Amazon launched Nova Sonic speech-to-speech AI for human-like interactions

—Outperforms OpenAI's voice models with ~ 80% less cost
—4.2% word error rate across languages
— 46.7% better accuracy than GPT-4o for noisy environments
—On Amazon Bedrock.

Amazon also dropped an upgraded Nova Reel 1.1 video model

—Delivers improved quality, style consistency
—Extends generations to 2 min via automated and manual, shot-by-shot modes
—Also available on Amazon Bedrock.

Google announced their new TPU 'Ironwood'. Each individual chip has a peak compute of 4,614 TFLOPs. Google is calling it 'a monumental leap in AI capability'.

Breakthrough in Stem Cell Modeling Could Transform Robotics and BCI

Researchers have achieved a remarkable breakthrough by developing a human ascending somatosensory assembloid (hASA) - a four-component model that integrates different types of organoids derived from human pluripotent stem cells to recreate the sensory pathway from periphery to central nervous system.

This model combines:
- Somatosensory organoids (hSeO)
- Dorsal spinal cord organoids (hdSpO)
- Thalamic organoids (hDiO)
Cortical organoids (hCO)

Together, they form a functional circuit that mimics how sensory information - such as pain, touch, and temperature - travels from sensory neurons to the spinal cord, thalamus, and ultimately the cerebral cortex.

Beyond Medical Applications

Robotics Revolution
This model could fundamentally transform how we build robots by:
Enhanced tactile perception: Creating robots that can "feel" their environment with human-like sensitivity
Pain-sensing robots: Developing machines that can detect potential damage to their systems
Biomimetic processing algorithms: Designing artificial neural networks based on the actual processing patterns of human sensory pathways
More intuitive human-robot interactions: Building robots that respond to touch in ways that feel natural to humans
Neural Interface Advancements
The insights from this model could also revolutionize brain-computer interfaces:
Bidirectional sensory feedback: Creating interfaces that not only read brain signals but can also send realistic sensory information back to the brain
High-fidelity sensory encoding: Understanding exactly how different sensations are encoded in neural activity patterns
Enhanced virtual reality experiences: Developing more realistic haptic feedback systems based on human sensory processing
Restoration of sensation: Creating neuroprosthetics that can restore touch and other sensations for individuals with spinal cord injuries
The Advantage of Human Models
What makes this breakthrough particularly valuable is that it's specifically based on human cells. Many previous technologies relied on animal models, which don't always translate well to human applications.

This model offers insights into uniquely human aspects of sensory processing, enabling technologies that are better optimized for human-machine integration.

The researchers have already demonstrated the model's value by studying how mutations in the NaV1.7 sodium channel (which cause either pain insensitivity or extreme pain disorders in humans) affect the synchronization of activity across the assembloid.

New Insights into the Brain's Predictive Power

A functional connectivity gradients as a common neural architecture for predictive processing in the human brain

Researchers explored functional connectivity gradients in the cerebral cortex, cerebellum, and hippocampus using fMRI data from healthy young adults.

They found that these brain structures share a common organization for predictive processing, following two key gradients: a model-error gradient (tracking predictions vs. errors) and a model-precision gradient (regulating attention and precision).

This "triangulation" of connectivity across regions suggests a unified neural architecture for how the brain anticipates and adapts to the world.

This discovery deepens our understanding of how the brain integrates sensory data, memory, and expectations. It could:

- Help study disorders like autism, schizophrenia, or depression, where predictive processing may be disrupted.
- Inspire more advanced AI models by mimicking the brain’s predictive mechanisms.
- Enhance neuroscience research on how different brain regions collaborate for complex behaviors.

OKX has partnered with Standard Chartered to launch a world-leading collateral mirroring program.

Teaming up with Brevan Howard and Franklin Templeton, this initiative lets institutional clients use crypto and tokenized money market funds as secure off-exchange collateral.

Backed by a top-tier G-SIB custodian and regulated under Dubai’s VARA framework, it’s a game-changer for safety and capital efficiency in digital asset trading.

