NEWS 21 December 2023 Clarification 23 December 2023 AI consciousness: scientists say we urgently need answers Researchers call for more funding to study the boundary between conscious and unconscious systems. By Mariana Lenharo Twitter Facebook Email A robot called Sophia being tested at Hanson Robotics, a robotics and artificial intelligence company in Hong Kong. A standard method to assess whether machines are conscious has not yet been devised.Credit: Peter Parks/AFP via Getty Could artificial intelligence (AI) systems become conscious? A trio of consciousness scientists say that, at the moment, no one knows — and they are expressing concern about the lack of inquiry into the question. In comments to the United Nations, three leaders of the Association for Mathematical Consciousness Science (AMCS) call for more funding to support research on consciousness and AI. They say that scientific investigations of the boundaries between conscious and unconscious systems are urgently needed, and they cite ethical, legal and safety issues that make it crucial to understand AI consciousness. For example, if AI develops consciousness, should people be allowed to simply switch it off after use? Such concerns have been mostly absent from recent discussions about AI safety, such as the high-profile AI Safety Summit in the United Kingdom, says AMCS board member Jonathan Mason, a mathematician based in Oxford, UK, and one of the authors of the comments. Nor did US President Joe Biden’s executive order seeking responsible development of AI technology address issues raised by conscious AI systems, Mason notes. “With everything that’s going on in AI, inevitably there’s going to be other adjacent areas of science which are going to need to catch up,” Mason says. Consciousness is one of them. The other authors of the comments were AMCS president Lenore Blum, a theoretical computer scientist at Carnegie Mellon University in Pittsburgh, Pennsylvania, and board chair Johannes Kleiner, a mathematician studying consciousness at the Ludwig Maximilian University of Munich in Germany. Not science fiction

A 2022 survey given to active researchers in the natural-language-processing community shows the stark divisions in this debate. One survey item asked whether the respondent agreed with the following statement about whether LLMs could ever, in principle, understand language: “Some generative model [i.e., language model] trained only on text, given enough data and computational resources, could understand natural language in some nontrivial sense.” Of 480 people responding, essentially half (51%) agreed, and the other half (49%) disagreed (26).

1. قرآن کریم و هوش مصنوعی 2. چه سئوالاتی را برای درک حقایق باید بپرسیم؟ 3. سئوالات ساده و کوتاه خوب هستند؟ 4. کدام سئوالات تأثیر بیشتری در درک دارند؟ 5. بدیهیات و اصول در هر موضوع کدامند؟ 6. رابطه متقابل قرآن وهوش مصنوعی چیست؟ 7. آیا قرآن برای درک بهتر علوم خوب است؟ 8. درک علوم شامل کاربرد صحیح آنها هم هست؟ 9. آیا علوم در درک بهتر قرآن مؤثّرند؟ 10. آیا فنون در درک بهتر قرآن تأثیر دارند؟ 11. آیا نگرشها در درک قرآن مفید هستند؟ 12. تعاریف برای هوش کدامند؟ 13. غریزه هم نوعی هوش است؟ 14. تعقّل و تفکّر با هوش ربط دارند؟ 15. انواع و درجات و نهایت هوش چیست؟ 16. انتظار از هوش مصنوعی چه باید باشد؟ 17. علوم مرتبط با هوش مصنوعی کدامند؟ 18. مهمترین ابزارها در هوش مصنوعی کدامند؟ 19. مبانی هوش مصنوعی چیست؟ 20. راستی! هوش مصنوعی چیست؟

For example, two standard benchmarks for assessing LLMs are the General Language Understanding Evaluation (GLUE) (27) and its successor (SuperGLUE) (28), which include large-scale datasets with tasks such as “textual entailment” (given two sentences, can the meaning of the second be inferred from the first?), “words in context” (does a given word have the same meaning in two different sentences?), and yes/no question answering, among others.

OpenAI’s GPT-3, with 175 billion parameters, performed surprisingly well on these tasks (5), and Google’s PaLM, with 540 billion parameters, performed even better (7), often equaling or surpassing humans on the same tasks.

The detailed nature of human concepts has been the subject of active debate for many years. Researchers disagree on the extent to which concepts are domain-specific and innate versus more general-purpose and learned (55–60), the degree to which concepts are grounded via embodied metaphors (61–63) and are represented in the brain via dynamic, situation-based simulations (64), and the conditions under which concepts are underpinned by language (65–67), by social learning (68–70), and by culture (71–73). In spite of these ongoing debates, concepts, in the form of causal mental models as described above, have long been considered to be the units of understanding in human cognition. Indeed, the trajectory of human understanding—both individual and collective—is the development of highly compressed, causally based models of the world analogous to the progression from Ptolemy’s epicycles to Kepler’s elliptical orbits and to Newton’s concise and causal account of planetary motion in terms of gravity. Humans, unlike machines, seem to have a strong innate drive for this form of understanding both in science and in everyday life (74). We might characterize this form of understanding as requiring few data, minimal or parsimonious models, clear causal dependencies, and strong mechanistic intuition.