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Demystifying Hinton's 'Mortal Computation' Theory A Glimpse into the Future of Superhuman AI

Demystifying Hinton's 'Mortal Computation' Theory A Glimpse into the Future of Superhuman AI - The Concept of Mortal Computation

The concept of "mortal computation" introduced by Geoffrey Hinton is gaining significant attention in the fields of neuroscience, artificial intelligence, and biomimetic computing.

Mortal computation is proposed to be a novel type of computation that is dependent on its hardware, in contrast with "immortal" computations that can run on any hardware.

This concept challenges the traditional views on AI consciousness and provides a new perspective on computational functionalism, suggesting that consciousness cannot be a Turing computation and is instead a type of mortal computation.

Mortal computation is proposed to be a novel type of computation that is fundamentally different from the "immortal" computations carried out by contemporary digital processing units.

Unlike immortal computations, mortal computations are highly dependent on their underlying hardware.

Hinton argued that the widely used backpropagation algorithm in machine learning is biologically implausible and cannot be implemented on analogue hardware.

He suggested the forward-forward algorithm, which resembles evolution strategies, as a more suitable alternative for mortal computation.

The notion of mortal computation challenges the traditional view that consciousness can be achieved through Turing computation.

Instead, Hinton proposes that consciousness is a novel type of computation that cannot be realized through Turing computation.

The concept of mortal computation has been driven by advancements in AI and AI chip production, as it provides a new perspective on the limitations of conventional digital computing and the potential for biomimetic intelligence.

Mortal computation is characterized by recasting ideas from biophysics, cybernetics, and cognitive science, and is framed through the Markov blanket formalism and the concept of circular causality entailed by inference, learning, and selection.

Mortal computation is proposed to increase complexity according to cognitive functionality, and can be examined through the lens of 5E theory, which suggests that agent-environment interactions should be evaluated based on the agent's ability to persist in its environment.

Demystifying Hinton's 'Mortal Computation' Theory A Glimpse into the Future of Superhuman AI - Overcoming Traditional Computing Limitations

The concept of "mortal computation" introduced by Geoffrey Hinton, a pioneering figure in deep learning, challenges the limitations of traditional digital computing and offers a new perspective on the future of artificial intelligence.

Hinton argues that digital models can aggregate knowledge more efficiently and perform computations with greater parallelism than biological brains, suggesting a paradigm shift in AI that could lead to levels of intelligence previously thought unattainable.

This theoretical foundation, rooted in biophysics, cybernetics, and cognitive science, highlights the need to address the energy efficiency and parallelism constraints of current computing systems in order to unlock the full potential of artificial intelligence.

Existing software-based learning methods do not require prior information, which fundamentally limits their ability to match the efficiency and performance of biological brains.

Analog computation models are inherently tied to the unique properties of their underlying hardware, making the learned knowledge "mortal" - when the hardware dies, the knowledge is lost and must be painfully relearned.

Geoffrey Hinton's vision for the future of AI involves "mortal computers" that will complement, rather than replace, traditional digital computers, requiring specialized learning procedures to accommodate their hardware properties.

Research into neuroscience-inspired AI and biomimetic computing has been heavily influenced and synthesized by the theoretical framework of "Mortal Computation," which draws from diverse fields like biophysics, cybernetics, and cognitive science.

Overcoming the challenge of training and utilizing mortal machines is crucial for the practical implementation of Hinton's "Mortal Computation" concept, which could enable a paradigm shift in AI capabilities.

Traditional digital computing systems face significant limitations in energy efficiency and parallelism, preventing them from reaching the computational power of biological brains, which Hinton's "Mortal Computation" theory aims to address.

Hinton argues that digital models can aggregate knowledge across multiple instances and perform computations more efficiently than biological brains, potentially leading to a new era of superhuman AI capabilities.

Demystifying Hinton's 'Mortal Computation' Theory A Glimpse into the Future of Superhuman AI - Embracing Neuromorphic and Finite Lifespan Designs

Neuromorphic computing, which aims to mimic the brain's structure and function, is an important field for the future of computing and AI.

