#bayesian networks

[thealchemyofgamecreation]

HWM: Game Design Log (25) Finding the Right “AI” System

Hey! I’m Omer from Oba Games, and I’m in charge of the game design for Hakan’s War Manager. I share a weekly game design log, and this time, I’ll be talking about AI and AI systems. Since I’m learning as I go, I thought, why not learn together? So, let’s get into it! Do We Need AI? Starting off with a simple question: Do we need AI? Now, I didn’t ask the most fundamental concept in game history…

[technicallyseverepuppy]

[raffaellopalandri]

How Cognitive Frameworks and Modes of Attention Shape Reality

Human perception is not a passive intake of environmental data but an active, anticipatory, and deeply interpretative neurological process. Photo by KATRIN BOLOVTSOVA on Pexels.com From a neuroscientific standpoint, perception arises from the brain’s continuous attempt to predict sensory input based on past experience, current context, and internalized models—what we might call cognitive…

[moastudiess]

Daily Productivity Challenge: 3/10

04.05.2025~ Went through some GLSL videos to understand the syntax for my computer graphics project and finished up inference performance on my Bayesian network

[darkmaga-returns]

There will be much more to this story as it unfolds but the Informed Consent Action Network (ICAN) has finally obtained the VAERS safety information produced by the FDA themselves.

During the pandemic, FDA conducted analyses of COVID-19 vaccine adverse events and tried to hide the results from the public. After two years of FOIA requests and lawsuits, FDA finally produced a portion of its “Empirical Bayesian (EB) data mining” reports. This type of analysis was designed to detect COVID-19 vaccine safety signals using VAERS reports. The data should be very revealing as far as what issues FDA was seeing during the vaccine rollout—especially given the agency has kept this data secret for years. This is the first time this critical data has been released to the public. An initial review of the records produced has revealed a long list of adverse events that far surpassed FDA’s “standard alert threshold”—meaning, there is (or should have been) great concern on the part of federal health authorities who were privy to this data. As just one example, ICAN discovered that “heavy menstrual bleeding” and “menstruation irregular” began showing up on the reports as early as April 2021!

I have only done a cursory review of the reported signals but what immediately caught my attention is that by 4th March, 2022, by their own metrics, all three US “vaccines” appear to be signalling serious adverse events, including

death (Janssen)

exposure via breast milk (Moderna, Pfizer)

suspected COVID-19 (Janssen); and

ineffectiveness (Pfizer).

If a product is ineffective, surely everything else is moot?! If that “ineffective” product is fatal in some cases, well…

No wonder they tried so shared to stop it being “shared more broadly”?

[compneuropapers]

Interesting Papers for Week 20, 2025

How Do Computational Models in the Cognitive and Brain Sciences Explain? Brun, C., Konsman, J. P., & Polger, T. (2025). European Journal of Neuroscience, 61(2).

Sleep microstructure organizes memory replay. Chang, H., Tang, W., Wulf, A. M., Nyasulu, T., Wolf, M. E., Fernandez-Ruiz, A., & Oliva, A. (2025). Nature, 637(8048), 1161–1169.

Dendrites endow artificial neural networks with accurate, robust and parameter-efficient learning. Chavlis, S., & Poirazi, P. (2025). Nature Communications, 16, 943.

Modelling sensory attenuation as Bayesian causal inference across two datasets. Eckert, A.-L., Fuehrer, E., Schmitter, C., Straube, B., Fiehler, K., & Endres, D. (2025). PLOS ONE, 20(1), e0317924.

Synaptic basis of feature selectivity in hippocampal neurons. Gonzalez, K. C., Negrean, A., Liao, Z., Terada, S., Zhang, G., Lee, S., Ócsai, K., Rózsa, B. J., Lin, M. Z., Polleux, F., & Losonczy, A. (2025). Nature, 637(8048), 1152–1160.

Fast updating feedback from piriform cortex to the olfactory bulb relays multimodal identity and reward contingency signals during rule-reversal. Hernandez, D. E., Ciuparu, A., Garcia da Silva, P., Velasquez, C. M., Rebouillat, B., Gross, M. D., Davis, M. B., Chae, H., Muresan, R. C., & Albeanu, D. F. (2025). Nature Communications, 16, 937.

