Amazon has launched Nova, a highly competitive family of foundation models.
Nova Pro, Lite and Flash set new standards for the intelligence that can be accessed at the price and speed these models are offered at.
* 75% more cost effective
* Support fine-tuning and distillation.
Nova Pro, Lite and Flash set new standards for the intelligence that can be accessed at the price and speed these models are offered at.
* 75% more cost effective
* Support fine-tuning and distillation.
Amazon
Amazon Nova – Базовая генеративная модель – Amazon Nova – AWS
Amazon Nova – это поколение ультрасовременных (SOTA) базовых моделей, предлагающих передовой анализ и лучшее в отрасли соотношение цены и производительности.
Starlink + robotics + weather forecasting = Clippership autonomous wind-powered cargo vessels.
Zero emission maritime shipping, faster and cheaper than what's possible now.
Zero emission maritime shipping, faster and cheaper than what's possible now.
MindSearch is an open-source AI Search Engine Framework with Perplexity Pro performance.
You can deploy your own Perplexity.ai-style search engine using either closed-source LLMs (GPT, Claude) or open-source LLMs (InternLM2.5-7b-chat).
You can deploy your own Perplexity.ai-style search engine using either closed-source LLMs (GPT, Claude) or open-source LLMs (InternLM2.5-7b-chat).
huggingface.co
MindSearch - a Hugging Face Space by internlm
Discover amazing ML apps made by the community
Bitcoin breaks $100,000 and extends record rally to $104,000
Key drivers behind Bitcoin's price growth:
1. Strong Institutional Investment
- Over $31 billion in net inflows through U.S. spot Bitcoin ETFs
- Increased corporate adoption, particularly led by MicroStrategy's Michael Saylor
2. Technical Factors
- Bitcoin's fourth halving in April, which tightened the supply
- Market capitalization reaching $2 trillion for the first time
3. Political Developments
- Donald Trump's victory in the U.S. presidential election
- Appointment of crypto-friendly officials to key positions:
- Paul Atkins nominated as SEC chair to replace Gary Gensler
- Scott Bessent selected for Treasury
- Howard Lutnik chosen for Commerce Department
- Potential formation of what could be the most pro-crypto cabinet to date
4. Market Speculation
- Growing speculation about potential Bitcoin national reserves
- Positive sentiment from regulatory changes under the new administration
- Breaking through psychological barriers ($90,000 in November, then $100,000)
For context, while this represents a 126% increase since January (from $44,000).
Key drivers behind Bitcoin's price growth:
1. Strong Institutional Investment
- Over $31 billion in net inflows through U.S. spot Bitcoin ETFs
- Increased corporate adoption, particularly led by MicroStrategy's Michael Saylor
2. Technical Factors
- Bitcoin's fourth halving in April, which tightened the supply
- Market capitalization reaching $2 trillion for the first time
3. Political Developments
- Donald Trump's victory in the U.S. presidential election
- Appointment of crypto-friendly officials to key positions:
- Paul Atkins nominated as SEC chair to replace Gary Gensler
- Scott Bessent selected for Treasury
- Howard Lutnik chosen for Commerce Department
- Potential formation of what could be the most pro-crypto cabinet to date
4. Market Speculation
- Growing speculation about potential Bitcoin national reserves
- Positive sentiment from regulatory changes under the new administration
- Breaking through psychological barriers ($90,000 in November, then $100,000)
For context, while this represents a 126% increase since January (from $44,000).
Microsoft: genAI agents are powerful but complex—how do we design them for transparency and human control?
At the heart of this challenge is establishing common ground, a concept from human communication.
A new paper identifies 12 key challenges in improving common ground between humans and AI agents.
At the heart of this challenge is establishing common ground, a concept from human communication.
A new paper identifies 12 key challenges in improving common ground between humans and AI agents.
Microsoft Research
Challenges in Human-Agent Communication - Microsoft Research
Explore key challenges in human-agent communication with generative AI and autonomous agents. Learn about transparency, control, and challenges for improving human-AI interaction.
Google DeepMind introduced Genie 2, capable foundation world model that, given a single prompt image, can generate an endless variety of action-controllable, playable 3D worlds.
Genie 2 could unlock the next wave of capabilities for embodied agents.
From first person real world scenes, to third person driving environments, Genie 2 generates worlds in 720p. Given an image, Genie 2 simulates world dynamics, creating a consistent environment playable with keyboard and mouse inputs.
This is DeepMind's SIMA following instructions within Genie 2's generative world. Most robotic training won't happen in base reality.
