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Welcome to an enlightening exploration of DNA Digital Data Storage — a groundbreaking advancement signaling a new era of technology. As we stand on the cusp of unprecedented tech evolution, DNA storage emerges as a game-changing alternative to traditional storage methods.

Understanding DNA Digital Data Storage is vital for anyone keen to dive deep into the future of data solutions. This video demystifies the intricacies behind this pioneering concept, shedding light on its potential to revolutionize how we perceive data storage and retrieval. With today’s rapid generation of digital data, our existing solutions are often overwhelmed. Enter DNA storage, which offers immense capacities, extending into petabytes and exabytes, all while consuming minimal physical space.

As technology enthusiasts, researchers, and innovators look for sustainable, long-term storage options, DNA-based storage presents a fascinating convergence of biology and digital tech. The microscopic strands of DNA possess the potential to store vast volumes of information, and this video unpacks the science behind this, making it comprehensible for everyone.

Whether you’re a tech enthusiast, an IT professional, or just curious about the advances in the digital realm, this video is tailored to satiate your curiosity. Join us in navigating the transformative world of DNA Digital Data Storage and understanding its pivotal role in ushering us into a new technology era.

#ai.
#artificialintelligence.
#dna.

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Welcome to our comprehensive guide on “Nano Robots.” In this enlightening video, we will take a look at what nano robots are, how they work, and the ways in which they are being used today. We will examine the potential of nano robots and how they could be used in the future. We will also discuss the advantages and disadvantages of using nano robots.

Nano robots are made up of very small robots that are only a few nanometers across and are powered by electricity, magnets, or light. These robots can be used for many different things, like fixing damaged cells, keeping an eye on and controlling the environment, and fighting off diseases and infections. Nano robots can also be used to do hard jobs like surgery, making things, and even going to space.

These robots are very accurate and good at what they do, which makes them perfect for use in medicine and industry. Nano robots can be programmed to do many different things, like keep an eye on and control the environment, find and fix damage, and even fight off diseases and infections. They can also be used for more complicated jobs, like surgery, making things, and going to space.

Nano robots have a lot of benefits, such as being small, accurate, and fast. They are also very flexible because they can be programmed to do many different things and used in many different ways. But there are some problems with using nano robots, such as the cost of making them and the chance that they will break down.

In the end, nano robots are an important part of the future of technology and robotics. They are very small and accurate, and they can be used for many different things. They have many pros and cons, but with the right programming and use, they can change the way people interact with the world around them.

#ai.

Frequency combs are revolutionizing optics, from telecommunications to astrophysics, but their complexity has been a roadblock.

Recent advancements in lithium tantalate technology have changed the game, creating a compact, user-friendly comb generator with incredible efficiency and bandwidth. This breakthrough could reshape fields like robotics and environmental monitoring, offering exciting new possibilities.

Frequency Combs in Modern Optics.

Open source and cheap DeepSeek means generative AI can push the exponential progress even faster. It runs on laptops!

“…digest the significance of DeepSeek’s AI reasoning model R1 published fully open-source last week…”


Yet R1 suggests that the thesis may be wrong.

Within no time, DeepSeek’s free-to-download app has https://www.reuters.com/technology/artificial-intelligence/c…1-27/” rel=“noopener” class=””>rocketed to the top of the App Store charts.

Its success follows the new Trump administration’s unveiling last week of its ambitious $500 billion https://fortune.com/2025/01/22/trump-openai-stargate-project…nk_clicks” rel=“noopener” class=””>Stargate AI program, which was designed to enshrine American dominance in technology for years to come.

A leading neuroscientist claims that a pong-playing clump of about a million neurons is “sentient”. What does that mean? Why did Cortical Labs teach a lab-grown brain to play pong? To study biological self-organization at the root of life, intelligence, and consciousness. And, according to their website, “to see what happens.” What’s next for biocomputing?

CORRECTIONS/Clarifications:
- The cells aren’t directly frozen in liquid nitrogen — they are put in vials and stored in liquid nitrogen (and you can’t buy them legally without credentials) https://www.atcc.org/products/pcs-201-010
- The sentience of some invertebrates, like octopuses, is generally agreed upon. Prominent scientists affirmed non-human consciousness in the Cambridge Declaration on Consciousness: https://philiplow.foundation/consciousness/
- The “Neanderthal neurons” are human cells that are “Neanderthalized” using genetic engineering: https://www.youtube.com/watch?v=5FBxnkzI9HU

DISCLAIMER: The explanations in this video are those proposed by the researchers, or my opinion. We are far from understanding how brains, or even neurons, work. The free energy principle is one of many potential explanations.

Support the channel: https://www.patreon.com/IhmCurious.

Footage from Cortical Labs: https://www.youtube.com/watch?v=neV3aZtTgVM
NASJAQ’s interview with founder Hon Weng Chong: https://www.youtube.com/watch?v=Y1R5k5QWPsY
Cortical Labs website: https://corticallabs.com.

Full paper on DishBrain: https://www.cell.com/neuron/fulltext/S0896-6273(22)00806-6

Once the detection mechanism is refined, the next milestone would be to interface that optical signal with a small experimental crystal. The choice of crystal is not arbitrary. Labs might experiment with rare-earth-ion-doped crystals like praseodymium-doped yttrium silicate, known for their capacity to store quantum information for microseconds to milliseconds, or possibly even seconds, under specialized conditions. At an early stage, the device would not store large swaths of complex data but might capture discrete bursts of neural activity corresponding to short-term memory formation. By demonstrating that these bursts can be reliably “written” into the crystal and subsequently “read” out at a later time, researchers would confirm the fundamental principle behind Hippocampus Sync-Banks: that ephemeral neural codes can be transcribed into a stable external medium.

Of course, storing a fleeting pattern is just one half of the puzzle. To realize the Sync-Bank concept fully, the same pattern must be reintroduced into the brain in a way that the hippocampus recognizes. Here, scientists would leverage neural stimulation techniques. In theory, the crystal would “release” the stored patterns in the form of carefully modulated optical or electrical signals. Specialized interfaces near or within the hippocampus—perhaps using microLED arrays or sophisticated electrode grids—would then convert those signals back into the language of the neurons. If the signals are replayed with the correct timing and intensity, the hippocampus might treat them as though they are its own native memory patterns, thereby reactivating the memory. Experimental validation could involve training an animal to associate a particular stimulus with a reward, capturing the neural trace, and then seeing if artificially stimulating that trace at a later time recalls the memory even in the absence of the original stimulus.

Such experiments would inevitably confront thorny technical issues. Neurons and synapses adapt or “rewire” themselves as learning progresses, and the hippocampus is far from static. Overlapping memory traces often share neurons, meaning that reintroducing one memory trace might partially interfere with or activate another. To address this, scientists would need real-time feedback loops that track how the hippocampus responds to artificial signals. Machine learning algorithms might adjust the reintroduced signal to better fit the updated neural state, ensuring that the stored pattern does not clash with changes in the memory landscape. In other words, a second or third generation of prototypes could incorporate adaptive feedback, not just a one-way feed of recorded data. This type of refinement would be crucial to the user’s experience, because we do not simply recall memories as static snapshots; each time we remember something, our brains incorporate subtle new contexts and associations.

THE ECONOMIC SINGULARITY IN 2 TO 3 YEARS.

“My guess is that by 2026 or 2027, we will have AI systems that are broadly better than almost all humans at almost all things,” Amodei (Anthropic CEO) said at the event.


Enter the new era of AI coworkers.