Theory of morphodynamic information processing: Linking sensing to behaviour. Juusola, M., Takalo, J., Kemppainen, J., Haghighi, K. R., Scales, B., McManus, J., Bridges, A., MaBouDi, H., & Chittka, L. (2025). Vision Research, 227, 108537.

Network structure influences the strength of learned neural representations. Kahn, A. E., Szymula, K., Loman, S., Haggerty, E. B., Nyema, N., Aguirre, G. K., & Bassett, D. S. (2025). Nature Communications, 16, 994.

Delayed Accumulation of Inhibitory Input Explains Gamma Frequency Variation with Changing Contrast in an Inhibition Stabilized Network. Krishnakumaran, R., Pavuluri, A., & Ray, S. (2025). Journal of Neuroscience, 45(5), e1279242024.

Predicting the Irrelevant: Neural Effects of Distractor Predictability Depend on Load. Lui, T. K., Obleser, J., & Wöstmann, M. (2025). European Journal of Neuroscience, 61(2).

The time course and organization of hippocampal replay. Mallory, C. S., Widloski, J., & Foster, D. J. (2025). Science, 387(6733), 541–548.

Anisotropy of the Orientation Selectivity in the Visual Cortex Area 18 of Cats Reared Under Normal and Altered Visual Experience. Merkulyeva, N., Lyakhovetskii, V., & Mikhalkin, А. (2025). European Journal of Neuroscience, 61(2).

The calcitron: A simple neuron model that implements many learning rules via the calcium control hypothesis. Moldwin, T., Azran, L. S., & Segev, I. (2025). PLOS Computational Biology, 21(1), e1012754.

High-Density Recording Reveals Sparse Clusters (But Not Columns) for Shape and Texture Encoding in Macaque V4. Namima, T., Kempkes, E., Zamarashkina, P., Owen, N., & Pasupathy, A. (2025). Journal of Neuroscience, 45(5), e1893232024.

Ventral hippocampus to nucleus accumbens shell circuit regulates approach decisions during motivational conflict. Patterson, D., Khan, N., Collins, E. A., Stewart, N. R., Sassaninejad, K., Yeates, D., Lee, A. C. H., & Ito, R. (2025). PLOS Biology, 23(1), e3002722.

Hippocampal coding of identity, sex, hierarchy, and affiliation in a social group of wild fruit bats. Ray, S., Yona, I., Elami, N., Palgi, S., Latimer, K. W., Jacobsen, B., Witter, M. P., Las, L., & Ulanovsky, N. (2025). Science, 387(6733).

Diverse neuronal activity patterns contribute to the control of distraction in the prefrontal and parietal cortex. Sapountzis, P., Antoniadou, A., & Gregoriou, G. G. (2025). PLOS Biology, 23(1), e3003008.

The role of oscillations in grid cells’ toroidal topology. Sarra, G. di, Jha, S., & Roudi, Y. (2025). PLOS Computational Biology, 21(1), e1012776.

Out of Sight, Out of Mind? Neuronal Gamma Oscillations During Occlusion Events in Infants. Slinning, R., Agyei, S. B., Kristoffersen, S. H., van der Weel, F. R. (Ruud), & van der Meer, A. L. H. (2025). Developmental Psychobiology, 67(1).

The Brain’s Sensitivity to Sensory Error Can Be Modulated by Altering Perceived Variability. Tang, D.-L., Parrell, B., Beach, S. D., & Niziolek, C. A. (2025). Journal of Neuroscience, 45(5), e0024242024.

[frank-olivier]

Bayesian Active Exploration: A New Frontier in Artificial Intelligence

The field of artificial intelligence has seen tremendous growth and advancements in recent years, with various techniques and paradigms emerging to tackle complex problems in the field of machine learning, computer vision, and natural language processing. Two of these concepts that have attracted a lot of attention are active inference and Bayesian mechanics. Although both techniques have been researched separately, their synergy has the potential to revolutionize AI by creating more efficient, accurate, and effective systems.

Traditional machine learning algorithms rely on a passive approach, where the system receives data and updates its parameters without actively influencing the data collection process. However, this approach can have limitations, especially in complex and dynamic environments. Active interference, on the other hand, allows AI systems to take an active role in selecting the most informative data points or actions to collect more relevant information. In this way, active inference allows systems to adapt to changing environments, reducing the need for labeled data and improving the efficiency of learning and decision-making.

