The lexicon of AI has become dizzying. In today's quick newsletter we cover three AI terms that anyone in public affairs or political advocacy should know, and why they are crucial for your work. Each term has a three letter acronym. So nine total letters is all you need today to sound like an expert.
Term 1: Natural Language Processing (NLP)
Natural language processing or "NLP," refers to computers understanding and working with human language. It's how computers read, understand, and respond to conversations, similar to how a human would, but with dramatically larger amounts of language inputs to draw on.
When you ask Siri, Alexa, or Google Translate a question, you're using NLP. The computer takes your natural human language, processes it to understand what you mean, and then figures out how to respond or translate it appropriately.
Note here the key difference from computer programming languages like Python or Java. Those languages follow exact rules. Instead, NLP deals with the messy, context-dependent way that humans actually communicate. This is part of what makes NLP so powerful—you don't need to be a computer programmer to operate it.
NLP has numerous implications for public affairs professionals including:
🔱 Understanding the content that people are consuming to identify the influencers of decision-makers.
🔱 Finding people who have consumed negative press about your client, so you can surgically respond.
🔱 Identifying advocates who are likely to engage with your issue.
Term 2: Large Language Model (LLM)
NLP is the foundation of large language models (LLMs), such as ChatGPT, Claude, and Gemini. LLMs are highly advanced text prediction systems that have been trained on massive amounts of content. Think of them as extremely sophisticated autocomplete systems that don't just predict the next word, but understand context, engage in conversations, and tackle complex tasks.
They're "large" because they contain hundreds ofbillions or even trillions of adjustable parameters that help them process and generate language.
LLMs have countless applications in advocacy work including:
🔱 Simplifying complex policy topics for policy-makers and advocates.
🔱 Finding statements that decision-makers have made on your issues.
🔱 Performing and explaining complicated math issues such as advertising campaign metrics.
🔱 Communications with clients.
Term 3: Automatic Content Recognition (ACR)
Automatic content recognition (ACR) refers to data collected by your TV that automatically identifies and tracks everything displayed on your screen. The TV essentially takes constant digital "fingerprints" of what's playing. Using NLP, advertisers can then process and understand the data to use it in a variety of ways. For advocates, this includes:
🔱 Identifying and retargeting audiences who have seen negative TV press about your client, or an attack ad from your opponent.
🔱 Determining audiences who have been underexposed or overexposed to cable or broadcast ads, and then supplementing these audiences with Connected TV (CTV) ads.
🔱 Determining the overall, potential reach of cable or broadcast buys versus CTV buys when building campaign plans.
To Summarize . . .
🔱 NLP (Natural Language Processing): How computers understand and work with human language.
🔱 LLM (Large Language Model): Highly advanced prediction systems that tackle complex tasks.
🔱 ACR (Automatic Content Recognition): Data collected by your TV that automatically identifies and tracks everything displayed on the screen.
Two Ways To Operationalize This Email
🔱 Say NLP, LLM, or ACR at your next meeting to sound cool, but not two or more of them in one sentence, unless your vibe is tech bro.
🔱 Look for ways to deploy NLP in your next media campaign, LLMs for your internal work, and ACR data on your next hot-button issue.
🔱 If you're enjoying this content, please consider forwarding this email to a colleague or friend. If you'd rather not forward it because you think the content makes you seem smarter compared to that annoying co-worker, it's a little petty, but we appreciate you.
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