🚨AI-Generated Videos : Can We Really Tell If an AI-Generated Video Is Fake? 🎥

As use of major LLMs become more widespread, we have been seeing a crazy influx of AI-generated content on our feeds. It used to be fairly easy to spot - with a character sporting six fingers or something phasing through a solid object; but with recent improvements, these videos are getting harder and harder to tell.

This new paper, published mid-August 2026, is an interesting read, specifically surveying crisis-themed content, titled:

Can We Defend Against AI-Generated Video Attacks on Real-World Crisis Events? A Systematic Evaluation of Detectors, Generators and Social Dissemination.

They systematically evaluate how well detectors perform on crisis-themed content, how generation conditions affect detectability, how humans perceive authenticity, or whether detectors remain reliable after videos are shared and altered during social dissemination.

📦 Core Contribution: RA-Bench Dataset

The authors also introduce RA-Bench, a large-scale benchmark for AI-generated video detection that uses real videos as anchors, matching real-world crisis scenarios:

• Total videos: 17,886

• Real anchors: 1,830 authentic crisis/event videos across 10 social-risk categories

• Generated clips: 16,056 synthetic clips created from:

  • ✅ 4 open-source generators

  • ✅ 5 closed-source generators

• Designed to reflect real-world crisis contexts rather than generic footage

🔍 They then tested different types of AI detectors, including specialised deepfake detectors and multimodal AI models, like CNNSpot, UnivFD and ReStraV .

🚨The results were sobering.

1. Current detection methods were not reliable for crisis scenarios.

2. No single type of detector consistently worked across different AI generators. A detector might perform well on videos from one generator but struggle with another.

3. Impact of social media dissemination: Uploading, compressing, resizing, and reposting a video can destroy the subtle clues detectors rely on, making fake videos even harder to spot.

4. Videos that were most convincing to humans were also harder for AI detectors to identify.

This is especially concerning since crisis events are high-stakes environments where misinformation can escalate rapidly, highlighting the need for detectors robust to evolving video generators.

Beyond AdamW: The Next Generation of Optimizers for Training AI Models 📈

For years, AdamW has been the undisputed workhorse of large language model (LLM) pretraining. 🖥️

AdamW is an element-wise, first-order optimizer that updates every parameter independently: simple, reliable and easy to scale. It powers many frontier models today. However, as researchers push toward larger models, longer training runs, and massive batch sizes, AdamW is beginning to show its weaknesses.

NVIDIA researchers found at global batch sizes approaching 100 million tokens per step, AdamW begins to lose effectiveness. hence a new study from NVIDIA explores a fundamental question:

Can we build optimizers that are smarter than AdamW and allow future AI models to train faster, more efficiently, and more reliably?

The New Contenders: Muon and SOAP 🚀

Unlike AdamW, which treats parameters independently, Muon and SOAP introduce structure-aware optimization, methods that use information about the geometry and relationships within neural network weights.

🌀 Muon — Spectral Orthogonalization: Making Gradient Updates More Balanced

• Matrix-aware updates: Optimizes the geometry of gradient updates rather than scaling parameters independently like AdamW.

• Balanced learning directions: Uses Newton-Schulz iterations to orthogonalize momentum updates and balance singular values.

• Memory efficient: Removes the need for AdamW-style second-moment statistics, reducing optimizer memory.

• Better scaling: Maintains stable training at larger batch sizes and model scales.

🌐 SOAP — Preconditioned Optimization with Adam-Style Adaptivity

• Uses parameter structure: Applies Shampoo-inspired Kronecker preconditioning to capture row and column correlations in weight matrices.

• Better optimization space: Rotates gradients into an eigenbasis where directions become far more effective to update.

• Adam-compatible: Performs adaptive updates like AdamW but in a more informative coordinate system.

• Second-order benefits: Gains curvature awareness without the prohibitive cost of full second-order optimization.

📌 This shift towards new structure-aware optimization approaches would allow for:

1. More efficient AI development

2. Lower computational costs

3. More capable future models

👉 To make this transition possible, NVIDIA has released open-sourced implementations in Megatron-LM and a standalone Emerging-Optimizers library to be available to the public.

AdamW is unlikely to disappear and will remain a staple for small-to-medium training runs. Its simplicity and maturity make it an excellent choice for many applications.

╰┈➤ˎˊ˗ But for frontier-scale training? It still remains to be seen, where it'll go next.

🩸Can a Blood Test Tell How Fast Your Brain Is Aging?