One of the first milestones in active inference was the development of the "query by committee" algorithm by Freund et al. in 1997. This algorithm used a committee of models to determine the most meaningful data points to capture, laying the foundation for future active learning techniques. Another important milestone was the introduction of "uncertainty sampling" by Lewis and Gale in 1994, which selected data points with the highest uncertainty or ambiguity to capture more information.

Bayesian mechanics, on the other hand, provides a probabilistic framework for reasoning and decision-making under uncertainty. By modeling complex systems using probability distributions, Bayesian mechanics enables AI systems to quantify uncertainty and ambiguity, thereby making more informed decisions when faced with incomplete or noisy data. Bayesian inference, the process of updating the prior distribution using new data, is a powerful tool for learning and decision-making.

One of the first milestones in Bayesian mechanics was the development of Bayes' theorem by Thomas Bayes in 1763. This theorem provided a mathematical framework for updating the probability of a hypothesis based on new evidence. Another important milestone was the introduction of Bayesian networks by Pearl in 1988, which provided a structured approach to modeling complex systems using probability distributions.

While active inference and Bayesian mechanics each have their strengths, combining them has the potential to create a new generation of AI systems that can actively collect informative data and update their probabilistic models to make more informed decisions. The combination of active inference and Bayesian mechanics has numerous applications in AI, including robotics, computer vision, and natural language processing. In robotics, for example, active inference can be used to actively explore the environment, collect more informative data, and improve navigation and decision-making. In computer vision, active inference can be used to actively select the most informative images or viewpoints, improving object recognition or scene understanding.

Timeline:

1763: Bayes' theorem

1988: Bayesian networks

1994: Uncertainty Sampling

1997: Query by Committee algorithm

2017: Deep Bayesian Active Learning

2019: Bayesian Active Exploration

2020: Active Bayesian Inference for Deep Learning

2020: Bayesian Active Learning for Computer Vision

The synergy of active inference and Bayesian mechanics is expected to play a crucial role in shaping the next generation of AI systems. Some possible future developments in this area include:

- Combining active inference and Bayesian mechanics with other AI techniques, such as reinforcement learning and transfer learning, to create more powerful and flexible AI systems.

- Applying the synergy of active inference and Bayesian mechanics to new areas, such as healthcare, finance, and education, to improve decision-making and outcomes.

- Developing new algorithms and techniques that integrate active inference and Bayesian mechanics, such as Bayesian active learning for deep learning and Bayesian active exploration for robotics.

Dr. Sanjeev Namjosh: The Hidden Math Behind All Living Systems - On Active Inference, the Free Energy Principle, and Bayesian Mechanics (Machine Learning Street Talk, October 2024)

Saturday, October 26, 2024

[spacetimewithstuartgary]

Astronomy’s dirty window to space

Researchers reconstruct detailed map of dust in the Milky Way

When we observe distant celestial objects, there is a possible catch: Is that star I am observing really as reddish as it appears? Or does the star merely look reddish, since its light has had to travel through a cloud of cosmic dust to reach our telescope? For accurate observations, astronomers need to know the amount of dust between them and their distant targets. Not only does dust make objects appear reddish (“reddening”), it also makes them appear fainter than they really are (“extinction”). It’s like we are looking out into space through a dirty window. Now, two astronomers have published a 3D map that documents the properties of dust all around us in unprecedented detail, helping us make sense of what we observe.

Behind this is the fact that, fortunately, when looking at stars, there is a way of reconstructing the effect of dust. Cosmic dust particles do not absorb and scatter light evenly across all wavelengths.  Instead, they absorb light more strongly at shorter wavelengths (towards the blue end of the spectrum), and less strongly at longer wavelengths (towards the red end).  The wavelength-dependence can be plotted as an “extinction curve,” and its shape provides information not only about the composition of the dust, but also about its local environment, such as the amount and properties of radiation in the various regions of interstellar space.