Healthcare AI Agents. New report.

What’s Inside?

The 7 core components of intelligent healthcare agents: Cognition, Memory, World Models, Reward, Emotion, Perception, and Action Systems.

A breakdown of 7 healthcare AI agent types – including ReAct, LLM-Enhanced, Memory-Enhanced, Tool-Enhanced, Self-Reflecting, Self-Learning, and Environment-Controlling.

A systems-level look at how these agents are architected – from Perceptual to Motivational layers.

Real-world and future-forward applications across clinical care, population health, operations, research, and more.

Alignment with major system challenges outlined by the Harvard Health Systems Innovation Lab.

OpenAI has rolled out a significant enhancement to ChatGPT's memory capabilities.

The AI can now reference your entire chat history to deliver more personalized responses based on your preferences and interests.

Unlike before, where ChatGPT only used specifically saved memories, it can now automatically analyze patterns across all your past conversations to provide more relevant assistance for writing, advice, learning, and more.

Memory isn't just another product feature. It signals a shift from episodic interactions (think a call center) to evolving ones (more like a colleague or friend).

Users maintain full control - you can opt out of this feature in settings or use temporary chats for private conversations. Currently available to Plus and Pro users (except in certain European regions).

Anthropic published a new quickstart - a minimal implementation of an LLM agent with MCP tools, loops, context management.

Very helpful if you're trying to learn what an agent actually looks like in code.

A new report by BIS - Leveraging tokenisation for payments and financial transactions

The report discusses the potential use cases of #tokenisation as well as ongoing applications in central banks.

Tokenising different forms of money, trading (possibly small fractions of) securities and posting collateral for a loan are just a few possibilities that could emerge.

In the use cases examined, all parties involved in a transaction may benefit from tokenisation.

For end users, transactions could be instantaneous, programmable and less costly. Users may also manage their digital assets directly, with transparency and immutability.

For banks and loan agents, tokenisation allows them to offer innovative financial products that could increase demand for their services. With a modern and user-friendly financial ecosystem, customer convenience and satisfaction may improve. Operational efficiency could also increase if tokenisation streamlines processes and reduces transaction costs through atomic settlement and smart contracts.

Participants may also benefit from improved risk management through secure collateral handling and greater confidence in the system's enforcement capabilities, as well as better regulatory compliance.

Real-world examples of how central banks are exploring tokenised solutions include the work of the Central Bank of Brazil (BCB) and the Central Bank of Colombia (BanRep).

○ From the private sector, tokenisation initiatives cover the Regulated Liability Network (RLN), the Orion asset tokenisation platform, the Goldman Sachs Digital Asset Platform (GS DAP) and those run by Brazilian companies.

In addition to use cases and progress to date, the report also explores future challenges of
tokenisation.

Several key questions arise for further exploration. For instance, how can tokenised systems be interoperable with each other and with other financial and payment systems?

What will comprise the set of safe settlement assets in a tokenised financial system?

These remain an important area for further work by central banks to help advance understanding of tokenisation in payment and financial systems and to prepare for the challenges and opportunities that may arise.

Google finally made the best model - not just in benchmarks but real use.

Clone Robotics gave a new glimpse of Protoclone, its synthetic android with over 200 DOF and 1,000+ myofibers

It's being built with principles of biomimetics, replicating all human soft tissues, with water serving as hydraulic fluid rather than motors.

In this paper, researchers are deliberately instructing agents to be deceptive in an experimental setup.

The models they tested successfully hid harmful features from oversight systems using steganography.

While this is a capability (rather than a propensity),it is important to empirically test how oversight systems could be fooled by malicious actors.

Vitalik Buterin dropped a new L1 centered privacy roadmap for Ethereum

- Privacy of on-chain payments
- Partial anonymization of on-chain activity within applications
- Privacy of reads to the chain (e.g., RPC calls)
- Network-level anonymization.

