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In a remote corner of the driest state in the country, Anne Heggli's team has been keeping watch over some of Earth’s oldest beings. Since 02012, Heggli’s NevCAN network has recorded conditions at the Nevada Bristlecone Preserve every eight minutes. The result is more than 120 million data points and a high-resolution portrait of how a bristlecone pine thrives. Paleoclimatologist Adam Csank showed how bristlecone microcores reveal the droughts and floods these trees survived long before us, a history that fine-tunes models of what's ahead. Artist and philosopher Jonathon Keats went further. “The most accurate clock,” he said, “is a tree.” His monumental artwork Centuries of the Bristlecone keeps bristlecone time, a clock calibrated to the trees' own growth over millennia. Together, Heggli, Keats and Csank made the case for long science: patient, continuous observation that will help us answer future urgent questions. “Data at this resolution,” Csank said, “is like a Rosetta Stone” for understanding nature’s resilience. At fourteen years young, the bristlecone record is just beginning, Heggli said, but it already holds the key to questions like these: How will bristlecones adapt? What happens when the droughts and floods they've weathered for millennia arrive faster and more frequently? “We can't answer the questions if we're not watching,” said Csank. “We are the stewards of this data,” Heggli concluded. “This is why we need long science.”
The ocean is not empty. It is a vast storage facility of memory agents. Ocean bodies use the chemical signatures of seawater for memory and intelligence in ways we can barely imagine. In her Talk, Melody Jue said our struggle to understand ocean memory comes from our terrestrial bias. This bias shapes what we try to protect and the technologies we develop. We must, she said, “deterritorialize the sensorium.” For example, the vertical depths of the Pacific carry thermal signatures of ancient ice ages. Arctic glaciers are laced with matrices of microbes retaining genes from before the Great Oxidation Event. Whale songs are also memory agents, passed down through generations, preserving the cultural histories of the planet's largest creatures. Corals hold memory, too. Those exposed earlier to changing ocean pH are more resilient to acidification. Meanwhile, human cultural memory is in danger of disappearing alongside these ecosystems. Jue pointed to Indigenous and traditional environmental knowledge at risk, like the Ama divers’ fishing tradition, as abalone populations drop. To better translate the ocean sensorium, Jue worked with interdisciplinary artists, musicians, divers, and researchers to develop soundscapes that help us “smell” with our ears, remapping chemosensation through synesthesia. Don’t miss the moment in the Talk where she plays two original music pieces that use the density and flow of sound to mimic chemical gradients of seawater. “The ocean teaches us humility,” Jue concluded. “It makes us confront our preconceptions about the planet and sensation.”
What if the solutions to humanity’s greatest challenges — on Earth and beyond — have already been invented by nature? In this forward-looking talk, evolutionary biologist and astrobiologist Dr. Lynn Rothschild explores how life’s patterns, materials, and mechanisms, refined over billions of years, can serve as a blueprint for building better futures on Earth and other planets. Drawing on insights from deep time, Dr. Rothschild will open the doors to “nature’s hardware store” — a vast, largely untapped reservoir of biological strategies available to scientists, engineers, and innovators. From self-healing materials and bio-inspired architecture to regenerative systems for space exploration, she reveals how biology is shaping the frontiers of technology and inspiring bold, surprisingly practical solutions to complex problems. Grounded in astrobiology and evolutionary insight, this talk invites us to rethink innovation through the lens of life itself and to explore what’s possible when we tap into nature’s storehouse of intelligence to solve the challenges of tomorrow. Lynn J. Rothschild is a research scientist at NASA Ames and Adjunct Professor at Brown University and Stanford University working in astrobiology, evolutionary biology and synthetic biology. Rothschild's work focuses on the origin and evolution of life on Earth and in space, and in pioneering the use of synthetic biology to enable space exploration. From 2011 through 2019 Rothschild served as the faculty advisor of the award-winning Stanford-Brown iGEM (international Genetically Engineered Machine Competition) team, exploring innovative technologies such as biomining, mycotecture, BioWires, making a biodegradable UAS (drone) and an astropharmacy. Rothschild is a past-president of the Society of Protozoologists, fellow of the Linnean Society of London, The California Academy of Sciences and the Explorer’s Club and lectures and speaks about her work widely.