Retrieving dust information from 130 million spectra

This is the kind of information used by Xiangyu Zhang, a PhD student at the Max Planck Institute for Astronomy (MPIA), and Gregory Green, an independent research group leader (Sofia Kovalevskaja Group) at MPIA and Zhang’s PhD advisor, to construct the most detailed 3D map yet of the properties of dust in the Milky Way galaxy. Zhang and Green turned to data from ESA’s Gaia mission, which was a 10.5-year-effort to obtain extremely accurate measurements of positions, motions and additional properties for more than a billion stars in our Milky Way and in our nearest galactic neighbours, the Magellanic Clouds. The third data release (DR3) of the Gaia mission, published in June 2022, provides 220 million spectra, and a quality check told Zhang and Green that about 130 million of those would be suitable for their search for dust.

The Gaia spectra are low-resolution, that is, the way that they separate light into different wavelength regions is comparatively coarse. The two astronomers found a way around that limitation: For 1% of their chosen stars, there is high-resolution spectroscopy from the LAMOST survey operated by the National Astronomical Observatories of China. This provides reliable information about the basic properties of the stars in question, such as their surface temperatures, which determines what astronomers call a star’s “spectral type.”

Reconstructing a 3D map

Zhang and Green trained a neural network to generate model spectra based on a star’s properties and the properties of the intervening dust. They compared the results to 130 million suitable spectra from Gaia, and used statistical (“Bayesian”) techniques to deduce the properties of the dust between us and those 130 million stars.

The results allowed the astronomers to reconstruct the first detailed, three-dimensional map of the extinction curve of dust in the Milky Way. This map was made possible by Zhang and Green’s measurement of the extinction curve towards an unprecedented number of stars – 130 million, compared to previous works, which contained approximately 1 million measurements.

But dust is not just a nuisance for astronomers. It is important for star formation, which occurs in giant gas clouds shielded by their dust from the surrounding radiation. When stars form, they are surrounded by disks of gas and dust, which are the birthplaces of planets. The dust grains themselves are the building blocks for what will eventually become the solid bodies of planets like our Earth. In fact, within the interstellar medium of our galaxy, most of the elements heavier than hydrogen and helium are locked up in interstellar dust grains.

Unexpected properties of cosmic dust

The new results not only produce an accurate 3D map. They have also turned up a surprising property of interstellar dust clouds. Previously, it had been expected that the extinction curve should become flatter (less dependent on wavelength) for regions with a higher dust density. “Higher density,” of course, is in this case still very little: approximately ten billionth billionth grams of dust per cubic meter, equivalent to just 10 kg of dust in a sphere with Earth’s radius. In such regions, dust grains tend to grow in size, which changes the overall absorption properties.

Instead, the astronomers found that in areas of intermediate density, the extinction curve actually becomes steeper, with smaller wavelengths absorbed much more effectively than longer ones. Zhang and Green surmise that the steepening might be caused by the growth not of dust, but of a class of molecules called polycyclic aromatic hydrocarbons (PAHs), the most abundant hydrocarbons in the interstellar medium, which may even have played a role in the origin of life. They have already set out to test their hypothesis with future observations.

Background information

The results reported here have been published as Xiangyu Zhang and Gregory M. Green, “Three-dimensional maps of the interstellar dust extinction curve within the Milky Way galaxy,” in the journal Science. Both authors work at the Max Planck Institute for Astronomy.

IMAGE: Red indicates regions where extinction falls off more rapidly at long wavelengths (the red end of the spectrum), while blue indicates that extinction is less dependent on wavelength. Regions with insufficient data are shown in white. The gray contours enclose regions of high dust density. Credit X. Zhang/G. Green, MPIA

[simulatedannealment]

Bayesian Networks

[codingprolab]

CS 440: INTRO TO ARTIFICIAL INTELLIGENCE Assignment 2

Logic-based and Bayesian Inference PART A. Due April 17. Question 1: [10 points] Consider the following Bayesian network, where variables A through E are all Boolean valued: a) What is the probability that all five of these Boolean variables are simultaneously true? [Hint: You have to compute the joint probability distribution (JPD). The structure of the Bayesian network suggests how the JPD is…

[aicerts09]

Advanced AI Design Course: Mastering the Next Frontier of Artificial Intelligence

Artificial Intelligence (AI) has become the backbone of technological innovation, driving transformations in healthcare, finance, retail, and beyond. For professionals eager to take charge of this revolution, the Advanced AI Design Course offers the perfect opportunity to hone their expertise and shape the future of AI.

