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We see consciousness in AI the same way we see faces in clouds, says neuroscientist Anil Seth. He explores the all-too-human tendency to project inner life onto machines that are brilliant mimics, not sentient beings, and gives a definitive answer to the urgent question: Will AI ever gain consciousness?

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Tracing the historical dynamics of science can reveal how scientific knowledge emerges and evolves over time. Because scientific knowledge is embedded in increasingly complex systems, comprising shifting relationships among people, the organisms and matter they study, technology, data, publications, and the concepts they utilize, scholars are looking beyond traditional historiographical methods towards quantitative and computational tools. Big data, network analysis, and machine learning enhance the scale and speed of analysis, but these methods often ignore or erase the critical roles that context (like time period, geography, and discipline) and different types of data (like image and audio data) play in the development of new knowledge. In this talk, I present context- and data-sensitive computational methods that extend efforts to model the evolution of science as a complex system. These methods reveal when new knowledge emerges and how the features of old scientific information constrain features of new scientific knowledge.

Read the full article at: www.mivideo.it.umich.edu

Binghamton Center of Complex Systems (CoCo) Extra Seminar March 24, 2026

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Network Science Colloquium Series, 09/24/2025

In 1905 the biologist Edmund Selous wrote of his wonderment when observing a flock of starlings flying overhead “they circle; now dense like a polished roof, now disseminated like the meshes of some vast all-heaven-sweeping net…wheeling, rending, darting…a madness in the sky”. He went on to speculate “They must think collectively, all at the same time, or at least in streaks or patches — a square yard or so of an idea, a flash out of so many brains”. Today, we still know relatively little about how the network of social interactions connect brains—and thus how sensing and information processing arises in such organismal collectives. Employing automated tracking, computational reconstruction of sensory information, and immersive ‘holographic’ virtual reality (VR) experiments, I will discuss newly-discovered geometric principles of collective decision-making that occur across scales of biological organization; from neural networks to the social networks of animal groups. I will also show how this finding can impact humans, including how it can be translated to highly effective control laws for swarming robots, as well as how it has transformed our understanding of locust swarms, one of the most destructive natural phenomena on Earth.

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Through a series of fascinating examples, physicist and data-visualisation specialist César Hidalgo shows how scientific laws of time, space and value allow us to chart how knowledge moves and spreads in the 21st century, helping us understand the emergence of hot and coldspots for scientific and economic growth and development.

Why is it that Silicon Valley in California or Zhongguancun in Beijing are such successful hubs for innovation, where other locations have failed? What sustains the exponential growth in some technologies, like computers, while we forgot how to make Polaroid film?

Watch at: youtu.be