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Neuralink is a neurotechnology company founded by Elon Musk in 2016 with the goal of. merging the human brain with artificial intelligence. The company aims to develop a. brain-machine interface that will enable humans to communicate with computers and other. devices directly through their thoughts. Neuralink’s ultimate vision is to create a symbiotic. relationship between humans and AI, where the brain and the computer work together to. enhance human capabilities. While there is a huge potential in this field, it could also turn out. to be extremely dangerous. Here’s why.
Jim Cantrell is an entrepreneur, strategist, subject matter expert in satellite systems, space system markets and road racer. Founder of StratSpace, Founder of Vintage Exotics Competition Engineering, early partner and VP at SpaceX.
In vitro biological neural networks (BNNs) interconnected with robots, so-called BNN-based neurorobotic systems, can interact with the external world, so that they can present some preliminary intelligent behaviors, including learning, memory, robot control, etc.
This work aims to provide a comprehensive overview of the intelligent behaviors presented by the BNN-based neurorobotic systems, with a particular focus on those related to robot intelligence.
In this work, we first introduce the necessary biological background to understand the 2 characteristics of the BNNs: nonlinear computing capacity and network plasticity. Then, we describe the typical architecture of the BNN-based neurorobotic systems and outline the mainstream techniques to realize such an architecture from 2 aspects: from robots to BNNs and from BNNs to robots.
The financial industry’s response to artificial intelligence has been all over the place. Now, Bank of America is weighing in very much on the side of the bots.
In a note to clients viewed by CNBC and other outlets, BofA equity strategist Haim Israel boasted that AI was one of its top trends to watch — and invest in — for the year, and used all kinds of hypey language to convince its clients.
“We are at a defining moment — like the internet in the ’90s — where Artificial Intelligence (AI) is moving towards mass adoption,” the client note reads, “with large language models like ChatGPT finally enabling us to fully capitalize on the data revolution.”
Researchers have developed a new model inspired by recent biological discoveries that shows enhanced memory performance. This was achieved by modifying a classical neural network.
Computer models play a crucial role in investigating the brain’s process of making and retaining memories and other intricate information. However, constructing such models is a delicate task. The intricate interplay of electrical and biochemical signals, as well as the web of connections between neurons and other cell types, creates the infrastructure for memories to be formed. Despite this, encoding the complex biology of the brain into a computer model for further study has proven to be a difficult task due to the limited understanding of the underlying biology of the brain.
Researchers at the Okinawa Institute of Science and Technology (OIST) have made improvements to a widely utilized computer model of memory, known as a Hopfield network, by incorporating insights from biology. The alteration has resulted in a network that not only better mirrors the way neurons and other cells are connected in the brain, but also has the capacity to store significantly more memories.
In episode 13 of the Quantum Consciousness series, Justin Riddle discusses how microtubules are the most likely candidate to be a universal quantum computer that acts as a single executive unit in cells. First off, computer scientists are trying to model human behavior using neural networks that treat individual neurons as the base unit. But unicellular organisms are able to do many of the things that we consider to be human behavior! How does a single-cell lifeform perform this complex behavior? As Stuart Hameroff puts it, “neuron doctrine is an insult to neurons,” referring to the complexity of a single cell. Let’s look inside a cell, what makes it tick? Many think the DNA holds some secret code or algorithm that is executing the decision-making process of the cell. However, the microscope reveals a different story where the microtubules are performing a vast array of complex behaviors: swimming towards food, away from predators, coordinating protein delivery and creation within the cell. This begs the question: how do microtubules work? Well, they are single proteins organized into helical cylinders. What is going on here? Typically, we think of a protein’s function as being determined by its structure but the function of a single protein repeated into tubes is tough to unravel. Stuart Hameroff proposed that perhaps these tubulin proteins are acting as bits of information and the whole tube is working as a universal computer that can be programmed to fit any situation. Given the limitations of digital computation, Roger Penrose was looking for a quantum computer in biology and Stuart Hameroff was looking for more than a digital computation explanation. Hence, the Hameroff-Penrose model of microtubules as quantum computers was born. If microtubules are quantum computers, then each cell would possess a central executive hub for rapidly integrating information from across the cell and to turn that information into a single action plan that could be quickly disseminated. Furthermore, the computation would get a “quantum” speed-up in that exponentially large search spaces could be tackled in a reasonable timeframe. If microtubules are indeed quantum computers, then modern science has greatly underestimated the processing power of a single cell, let alone the entire human brain.
~~~ Timestamps ~~~ 0:00 Introduction. 3:08 “Neuron doctrine is an insult to neurons” 8:23 DNA vs Microtubules. 14:20 Diffusion vs Central Hub. 17:50 Microtubules as Universal Computers. 23:40 Penrose’s Quantum Computation update. 29:48 Quantum search in a cell. 33:25 Stable microtubules in neurons. 35:18 Finding the self in biology.
Technological Singularity is expected to trigger profound changes in both social and technogenic structures. The introduction of Artificial General Intelligence (AGI), that can be both disembodied and embodied as smart robots, will bring about a multitude of issues, including human unemployment or underemployment, ethical considerations regarding the treatment of robots, the challenge of managing their runaway superintelligence, and most importantly, harnessing the potential of human-machine convergence.