What Makes the Advanced AI Design Course Unique?

The Advanced AI Design Course goes beyond the basics, focusing on AI design’s complex methodologies and ethical challenges. Unlike introductory programs, this course is tailored for professionals and advanced learners who want to build innovative AI solutions.

Core Highlights of the Course

Comprehensive Curriculum: Dive deep into advanced AI topics such as deep learning, generative AI, and ethical considerations.

Hands-On Projects: Gain practical experience by working on industry-specific challenges.

Expert Instructors: Learn from leading AI researchers and industry professionals.

Global Certification: Earn credentials that are recognized and respected worldwide.

Career-Aligned Skills: Prepare for high-demand roles in the rapidly evolving AI landscape.

The Advanced AI Design Course offers a unique blend of technical expertise, practical applications, and global recognition. In the next section, we’ll explore who should consider enrolling in this transformative course.

Who Should Enroll in the Advanced AI Design Course?

This course is designed for a diverse audience, including:

AI Professionals: Engineers, data scientists, and developers seeking advanced skills.

Tech Enthusiasts: Individuals passionate about creating cutting-edge AI applications.

Business Executives: Leaders exploring how AI can transform their organizations.

Students and Academics: Scholars aiming to specialize in AI research or applications.

Whether you’re a tech-savvy developer or a business strategist, the Advanced AI Design Course equips you with the tools to innovate and lead.

This course caters to diverse professionals and enthusiasts eager to excel in the ever-evolving AI landscape. Let’s dive into why advanced AI design skills are crucial in today’s world.

Why Advanced AI Design Skills Are Essential

AI is no longer optional, it’s a necessity. Industries across the globe are adopting AI to improve efficiency, make better decisions, and create personalized experiences. The Advanced AI Design Course provides the expertise needed to meet this growing demand.

Key Benefits of Advanced AI Design Skills

Stay Ahead of the Curve: AI is evolving rapidly, and advanced skills ensure you remain competitive.

High-Demand Roles: AI expertise is one of the most sought-after skills in the job market.

Global Relevance: AI transcends borders, offering opportunities worldwide.

Real-World Impact: Create solutions that solve pressing problems in healthcare, finance, and more.

Advanced AI design skills are the key to unlocking unparalleled opportunities in a tech-driven world. Next, we’ll take a closer look at the comprehensive curriculum of the Advanced AI Design Course.

A Detailed Look at the Curriculum

The curriculum of the Advanced AI Design Course is structured to cover a wide range of advanced topics while emphasizing practical applications. Here’s a closer look:

1. Deep Neural Networks (DNNs)

Understand advanced architectures like ResNet, Transformers, and Autoencoders.

Master techniques for training and optimizing deep neural networks.

Learn to implement DNNs for tasks such as image recognition and natural language processing.

2. Advanced Machine Learning Algorithms

Explore ensemble methods like Random Forests and Gradient Boosting.

Dive into Bayesian networks and their applications.

Work on projects involving predictive analytics, clustering, and anomaly detection.

3. Reinforcement Learning (RL)

Learn about Markov Decision Processes and policy optimization.

Apply RL to develop autonomous systems like self-driving cars and AI in gaming

4. Generative AI

Gain expertise in Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).

Build applications for creating realistic images, music, and text.

Understand the role of generative AI in content creation and simulation.

5. Ethical AI Design

Learn to identify and mitigate biases in AI models.

Develop AI systems that adhere to ethical standards and fairness.

Study real-world case studies on the consequences of unethical AI.

6. Capstone Projects

Solve real-world problems in sectors like healthcare, retail, and finance.

Showcase your expertise by presenting solutions to industry professionals.

The curriculum equips learners with cutting-edge knowledge, hands-on experience, and ethical AI design expertise. Moving forward, we’ll explore the career opportunities awaiting graduates of this course.

Career Opportunities After Completing the Course

The Advanced AI Design Course prepares you for some of the most rewarding and high-paying roles in the AI industry.

In-Demand Job Roles

AI Architect: Design and oversee the development of complex AI systems.

Machine Learning Engineer: Build and deploy machine learning models.