What if a blood sample could reveal which parts of your body are aging the fastest, and even predict your risk of developing diseases years before symptoms appear? 🤔❓

A new study published by Stanford researchers analyzed blood plasma proteins with nearly 3,000 proteins across 44,498 UK Biobank participants for organ aging, and 7,000+ proteins in 60,542 individuals for cell-type-specific aging.

Using Machine Learning, the scientists figured out how much composite protein “signature” represented each organ and let the models assign biological ages to 11 organ systems and 40+ cell types.

😮 Turns out one-third of us have at least one organ that's aging dramatically faster or slower than the rest.

They discovered that aging is far from uniform. Instead, different cells and organs age at different rates, meaning two people of the same chronological age can have very different biological aging profiles, where 1 in 4 participants had multiple extremely aged or youthful organs; 20–25% showed accelerated aging in a single cell type, while 1–3% had >10 rapidly aging cell types.

In the study, they claim that cellular aging signatures were uniquely associated with disease status:

• 🧠Astrocytes (brain support cells): Extreme astrocyte aging demonstrated a 12.59-fold increased risk of incident Alzheimer’s disease (AD).

• 💪🏻Skeletal muscle cells: People with aged muscle cells had a 12.7× higher risk of amyotrophic lateral sclerosis (ALS), detectable three years before symptoms appear.

• 🫁 Respiratory cells: Smokers with aged lung epithelial cells faced 58% higher lung cancer risk than smoking alone would predict.

🍀Interestingly, new findings on Alzheimer’s disease offer hopeful news for carriers of the APOE4 gene that has long been linked to Alzheimer’s.

1. The predictive power of astrocyte aging (HR = 5.16) was comparable to APOE4 carrier status itself (HR = 5.30).

2. APOE4 gene + aged astrocytes three times higher risk compared to an average APOE carrier.

3. In contrast, APOE4 carriers with youthful astrocytes appeared to neutralise the genetic risk completely as none of their 23 APOE4/4 carriers developed Alzheimer's.

4. Youthful astrocytes reduced overall AD risk by over 60%

Side Note: ♀️ Women seemed to be more vulnerable to the harmful associations in both APOE4 gene and aged astrocyes.

🧬The researchers also identified an inverse pattern via biological age quantification from blood for the APOE gene variants:

• APOE4 → astrocytes age faster, macrophages age slower

• APOE2 (the protective variant) → the exact opposite: younger astrocytes, older macrophages

Together, these findings highlight a shift in how we understand aging. Chronological age is merely one measure, and this algorithm may provide a more precise picture of one's health trajectories by decoding cellular aging signatures from blood.

✨ Check out the these links for a deeper read:

1. More on the APOE Gene 🧬

2. Stanford Medicine Newsletter: Biological age of our organs and cell types within them predict our disease risk, longevity.

😴 Sleep Is More Than Just Rest: The Hidden Link Between Sleep Disorders and Dementia 💤

Most of us know that a bad night's sleep makes us feel tired, irritable, and unable to concentrate the next day. But growing scientific evidence suggests that POOR SLEEP may have much more serious long-term consequences, increasing the risk of developing dementia and Alzheimer's disease (AD).

Let's look at some meta-analyses, published seven years apart, provide compelling evidence linking sleep disorders with dementia.

In Shi et al., 2017 (247,000 people): With 18 longitudinal studies - consolidated that sleep disturbances are an independent risk factor, not merely an early symptom of dementia.

• Insomnia was mainly linked to a higher risk of AD.

• Obstructive Sleep Apnea (OSA) was associated with increased risk of all-cause dementia, AD, and vascular dementia (VasD).

❓However, evidence was limited, with most studies relying on self-reported sleep.

In Ungvari et al., 2025 : Analysed 39 studies

• Insomnia raises all-cause dementia risk by 36%, AD by 49%, and VasD by 59%.

• OSA raises risk by 33% in all-cause dementia and 45% in AD.

In Zhang et al., 2025 : Analysed 76 studies

• Excessive Daytime Sleepiness (EDS): +41% in all-cause and +85% in VasD

• Sleep-Related Movement Disorders: +153% VasD (showing restless legs/periodic limb movements are primarily vascular risk factors).

💊 Ungvari et al., 2025 raises the question of whether treating sleep disorders can mitigate dementia risk and states that it remains an important avenue for future research, particularly mentioning:

• CPAP (Continuous Positive Airway Pressure) for Obstructive Sleep Apnea (OSA).

