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Artificial intelligence (AI) is revolutionizing industries, streamlining processes, and, hopefully, on its way to improving the quality of life for people around the world — all very exciting news. That said, with the increasing influence of AI systems, it’s crucial to ensure that these technologies are developed and implemented responsibly.

Responsible AI is not just about adhering to regulations and ethical guidelines; it is the key to creating more accurate and effective AI models.

Vrgineers and Advanced Realtime Tracking demonstrate the combination of XTAL 3 headset and SMARTTRACK3/M in a mixed reality pilot trainer. The partnership between these two technological companies started in 2018. At IT2EC 2023 in Rotterdam, the integrated SMARTTRACK3/M into an F-35-like Classroom Trainer manufactured and delivered to USAF and RAF will be for display. This unique combination of the latest ART infrared all-in-one hardware and Vrgineers algorithms for cockpit motion compensation creates an unseen immersion for every mixed reality training. One of the challenges in next-generation pilot training using virtual technology and motion platforms is the alignment of the pilot’s position in the cockpit. By overcoming this issue, the simulator industry is moving forward to eliminate the disadvantages of simulated training.

“We are continuously working on removing the technological challenges of modern simulators, one of which is caused by front-facing camera position distance from users’ eyes. We are developing advanced algorithms for motion compensation to minimize the shift between virtual and physical scene, making experience realistic. The durability and compact size of SMARTTRACK3/M, which was optimized for using in cockpits, allows us as training device integrator to make it a comprehensive part of a simulation,” says Marek Polcak, CEO of Vrgineers.

“This is the application SMARTTRACK3/M was designed for., We have taken the proven hardware from the SMARTTRACK3 and adapted it to the limited space available. As a result, we have the precision and the reliability of a seasoned system in a form factor fitting to simulator cockpits” says Andreas Werner, business development manager for simulations at ART.

Many current computational models that aim to simulate cortical and hippocampal modules of the brain depend on artificial neural networks. However, such classical or even deep neural networks are very slow, sometimes taking thousands of trials to obtain the final response with a considerable amount of error. The need for a large number of trials at learning and the inaccurate output responses are due to the complexity of the input cue and the biological processes being simulated. This article proposes a computational model for an intact and a lesioned cortico-hippocampal system using quantum-inspired neural networks. This cortico-hippocampal computational quantum-inspired (CHCQI) model simulates cortical and hippocampal modules by using adaptively updated neural networks entangled with quantum circuits. The proposed model is used to simulate various classical conditioning tasks related to biological processes. The output of the simulated tasks yielded the desired responses quickly and efficiently compared with other computational models, including the recently published Green model.

Several researchers have proposed models that combine artificial neural networks (ANNs) or quantum neural networks (QNNs) with various other ingredients. For example, Haykin (1999) and Bishop (1995) developed multilevel activation function QNNs using the quantum linear superposition feature (Bonnell and Papini, 1997).

The prime factorization algorithm of Shor was used to illustrate the basic workings of QNNs (Shor, 1994). Shor’s algorithm uses quantum computations by quantum gates to provide the potential power for quantum computers (Bocharov et al., 2017; Dridi and Alghassi, 2017; Demirci et al., 2018; Jiang et al., 2018). Meanwhile, the work of Kak (1995) focused on the relationship between quantum mechanics principles and ANNs. Kak introduced the first quantum network based on the principles of neural networks, combining quantum computation with convolutional neural networks to produce quantum neural computation (Kak, 1995; Zhou, 2010). Since then, a myriad of QNN models have been proposed, such as those of Zhou (2010) and Schuld et al. (2014).

In this, we present QuASeR, a reference-free DNA sequence reconstruction implementation via de novo assembly on both gate-based and quantum annealing platforms. This is the first time this important application in bioinformatics is modeled using quantum computation. Each one of the four steps of the implementation (TSP, QUBO, Hamiltonians and QAOA) is explained with a proof-of-concept example to target both the genomics research community and quantum application developers in a self-contained manner. The implementation and results on executing the algorithm from a set of DNA reads to a reconstructed sequence, on a gate-based quantum simulator, the D-Wave quantum annealing simulator and hardware are detailed. We also highlight the limitations of current classical simulation and available quantum hardware systems. The implementation is open-source and can be found on https://github.com/QE-Lab/QuASeR.

Citation: Sarkar A, Al-Ars Z, Bertels K (2021) QuASeR: Quantum Accelerated de novo DNA sequence reconstruction. PLoS ONE 16: e0249850. https://doi.org/10.1371/journal.pone.

Editor: Archana Kamal, University of Massachusetts Lowell, UNITED STATES.

I for one welcome our godlike AI overlords we should be so lucky to have them lol 😆 😄They can help with humanities homework 😄

With algorithms starting to govern more and more aspects of our lives, it was only a matter of time until someone started to deify them.

Continue reading “The Radical Movement to Worship AI as a New God” »

This could lead to chat gpt infinite ♾️ ✨️

Machine learning is a fascinating and exciting field within computer science. Recently, this excitement has been transferred to the quantum information realm. Currently, all proposals for the quantum version of machine learning utilize the finite-dimensional substrate of discrete variables. Here we generalize quantum machine learning to the more complex, but still remarkably practical, infinite-dimensional systems. We present the critical subroutines of quantum machine learning algorithms for an all-photonic continuous-variable quantum computer that can lead to exponential speedups in situations where classical algorithms scale polynomially. Finally, we also map out an experimental implementation which can be used as a blueprint for future photonic demonstrations.

“AI is a challenge for global governance,” says a regulations expert.

The Cyberspace Administration of China (CAC), China’s internet regulator, proposed rules to govern artificial intelligence (AI) tools like OpenAI’s ChatGPT on Tuesday.

“China supports the independent innovation, popularization and application and international cooperation of basic technologies such as AI algorithms and frameworks,” CAC said in the draft regulation published on its website.

Continue reading “China plans new AI regulations after Alibaba, Baidu, Huawei launch tech” »

Noisy intermediate-scale quantum algorithms, which run on noisy quantum computers, should be carefully designed to boost the output state fidelity. While several compilation approaches have been proposed to minimize circuit errors, they often omit the detailed circuit structure information that does not affect the circuit depth or the gate count. In the presence of spatial […]…

In the math of particle physics, every calculation should result in infinity. Physicists get around this by just ignoring certain parts of the equations — an approach that provides approximate answers. But by using the techniques known as “resurgence,” researchers hope to end the infinities and end up with perfectly precise predictions.