Roadmap

1. Integrate privacy tools like Railgun and Privacy Pools directly into existing #wallets, enabling shielded balances and default private sends without requiring separate privacy-focused wallets.

2. Adopt a 'one address per application' model to prevent linking user activities across different applications, necessitating #privacy-preserving send-to-self #transactions.

3. Implement FOCIL and EIP-7701 to enhance account abstraction, allowing privacy #protocols to operate without relays and improving censorship resistance.

4. Incorporate TEE-based RPC privacy solutions in wallets as a short-term measure, with a plan to transition to more robust Private Information Retrieval (PIR) methods as they become viable.

5. Encourage wallets to connect to multiple RPC nodes, possibly through mixnets, and use different nodes per #dApp to reduce metadata leakage.

6. Develop proof aggregation protocols to lower gas costs by allowing multiple privacy transactions to share a single on-chain proof.

7. Create privacy-preserving keystore wallets that enable users to update account verification logic across L1 and L2 without publicly linking their notes.

The goal here is to make private transactions the norm, keep activity within each app transparent, but break the links between what users do across different apps, all while shielding them from snooping by adversaries watching the chain or running RPC infrastructure.

Looking ahead and some open questions:

1. One concern is how this roadmap interacts with on-chain #identity.

2. If users rotate addresses for every app (as proposed), what happens to systems like ENS that link your name to a wallet?

3. For example, how do you keep using your ENS name for voting or signing public attestations while shielding your #DeFi activity?

4. Another challenge is PIR performance. Current private query schemes are too slow for many real-time RPC use cases. So while TEEs offer a viable intermediate step, Ethereum will need more efficient PIR primitives to fully decouple wallet queries from surveillance.

Notably, its mention was missing from the roadmap despite the lifting of OFAC sanctions.

It’s cool! Hugging Face buys a humanoid robotics startup Pollen Robotics

Pollen, which was founded in 2016 and is based in the French city of Bordeaux, had raised €2.5 million in venture capital funding to date.

The move will see Hugging Face selling Pollen’s $70,000 humanoid robot Reachy 2, which is designed for academic research, education, and testing “embodied AI” applications.

Pollen’s robots are designed to run open-source software, including freely-available AI models, as well as to allow users to potentially modify the physical design of the robot.

Hugging Face has increasingly moved into robotics in the past year. “Robotics is going to be the next frontier that AI will unlock,” Thomas Wolf, Hugging Face’s co-founder and chief scientist, told Fortune.

He said new AI “world models” were contributing to rapid progress in robotics and that having AI embodied in devices like robots might also help solve remaining challenges to achieving human-like artificial general intelligence.

OpenAI is set to release new models o3 and o4-mini as soon as this week that can suggest new types of scientific experiments, like for nuclear fusion or pathogen detection, by combining knowledge from multiple fields at once, according to three people who have tested the models.

They're starting to generate new scientific ideas, connecting concepts across fields like physics, biology, and engineering — the way a Tesla or Feynman might’ve done.

New Mistral Cookbook: a Multi-Agent Earnings Call Analysis System that turns lengthy and complex financial discussions into clear, actionable insights in minutes.

Google DeepMind has started hiring for post AGI research

OpenAI published 3 new guides:

AI in the Enterprise

A practical guide to building AI agents

Identifying and scaling AI use cases.

Google presents how new data permeates LLM knowledge and how to dilute it

Veo 2, Google’s SOTA video model, is rolling out to Gemini Advanced + Whisk

You can create 8s, high-res videos from text prompts fluid character movement + lifelike scenes across a range of styles.

Tip: the more detailed your denoscription, the better.

Plus, you can try Veo 2 using Whisk from Google labs.

Just input images, blend them together, and – now –  “animate” to bring your creation to life. Available for all Google One AI Premium subscribers today.

Convergent Research released map of the things that need solving in science and R&D

gap-map.org is a tool to help you explore the landscape of R&D gaps holding back science - and the bridge-scale fundamental development efforts that might allow humanity to solve them, across almost two dozen fields