial intelligence: Simulates human intelligence in machines for things like problem solving, identifying patterns and decision-making. This is a broad definition of its capabilities, Simons said. AI has been used since the late ’90s to help you sort email, play games on your Nokia cell phone and use predictive text on search engines.
- Machine learning: This is a subset of AI where systems learn from data, improving patterns over time through explicit programming. Platforms like Sprinklr can identify trends, mentions and provide real-time insights using machine learning, Simons said.
- Generative AI: Can quickly generate content, provide answers to queries and analyze data based on what it has been programmed to do. An example of Gen AI are GPTs like Claude, Perplexity and Copilot.
- Large Language Model: A deep-learning model that is trained on massive data sets to understand and generate human language in a coherent way, Simons said. Grammarly is a great example of this, she said.
- Natural Language Processing: A type of AI that allows machines to understand and interpret human languages. Think of Google Translate, Simon said.
- Deep learning: A type of machine learning that uses networks to recognize patterns and learn from large data sets. “YouTube does this well,” Simons said. “YouTube uses deep learning to recommend personalized videos by analyzing a user’s watch history and identifying patterns, preferences and behavior.”
- Hallucinations: Plausible sounding but factually incorrect or nonsensical information. ChatGPT and Claude have been guilty of this and you must be extremely cautious about the information you’re provided, Simons said. “A lot of the content this is generating should be thought of as more of a first draft,” she said. “Always fact check.”
Simons cautions that while there are many new tools being built every day that can optimize workflows using AI, as communicators, it’s essential that safety and ethics are top of mind.
Learn more from Ragan Training and watch Simons’ full presentation here.
Courtney Blackann is a communications reporter. Connect with her on LinkedIn or email her at courtneyb@ragan.com.
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