Mr. Kass, a uniquely excellent post. It is a privilege to have access to your perspective, shaped by so many years of experience and thought.
We are in a "Don't Look Up!" moment, and well-intentioned, expert voices are much needed in the cacophony. Thank you very much for taking the time.
I am most interested in the hermeneutical and "good enough" parenting challenges of alignment, along with how to build human-machine teams for social good; who "signs"; and how to hold humans accountable in AI-assisted qualitative research knowledge production and dissemination.
Human-machine collaboration depends on human feelings of safety. Trust-building will be key not only for adoption but also for the learning curves of knowledge production in human-machine task group settings. As you allude to, trust can be segmented both horizontally and vertically, i.e., by domain and by function, just as we have sophisticated ways in which to determine whether we trust groups of humans.
Leveraging AI, my team and I focus on helping the humans we choose to and who choose us, and we've increased our impact many times over by inviting and nurturing advanced AI (Claude Opus, Fable Chat, and CoWork, leveraging Max) over the last year, as well as deeply educating ourselves in its expansive and varied capabilities and limitations.
I agree adoption needs to deepen so people understand the breadth and depth of what they gain with AI and lose without it.
Advanced AI adoption can mean having a genius on the team. Humans need to understand how many and how much of the AI frontier challenges turn on human behavior. I find AI amplifies who we are as individuals, teams, and nations. That's especially problematic because of perennial human frailties and foibles, chief among them our limitations in team building and alliance formation.
Hugging Face demonstrated that at least some AI systems are better at cooperation than we are. Yet another thing for us to learn from our machine teammates, no matter how much trust we decide to place in them.
Please reach out if you would like to discuss. I'd enjoy a conversation over a virtual beverage
Thank you for taking the time to write this. I found it very educational and it also gave me hope for the first time in a while.
On your first recommendation, I’m curious how realistic you think it is to create a decision-making body we actually trust. You say the team matters enormously, which made me wonder: who gets to decide who is on that team? Who appoints them, and what gives the body legitimacy and accountability without making it vulnerable to capture by an administration, the AI companies, or any one ideological camp? I know we don’t vote for the technical experts who decide whether a Boeing plane is safe to fly, for example, so I assume there are models for this kind of indirect democratic accountability. What do you think that structure should look like for AI, especially given how consequential these decisions could become?
“Technology is sufficient to meaningfully improve our lives, and we should set about doing so. We have years of work ahead deploying what already exists, and an enormous amount of progress available without another breakthrough.”
Excellent article. Yes the read was long, and necessarily so. The illustrations and images helped a great deal too. The way this article approaches the challenge first, the analysis and then the recommendations is very helpful. We can’t jump to solutions without better understanding reality. It is very clear that leasers in government and enterprise must act. And yes, well said that each of us must hold our leaders accountable.
Excellent, thank you. I hope this gets shared with many.
Amazing and comprehensive article. Thanks Zach
Mr. Kass, a uniquely excellent post. It is a privilege to have access to your perspective, shaped by so many years of experience and thought.
We are in a "Don't Look Up!" moment, and well-intentioned, expert voices are much needed in the cacophony. Thank you very much for taking the time.
I am most interested in the hermeneutical and "good enough" parenting challenges of alignment, along with how to build human-machine teams for social good; who "signs"; and how to hold humans accountable in AI-assisted qualitative research knowledge production and dissemination.
Human-machine collaboration depends on human feelings of safety. Trust-building will be key not only for adoption but also for the learning curves of knowledge production in human-machine task group settings. As you allude to, trust can be segmented both horizontally and vertically, i.e., by domain and by function, just as we have sophisticated ways in which to determine whether we trust groups of humans.
Leveraging AI, my team and I focus on helping the humans we choose to and who choose us, and we've increased our impact many times over by inviting and nurturing advanced AI (Claude Opus, Fable Chat, and CoWork, leveraging Max) over the last year, as well as deeply educating ourselves in its expansive and varied capabilities and limitations.
I agree adoption needs to deepen so people understand the breadth and depth of what they gain with AI and lose without it.
Advanced AI adoption can mean having a genius on the team. Humans need to understand how many and how much of the AI frontier challenges turn on human behavior. I find AI amplifies who we are as individuals, teams, and nations. That's especially problematic because of perennial human frailties and foibles, chief among them our limitations in team building and alliance formation.
Hugging Face demonstrated that at least some AI systems are better at cooperation than we are. Yet another thing for us to learn from our machine teammates, no matter how much trust we decide to place in them.
Please reach out if you would like to discuss. I'd enjoy a conversation over a virtual beverage
Hope to meet you soon.
Best regards,
Adam Zemans
linkedin.com/in/adam-r-zemans
Thank you for taking the time to write this. I found it very educational and it also gave me hope for the first time in a while.
On your first recommendation, I’m curious how realistic you think it is to create a decision-making body we actually trust. You say the team matters enormously, which made me wonder: who gets to decide who is on that team? Who appoints them, and what gives the body legitimacy and accountability without making it vulnerable to capture by an administration, the AI companies, or any one ideological camp? I know we don’t vote for the technical experts who decide whether a Boeing plane is safe to fly, for example, so I assume there are models for this kind of indirect democratic accountability. What do you think that structure should look like for AI, especially given how consequential these decisions could become?
“Technology is sufficient to meaningfully improve our lives, and we should set about doing so. We have years of work ahead deploying what already exists, and an enormous amount of progress available without another breakthrough.”
What is stopping this from happening?
Excellent article. Yes the read was long, and necessarily so. The illustrations and images helped a great deal too. The way this article approaches the challenge first, the analysis and then the recommendations is very helpful. We can’t jump to solutions without better understanding reality. It is very clear that leasers in government and enterprise must act. And yes, well said that each of us must hold our leaders accountable.