But most deep learning models are loosely based on the brain’s inner workings. AI agents are increasingly endowed with human-like decision-making algorithms. The idea that machine intelligence could become sentient one day no longer seems like science fiction.
How could we tell if machine brains one day gained sentience? The answer may be based on our own brains.
A preprint paper authored by 19 neuroscientists, philosophers, and computer scientists, including Dr. Robert Long from the Center for AI Safety and Dr. Yoshua Bengio from the University of Montreal, argues that the neurobiology of consciousness may be our best bet. Rather than simply studying an AI agent’s behavior or responses—for example, during a chat—matching its responses to theories of human consciousness could provide a more objective ruler.
Swift is the culmination of years of AI and machine learning research by the University of Zurich. In 2021, the team set an earlier iteration of the flight control algorithm that used a series of external cameras to validate its position in space in real-time, against amateur human pilots, all of whom were easily overmatched in every lap of every race during the test. That result was a milestone in its own right as, previously, self-guided drones relied on simplified physics models to continually calculate their optimum trajectory, which severely lowered their top speed.
This week’s result is another milestone, not just because the AI bested people whose job is to fly drones fast, but because it did so without the cumbersome external camera arrays= of its predecessor. The Swift system “reacts in real time to the data collected by an onboard camera, like the one used by human racers,” an UZH Zurich release reads. It uses an integrated inertial measurement unit to track acceleration and speed while an onboard neural network localizes its position in space using data from the front-facing cameras. All of that data is fed into a central control unit — itself a deep neural network — which crunches through the numbers and devises a shortest/fastest path around the track.
Artificial Intelligence has transformed how we live, work, and interact with technology. From voice assistants and chatbots to recommendation algorithms and self-driving cars, AI has suddenly become an integral part of our daily lives, just a few months after the release of ChatGPT, which kickstarted this revolution.
However, with the increasing prevalence of AI, a new phenomenon called “AI fatigue” has emerged. This fatigue stems from the overwhelming presence of AI in various aspects of our lives, raising concerns about privacy, autonomy, and even the displacement of human workers.
AI fatigue refers to the weariness, frustration, or anxiety experienced by individuals due to the overreliance on AI technologies. While AI offers numerous benefits, such as increased efficiency, improved decision-making, and enhanced user experiences, it also presents certain drawbacks. Excessive dependence on AI can lead to a loss of human agency, diminishing trust in technology, and a feeling of disconnection from the decision-making process.
Michael Levin is a Distinguished Professor in the Biology department at Tufts University. He holds the Vannevar Bush endowed Chair and serves as director of the Allen Discovery Center at Tufts and the Tufts Center for Regenerative and Developmental Biology. To explore the algorithms by which the biological world implemented complex adaptive behavior, he got dual B.S. degrees, in CS and in Biology and then received a PhD from Harvard University. He did post-doctoral training at Harvard Medical School, where he began to uncover a new bioelectric language by which cells coordinate their activity during embryogenesis. The Levin Lab works at the intersection of developmental biology, artificial life, bioengineering, synthetic morphology, and cognitive science.
✅TIMESTAMPS: 0:00 – Introduction. 1:27 – The Prisoner’s Dilemma (Game Theory applied to Life) 7:55 – Computational Boundary of the Self. 10:17 – “Goal States” & “Cognitive Light Cones” 13:55 – To Naturalise Cognition. 19:00 – The Hard Problem of Consciousness. 23:10 – Defining Consciousness. 27:14 – The Field of Diverse Intelligence. 43:25 – Who inspired Mike within his field. 46:52 – Is Mike a Panpsychist? 52:09 – Thoughts on Illusionism. 55:44 – Links to IIT 57:56 – Technological Approach to Mind Everywhere (TAME 2.0) 1:02:14 – Proof of Humanity Certification. 1:10:00 – Phase Transitions in Mathematics. 1:15:26 – Bioelectric Medicine. 1:21:06 – Can Cells Think? What is the Self? Is Man a Machine? 1:28:55 – Metacognition & Cloning. 1:35:49 – Teleology, Teleonomy & Teleophobia. 1:50:08 – All Intelligence is Collective Intelligence. 1:54:33 — Conclusion.
Video Title: What is The Field of Diverse Intelligence? Hacking the Spectrum of Mind & Matter | Michael Levin.
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Catastrophic forgetting, an innate issue with backpropagation learning algorithms, is a challenging problem in artificial and spiking neural network (ANN and SNN) research.
The brain has somewhat solved this problem using multiscale plasticity. Under global regulation through specific pathways, neuromodulators are dispersed to target brain regions, where both synaptic and neuronal plasticity are modulated by neuromodulators locally. Specifically, neuromodulators modify the capacity and property of neuronal and synaptic plasticity. This modification is known as metaplasticity.
Researchers led by Prof. Xu Bo from the Institute of Automation of the Chinese Academy of Sciences and their collaborators have proposed a novel brain-inspired learning method (NACA) based on neural modulation dependent plasticity, which can help mitigate catastrophic forgetting in ANN and SNN. The study was published in Science Advances on Aug. 25.
Consciousness is usually ascribed to a specific set of mechanisms and functional capabilities of the complex brain. Importantly, those mechanisms (ion channels, electrical networks, neurotransmitter machinery) long pre-date the evolutionary innovation of nervous systems. Moreover, the algorithms and competencies such as memory, decision-making, and information integration likewise have an ancient evolutionary origin: before they controlled moving the body through 3D space, electrical networks moved body configurations through anatomical morphospace. In this talk, I will describe how we view the morphogenesis during embryonic development and regeneration as the behavior of a collective intelligence, which has many problem-solving capacities. I will describe the tools we have developed, paralleling neuroscientists’ attempts to read and write mental content by control of electrophysiology, to decode and re-write the pattern memories of the body. This has significant implications not only for biomedicine and evolutionary biology, but also for questions about consciousness and the scaling of coherent Selves from agential materials. I will conclude with some conjectures about what this new field offers the science of consciousness, in the form of new embodied living creatures that are outside the natural evolutionary stream of Earth, and the quest for theories of consciousness.-https://www.drmichaellevin.org/ Participate in our online research survey-Survey on Diverse intelligence-https://tufts.qualtrics.com/jfe/form/SV_eE51vKE34q3hexo (takes 9 minutes). Thank you.
In an era of growing digitalisation, data centers have emerged as the fundamental support of our technological framework. However, worries persist over the ecological effects due to their swift growth and power-demanding activities. These data centers rank among the planet’s most energy-intensive establishments, drawing substantial electricity to fuel servers, cooling mechanisms, and auxiliary apparatus vital for their operations. Such elevated energy usage significantly affects the environment by adding to greenhouse gas discharges and ushering climate change.
The AI Power Consumption Challenge
The growing surge of AI (Artificial Intelligence) in recent years has been a remarkable and transformative phenomenon. However, AI models and algorithms are highly resource-intensive and consume significant amounts of power. Training AI models involve massive computational workloads, often requiring specialised hardware accelerators like GPUs, which consume substantial energy. This power consumption is a major concern when it comes to making data centers greener.
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