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Category: information science – Page 67

AI Generated Content and Academic Journals

What are good policy options for academic journals regarding the detection of AI generated content and publication decisions? As a group of associate editors of Dialectica note below, there are several issues involved, including the uncertain performance of AI detection tools and the risk that material checked by such tools is used for the further training of AIs. They’re interested in learning about what policies, if any, other journals have instituted in regard to these challenges and how they’re working, as well as other AI-related problems journals should have policies about. They write: As associate editors of a philosophy journal, we face the challenge of dealing with content that we suspect was generated by AI. Just like plagiarized content, AI generated content is submitted under false claim of authorship. Among the unique challenges posed by AI, the following two are pertinent for journal editors. First, there is the worry of feeding material to AI while attempting to minimize its impact. To the best of our knowledge, the only available method to check for AI generated content involves websites such as GPTZero. However, using such AI detectors differs from plagiarism software in running the risk of making copyrighted material available for the purposes of AI training, which eventually aids the development of a commercial product. We wonder whether using such software under these conditions is justifiable. Second, there is the worry of delegating decisions to an algorithm the workings of which are opaque. Unlike plagiarized texts, texts generated by AI routinely do not stand in an obvious relation of resemblance to an original. This renders it extremely difficult to verify whether an article or part of an article was AI generated; the basis for refusing to consider an article on such grounds is therefore shaky at best. We wonder whether it is problematic to refuse to publish an article solely because the likelihood of its being generated by AI passes a specific threshold (say, 90%) according to a specific website. We would be interested to learn about best practices adopted by other journals and about issues we may have neglected to consider. We especially appreciate the thoughts of fellow philosophers as well as members of other fields facing similar problems. — Aleks…

Emerging Memristive Artificial Synapses and Neurons for Energy-Efficient Neuromorphic Computing

Memristors have recently attracted significant interest due to their applicability as promising building blocks of neuromorphic computing and electronic systems. The dynamic reconfiguration of memristors, which is based on the history of applied electrical stimuli, can mimic both essential analog synaptic and neuronal functionalities. These can be utilized as the node and terminal devices in an artificial neural network. Consequently, the ability to understand, control, and utilize fundamental switching principles and various types of device architectures of the memristor is necessary for achieving memristor-based neuromorphic hardware systems. Herein, a wide range of memristors and memristive-related devices for artificial synapses and neurons is highlighted. The device structures, switching principles, and the applications of essential synaptic and neuronal functionalities are sequentially presented. Moreover, recent advances in memristive artificial neural networks and their hardware implementations are introduced along with an overview of the various learning algorithms. Finally, the main challenges of the memristive synapses and neurons toward high-performance and energy-efficient neuromorphic computing are briefly discussed. This progress report aims to be an insightful guide for the research on memristors and neuromorphic-based computing.

Keywords: artificial neural networks; artificial neurons; artificial synapses; memristive electronic devices; memristors; neuromorphic electronics.

© 2020 Wiley-VCH GmbH.

Euler’s Identity: ‘The Most Beautiful Equation’

Euler’s Identity:

The most beautiful equation in mathematics that combines five of the most important constants of nature: 0, 1, π, e and i, with the three fundamental operations: addition, multiplication and exponentiation.

It’s mystical.

Euler’s identity is an equality found in mathematics that has been compared to a Shakespearean sonnet and described as “the most beautiful equation.” It is a special case of a foundational equation in complex arithmetic called Euler’s Formula, which the late great physicist Richard Feynman called in his lectures “our jewel” and “the most remarkable formula in mathematics.”

In an interview with the BBC, Prof David Percy of the Institute of Mathematics and its Applications said Euler’s Identity was “a real classic and you can do no better than that … It is simple to look at and yet incredibly profound, it comprises the five most important mathematical constants.”

