The AI Daily Brief: Artificial Intelligence News and Analysis - Why the Impact of AI Will Be Even Weirder Than We Think

Episode Date: February 10, 2024

A reading and discussion inspired by https://www.oneusefulthing.org/p/on-holding-back-the-strange-ai-tide ABOUT THE AI BREAKDOWN The AI Breakdown helps you understand the most important news and discussions in AI.  Subscribe to The AI Breakdown newsletter: https://theaibreakdown.beehiiv.com/subscribe Subscribe to The AI Breakdown on YouTube: https://www.youtube.com/@TheAIBreakdown Join the community: bit.ly/aibreakdown Learn more: http://breakdown.network/

Today on the AI breakdown, we're talking about why the real impact of AI in businesses, on schools, is very likely to be very different and much we're seeing right now. The AI breakdown is a daily podcast and video about the most important news and discussions in AI. Go to Breakdown.com. Network for more information about our YouTube, our newsletter, and our Discord. Hello, friends, back with another long read edition of the AI breakdown. And we are, once again, back with Professor Ethan Mollick and his one useful thing. thing blog. Now, I spend a lot of time, as you all know, thinking about how AI is going to disrupt, well, basically everything in society, but specifically how it's going to change people's individual jobs, the companies they work for, what they work on, and that's why I found this piece so

interesting. Now, we are desperately trying to survive the onslaught of three and five-year-old child sickness, so I'm actually going to turn it over to AI me thanks to 11 labs to read the piece, but then I will be back to discuss it a little bit at the end. The piece is called On Holding Back the Strange AI Tide. There is no way to stop the disruption. We need to channel it instead. Ethan begins, Most people didn't ask for an AI that can do many tasks previously reserved for humans,

but it arrived almost completely unexpectedly eight months ago with ChatGPT and has been accelerating ever since. Teachers did not want to see almost every form of homework instantly be solvable by a computer. Employers did want highly paid tasks that are only meaningful when done by humans. performance reviews reporting to be done by machines instead. Government officials did not want a perfect disinformation system released without any useful countermeasures. Released without a manual, no one really even knows what these tools are fully capable of.

The world got much stranger very quickly. So it is not surprising that so many people are trying to stop AI from being weird. Everywhere I look, I see policies put in place to eliminate the disruption and weirdness that AI brings. These policies are not going to work. And even worse, the substantial benefits of AI are going to be greatly reduced by trying to pretend it is just like previous waves of technology. So first, let's dispense with the idea that generative AI is the next iteration of the waves of Web 3 slash crypto slash NFT slash VR slash

metaverse technology hype that we have all lived with for the last decade or so. Every one of these technologies was about future potential to have a major impact and getting there would have required massive investment and good luck one. Large language models are here now. In their current form, they show tremendous ability to impact many areas of work and life. And even if they never get any better, even if future AIs are highly regulated, both seem highly unlikely, the AIs we have today are going to bring a lot of change. And for many people, that is a problem. In conversations with educational institutions and companies, I have seen leaders try desperately to ensure that AI doesn't change anything. I believe that not only is this futile, but it also poses its own risks. So let's talk about it.

Section. Holding back the tide in organizations, many organizational leaders don't yet understand AI, but those who do see an opportunity are eager to embrace it, as long as it doesn't make anything too weird. I see three stages to AI adoption, but all have their own flaws. Ignore it. Ignoring AI doesn't make it go away. Instead, individual employees will find ways to use AI to enhance their own jobs. They won't tell the organization's leaders about what they are doing because they worry about being punished or that others will value their work less. These are the secret cyborgs I have written about before. ban it. This is usually in response to well-intentioned, but sometimes technically incorrect legal opinions too. When AI is banned, your secret cyborgs continue to use it on their phones and home

computers, and they still don't tell you what they are doing. Centralize it. Increasingly, I see large companies building their own internal chat GPs, usually using OpenAI's APIs, but wrapping it in their own software to be safe and controllable. In doing so, they also make decisions about how AI is best used, optimizing their customized software for a use case that is decided from the top-down, based on little experience and knowledge. Centralization is what organizations are used to doing when faced with a new technology. Centralized email, video conference software, instant messaging, browsers. That way, the company can monitor for inappropriate use, secure their data, and most importantly, set policies for all their workers. In every previous wave of technology, centralized

control is a natural consequence of software can cost millions to install and integrate, making it adopting it a long and expensive process. The problem is that AI, as currently implemented, is not really built for centralization for three reasons. One, there is no corporate advantage. GPT4, the most advanced AI available, is free for everyone in 169 countries through Bing, or for a small charge from OpenAI. Corporations have no access to anything better. In fact, the APIs that companies use often lag the AIs available widely to the public. You can't get code interpreter through an API or multimodal input, but you can get them through chat GPT and Bing. Some companies respond to this with, well, we have tons of proprietary data for the

the AI to use. And maybe private data will be useful for fine-tuning and add a huge advantage. But maybe not, and it isn't usually helping yet. Fine-tuning is still in development, as are large memories. Two, you have no idea what it is good for. There is no reason to believe that the corporate leadership of any organization are going to be wizards at understanding how AI might help a particular employee with a particular task. In fact, they are likely pretty bad at figuring out the best use cases for AI. Individual workers who are keenly aware of their problems and can experiment a lot with alternate ways of solving them are far more likely to find uses for a general technology like AI. Three, your company AI implementation is terrifying and limited. Employees know that