  • Has been found to improve cognitive performance, particularly in executive function and memory

• Cognitive Behavioral Therapy for Insomnia (CBT-I)

🧠 Why might poor sleep affect the brain?

During deep sleep, the brain activates the glymphatic system, a specialised waste-clearance pathway that removes metabolic waste products, including amyloid-beta proteins associated with Alzheimer's disease.

Poor or fragmented sleep may reduce this nightly "brain cleaning" process, allowing harmful proteins to accumulate. Sleep disorders may also contribute through:

• 🫀 Reduced oxygen supply to the brain (particularly in sleep apnea)

• 🩸 Increased inflammation

• ❤️ Higher blood pressure and cardiovascular disease

• 🧠 Greater oxidative stress

• 🔄 Disruption of normal circadian rhythms

All of these factors have independently been linked to cognitive decline and neurodegeneration.

💡 A Note for Caution: Remember that these factors are links / associations and not a direct cause.

They are more of an INDICATOR of things that often go hand-in-hand and have have a bilateral relationship, for example: People with dementia may experience increased daytime sleepiness and cognitive impairment.

While improving sleep cannot guarantee prevention of dementia, taking sleep seriously may be one of the most accessible ways to protect brain health as we age.

• 🧠 Your brain works hard all day. Give it the sleep it needs to repair, recover, and protect itself for the future.

USB C works for an external monitor when connected through a hub but not directly??

I’m having a strange issue with my portable monitor:

1. Connecting the monitor directly to my laptop’s USB-C port → it does not work.

2. Connecting the same monitor using the same USB-C cable through a USB-C hub (from the exact same laptop USB-C port) → it works perfectly.

3. The hub is not connected to external power.

4. This exact monitor worked with a previous laptop with the same C-to-C cable.

When I initially couldn't use this port directly, I chalked it up to having a non-display support USB-C port... But now it workks through a hub?!!?

Does anyone know why this happens? Is the hub changing the USB-C negotiation, enabling DisplayPort Alt Mode differently, or is there some compatibility issue between the laptop and monitor?

Would appreciate any insights from people familiar with USB-C/DisplayPort Alt Mode! 🙏

Vision Mamba: A New Architecture Changing Computer Vision 🚀

For years, computer vision has relied on two dominant architectures: CNNs and Transformers. >>> Enter Mamba, built on State Space Models (SSMs).

> CNNs: Fast, but limited local field → struggle with long-range dependencies

> Transformers: Great global context, but high complexity → slow, high memory use

> Mamba: → delivers linear complexity + global modeling

Read more on this: Vision Mamba: A Comprehensive Survey and Taxonomy

⚙️ Key Technical Innovations

1. Selective SSM (S6) Mechanism

• Parameters B, C, Δ are input-dependent (time-varying, not fixed)

• Dynamically updates hidden state → focuses only on relevant features

• Achieves linear complexity O(L) — speed and memory scale proportionally with input size

• Mamba-2 further optimizes with vectorized computation and GPU-friendly design, matching Transformer hardware efficiency

2. Adapting SSMs to 2D Visual Data

• Vim: Adds bidirectional scanning and positional awareness to overcome unidirectional bias

• VMamba: Introduces Cross-Scan Strategy (SS2D) — traverses images in four directions to turn 2D grids into ordered sequences without breaking spatial structure

• Later variants: Local, atrous, and deformable scanning to balance fine detail and global context

📊 Key Advantages

Unlike older sequence models, Vision Mamba does not treat every part of the image equally. Instead, it uses its selective mechanism to prioritize meaningful features — edges, textures, and objects — while compressing or discarding irrelevant background.

1. Efficiency: 2–5× faster inference, lower FLOPs and memory footprint than comparable Transformers

2. Scalability: Linear complexity works smoothly for sequences of 10,000+ tokens without performance collapse

3. Flexibility: Easily hybridized with CNNs to retain strong local feature extraction, or paired with attention layers for maximum expressiveness

4. Performance: Matches or exceeds state-of-the-art results across classification, segmentation, restoration, and detection

📌 Where It’s Applied

• High/Mid-level: Classification, detection, segmentation, video understanding

• Low-level: Restoration, denoising, super-resolution

• 3D: Point clouds, reconstruction, volumetric medical data

• Vertical domains: Medical imaging, remote sensing, multimodal vision-language

🔭 Current Limitations & Future Directions

• Scanning dependency: Performance is sensitive to scanning order; predefined paths may not always match complex scene structure

• Local detail gap: Pure SSMs sometimes lack fine-grained detail compared to CNNs

• Stability: Larger pure Mamba models can face training instability, though hybrid designs mitigate this

Moving forward, the focus is on adaptive scanning, tighter integration with attention, and better pretraining strategies to scale Vision Mamba into a true foundation backbone.

