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Category: robotics/AI – Page 318

Discussing 3 Recently Published Papers with Michael Levin

Michael Levin is a scientist at Tufts University; his lab studies anatomical and behavioral decision-making at multiple scales of biological, artificial, and hybrid systems. He works at the intersection of developmental biology, artificial life, bioengineering, synthetic morphology, and cognitive science. Respective papers are linked below.

Round 1 Interview | What are Cognitive Light Cones? • What are Cognitive Light Cones? (Mich…
Round 2 Interview | Agency, Attractors, & Observer-Dependent Computation in Biology & Beyond • Agency, Attractors, & Observer-Depend…

Bioelectric Networks: The cognitive glue enabling evolutionary scaling from physiology to mind https://link.springer.com/article/10…
Darwin’s Agential Materials: Evolutionary implications of multiscale competency in developmental biology https://link.springer.com/article/10…
Biology, Buddhism, and AI: Care as the Driver of Intelligence https://www.mdpi.com/1099-4300/24/5/710

Bioelectric Networks as \.

Tiny device processes hand movement in real time, storing visual memories with brain-like efficiency

Engineers at RMIT University have invented a small “neuromorphic” device that detects hand movement, stores memories and processes information like a human brain, without the need for an external computer.

The findings are published in the journal Advanced Materials Technologies.

Team leader Professor Sumeet Walia said the innovation marked a step toward enabling instant visual processing in autonomous vehicles, advanced robotics and other next-generation applications for improved .

On Memory as a Self-Adapting Agent

We discuss Michael Levin’s paper “Self-Improvising Memory: A Perspective on Memories as Agential, Dynamically Reinterpreting Cognitive Glue.” Levin is a scientist at Tufts University, his lab studies anatomical and behavioral decision-making across biological, artificial, and hybrid systems. His work spans developmental biology, artificial life, bioengineering, synthetic morphology, and cognitive science. 🎥 Next, watch my first interview with Michael Levin… What are Cognitive Light Cones? • What are Cognitive Light Cones? (Mich… ❶ Memories as Agents 0:00 Introduction 1:40 2024 Highlights from Levin Lab 3:20 Stress sharing paper summary 6:15 Paradox of change: Species persist don’t evolve 7:20 Bow-tie architectures 10:00 🔥 Memories as messages from your past self 12:50 Polycomputing 16:45 Confabulation 17:55 What evidence supports the idea that memories are agential? 22:00 Thought experiment: Entities from earth’s core ❷ Information Patterns 31:30 Memory is not a filing cabinet 32:30 Are information patterns agential? 35:00 🔥 Caterpillar/butterfly… sea slug memory transfer 37:40 Bow-tie architectures are EVERYWHERE 43:20 Bottlenecks “scary” for information ❸ Connections & Implications 45:30 🔥 Black holes/white holes as bow-ties (Lee Smolin) 47:20 What is confabulation? AI hallucinations 52:30 Gregg Henriques & self-justifying apes… all good agents storytellers 54:20 Information telling stories… Joseph Campbell’s journey for a single cell 1:00:50 What comes next? 🚾 Works Cited 🚩 Self-Improvising Memory: A Perspective on Memories as Agential, Dynamically Reinterpreting Cognitive Glue https://www.mdpi.com/1099-4300/26/6/481 https://thoughtforms.life/suti-the-se… our way to health with robot cells | Michael Levin (Big Think 2023) • Biohacking our way to health with rob… https://peregrinecr.com/ 🚀 What is this channel? Exploring Truth in philosophy, science, & art. We’ll uncover concepts from psychology, mythology, spirituality, literature, media, and more. If you like Lex Fridman or Curt Jaimungal, you’ll love this educational channel. p.s. Please subscribe! Young channel here. =) #science #memory #biology #computing #mind #intelligence #attractor #polycomputing #bioelectric #cybernetics #research #life

Michael Levin—The Future of Intelligence: Synthbiosis

At the Artificiality Summit 2024, Michael Levin, distinguished professor of biology at Tufts University and associate at Harvard’s Wyss Institute, gave a lecture about the emerging field of diverse intelligence and his frameworks for recognizing and communicating with the unconventional intelligence of cells, tissues, and biological robots. This work has led to new approaches to regenerative medicine, cancer, and bioengineering, but also to new ways to understand evolution and embodied minds. He sketched out a space of possibilities—freedom of embodiment—which facilitates imagining a hopeful future of \.

Michael Levin — Non-neural intelligence: biological architecture problem-solving in diverse spaces

Recorded 6 November 2024. Michael Levin of Tufts University presents “Non-neural intelligence: biological architectures for problem-solving in diverse spaces” at IPAM’s Naturalistic Approaches to Artificial Intelligence Workshop. Abstract: The familiar, readily-recognized intelligence of brainy animals has long served as inspiration for AI. However, biological intelligence is far older than neurons, and indeed than multicellularity. My lab studies problem-solving in cells, tissues, and even subcellular components, operating in different spaces and at different scales than conventional intelligent agents. In this talk, I will describe a framework for detecting, communicating with, and creating collective intelligences, and show examples of how the fundamental properties of life suggest novel approaches for ethically relating to diverse and fascinating engineered and hybrid intelligences. Learn more online at: https://www.ipam.ucla.edu/programs/wo…

AI tools may be weakening the quality of published research, study warns

Artificial intelligence could be affecting the scientific rigor of new research, according to a study from the University of Surrey.

The research team has called for a range of measures to reduce the flood of “low-quality” and “science fiction” papers, including stronger peer review processes and the use of statistical reviewers for complex datasets.

In a study published in PLOS Biology, researchers reviewed papers proposing an association between a predictor and a health condition using an American government dataset called the National Health and Nutrition Examination Survey (NHANES), published between 2014 and 2024.

To save nature, AI needs our help

AI is a computing tool. It can process and interrogate huge amounts of data, expand human creativity, generate new insights faster and help guide important decisions. It’s trained on human expertise, and in conservation that’s informed by interactions with local communities or governments—people whose needs must be taken into account in the solutions. How do we ensure this happens?

Last year, Reynolds joined 26 other conservation scientists and AI experts in an “Horizon Scan”—an approach pioneered by Professor Bill Sutherland in the Department of Zoology—to think about the ways AI could revolutionize the success of global biodiversity conservation. The international panel agreed on the top 21 ideas, chosen from a longlist of 104, which are published in the journal Trends in Ecology and Evolution.

Some of the ideas extrapolate from AI tools many of us are familiar with, like phone apps that identify plants from photos, or birds from sound recordings. Being able to identify all the species in an ecosystem in real time, over long timescales, would enable a huge advance in understanding ecosystems and species distributions.

AI-generated exam submissions evade detection at UK university

In a test of the examinations system of the University of Reading in the UK, artificial intelligence (AI)-generated submissions went almost entirely undetected, and these fake answers tended to receive higher grades than those achieved by real students. Peter Scarfe of the University of Reading and colleagues present these findings in the open-access journal PLOS ONE on June 26.

In recent years, AI tools such as ChatGPT have become more advanced and widespread, leading to concerns about students using them to cheat by submitting AI-generated work as their own. Such concerns are heightened by the fact that many universities and schools transitioned from supervised in-person exams to unsupervised take-home exams during the COVID-19 pandemic, with many now continuing such models. Tools for detecting AI-generated written text have so far not proven very successful.

To better understand these issues, Scarfe and colleagues generated answers that were 100% written by the AI chatbot GPT-4 and submitted on behalf of 33 fake students to the examinations system of the School of Psychology and Clinical Language Sciences at the University of Reading. Exam graders were unaware of the study.

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