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

“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.

“It gets back to this core question. I just wish I was a young scientist going into this because that's the question to answer: Why AI comes out with what it does. That's the burning question. It's like it's bigger than the origin of the universe to me as a scientist, and here's the reason why. The origin of the universe, it happened. That's why we're here. It's almost like a historical question asking why it happened. The AI future is not a historical question. It's a now and future question.
I'm a huge optimist for AI, actually. I see it as part of that process of climbing its own mountain. It could do wonders for so many areas of science, medicine. When the car came out, the car initially is a disaster. But you fast forward, and it was the key to so many advances in society. I think it's exactly the same as AI. The big challenge is to understand why it works. AI existed for years, but it was useless. Nothing useful, nothing useful, nothing useful. And then maybe last year or something, now it's really useful. There seemed to be some kind of jump in its ability, almost like a shock wave. We're trying to develop an understanding of how AI operates in terms of these shockwave jumps. Revealing how AI works will help society understand what it can and can't do and therefore remove some of this dark fear of being taken over. If you don't understand how AI works, how can you govern it? To get effective governance, you need to understand how AI works because otherwise you don't know what you're going to regulate.”

Neil Johnson is a physics professor at George Washington University. His new initiative in Complexity and Data Science at the Dynamic Online Networks Lab combines cross-disciplinary fundamental research with data science to attack complex real-world problems. His research interests lie in the broad area of Complex Systems and ‘many-body’ out-of-equilibrium systems of collections of objects, ranging from crowds of particles to crowds of people and from environments as distinct as quantum information processing in nanostructures to the online world of collective behavior on social media.