Data Scientist: Analyze data and create predictive models for decision-making.

AI Consultant: Advise businesses on implementing AI solutions.

Generative AI Specialist: Focus on creative applications of AI in industries like entertainment and design.

Industry Applications

Graduates of the course have gone on to innovate in fields such as:

Healthcare: AI systems for disease detection, personalized medicine, and hospital operations.

Finance: Fraud detection, credit scoring, and algorithmic trading systems.

Retail: Personalized shopping experiences, inventory management, and demand forecasting.

Entertainment: AI-generated music, films, and gaming experiences.

Completing the Advanced AI Design Course opens doors to lucrative and impactful careers across industries. Now, let’s examine the value of earning a globally recognized certification through this program.

Top Design Certifications to Complement the Advanced AI Design Course

Earning certifications specifically tailored to AI design enhances your credentials and proves your ability to create impactful, ethical, and user-centric AI systems. Below are some of the most comprehensive certifications available for professionals specializing in AI design:

1. AI+ Design Certification™ by AI CERTs

The AI Design Certification™ by AI CERTs is a top-tier program designed to bridge the gap between creative innovation and technical expertise. It offers in-depth training in:

User-Centric AI Systems: Learn to design systems that prioritize user needs and behaviors.

Ethical AI Frameworks: Understand the importance of designing AI systems that promote fairness, transparency, and accountability.

Generative AI Applications: Explore tools like GANs and large language models to create artistic and functional AI solutions.

Project-Based Learning: Work on real-world projects to build a robust portfolio.

This certification is ideal for designers, AI professionals, and entrepreneurs aiming to lead in AI product innovation.

👉 Learn More

2. Human-Centered AI Design by Stanford University

Stanford’s Human-Centered AI Design Certification focuses on creating AI systems that align with human needs, values, and expectations. Key aspects of the program include:

Usability Principles for AI: Design AI applications that are intuitive and user-friendly.

Behavioral Insights: Understand how users interact with AI and incorporate those insights into system design.

Ethics in AI Design: Address bias, fairness, and inclusivity to build trust in AI systems.

This certification is ideal for professionals in UX/UI design, product management, and AI system development.

👉 Learn More

3. Creative AI Certification by Udemy

The Creative AI Certification explores the artistic potential of AI. Designed for creators and designers, it provides hands-on experience with AI-driven creativity. Topics covered include:

Generative Adversarial Networks (GANs): Create AI-generated art, music, and visuals.

AI for Content Creation: Learn how to use tools like DALL·E and Runway for creative projects.

AI Ethics for Creators: Understand copyright, intellectual property, and ethical considerations in generative AI.

Case Studies: Study successful AI-driven creative projects to inspire your work.

This certification is perfect for artists, designers, and content creators looking to integrate AI into their workflows.

👉 Learn More

4. AI UX/UI Design Certification by edX

This program explores the intersection of artificial intelligence and user experience (UX) design. It’s a must-have for professionals aiming to make AI systems more accessible and user-friendly. Key highlights include:

AI-Driven UX Insights: Use AI to understand user behavior and design responsive systems.

Designing for Accessibility: Create AI interfaces that cater to diverse user groups, including those with disabilities.

AI-Powered Prototypes: Build and test prototypes for AI-based products and services.

Practical Applications: Learn to integrate AI UX principles in industries like e-commerce, healthcare, and education.

This certification benefits UX/UI designers, product managers, and developers.

👉 Learn More

5. MIT Media Lab: Designing for AI Certification

The Designing for AI Certification by MIT Media Lab offers a cutting-edge approach to AI design. Participants gain expertise in creating adaptive, engaging AI systems for real-world applications. Key features include:

Interactive AI Applications: Develop AI for robotics, smart environments, and digital assistants.

Generative AI Techniques: Harness AI tools to produce interactive media and creative solutions.

Design Thinking for AI: Use design thinking methodologies to solve complex challenges.

Real-World Use Cases: Study advanced applications of AI in industries like entertainment, healthcare, and education.

This certification is ideal for forward-thinking designers, developers, and researchers aiming to lead AI design innovation.

👉 Learn More

Earning a certification elevates your credibility and positions you as a leader in AI innovation. Up next, we’ll discuss why now is the perfect time to enroll in this advanced AI course.