Genie 2 could unlock the next wave of capabilities for embodied agents.
From first person real world scenes, to third person driving environments, Genie 2 generates worlds in 720p. Given an image, Genie 2 simulates world dynamics, creating a consistent environment playable with keyboard and mouse inputs.
This is DeepMind's SIMA following instructions within Genie 2's generative world. Most robotic training won't happen in base reality.
Google DeepMind
Genie 2: A large-scale foundation world model
Generating unlimited diverse training environments for future general agents
❤5
Researchers have created a virtual laboratory staffed by ‘AI scientists’ — LLMs with defined scientific roles — that collaborate to achieve goals set by people.
The team trained one LLM to be the work’s principal investigator (PI) and a second to act as a ‘scientific critic’.
The ‘PI’ then trained three further LLMs to support the research efforts.
Each worked independently, but the group came together for short ‘team meetings’ overseen by a human.
When tasked with designing antibody fragments that can bind to the virus that causes COVID-19, the AI team proposed 92 structures in a fraction of the time it would have taken an all-human research group.
The team trained one LLM to be the work’s principal investigator (PI) and a second to act as a ‘scientific critic’.
The ‘PI’ then trained three further LLMs to support the research efforts.
Each worked independently, but the group came together for short ‘team meetings’ overseen by a human.
When tasked with designing antibody fragments that can bind to the virus that causes COVID-19, the AI team proposed 92 structures in a fraction of the time it would have taken an all-human research group.
Nature
Virtual lab powered by ‘AI scientists’ super-charges biomedical research
Nature - Could human–AI collaborations be the future of interdisciplinary studies?
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Revolutionary Breakthrough: Real-Time Inflammation Tracking Becomes Reality
In a groundbreaking development, scientists at CZ Biohub Chicago and Northwestern University have created the first-ever device capable of continuously monitoring inflammation in real-time.
This innovation could revolutionize how we understand and treat numerous diseases, as inflammation is linked to approximately 50% of all human deaths.
The team developed an ingenious solution to a long-standing challenge in medical monitoring.
Their "pendulum" sensors, thinner than three human hairs, can detect inflammatory proteins under the skin continuously – something previously thought impossible due to the strong binding properties of proteins.
The beauty of this breakthrough lies in its elegant simplicity. Rather than relying on new materials or AI, the team created a clever mechanical solution: sensors that literally "shake off" measured proteins to make room for new ones, similar to a dog's toy picking up and releasing balls. This enables continuous measurement of inflammation markers in living tissue.
The implications are staggering. Just as continuous glucose monitors transformed diabetes care, these sensors could revolutionize treatment for conditions like:
- Rheumatoid arthritis
- Crohn's disease
- Heart disease
- Cancer
- Diabetes complications
Initial tests in diabetic rats have shown remarkable accuracy, with the device even detecting minor inflammation from insulin injections. The biocompatible device can safely operate for at least two weeks, bringing us one step closer to a future where inflammation-related diseases could be monitored and prevented before symptoms appear.
In a groundbreaking development, scientists at CZ Biohub Chicago and Northwestern University have created the first-ever device capable of continuously monitoring inflammation in real-time.
This innovation could revolutionize how we understand and treat numerous diseases, as inflammation is linked to approximately 50% of all human deaths.
The team developed an ingenious solution to a long-standing challenge in medical monitoring.
Their "pendulum" sensors, thinner than three human hairs, can detect inflammatory proteins under the skin continuously – something previously thought impossible due to the strong binding properties of proteins.
The beauty of this breakthrough lies in its elegant simplicity. Rather than relying on new materials or AI, the team created a clever mechanical solution: sensors that literally "shake off" measured proteins to make room for new ones, similar to a dog's toy picking up and releasing balls. This enables continuous measurement of inflammation markers in living tissue.
The implications are staggering. Just as continuous glucose monitors transformed diabetes care, these sensors could revolutionize treatment for conditions like:
- Rheumatoid arthritis
- Crohn's disease
- Heart disease
- Cancer
- Diabetes complications
Initial tests in diabetic rats have shown remarkable accuracy, with the device even detecting minor inflammation from insulin injections. The biocompatible device can safely operate for at least two weeks, bringing us one step closer to a future where inflammation-related diseases could be monitored and prevented before symptoms appear.
CZ Biohub
Protein sensors track inflammation
Implantable device provides an impactful new way to monitor health and disease
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Google released PaliGemma 2, a vision language model family that comes in various sizes: 3B, 10B, 28B, based on Gemma 2 and SigLIP, comes with transformers support day-0 .
