Train Humans, Not AI

Today on the AI Breakdown, we're reading an essay about why we're thinking about AI and jobs in entirely the wrong ways. The AI Breakdown is a daily podcast and video about the most important news and discussions in AI. Go to breakdown.network for more information about our YouTube, our newsletter and our discord. Hello friends, welcome back to the AI Breakdown. And this is is a Long Reads edition and we are lucky today to have a essay from Esther Dyson. Now Esther Dyson has been writing about technology for decades now and is a really, really thoughtful person. And the piece that she wrote for the Information was called don't fuss about training AIs train our kids. People worried about AI taking their jobs are competing with a myth. Instead, people should train themselves to be better humans. So what we're going to do is read some excerpts and discuss other parts of this and use it as the basis for a larger conversation. Esther writes, Humanity is waking up to the challenges and opportunities of artificial intelligence, but we don't yet understand our role. People talk about unexplainable AI when they should be more concerned about the unexplainable humans running the companies that develop AI. People worried about AI taking their jobs and taking control or competing with a myth. Instead, people should train themselves to be better humans even as they develop better AI. People are still in control, but they need to use that control wisely, ethically and carefully. The first step is to understand the fundamental difference between humans and AI. We are analog chemical beings with emotions and feelings compared with machines. We think slowly and we act too fast, failing to consider the long term consequences of our behavior which AI can help predict. We should not compete with AI, we should use it. At the same time we should become better humans, more self aware and more understanding of the world around us, better able to understand our own and others motivations. We should know enough to manipulate ourselves and resist manipulation by others. This is the solution to the problem of AI stealing jobs or evading our control. If we manage AI correctly, we can automate routine tasks and use the money and time saved to allow humans to do more meaningful work. That work starts with training other humans and kids. Learning from well paid engaged caregivers. Patients talking with real doctors and nurses, not just bots and machines. Students learning not just to remember facts but to ask provocative questions. Teenagers interacting with human mentors instead of influencers trained by algorithms. The important training is not STEM coding or how AI works. The market will take care of that. It's about how people work and how businesses so often make money by manipulating people to buy things they might not need. Instead, people people can learn how to manipulate themselves. So let's move off of direct quotation from this piece and do a little bit more Summary Esther makes the argument that training is recursive and argues that there are three types of training roles. Meta trainers are the people who learn or create and teach any sort of formal curriculum and do so for their job. This is, yes, of course, the people who exist at colleges and schools, but it's also people who train managers, coaches, healthcare workers, et cetera. Now the frontline trainers are people who aren't specifically paid to be trainers, but as part of their job or interaction with the world, they end up training people a lot as well. So think religious leaders, counselors, and she says, most important, parents. Esther writes, these frontline humans encourage individuals to understand themselves and others, and they pay personal attention to those they train. The last category she identifies are trainees, and at any given time anyone can be a trainee. Obviously children are a clear example parents, but all of us, at some point, in some context throughout our lives, are trainees. In the long run, Esther argues, everyone should become a frontline trainer of the next generation within their own families. Many should also be employed as trainers and meta trainers, passing on formal knowledge and insights to the next generation of trainers before we get to the main part of the episode, one quick note about the AI Education beta that you have been hearing about all week. This is the learning program we've been running for a couple months now that's all about tutorials, challenges, basically getting you learning AI by actually using AI faster, more effectively, more efficiently, and hopefully in a way that's more fun too. If you sign up, you'll get access to a library of 60 plus tutorials, case studies and challenges, new lessons that drop every weekday, and a passionate community of like minded learners. We cover everything from LLMs and prompt engineering to image generators, voice synthesizers, AI, no cold tools, as well as professional applications like presentation generation, website building, and more. Due to a bunch of people saying that the window for enrollment was too short, I'm actually going to extend it to Sunday night at 11:59 East coast time, so you have a couple more days to register. Go to Bit ly AIBETA to learn more and sign up. I'd love to see you there, but now let's get back to the show. So you're probably getting the idea that a lot of what Esther is talking about in this essay is about how the rise of the machines, paradoxically in some ways gives rise to an understanding of the importance of fundamental social relationships between humans. One of the things that I often talk about when it comes to how to handle the coming disruption from AI when it comes to the workplace