Blaise Agüera y Arcas’s talk took us on a journey through What is Intelligence?, his groundbreaking new work connecting the evolutionary dots between life, computation, and symbiogenesis. He explores how, in our symbiotic world, things combine to make larger things all the time. We might think of humanity in terms of the individual — but we're already part of everything we're creating, which is in turn co-creating us. In the story of technology and humanity, are we distinct from the technologies that we make? Agüera y Arcas' cuts through the essentialist dogma with a functionalist view: Biological computing — computation through DNA, RNA, and proteins — is not a strange outcropping of life but its very nature. Blaise Agüera y Arcas is a VP and Fellow at Google, where he is the CTO of Technology & Society and founder of Paradigms of Intelligence (Pi). Pi is an organization working on basic research in AI and related fields, especially the foundations of neural computing, active inference, sociality, evolution, and artificial life. During his tenure at Google, Blaise has innovated on-device machine learning for Android and Pixel; invented Federated Learning, an approach to decentralized model training that avoids sharing private data; and founded the Artists + Machine Intelligence program.
What if AI is not here actually to replace us, but to remind us who we actually are? That was the question at the heart of Kim Carson’s Long Now Talk. In _Inspired by Intelligence: Purpose and Creativity in the AI Era_, Carson challenged us to avoid the easy narratives of tech-driven utopia and dystopia, charting a course through those two extremes that made the case for AI not as a way to make humans unnecessary but to emphasize our most important creative capacities. In her talk, Carson drew on her experience working in AI at organizations like IBM, where she helped lead Watson Education, which helped connect educators in underserved communities to AI technology, in the name of facing down some of the wickedest problems in society. But she also drew on her own more personal engagement with AI, discussing at length the nuances of how she uses personalized versions of generative pre-trained transformers as collaborators and enablers for creativity. For Carson, AI is a sort of tool for thought — a mirror that we can use to re-inspire ourselves towards greater creativity. Accompanied by video art made using the SORA text-to-video model by Charles Lindsay, she made the case that AI could be used not just for automating labor but also for reclaiming human agency. That means using these new technological modes as enablers for human thought and action, while recognizing their gaps, too — the questions about ourselves that only we can answer, no matter how sophisticated our technology becomes. Throughout her talk, Carson expounded upon the power of vulnerability. The ability to use AI tools to help us reconnect with ourselves, to jar us into seeing our own identities and creative capacities in new lights, is one that will fundamentally help us change our world. In Carson’s view, vulnerability and creativity are the necessary precursors to any sort of technological innovation. As she ended her remarks, Kim made one final note on how we can make a better world collaboratively and creatively: our society does not need “more optimization, it needs more imagination.”
More in science
This question will not go away. I am getting asked this questions at conferences now. The media also seems to think this is a compelling question, and they are fueled in part by the tech industry itself, some of whom indulge this question. I will admit the answer has become more complex and nuanced than a simple “no”, but it is still mostly no. Certainly the rise of neural networks, deep learning, transformer technology, and specifically large language models have ushered in the era of – are we really sure current AI is not conscious? Our intuition tells us that they may be – LLMs are getting scary in how sophisticated they are and in their ability to simulate complex human conversations. Five years ago I was with the crowd who argued that LLMs are basically really good auto-complete apps, but this pithy sentiment no longer covers it. Even if they do not have a whisper of conscious awareness, they employ context-dependent logic. They are “intelligent” in their own way. And they are rapidly getting more impressive. So I get why people would be asking the question. Everyone I know who works with AI apps has their example of when they were blown away by an interaction with an LLM. For me, I was trouble-shooting a web-based app I was using, and ChatGPT was very effective at helping with the trouble-shooting and producing a differential problem list. At one point it asked for a screen capture so it could have more information, and it gave me a cogent analysis. But here’s the part I found surprising – it “noticed” another window open in the background and inferred from this that I was also using this other app in the process, and used this to generate further potential sources of the problem. As impressive as this is, I still think they are not conscious. I think the illusion of consciousness is generated by the fact that LLMs are simulating language, and we associate language with consciousness. No one thinks Midjourney, an art making program, is conscious, even though this is based on the same fundamental technology. Further, LLMs are still brittle in their own way, although the brittleness is being rapidly programmed out of them. What this means is that you can break them, or easily confuse them, in