Vision Mamba: A Comprehensive Survey and Taxonomy

Any Advice for the best renter friendly 'tape' that can hold a fair amount of weight?

Hey guys. Wanted to seek some advice on what tape would be good to hang up some string lights -- Nano tape and Blu-tack have previously worked for me on other things, but this round seems to keep dropping every 1-2 weeks.

🫚 Ginseng for Alzheimer's and Parkinson's ?!! 🧠✨

Chinese Medicine has been passed down for thousands of years, but only recently are we really analysing the ancient wisdom, and there seem to be some truth to them. 🧐

Ginseng, in particular, was long hailed as a miracle medicine that could save people who were close to death’s door. Well, research has found that Ginseng could work for two of the most devastating degenerative diseases: Alzheimer’s and Parkinson’s disease.

Although Alzheimer's and Parkinson's disease manifests very differently, one with physical ailments and the other in mental state.

They actually share several underlying pathological mechanisms 🧬, including oxidative stress, chronic neuroinflammation, mitochondrial dysfunction, and programmed cell death (apoptosis) 💀.

A meta‑analysis published by Pharmacological Research reviewed & confirmed that ginsenosides, the main bioactive compounds in ginseng, can deliver powerful neuroprotective effects and show great promise as a new therapeutic option.

Ginsenosides have been reported to:

• Stop toxic proteins (amyloid‑beta, tau, alpha‑synuclein) from clumping and damaging the brain cells

• Lower chronic brain inflammation, a major driver of cell death

• Boost antioxidant defence and reduce oxidative stress

• Protect neurons, improve signal transmission, and even support the growth of new brain cells

Unlike single‑target drugs that have not been of much use, researches contemplate whether the effectiveness seen in ginsenosides work because they target multiple pathways at once, which may have been necessary to curb these complex diseases.

Specifically:

• Rg1 stands out for boosting cognition and memory

• Rb1 offers neuroprotection by stopping harmful protein tangles

• Rare ginsenosides like Rg3, Rh2, and Compound K are even more exciting: formed through metabolism or special processing, they are absorbed better and show stronger protective effects than the more common forms.

While more human clinical trials are needed, the science is becoming increasingly difficult to ignore. An herb once praised in ancient texts for preserving vitality is now attracting serious attention from neuroscientists worldwide.

Camera Specifications on Smartphones having 200MP. Will we even know the difference?

Most of the screens we own are mostly maxed out at 4K, maybe 8K for a TV. A quick search says 4K uses ~8.3MP per frame.

Or can any camera enthusiasts let me know what would they actually consider when buying a camera/smartphone? I've seen friends purposefully buy certain cameras for certain 'filter' effects - how can we know? or is it - just a feeling thing?

🗺️ ABot‑Earth 0.5: Building the World in 3D with AI

Turn any ordinary satellite photo into a detailed, fly‑through 3D model of the Earth — in less than 10 minutes per square kilometre.

That is exactly what ABot‑Earth 0.5 , developed by Alibaba’s AMAP team, just published this month. The novel generative model formulated directly with the 3D Gaussian Splatting (3DGS) representation. It changes how we map, view, and interact with our planet.

🔍 Zoom from Space down to street level

• ABot‑Earth uses only standard satellite images as input.

• It generates 1 km² of detailed 3D terrain in under 10 minutes, and has consistent geometry and textures that match real‑world physics.

• It already covers 300+ cities across 190+ countries, and can fill in areas where no 3D scans exist at all.

• Using the LOD Quadtree Tile Hierarchy tiles, it has 6 levels of built‑in detail.

✅ Fast, low‑cost, and everywhere

Traditional 3D mapping needs expensive planes, LiDAR scanners, and months of work — and still only covers major cities. ABot‑Earth can generate 3D terrain for any spot on Earth, even remote or poorly mapped regions, at a tiny fraction of the usual cost.

Whether you’re curious to see what a remote area looks like in 3D, planning a project, or just love exploring our world, this is your new window to the Earth.

🤖 More than just pretty pictures

These models are simulation‑ready: PERFECT for:

• Training drones for autonomous delivery

• Planning cities - infrastructure design, and traffic simulation

• Generating instant 3D views for disaster response.

Where would you zoom in first? 🌍

>>> Check it out at: ABot Earth Studio · 即刻生成你的星球