AI & The Future of Humanity: Artificial Intelligence, Technology, VR, Algorithm, Automation, ChatBPT, Robotics, Augmented Re The Creative Process

“Generative AI, particularly Large Language Models, they seem to be engaging in conversation with us. We ask questions, and they reply. It seems like they're talking to us. I don't think they are. I think they're playing a game very much like a game of chess. You make a move and your chess computer makes an appropriate response to that move. It doesn't have any other interest in the game whatsoever. That's what I think Large Language Models are doing. They're just making communicative moves in this game of language that they've learned through training on vast quantities of human-produced text.”

Is consciousness an illusion? Is it just a complex set of cognitive processes without a central, subjective experience? How can we better integrate philosophy with everyday life and the arts?
Keith Frankish is an Honorary Professor of Philosophy at the University of Sheffield, a Visiting Research Fellow with The Open University, and an Adjunct Professor with the Brain and Mind Programme in Neurosciences at the University of Crete. Frankish mainly works in the philosophy of mind and has published widely about topics such as human consciousness and cognition. Profoundly inspired by Daniel Dennett, Frankish is best known for defending an “illusionist” view of consciousness. He is also editor of the journal Illusionism as a Theory of Consciousness and co-edits, in addition to others, The Cambridge Handbook of Cognitive Science.

“I'd like to focus more on the immediate harms that the kinds of AI technologies we have today might pose. With language models, the kind of technology that powers ChatGPT and other chatbots, there are harms that might result from regular use of these systems, and then there are harms that might result from malicious use. Regular use would be how you and I might use ChatGPT and other chatbots to do ordinary things. There is a concern that these systems might reproduce and amplify, for example, racist or sexist biases, or spread misinformation. These systems are known to, as researchers put it, “hallucinate” in some cases, making up facts or false citations. And then there are the harms from malicious use, which might result from some bad actors using the systems for nefarious purposes. That would include disinformation on a mass scale. You could imagine a bad actor using language models to automate the creation of fake news and propaganda to try to manipulate voters, for example. And this takes us into the medium term future, because we're not quite there, but another concern would be language models providing dangerous, potentially illegal information that is not readily available on the internet for anyone to access. As they get better over time, there is a concern that in the wrong hands, these systems might become quite powerful weapons, at least indirectly, and so people have been trying to mitigate these potential harms.”

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.