Toggle light / dark theme

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.

Capcom is experimenting with generative AI to create the “hundreds of thousands” of ideas needed for in-game environments.

As video game development costs rise, publishers are increasingly looking to controversial AI tools to speed up work and cut costs. Call of Duty reportedly sold an “AI-generated cosmetic” for Call of Duty: Modern Warfare 3 in late 2023, and fans accused Activision of using generative AI again for a loading screen last year. EA said in September that AI was “the very core” of its business.

In a new interview with Google Cloud Japan, Kazuki Abe, a technical director at Capcom who has worked on huge titles like Monster Hunter: World and Exoprimal, explained how the company is experimenting with implementing AI in its game development processes.

Researchers at the Technical University of Darmstadt and the Helmholtz Center Dresden-Rossendorf have developed flexible robot wings that are moved by magnetic fields. Inspired by the efficiency and adaptability of the wings of the monarch butterfly, they enable precise movements without electronics or batteries.

This bio-inspired development could fundamentally change , rescue operations and biomedical applications.

Monarch butterflies are known for their outstanding endurance and adaptability. Every year, they cover thousands of kilometers on their migrations between Canada and Mexico. The key to this feat lies in their unique wings, which allow the insects to fly energy-efficiently through a combination of active movement and passive bending.

“If you lease it like you lease a car, a $30,000 car, your price point per month is 300 bucks,” says author, futurist, investor, doctor, and engineer Peter Diamandis in a recent TechFirst podcast. “And that translates amazingly to $10 a day and 40 cents an hour. So you’ve got labor that’s waiting for whatever your wish is. You know, clean up the house, go mow the lawn, you know, please change the baby’s diapers.”

In today’s AI news, a majority of senior executives across multiple industries expect AI to fundamentally reshape their businesses in the next 12 to 24 months, according to KPMG’s latest AI Quarterly Pulse Survey. According to the survey, 68% of executives plan to invest between $50M and $250M into GenAI over the next 12 months, marking a substantial increase from 45% in Q1 of 2024.

S chief AI scientist, Yann LeCun, the biggest takeaway from DeepSeek In other advancements, hot healthcare startup Rad AI has raised a Series C funding round. The company, which creates AI-powered tools for radiologists, grabbed $60 million dollars of fresh funding in a Series C round led by Transformation Capital, according to two sources, the new fundraise valued Rad AI at $525 million.

Meanwhile, Alphabet’s Google, already facing an unprecedented regulatory onslaught, is looking to shape public perception and policies on artificial intelligence ahead of a global wave of AI regulation. A key priority comes in building out educational programs to train the workforce on AI. “Getting more people and organizations, including governments, familiar with AI and using AI tools, makes for better AI policy and opens up new opportunities.”

T be fixated on the best big model … + Then, join renowned investor Ray Dalio of Bridgewater Associates, for an engaging fireside chat with Merantix Capital Co-Founder, Rasmus Rothe exploring the enormous potential of artificial intelligence in decision-making, innovation, and global investing.

And, artificial general intelligence could possess the versatility to reason, learn and innovate in any task. But with rising concerns about job losses, surveillance and deepfakes, will AGI be a force for progress or a threat to the very fabric of humanity?

We close out with, a thought-provoking panel discussion, moderated by Becky Anderson, Anchor & Managing Editor of CNN Abu Dhabi, featuring Ian Bremmer, President and Founder of Eurasia Group and GZERO Media; Nadia Calviño, President of the European Investment Bank; Ngozi Okonjo-Iweala, Director General of the WTO; Brad Smith, Vice Chair and President of Microsoft; and Peng Xiao, CEO of G42.

Thats all for today, but AI is moving fast, Like, comment, and subscribe for more AI news! Please vote for me in the Entrepreneur of Impact Competition today! Thank you for supporting my partners and I, it’s how I keep Neural News Network free.

We report the use of a multiagent generative artificial intelligence framework, the X-LoRA-Gemma large language model (LLM), to analyze, design and test molecular design. The X-LoRA-Gemma model, inspired by biological principles and featuring ~7 billion parameters, dynamically reconfigures its structure through a dual-pass inference strategy to enhance its problem-solving abilities across diverse scientific domains. The model is used to first identify molecular engineering targets through a systematic human-AI and AI-AI self-driving multi-agent approach to elucidate key targets for molecular optimization to improve interactions between molecules. Next, a multi-agent generative design process is used that includes rational steps, reasoning and autonomous knowledge extraction. Target properties of the molecule are identified either using a Principal Component Analysis (PCA) of key molecular properties or sampling from the distribution of known molecular properties. The model is then used to generate a large set of candidate molecules, which are analyzed via their molecular structure, charge distribution, and other features. We validate that as predicted, increased dipole moment and polarizability is indeed achieved in the designed molecules. We anticipate an increasing integration of these techniques into the molecular engineering workflow, ultimately enabling the development of innovative solutions to address a wide range of societal challenges. We conclude with a critical discussion of challenges and opportunities of the use of multi-agent generative AI for molecular engineering, analysis and design.