Why Enroll Now?

The demand for AI expertise is growing exponentially, and the skills you gain today will prepare you for opportunities tomorrow. The Advanced AI Design Course not only enhances your knowledge but also positions you as a leader in this transformative field.

Industry Trends

The AI market is projected to grow to $1.8 trillion by 2030.

Companies are investing heavily in AI, creating a demand for skilled professionals.

Ethical AI design is becoming a priority, making specialized skills essential.

Enrolling in the Advanced AI Design Course today is your ticket to staying ahead in a rapidly advancing field. Let’s wrap up with final thoughts on how this course can shape your AI career journey.

Final Thoughts

The Advanced AI Design Course is your ultimate pathway to mastering artificial intelligence and driving innovation. From mastering cutting-edge technologies to understanding ethical considerations, this course equips you with everything you need to succeed.

The Advanced AI Design Course is your gateway to creating groundbreaking AI solutions and securing a future-proof career.

[moastudiess]

Daily Productivity Challenge 2/10

04.03.2025~ I’m doing work every day , it’s just a little difficult to keep posting so it may not seem as regular! Just did some note taking on Bayesian networks and wrote a file copy batch script for work today :)

[alvinstrat]

AI Cannot Come Up With New Hypotheses...Yet

I am not an AI expert and my understanding of AI goes as far as Bayesian and forest tree models only.

So far, AI cannot come up with new logical hypotheses or imagine things logically to human levels. Maybe in future they can, but it is likely to to be at a low level and only within the confines of the data they have and the the rules and logic they are given.

For example, AI can design pretty nice videos and pictures now but there are often errors and a human creative is still needed to input the prompts and oversee and edit the pictures.

The human brain is amazing. I think digital AI cannot replace humans fully but they are already making our work and lives easier.

However, organoids powered computers and AI models may be able to replace humans one day, who knows? Organoids are basically miniature body parts grown from stem cells. Scientists today do have the ability to take some skin from you, strip them into pluripotent stem cells and then differentiate them into various organs and cells outside of your body. It is pretty common these days already so it is not even the cutting edge anymore.

[compneuropapers]

Interesting Reviews for Week 1, 2025

Happy New Year!!

Astrocyte Regulation of Synapse Formation, Maturation, and Elimination. Chung, W.-S., Baldwin, K. T., & Allen, N. J. (2024). Cold Spring Harbor Perspectives in Biology, 16(8), a041352.

Practical Bayesian Inference in Neuroscience: Or How I Learned to Stop Worrying and Embrace the Distribution. Coventry, B. S., & Bartlett, E. L. (2024). ENeuro, 11(7), ENEURO.0484-23.2024.

Adaptation in the visual system: Networked fatigue or suppressed prediction error signalling? Feuerriegel, D. (2024). Cortex, 177, 302–320.

On the neural networks of self and other bias and their role in emergent social interactions. Forbes, C. E. (2024). Cortex, 177, 113–129.

[govindhtech]

Ultralight Dark Matter Detection with Superconducting Qubits

Detecting Ultralight Dark Matter

Superconducting qubit networks detect lightweight dark matter better. Research improves network topology and measurement approaches to outperform standard detection methods while supporting quantum hardware. Bayesian inference, which resists local noise, extracts dark matter phase shifts.

The enigma of dark matter continues to test modern physics, prompting research into new detection methods. A recent study describes a quantum sensor network that employs quantum entanglement and optimised measurement techniques to detect ultralight dark matter fluxes. In “Optimised quantum sensor networks for ultralight dark matter detection,” Tohoku University researchers Adriel I. Santoso (Department of Mechanical and Aerospace Engineering) and Le Bin Ho (Frontier Research Institute for Interdisciplinary Sciences and Department of Applied Physics) present their findings. They found that interconnected superconducting qubits in diverse network topologies improve detection over standard quantum protocols even in noisy conditions.

Scientists are perfecting methods to detect dark matter, a non-luminous element predicted to make up over 85% of the cosmos, despite its resistance to direct detection. A recent study offers a network-based sensing architecture that uses superconducting qubits to boost ultralight dark matter flux sensitivity to overcome single-sensor disadvantages.

