AI Is Here—and Everywhere | Quality Digest

Innovation

AI Is Here—and Everywhere

Three AI researchers look to the challenges ahead in 2024

Published: Wednesday, January 31, 2024 – 12:01

The year 2023 was one of AI hype. Regardless of whether the narrative was that AI was going to save the world or destroy it, it often felt as if visions of what AI might be someday overwhelmed the current reality. And though anticipating future harms is a critical component of overcoming ethical debt in tech, getting too swept up in the hype risks creating a vision of AI that seems more like magic than a technology that can still be shaped by explicit choices. But taking control requires a better understanding of that technology.

One of the major AI debates of 2023 was around the role of ChatGPT and similar chatbots in education. This time last year, most relevant headlines focused on how students might use it to cheat and how educators were scrambling to keep them from doing so—in ways that often did more harm than good.

However, throughout the year, people recognized that a failure to teach students about AI might put them at a disadvantage, and many schools rescinded their bans. This is not to say we should be revamping education to put AI at the center of everything. But if students don’t learn about how AI works, they won’t understand its limitations—and therefore how it’s useful and appropriate to use and how it’s not. This is true not only for students; the more people understand how AI works, the more empowered they are to use it and critique it.

So our prediction, or perhaps hope, for 2024 is that there will be a huge push to learn. In 1966, Joseph Weizenbaum, the creator of the ELIZA chatbot, wrote that machines are “often sufficient to dazzle even the most experienced observer,” but that once their “inner workings are explained in language sufficiently plain to induce understanding, its magic crumbles away.” The challenge with generative AI is that, in contrast to ELIZA’s very basic pattern-matching and substitution methodology, it’s much more difficult to find language “sufficiently plain” to make the magic crumble away.

We think it’s possible to make this happen, and hope that universities that are rushing to hire more technical AI experts put just as much effort into hiring AI ethicists. Hopefully, media outlets will help cut through the hype; everyone reflects on their own uses of this technology and its consequences; and tech companies listen to informed critiques in considering what choices continue to shape the future.

Many of the challenges in the year ahead have to do with problems of AI that society is already facing.

Kentaro Toyama, professor of community information, University of Michigan

In 1970, Marvin Minsky, the AI pioneer and neural network skeptic, told Life magazine, “In from three to eight years, we will have a machine with the general intelligence of an average human being.” With singularity—the moment artificial intelligence matches and begins to exceed human intelligence—not quite here yet, it’s safe to say that Minsky was off by at least a factor of 10. It’s perilous to make predictions about AI.

Still, making predictions for a year out doesn’t seem quite as risky. What can be expected of AI in 2024?

First, the race is on! Progress in AI had been steady since the days of Minsky’s prime, but the public release of ChatGPT in 2022 kicked off an all-out competition for profit, glory, and global supremacy. Expect more powerful AI in addition to a flood of new AI applications.

The big technical question is how soon and how thoroughly AI engineers can address the current Achilles’ heel of deep learning—what might be called generalized hard reasoning, things like deductive logic. Will quick tweaks to existing neural-net algorithms be sufficient, or will a fundamentally different approach be required, as neuroscientist Gary Marcus suggests? Armies of AI scientists are working on this problem, so expect some headway in 2024.

Meanwhile, new AI applications are likely to result in new problems, too. You might soon start hearing about AI chatbots and assistants talking to each other, having entire conversations on your behalf but behind your back. Some of it will go haywire—comically, tragically, or both. Deepfakes, AI-generated images, and videos that are difficult to detect are likely to run rampant despite nascent regulation, causing more sleazy harm to individuals and democracies everywhere. And there are likely to be new classes of AI calamities that wouldn’t have been possible even five years ago.

Speaking of problems, the very people sounding the loudest alarms about AI—like Elon Musk and Sam Altman—can’t seem to stop themselves from building ever more powerful AI. Expect them to keep doing more of the same. They’re like arsonists calling in the blaze they stoked themselves, begging the authorities to restrain them. And along those lines, the greatest hope for 2024—though it seems slow in coming—is stronger AI regulation at national and international levels.

Anjana Susarla, professor of information systems, Michigan State University

In the year since the unveiling of ChatGPT, the development of generative AI models is continuing at a dizzying pace. In contrast to ChatGPT a year back, which took in textual prompts as inputs and produced textual output, the new class of generative AI models are trained to be multimodal, meaning the data used to train them come not only from textual sources such as Wikipedia and Reddit but also from videos on YouTube, songs on Spotify, and other audio and visual information. With the new generation of multimodal large language models (LLMs) powering these applications, you can use text inputs to generate not only images and text but also audio and video.

Companies are racing to develop LLMs that can be deployed in a variety of hardware and applications, including running an LLM on your smartphone. The emergence of these lightweight LLMs and open-source LLMs could usher in a world of autonomous AI agents—a world that society isn’t necessarily prepared for.

These advanced AI capabilities offer immense transformative power in applications ranging from business to precision medicine. A chief concern is that such advanced capabilities will pose new challenges for distinguishing between human-generated content and AI-generated content, as well as posing new types of algorithmic harms.

A flood of AI-generated content primed to exploit algorithmic filters and recommendation engines could soon overpower critical functions such as information verification, information literacy, and serendipity provided by search engines, social media platforms, and digital services.

The deluge of synthetic content produced by generative AI could spawn a world where malicious people and institutions can manufacture synthetic identities and orchestrate large-scale misinformation. A flood of AI-generated content primed to exploit algorithmic filters and recommendation engines could soon overpower critical functions such as information verification, information literacy, and serendipity provided by search engines, social media platforms, and digital services.

The Federal Trade Commission has warned about fraud, deception, infringements on privacy, and other unfair practices enabled by the ease of AI-assisted content creation. While digital platforms such as YouTube have instituted policy guidelines for disclosing AI-generated content, there’s a need for greater scrutiny of algorithmic harms from agencies by the FTC and lawmakers working on privacy protections such as the American Data Privacy & Protection Act.

A new bipartisan bill introduced in Congress aims to codify algorithmic literacy as a key part of digital literacy. With AI increasingly intertwined with everything people do, it’s clear that the time has come to focus not on algorithms as pieces of technology but to consider the contexts the algorithms operate in: people, processes, and society.

Published Jan. 3, 2024, on The Conversation.

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