Inboxes are already bursting with spam. Epoxydude/fStop via Getty Images
Future of spam
Chances are you’ve heard about the advances in generative large language models like ChatGPT. The task these generative LLMs perform is deceptively simple: given a text sequence, predict which token – think of this as a part of a word – comes next. Then, predict which token comes after that. And so on, over and over.
Somehow, training on that task alone, when done with enough text on a large enough LLM, seems to be enough to imbue these models with the ability to perform surprisingly well on a lot of other tasks.
Multiple ways to use the technology have already emerged, showcasing the technology’s ability to quickly adapt to, and learn about, individuals. For example, LLMs can write full emails in your writing style, given only a few examples of how you write. And there’s the classic example – now over a decade old – of Target figuring out a customer was pregnant before she did.
Spammers and marketers alike would benefit from being able to predict more about individuals with less data. Given your LinkedIn page, a few posts and a profile image or two, LLM-armed spammers might make reasonably accurate guesses about your political leanings, marital status or life priorities.
Our research showed that LLMs could be used to predict which word an individual will say next with a degree of accuracy far surpassing other AI approaches, in a word-generation task called the semantic fluency task. We also showed that LLMs can take certain types of questions from tests of reasoning abilities and predict how people will respond to that question. This suggests that LLMs already have some knowledge of what typical human reasoning ability looks like.
If spammers make it past initial filters and get you to read an email, click a link or even engage in conversation, their ability to apply customized persuasion increases dramatically. Here again, LLMs can change the game. Early results suggest that LLMs can be used to argue persuasively on topics ranging from politics to public health policy.
Good for the gander
AI, however, doesn’t favor one side or the other. Spam filters also should benefit from advances in AI, allowing them to erect new barriers to unwanted emails.
Spammers often try to trick filters with special characters, misspelled words or hidden text, relying on the human propensity to forgive small text anomalies – for example, “c1îck h.ere n0w.” But as AI gets better at understanding spam messages, filters could get better at identifying and blocking unwanted spam – and maybe even letting through wanted spam, such as marketing email you’ve explicitly signed up for. Imagine a filter that predicts whether you’d want to read an email before you even read it.
Despite growing concerns about AI – as evidenced by Tesla, SpaceX and Twitter CEO Elon Musk, Apple founder Steve Wozniak and other tech leaders calling for a pause in AI development – a lot of good could come from advances in the technology. AI can help us understand how weaknesses in human reasoning might be exploited by bad actors and come up with ways to counter malevolent activities.
All new technologies can result in both wonder and danger. The difference lies in who creates and controls the tools, and how they are used.
John Licato, Assistant Professor of Computer Science and Director of AMHR Lab, University of South Florida
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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