Philosophy, Ideas, Critical Thinking, Ethics & Morality: The Creative Process: Philosophers, Writers, Educators, Creative Thi The Creative Process

“There's been some interesting research recently about the fact that we don't really know how many people are losing lives because of extreme heat, right? Extreme events are climate change-exacerbated events, which I call climate shocks in the book. We haven't been counting that. I think we will start counting it and we'll get a better sense of the trends, but there's no doubt that the numbers are going up, even though we aren't sure about the exact numbers globally. We need a lot more people pushing back against power, particularly given the way that fossil fuel interests are continuing to push greenwash to try to continue to affect decision-making. The Trump administration is trying very hard to attract funds from oil companies with the promise that all regulations will be gone. And fossil fuel interests are supporting elected officials and pushing for decision-making that reflects their interests, which is not in the interest of the planet and certainly not in the interest of the general public. I know at the most recent COP, OPEC had its own pavilion. You would have never seen that back in the 1990s, but nowadays, they are trying to present themselves as a pathway towards the other side of the climate crisis, which is just absolutely not true.”

How can we make the radical social changes needed to address the climate crisis? What kind of large ecological disaster or mass mobilization in the streets needs to take place before we take meaningful climate action?

Dana R. Fisher is the Director of the Center for Environment, Community, & Equity and Professor in the School of International Service at American University. Fisher’s research focuses on questions related to democracy, civic engagement, activism, and climate politics. Current projects include studying political elites’ responses to climate change, and the ways federal service corps programs in the US are integrating climate into their work. She is a self-described climate-apocalyptic optimist and co-developed the framework of AnthroShift to explain how social actors are reconfigured in the aftermath of widespread perceptions and experiences of risk. Her seventh book is Saving Ourselves: From Climate Shocks to Climate Action.

“There’s a lot of greenwashing that's going on these days. It is great marketing. And that was really the reason why I wrote this book. I had started to see the patterns. You can start to tell them the companies that are genuinely doing it versus the companies that are just talking about it. So that was one indicator, you know, a company that would send out a press release about their goals and what they anticipated to do in the next 5 to 10 years was very different from companies who had said, you know what, this is what we've achieved. Regenerative started coming into the lexicon, the term in 2017, 2018. And regenerative means to regenerate, means to bring life into something. To sustain means to keep the status quo. And regenerative looks at things from a very holistic lens. You know, it's like if you're going to run a regenerative farm, it's all the different components of the farm and the ecosystem ideally come within the ecosystem.”

What is regenerative business? How can we create a business mindset that addresses social, economic and environmental issues?
Esha Chhabra has written for national and international publications over the last 15 years, focusing on global development, the environment, and the intersection of business and impact. Her work has been featured in The New York Times, The Economist, The Guardian, and other publications. She is the author of Working to Restore: Harnessing the Power of Business to Heal the Earth.

“One very interesting question is what might we learn from recent developments in AI about how humans learn and process language. The full story is a little bit more complicated because, of course, humans don't learn from the same kind of data as language models. Language models learn from this internet-scraped data that includes New York Times articles, blog posts, thousands of books, a bunch of programming code, and so on. That's very different from the kind of linguistic input that children learn from, which is child-directed speech from parents and relatives that’s much simpler linguistically. That said, there is a lot of interesting research trying to train smaller language models on data that is similar to the kind of input that a child might receive when they are learning language. You can, for example, strap a head mounted camera on a young child's head for a few hours every day and record whatever auditory information the child has access to, which would include any language spoken around or directed at the child. You can then transcribe that, and use that data set to train a language model on the child-directed speech that a real human would have received during their development. So some artificial models are learning from child-directed speech, and that might eventually go some way towards advancing debates about what scientists and philosophers call “nativism versus empiricism” with respect to language acquisition—the nature/nurture debate: Are we born with a universal innate grammar that enables us to learn the rules of language, as linguists like Noam Chomsky have argued, or can we learn language and its grammatical rules just from raw data, just from hearing and being exposed to language as children?If these language models that are trained from this kind of realistic datasets of child-directed speech manage to learn grammar, to learn how to use language in the way children do, then that might put some pressure on the nativist claim that there is this innate component to language learning that is part of our DNA, as opposed to just learning from exposure to language itself.”

How can we ensure that AI is aligned with human values? What can AI teach us about human cognition and creativity?

Dr. Raphael Millière is Assistant Professor in Philosophy of AI at Macquarie University in Sydney, Australia. His research primarily explores the theoretical foundations and inner workings of AI systems based on deep learning, such as large language models. He investigates whether these systems can exhibit human-like cognitive capacities, drawing on theories and methods from cognitive science. He is also interested in how insights from studying AI might shed new light on human cognition. Ultimately, his work aims to advance our understanding of both artificial and natural intelligence.