Universal and High-Fidelity Resolution Extending for Fluorescence Microscopy Using a Single-Training Physics-Informed Sparse Neural Network

As a supplement to optical super-resolution microscopy techniques, computational super-resolution methods have demonstrated remarkable results in alleviating the spatiotemporal imaging trade-off. However, they commonly suffer from low structural fidelity and universality. Therefore, we herein propose a deep-physics-informed sparsity framework designed holistically to synergize the strengths of physical imaging models (image blurring processes), prior knowledge (continuity and sparsity constraints), a back-end optimization algorithm (image deblurring), and deep learning (an unsupervised neural network). Owing to the utilization of a multipronged learning strategy, the trained network can be applied to a variety of imaging modalities and samples to enhance the physical resolution by a factor of at least 1.67 without requiring additional training or parameter tuning.

Post-silicon nano-electronic device and its application in brain-inspired chips

As information technology is moving toward the era of big data, the traditional Von-Neumann architecture shows limitations in performance. The field of computing has already struggled with the latency and bandwidth required to access memory (“the memory wall”) and energy dissipation (“the power wall”). These challenging issues, such as “the memory bottleneck,” call for significant research investments to develop a new architecture for the next generation of computing systems. Brain-inspired computing is a new computing architecture providing a method of high energy efficiency and high real-time performance for artificial intelligence computing. Brain-inspired neural network system is based on neuron and synapse. The memristive device has been proposed as an artificial synapse for creating neuromorphic computer applications. In this study, post-silicon nano-electronic device and its application in brain-inspired chips are surveyed. First, we introduce the development of neural networks and review the current typical brain-inspired chips, including brain-inspired chips dominated by analog circuit and brain-inspired chips of the full-digital circuit, leading to the design of brain-inspired chips based on post-silicon nano-electronic device. Then, through the analysis of N kinds of post-silicon nano-electronic devices, the research progress of constructing brain-inspired chips using post-silicon nano-electronic device is expounded. Lastly, the future of building brain-inspired chips based on post-silicon nano-electronic device has been prospected.

Keywords: brain-inspired chips; neuron; phase change memory; post-silicon nano-electronic device; resistive memory; synapse.

Copyright © 2022 Lv, Chen, Wang, Li, Xie and Song.

Black Hole and General relativity — gravity theory ‚Einstein field ‚sch equation|Ramanujan number

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Taming the Machine, with Nell Watson

Those who rush to leverage AI’s power without adequate preparation face difficult blowback, scandals, and could provoke harsh regulatory measures. However, those who have a balanced, informed view on the risks and benefits of AI, and who, with care and knowledge, avoid either complacent optimism or defeatist pessimism, can harness AI’s potential, and tap into an incredible variety of services of an ever-improving quality.

These are some words from the introduction of the new book, “Taming the machine: ethically harness the power of AI”, whose author, Nell Watson, joins us in this episode.

Nell’s many roles include: Chair of IEEE’s Transparency Experts Focus Group, Executive Consultant on philosophical matters for Apple, and President of the European Responsible Artificial Intelligence Office. She also leads several organizations such as EthicsNet.org, which aims to teach machines prosocial behaviours, and CulturalPeace.org, which crafts Geneva Conventions-style rules for cultural conflict.

Selected follow-ups:

• Nell Watson’s website (https://www.nellwatson.com/)
• Taming the Machine (https://tamingthemachine.com/) — book website.
• BodiData (https://www.bodidata.com/) (corporation)
• Post Office Horizon scandal: Why hundreds were wrongly prosecuted (https://www.bbc.co.uk/news/business-5…) — BBC News.
• Dutch scandal serves as a warning for Europe over risks of using algorithms (https://www.politico.eu/article/dutch…) — Politico.
• Robodebt: Illegal Australian welfare hunt drove people to despair (https://www.bbc.co.uk/news/world-aust…) — BBC News.
• What is the infected blood scandal and will victims get compensation? (https://www.bbc.co.uk/news/health-485…) — BBC News.
• MIRI 2024 Mission and Strategy Update (https://intelligence.org/2024/01/04/m…) — from the Machine Intelligence Research Institute (MIRI)
• British engineering giant Arup revealed as $25 million deepfake scam victim (https://edition.cnn.com/2024/05/16/te…) — CNN
• Zersetzung psychological warfare technique (https://en.wikipedia.org/wiki/Zersetzung) — Wikipedia.

Music: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain Declaration.

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