and the economy is that we're probably going to have to go through a period of renegotiating the social contract. What I mean by that is, of course, what people expect of society and what people are expected to give to society. Obviously the social contract of the post World War II American world, where you went to high school, got a college degree, got a 40 hour a week job and had enough money to buy a house and take some vacations, then retire in your 60s, doesn't exist anymore. But in its place, we haven't proactively introduced some alternative expectation. We just have the uncomfortable mismatch where generations are living through entirely different realities in parallel with one another. I think in some ways what I read in Esther's piece is an argument for what one approach to a renegotiated social contract might look like. She's placing an incredible amount of emphasis on things that we take for granted now as just stuff you do, particularly being parents. It's very clear that she's saying that in this world of machines doing automated tasks, we should take advantage of the new time and capacity we have. And when we talk about more meaningful, higher order work that includes raising families. Esther writes frontline trainers are crucial to raising healthy, resilient, curious children who will grow into adults capable of loving others and overcoming challenges. She said there's no formal curriculum for that, but but is about training kids to do two fundamental things. First, to develop the emotional security to think long term. And second, she writes, kids need to understand themselves and understand the motivations of the people, institutions and social media they interact with. That's how to combat fake news or the distrust of real news. It is less about traditional media literacy and more about understanding why am I seeing this news? Are they trying to get me angry or just using me to sell ads? Now she talks about how little we support this type of training in our society, how difficult it is for parents to actually do this as well. How many things get in the way of this. Luckily, Esther says there are some examples of programs that are trying to redress this balance. The example she gives is something called the Nurse Family Partnership. It's a community health program that operates in more than 40 states for first time moms and children that are impacted by social and economic inequality and other risk factors. Mothers who are enrolled in the program get paired with a registered nurse early in their pregnancy and receive ongoing nurse visits all the way through the child's second birthday. This creates a stable long term relationship, someone that parents can turn to for advice and for help. And this program, the NFP, serves 55,000 families a year in the U.S. now, the Pacific Institute for Research and Evaluation, which is a nonprofit think tank, looked into the long term benefits of NFP and found that every dollar spent in the program yields $6.50 in benefits over 18 years. That's in the form of lower health care costs, improved health, improved educational attainment and job performance, and reduced crime and welfare. Now, pragmatist that she is, Esther also asks, even if we were to decide that this was important to society, how would we pay for these types of programs? She said in the absence of increasing taxes on corporations, a few more realistic steps might be one leaning on the private sector's training efforts, which she says focus on training the meta trainers rather than the frontline ones. Basically, she says that some of that frontline training could come into the meta training. She points out that AI can be used to demonstrate the value of training. Finally, she suggests that we stop talk about spending on health care and other social support and reframe it as investing in people the same way, she says, as we think about investing in other public infrastructure. Ultimately, she says, this may be too optimistic, but it is how we can make the best use of the power and efficiency AI represents and protect ourselves against its misuse. Don't leave it to the corporations and politicians with agendas they don't want to explain. So like I said, overall, what I think this piece represents is an exploration of how we might find that human social relationships are the most important currency in a world where machines can do so much of everything else. It's going to take a lot if we decide that's the case, to rewire society to better appreciate those things. Just ask any mom who's lumped into some stay at home category and has the work that they do undervalued systematically by people who haven't been in their shoes before. But I think that the scale of the disruption that we're talking about calls for thinking in these big ways. It's still really early in the artificial intelligence revolution. We are still just coming to understand what the implications of this shift might be. Another way to put that is that we still have a lot of agency to shape how we want it to go. We can have these conversations in parallel and figure out what world we want to design thanks to the benefit that these technologies offer. I would suggest humbly, that coming at this conversation exclusively from a place of fear is likely not the way that we're going to actually make progress on that front though. And so I am very glad to people like Esther and others who are starting to try to prod and pro can have that conversation. Anyways, let me know what you think about all this. Come join us on the AI Breakdown Discord. You can find a link in our show notes or on the website. As you know breakdown.network. i appreciate you guys listening, wherever you are. Until next time. Peace.