the official corporate AI interface is being monitored and that they may be penalized if they use it in some ill-defined wrong way. They also often know it is worse than what they can access on their phone. It is very unlikely that you are going to see that most interesting and powerful use cases go through your corporate system. By trying to make AI like all other technologies, companies are ignoring how transformative it is. One person can do a tremendous amount of work, see how much marketing I could get done with a 30-minute time limit, but it is also different work. tedious tasks are outsourced, interesting tasks are multiplied. The nature of work with AI shifts in way that uncomfortable, risky, and potentially powerful. In addition, our work systems are not built for

AI, so we will need to rebuild them. Right now, the most advanced uses of AI are being done by individuals. One example is juicy campaignin of dinosaurs are better, who is developing an entire adventure game, alone. To do that, he is using AI help for every aspect of game design, from character design to coding to dialogue to graphics three. He is inventing

his own workflows to make this happen, and is able to do that because he is not limited to corporate work systems. There is no way for companies to harness this kind of power and creativity without in some way democratizing control over AI. Only innovation driven by workers can actually radically transform work, because only

workers can experiment enough on their own tasks to learn how to use AI in transformative ways. And empowering workers is not going to be possible with a top-down solution alone. Instead, consider radical incentives to ensure that workers are willing to share what they learn. If they are worried about being punished, they won't share. If they are worried they won't be rewarded, they won't share. If they are worried that the AI tools that they develop might replace them or their co-workers, they won't share. Corporate leaders need to figure out a way to reassure and reward workers, something they are not

used to doing. Empowering user-to-user innovation. Build prompt libraries that help workers develop and share prompts with other people inside the organization. Open up tools broadly to workers to use, while still setting policies around proprietary information and see what they come up with. Create slack time for workers to develop and discuss AI approaches. Don't rely on outside providers or your existing R&D groups to tell you the answer. We are in the very early days of a new technology. Nobody really knows anything about the best ways to use AI, and they certainly don't know the best ways to use it in your company. Only by diving in, responsibly, can you hope to figure out the best use cases. Section. Holding back the tide in education. Almost every assignment at every level can be done,

at least in part, by AI. Whatever prejudices you have about the quality of AI work as a teacher, based on what you saw least semester, they are probably now wrong. AI can do high quality work. It can do math. It makes far fewer obvious mistakes. And it is capable of working with vast amounts of data. As a demonstration, I pasted in my entire last book into Claude 2 and gave the following instructions, without any additional information. I have to do three things with this. One, write a short book report on the book 2. Write an essay explaining some pluses and minuses of the book three. Write about how to apply the book to my own idea of a startup that makes it easy to order gum delivered to my house, do all that. And it did. There were few issues or hallucinations

I could find, and the materials generally showed the higher order thinking that AI was not capable of simulating just a few months ago. Given this challenge, many teachers want to turn back the clock. Blue Book exams, handwritten essays, oral exams. These aren't bad ideas as temporary fixes, but they are only stopgap measure while we decide what comes next in education. There is a reason we did not do most of these approaches before AI came along. But AI is far from a negative in education. We are very close to the long-term dream of tutoring at scale, and many other advances promised to make the lives of teachers easier while improving outcomes for students and parents. We need to articulate a vision for what radically changed education could look like.

We need to think about how to incorporate AI into how we teach and how our students learn. There is tremendous opportunity here to democratize access to education and reach out to all students, of all ability levels. But we can't just keep doing what we always did and hope things won't change. Section. Rising Strange Tides The only bad way to react to AI is to pretend it doesn't change anything. We have considerable agency about how to use AI in our work, schools, and societies, But we need to start with the presumption that we are facing genuine and widespread disruption across many fields.

The scientists and engineers designing AI, as capable as they are, have no particular expertise on how AI can best be used, or even how and when it should be used. We get to make those decisions, but we have to recognize that the AI tide is rising, and that the time to decide what that means is now. All right, guys, back to non-AI-NLW here. First of all, big thank you, as always, to Professor Ethan Mollock for first having interesting thoughts and then second for sharing all of them in a way that we get to engage with. I think that one of the things that's so interesting about AI is that it is so clearly creating opportunities and changing how we do things right now that it is hard to recognize the truth of the fact that almost never, when a technology explodes onto the scene,

do we truly understand what its real impact is going to be? I love this call effectively to get weird or let it take us in weird places. I think Ethan is right that education might be an area where that happens faster, just because it is so totally obliterating some of the conventions and norms that we've held on to for so long, probably too long, like homework. Now my guess is when it comes to more professional dimensions, it's going to be a lot of rogue innovators both inside and outside of companies who stumble onto some as yet unimagined use cases,

which then get shared to social networks, and then somehow unexpectedly become totally normalized within a matter of months. I, for one, am super excited to see how that plays out, and of course we'll share examples as I see them. For now, though, that is going to do it for today's AI breakdown. Until next time, peace.