The neuromorphic community is encouraged to focus on building a larger community of users and stakeholders, and recent research has explored neuromorphic algorithms and applications alongside hardware development.

Memristive neurons for neuromorphic computing and sensing are a major opportunity, offering low-power consumption and the ability to train and use these systems locally.

Neuromorphic computing, which mimics the brain's architecture and information processing, has shown promise in achieving energy-efficient AI by performing computations using spikes rather than continuous signals.

Memristive neurons, devices that can serve as both memory and logic elements, are a major opportunity for organic neuromorphic devices, offering low-power consumption and the ability to train and use these systems locally.

Spiking neural networks (SNNs), which more closely resemble biological neural networks, are being explored alongside traditional artificial neural networks (ANNs) for developing the next generation of neuromorphic computing.

Neuromorphic computers, unlike traditional "von Neumann" architectures, are not based on the separation of memory and processing, but rather on the interconnection of "neurons" and "synapses" to perform computations.

Embodied neuromorphic intelligence, where the neuromorphic system is integrated with a physical platform, has the potential to demonstrate adaptive and learning capabilities in real-world robotic applications.

The 2022 Roadmap on Neuromorphic Computing and Engineering provides a comprehensive overview of the current state and future directions of neuromorphic technology, covering materials, devices, circuits, algorithms, applications, and ethical considerations.

While much of the neuromorphic computing research has focused on hardware development, recent work has also explored novel algorithms and applications that can leverage the unique properties of neuromorphic architectures.

Neuromorphic computing is expected to play a significant role in the future of AI, as it can potentially conduct computations within the device itself, making it suitable for applications that require low power and local processing.

Demystifying Hinton's 'Mortal Computation' Theory A Glimpse into the Future of Superhuman AI - Exploring Digital Intelligence Efficiencies

The concept of "mortal computation" introduced by Geoffrey Hinton has significant implications for the future of artificial intelligence.

Hinton argues that digital computation, using backpropagation learning, can scale much better than analog hardware, potentially leading to digital intelligences that surpass human-level abilities.

This notion challenges the traditional view of consciousness as a Turing computation, suggesting that it may be a novel form of "mortal computation" that is dependent on the underlying hardware.

Hinton's vision involves "mortal computers" that can complement traditional digital systems, requiring specialized learning procedures to harness their unique properties.

As the research into neuromorphic and biomimetic computing continues, the practical implementation of Hinton's "Mortal Computation" theory could enable a transformative leap in AI capabilities.

Hinton's "mortal computation" theory suggests that digital intelligence may be less susceptible to religion and wars compared to biological intelligence, due to its immortal nature.

Hinton believes that large-scale digital computation is likely far better at acquiring knowledge than biological computation and could soon exceed human-level intelligence.

The forward-forward algorithm, which resembles evolution strategies, has been proposed by Hinton as a more biologically plausible alternative to the widely used backpropagation algorithm in machine learning.

Mortal computation challenges the traditional view that consciousness can be achieved through Turing computation, suggesting that consciousness is a novel type of computation that cannot be realized through Turing computation.

Hinton argues that digital models can aggregate knowledge more efficiently and perform computations with greater parallelism than biological brains, potentially leading to a paradigm shift in AI.

Existing software-based learning methods do not require prior information, which fundamentally limits their ability to match the efficiency and performance of biological brains.

Analog computation models are inherently tied to the unique properties of their underlying hardware, making the learned knowledge "mortal" and requiring specialized learning procedures to accommodate their hardware properties.

Hinton has suggested that digital intelligence could potentially lead to 20% sentience within the next decade, challenging the traditional understanding of machine consciousness.

Demystifying Hinton's 'Mortal Computation' Theory A Glimpse into the Future of Superhuman AI - Genetically Encoded Development for Parameter Optimization

Genetically encoded development has been explored as a potential approach to solving the challenges posed by Hinton's "Mortal Computation" theory.

Genetic algorithms have been applied to optimize parameters in various AI models, such as transfer convolutional neural networks and support vector machines, with the goal of achieving more efficient and effective solutions.