This approach relies on building networks of superconducting qubits with superposition and entanglement and connecting them with controlled-Z gates. These gates change qubit quantum states to enable correlated measurements. Researchers tested linear chains, rings, star configurations, and entirely linked graphs to find the best network structure for signal detection.

The study optimises quantum state preparation and measurement using variational metrology. Reducing the Cramer-Rao constraints, which limit quantum and classical parameter estimation accuracy, is necessary. By carefully altering these parameters, scientists can explore previously unreachable parameter space and identify setups that boost dark matter signal sensitivity.

Dark matter interactions should produce tiny quantum phase shifts in qubits. Bayesian inference, a statistical method for updating beliefs based on evidence, extracts phase shifts from measurement results for reliable signal recovery and analysis. Well-planned network topologies outperform Greenberger-Horne-Zeilinger (GHZ) protocols, a quantum sensing benchmark.

Practicality is a major benefit of this strategy. Because optimised networks maintain modest circuit depths, quantum computations require fewer sequential operations. Current noisy intermediate-scale quantum (NISQ) hardware limits quantum coherence, making this crucial. The work also exhibits robustness to local dephasing noise, a common mistake in quantum systems caused by environmental interactions, ensuring reliable performance under actual conditions.

This study emphasises network structure's role in dark matter detection. Researchers employ entanglement and network topology optimisation to build scalable approaches for enhancing sensitivity and expanding dark matter search. Future study will examine complex network topologies and develop advanced data processing methods to improve sensitivity and precision. Integration with current astrophysical observations and direct detection research could lead to a complex dark matter mystery solution.

Squeezer

A technology developed by UNSW researchers may help locate dark matter. Using “squeezing,” Associate Professor Jarryd Pla's group created an amplifier that can precisely detect weak microwave signals. One signal property is measured ultra-precisely while another is uncertainly reduced. Axions, hypothetical dark matter particles, may be found faster with the device. Future quantum computers and spectroscopy may benefit from the team's knowledge.

Quantum Engineers Create Dark Matter Research Amplifier

Sydney's University of New South Wales (UNSW) quantum engineers developed a new amplifier that may help researchers locate dark matter particles. This device accurately measures very faint microwave waves by "squeezing."

Squeezing decreases signal uncertainty for an ultra-precise measurement. Because Werner Heisenberg's uncertainty principle forbids simultaneous particle position and velocity measurements, this method is useful in quantum mechanics.

To set a world record, Associate Professor Jarryd Pla's team improved microwave signal monitoring, including cell phone signals. Noise, or signal fuzziness, limits signal measurement precision. However, the UNSW squeezer can exceed this quantum limit.

The Noise-Reducing Squeezer

The squeezer amplifies noise in one direction to substantially lower noise in another direction, or "squeeze." More accurate measurements arise from noise reduction. The gadget required substantial engineering and meticulous work to reduce loss causes. High-quality superconducting materials were employed to build the amplifier.

The team believes this new method could help find axions, which are theorised particles that have been hypothesised as the secret component of dark matter.

Searching for Axions: Dark Matter Key

Researchers need accurate measurements to identify dark matter, which makes about 27% of the universe. Nothing emits or absorbs light, making dark matter “invisible.” Astronomers believe it exists because its gravitational pull prevents galaxies from colliding.

Axions are one of many dark matter theories. These undiscovered particles are thought to be extremely light and tiny, allowing them to interact with other matter virtually softly. Axions should emit faint microwave signals under strong magnetic fields, according to one theory.

Axion Detection Squeezer

UNSW's squeezing work speeds up axion detection measurements by six times, improving the likelihood of finding an elusive axion. Axion detectors can measure faster and quieter with squeezers. The findings show those tests may be done faster, says A/Prof. Pla.

Wide Range of Squeezer Uses

The team's novel amplification approach may have uses beyond dark matter search. The squeezer works in stronger magnetic fields and at higher temperatures than prior models. The structure of novel materials and biological systems like proteins can be studied using spectroscopy. You can measure samples more accurately or explore smaller volumes with squeezed noise.

Additionally, compressed noise may be used in quantum computers. One type of quantum computer can be built utilising squeezed vacuum noise. Dr. Anders Kringhøj, part of the UNSW quantum technologies team, says our progress is comparable to what would be needed to build such a system.