Tags; #science #neuroscience #happiness #happiness #neurodegenerativediseases #disease #health #mentalhealth #sleep #neuroscientist #disease #education #success.
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About me:
I am Shambhu Yadav, Ph.D., a research scientist at Harvard Medical School (Boston, MA, USA). I also work (for fun) as a Science Journalist, editor, and presenter on a YouTube channel. Science Communication is my passion.

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Disclaimer 1: The video content is for educational and informational purposes only, not a substitute for professional medical advice, diagnosis, or treatment. Always consult your physician or qualified healthcare provider regarding any medical condition. Do not disregard or delay seeking professional medical advice based on information from this video. Any reliance on the information provided is at your own risk.
Disclaimer 2: The Diary Of A Scientist (DOAS) channel does not promote or encourage any unusual activities, and all content provided by this channel is meant for EDUCATIONAL purposes only.

*Credits and thanks**
The video was recorded using iPhone and edited using Adobe Premiere Pro: a timeline-based and non-linear video editing software.
Music source: Epidemic sound.

Imagine smartphones that can diagnose diseases, detect counterfeit drugs or warn of spoiled food. Spectral sensing is a powerful technique that identifies materials by analyzing how they interact with light, revealing details far beyond what the human eye can see.

Traditionally, this technology required bulky, expensive systems confined to laboratories and industrial applications. But what if this capability could be miniaturized to fit inside a smartphone or wearable device?

Researchers at Aalto University in Finland have combined miniaturized hardware and intelligent algorithms to create a powerful tool that is compact, cost-effective, and capable of solving real-world problems in areas such as health care, food safety and autonomous driving. The research is published in the journal Science Advances.

A research team at POSTECH has developed a novel multidimensional sampling theory to overcome the limitations of flat optics. Their study not only identifies the constraints of conventional sampling theories in metasurface design but also presents an innovative anti-aliasing strategy that significantly enhances optical performance. Their findings were published in Nature Communications.

Flat optics is a cutting-edge technology that manipulates light at the nanoscale by patterning ultra-thin surfaces with nanostructures. Unlike traditional optical systems that rely on bulky lenses and mirrors, flat optics enables ultra-compact, high-performance optical devices. This innovation is particularly crucial in miniaturizing smartphone cameras (reducing the “camera bump”) and advancing AR/VR technologies.

Metasurfaces, one of the most promising applications of flat optics, rely on hundreds of millions of nanostructures to precisely sample and control the phase distribution of light. Sampling, in this context, refers to the process of converting analog optical signals into discrete data points—similar to how the human brain processes visual information by rapidly capturing multiple images per second to create continuous motion perception.

Google’s February 2025 update patches 47 Android flaws, including CVE-2024–53104, exploited in the wild, and CVE-2024–45569 in Qualcomm WLAN.

Scientists have successfully achieved a quantum collective behavior of macroscopic mechanical oscillators, unlocking new possibilities in quantum technology.

Quantum technologies are radically transforming our understanding of the universe. One emerging technology are macroscopic mechanical oscillators, devices that are vital in quartz watches, mobile phones, and lasers used in telecommunications. In the quantum realm, macroscopic oscillators could enable ultra-sensitive sensors and components for quantum computing, opening new possibilities for innovation in various industries.

Controlling mechanical oscillators at the quantum level is essential for developing future technologies in quantum computing and ultra-precise sensing. But controlling them collectively is challenging, as it requires near-perfect units, i.e. identical.

In today’s AI news, Mark Zuckerberg announced a huge leap in Meta Platforms’s capital spending this year to between $60 billion to $65 billion, an increase driven by artificial intelligence and a massive new data center.

Zuckerberg plans to increase the company’s capital expenditures by as much as roughly 70% over 2024.

In other advancements, Hugging Face has achieved a remarkable breakthrough in AI, introducing vision-language models that run on devices as small as smartphones while outperforming their predecessors that require massive data centers. The company’s new SmolVLM-256M model, requiring less than one gigabyte of GPU memory, surpasses the performance of its Idefics 80B model from just 17 months ago — a system 300 times larger.

And, Anthropic has launched a new feature for its “Claude” family of AI models, one that enables the models to cite and link back to sources when answering questions about uploaded documents. The new feature, appropriately dubbed “Citations,” is now available for developers through Anthropic’s API.

Meanwhile, can AI agents reliably click on all images showing motorcycles or traffic lights for us? It might be too early to tell, considering that a robot will essentially have to tell a website that it is not a robot. However, it looks like at least one of OpenAI’s Operator users was able to have the AI agent beat CAPTCHAs for him.

Google’s Identity Check locks sensitive Android settings behind biometrics, enhancing security outside trusted locations.

UBTech partners with Apple’s manufacturing partner, Foxconn, to build the iPhone using Walker S1 humanoid robots.

I was recently a co-author on a paper about anticipatory governance and genome editing. The lead author was Jon Rueda, and the others were Seppe Segers, Jeroen Hopster, Belén Liedo, and Samuela Marchiori. It’s available open access here on the Journal of Medical Ethics website. There is a short (900 word) summary available on the JME blog. Here’s a quick teaser for it:

Transformative emerging technologies pose a governance challenge. Back in 1980, a little-known academic at the University of Aston in the UK, called David Collingridge, identified the dilemma that has come to define this challenge: the control dilemma (also known as the ‘Collingridge Dilemma’). The dilemma states that, for any emerging technology, we face a trade-off between our knowledge of its impact and our ability to control it. Early on, we know little about it, but it is relatively easy to control. Later, as we learn more, it becomes harder to control. This is because technologies tend to diffuse throughout society and become embedded in social processes and institutions. Think about our recent history with smartphones. When Steve Jobs announced the iPhone back in 2007, we didn’t know just how pervasive and all-consuming this device would become. Now we do but it is hard to put the genie back in the bottle (as some would like to do).

The field of anticipatory governance tries to address the control dilemma. It aims to carefully manage the rollout of an emerging technology so as to avoid the problem of losing control just as we learn more about the effects of the technology. Anticipatory governance has become popular in the world of responsible innovation and design. In the field of bioethics, approaches to anticipatory governance often try to anticipate future technical realities, ethical concerns, and incorporate differing public opinion about a technology. But there is a ‘gap’ in current approaches to anticipatory governance.

7,500 milliamp-hours, Xiaomi? That would be impressive.