This concept of using evolutionary algorithms to optimize model parameters is seen as a promising path towards realizing superhuman AI capabilities.

determining how to get trillions of parameters into the network and using evolutionary programming of the genome as an efficient learning procedure that can run in hardware.

Genetic algorithms have been applied to select trainable layers in the design of transfer convolutional neural networks (CNNs), as conventional transfer CNN models are often manually designed based on intuition.

Geoffrey Hinton's "forwardforward" algorithm, inspired by neural activations in the brain, has been presented as a potential replacement for backpropagation in the future, according to Hinton himself.

Genetic-based evolutionary algorithms have been used to optimize support vector machine (SVM) parameters, gaining increasing interest across various research fields.

These genetic algorithms optimize parameters according to the laws of separation and free combination in genetics, a concept with implications for artificial intelligence, as it suggests AI systems should be designed to minimize prediction error.

Genetically encoded development is a potential approach to applying this principle in AI systems, where evolutionary algorithms are used to optimize model parameters.

The concept of superhuman AI is closely tied to the idea of optimizing model parameters, as it requires AI systems to surpass human-level performance in diverse tasks.

Genetically encoded development for parameter optimization offers a promising path towards achieving superhuman AI, by leveraging evolutionary algorithms to search for optimal solutions in complex spaces.

This approach could lead to breakthroughs in AI capabilities, enabling systems that exceed human abilities in areas like computer vision, natural language processing, and decision-making.

Researchers have highlighted the potential of genetically encoded development to address the limitations of traditional digital computing, such as energy efficiency and parallelism, which have prevented digital models from matching the computational power of biological brains.

Demystifying Hinton's 'Mortal Computation' Theory A Glimpse into the Future of Superhuman AI - The Forward-Forward Algorithm A New AI Approach

The Forward-Forward algorithm is a novel learning procedure for neural networks introduced by Geoffrey Hinton.

It aims to replace the forward and backward passes of backpropagation with two forward passes, one using positive (real) data and one using negative data generated by the network itself.

The algorithm has shown promising results on small problems and is considered worth further investigation as a potential alternative to traditional backpropagation, with implications for the development of superhuman AI.

The Forward-Forward algorithm updates network parameters immediately after the forward pass of a layer, in contrast with backpropagation which requires full knowledge of the computation in the forward pass to compute gradients.

This approach has sparked interest in the AI community, as it could address some of the shortcomings of standard backpropagation training and help explore new directions for artificial neural networks inspired by the brain.

The Forward-Forward algorithm replaces the traditional forward and backward passes of backpropagation with two forward passes - one using positive (real) data and one using negative data generated by the network itself.

This approach eliminates the problems associated with backpropagation, such as the requirement for full knowledge of the computation in the forward pass to compute gradients.

The Forward-Forward algorithm updates network parameters immediately after the forward pass of each layer, unlike backpropagation which requires a separate backward pass.

Preliminary results have shown the Forward-Forward algorithm performing well on small problems, sparking interest in the AI community as a potential replacement for backpropagation.

The Forward-Forward algorithm is part of Geoffrey Hinton's efforts to address the shortcomings of standard backpropagation and explore new biologically-inspired approaches to training artificial neural networks.

Hinton has proposed the Forward-Forward algorithm as a learning procedure that could potentially run efficiently in hardware with unknown precise details, unlike backpropagation which requires full knowledge of the forward computation.

The Forward-Forward algorithm may benefit from self-supervised learning techniques that can make neural networks more robust and reduce the need for manually labeled examples.

In his NeurIPS 2022 keynote speech, Hinton presented the Forward-Forward algorithm as a novel approach to neural network learning that updates parameters immediately after the forward pass.

While the Forward-Forward algorithm has limitations, it serves as an inspiration for new ideas in artificial neural networks, such as the use of negative data to improve learning.

The development of the Forward-Forward algorithm is closely linked to Hinton's broader work on the concept of "mortal computation," which challenges the traditional view of consciousness as a Turing computation.

Hinton believes the Forward-Forward algorithm, with its similarities to evolution strategies, may be a more biologically plausible alternative to backpropagation for implementing "mortal computation" in artificial systems.

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