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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.

✅EPISODE LINKS:
👉Round 1: https://youtu.be/v6gp-ORTBlU
👉Mike’s Website: https://drmichaellevin.org/
👉New Website: https://thoughtforms.life.
👉Mike’s Twitter: https://twitter.com/drmichaellevin.
👉Mike’s YouTube: https://youtube.com/@drmichaellevin.
👉Mike’s Publications: https://tinyurl.com/yc388vvk.
👉The Well: https://www.youtube.com/watch?v=0a3xg4M9Oa8 & https://youtu.be/XHMyKOpiYjk.
👉Aeon Essays: https://aeon.co/users/michael-levin.

Continue reading “What is The Field of Diverse Intelligence? Hacking the Spectrum of Mind & Matter | Michael Levin” »

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.

Edited by Emilio Manzotti.
https://github.com/emilim/

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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Welcome to our latest educational video, “The 9 AI Skills You Need NOW to Stay Ahead of 97% of People.” This video is designed for anyone eager to take a deep dive into the world of artificial intelligence and machine learning. Our goal is to provide you with the most essential AI skills needed to excel in this rapidly evolving field, keeping you ahead of the curve and well-positioned in the job market.

In this comprehensive guide, we explore nine fundamental AI skills, ranging from understanding algorithms to deep learning, data science, natural language processing, computer vision, robotics, and more. We also provide practical tips on how to apply these skills in real-world scenarios, whether you’re an AI enthusiast or a seasoned professional.

Continue reading “The 9 AI Skills You Need NOW to Stay Ahead of 97% of People” »

Researchers have developed a new technique that combines the strengths of human cognition with algorithmic logic.

It sometimes presents incorrect steps to arrive at the answer, because it is designed to base conclusions on precedent. And a precedent based on a given data set is limited to the confines of the data set. This, says Microsoft, leads to “increased costs, memory, and computational overheads.”

AoT to the rescue. The algorithm evaluates whether the initial steps—” thoughts,” to use a word generally associated only with humans—are sound, thereby avoiding a situation where an early wrong “thought” snowballs into an absurd outcome.

Though not expressly stated by Microsoft, one can imagine that if AoT is what it’s cracked up to be, it might help mitigate the so-called AI “hallucinations”—the funny, alarming phenomenon whereby programs like ChatGPT spits out false information. In one of the more notorious examples, in May 2023, a lawyer named Stephen A. Schwartz admitted to “consulting” ChatGPT as a source when conducting research for a 10-page brief. The problem: The brief referred to several court decisions as legal precedents… that never existed.

This post is also available in: עברית (Hebrew)

At a crucial time when the development and deployment of AI are rapidly evolving, experts are looking at ways we can use quantum computing to protect AI from its vulnerabilities.

Machine learning is a field of artificial intelligence where computer models become experts in various tasks by consuming large amounts of data, instead of a human explicitly programming their level of expertise. These algorithms do not need to be taught but rather learn from seeing examples, similar to how a child learns.