order to reveal that they are simulating language rather than truly thinking. This brittleness is revealed in several ways – sensitivity to exact prompt wording, accepting false premises in the prompt, and failure to recognize constraints. If changing the language but not the real meaning of a prompt gives different answers, then the LLM is responding statistically to the language, not genuinely reasoning about the information. Each iteration of LLMs is getting better at plugging these brittleness holes, but they are still there. Still, this doesn’t mean that there is no contextual reasoning happening. I think the best current summary is that LLMs are a combination of statistical language prediction (the auto-complete thing) and contextual reasoning. This doesn’t mean that they are conscious, however. What I believe it means is that humans find it surprising how much reasoning a machine can do without consciousness. Perhaps our own reasoning is more procedural than true deep thinking than we perceive or feel comfortable truly grappling with. We are also algorithmic machines, with different strengths and weaknesses than LLMs. But we are also conscious. So what is the difference? The difficulty in answering this question is that neuroscientists are still not certain what causes consciousness in the first place. This does not mean it’s a complete mystery or we know nothing – it means it is a complex multifactorial, and perhaps emergent, process that is not easily understood or explained. But we do have a lot of knowledge about it. There is no reason, in my opinion, to think that the different is biology vs silicon. I do believe that silicon can become conscious. Rather, it’s the process that matters, and we know some things about the process. Human consciousness requires that the brain be constantly self-activated. Turn off this activation (the brainstem activating system) and you will be in a coma – not conscious. We also know that the brain is constantly active and communicating with itself, in an endless loop of one network in the brain talking to other networks which talk to other networks and back to the original network, in a constant stream. It is this constant stream that is our consciousness. We also know there is a threshold, at least for wakeful consciousness. You need at least 45% of your cortex to be functional in order to generate wakeful consciousness. Are people who are not wakeful still conscious to some degree? Probably, but it’s difficult to understand what this means. If you are not making memories, for example, then we cannot interrogate what that consciousness was like. If people can still respond to their environment, does that imply consciousness, or could that simply be an unconscious reaction? For straightforward LLMs – prompt-response, I do not think they can be conscious, at least not human-level conscious. They are doing none of the things that are necessary for vertebrate consciousness. Things get a bit trickier for agentic AIs, that can iterate their own behavior, but still this is more like a self-iterative prompt than a true stream of consciousness. But admittedly, frontier agentic AI is starting to blur the lines a bit. But here’s the thing – even if I cannot say with absolute certainty that some of these frontier models have still not crossed over some fuzzy line into a type of consciousness, I can say that this consciousness must be fairly minimal compared to human-level consciousness. I can also say that such minimal consciousness is not what is speaking to you. It is not reasoning and generating the output of LLMs. We know what’s doing that, and it does not require consciousness or result from any process that can be reasonably inferred to be conscious (again, it may just be a prompt-response). Therefore, arguing that we cannot rule out some consciousness emerging from the processing of massive data centers does not mean you are speaking to that consciousness through your LLM. I may turn out to be wrong, but I don’t think we will back into AI consciousness through some unanticipated emergent property. I think we will have to design systems to do the kinds of things necessary for consciousness. It may be a deliberate attempt to make a conscious AI, or many of those elements may have some separate utility. Either way we will be able to look at the system and see that it is functioning in a way that can plausibly result in consciousness, and that consciousness will be proportional to the power and functionality of the system. LLMs are not it. They are not even on a path to become conscious. They are doing contextual reasoning, and it is getting steadily more impressive, but they are doing it without consciousness. The post Could AI Be Conscious? first appeared on NeuroLogica Blog.
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Economics often starts with an assumption that people are rational maximizers of their own self-interest; but it doesn't stop there. […]
I’m an Australian father of four, living in the US. Something of a corner case. Nevertheless, getting my kids Australian citizenship by descent, and then Australian passports so we can fly home legally, has kicked off a carnival of errors with a very dark side. Australia is generally thought of as a developed western liberal country with relatively high state capacity. And I have generally found that at least state government services, now administered through centralized offices, have become more efficient over time. Passport applications for children living overseas, however, remain a time machine with a difficult security overhang. Australian …