Model collection.
Model collection.
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NVIDIA introduced NVILA, a family of open VLMs designed to optimize both efficiency and accuracy.
Model arch focuses on scaling up spatial and temporal resolutions, and then compressing visual tokens, allowing for efficient processing of high resolutions.
Also uses "DeltaLoss" data pruning and FP8 training.
Competitive with proprietary VLMs on visual understanding benchmarks.
Model arch focuses on scaling up spatial and temporal resolutions, and then compressing visual tokens, allowing for efficient processing of high resolutions.
Also uses "DeltaLoss" data pruning and FP8 training.
Competitive with proprietary VLMs on visual understanding benchmarks.
arXiv.org
NVILA: Efficient Frontier Visual Language Models
Visual language models (VLMs) have made significant advances in accuracy in recent years. However, their efficiency has received much less attention. This paper introduces NVILA, a family of open...
⚡️OpenAI aims to attract more investment by removing 'AGI' clause with Microsoft
OpenAI is in discussions to remove a clause that shuts Microsoft out of the start-up's most advanced models when it achieves AGI, as it seeks to unlock future investments.
As per the current terms, when OpenAI creates AGI - defined as a "highly autonomous system that outperforms humans at most economically valuable work" - Microsoft's access to such a technology would be void.
OpenAI is exploring removing the condition from its corporate structure, enabling Microsoft to continue investing in and accessing all OpenAI technology after AGI is achieved.
The clause was included to protect the technology from being misused for commercial purposes, giving its ownership to OpenAI's non-profit board.
OpenAI's board is discussing the options and a final decision has not been made.
Microsoft-backed OpenAI was working on a plan to restructure its core business into a for-profit benefit corporation no longer governed by its non-profit board
OpenAI is in discussions to remove a clause that shuts Microsoft out of the start-up's most advanced models when it achieves AGI, as it seeks to unlock future investments.
As per the current terms, when OpenAI creates AGI - defined as a "highly autonomous system that outperforms humans at most economically valuable work" - Microsoft's access to such a technology would be void.
OpenAI is exploring removing the condition from its corporate structure, enabling Microsoft to continue investing in and accessing all OpenAI technology after AGI is achieved.
The clause was included to protect the technology from being misused for commercial purposes, giving its ownership to OpenAI's non-profit board.
OpenAI's board is discussing the options and a final decision has not been made.
Microsoft-backed OpenAI was working on a plan to restructure its core business into a for-profit benefit corporation no longer governed by its non-profit board
Reuters
OpenAI aims to attract more investment by removing 'AGI' clause with Microsoft, FT reports
OpenAI is in discussions to remove a clause that shuts Microsoft out of the start-up's most advanced models when it achieves "artificial general intelligence", as it seeks to unlock future investments, the Financial Times reported on Friday.
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Zuck just dropped LLAMA 3.3 is a new 70 billion parameter text model that performs about as well as their 405 billion parameter model.
So that is the last LLAMA 3 release. The next stop is LLAMA 4.
Also Zuck announced a 2 GIGAWATT PLUS DATA CENTER in Louisiana.
HF.
So that is the last LLAMA 3 release. The next stop is LLAMA 4.
Also Zuck announced a 2 GIGAWATT PLUS DATA CENTER in Louisiana.
HF.
Industry Leading, Open-Source AI | Llama
Discover Llama 4's class-leading AI models, Scout and Maverick. Experience top performance, multimodality, low costs, and unparalleled efficiency.
A new tutorial on RL by Kevin Patrick Murphy, a Research Scientist at Google DeepMind who also wrote several comprehensive, well-regarded textbooks on ML/DL.
arXiv.org
Reinforcement Learning: An Overview
This manuscript gives a big-picture, up-to-date overview of the field of (deep) reinforcement learning and sequential decision making, covering value-based methods, policy-based methods,...
🦄5
Multi_agent_systems_III_beyond_single_agents_1733748286.pdf
876.1 KB
What Comes After AI Agents?
By Dr. Egor Kraev, Head of AI at Wise
Over the past year, we've witnessed a boom in AI agents. Many frameworks have emerged - Langchain, Autogen, CrewAI, and others. New solutions and approaches appear weekly. But what's next? Where is this technology heading?
Why Agents Alone Are Not Enough
The current approach of trying to solve everything with "smart agents" is like trying to build a house using only a hammer. Yes, a hammer is a great tool, but for quality results, you need a whole set of tools.
The Future Lies in Compound Systems
The next stage of development is compound systems, where AI agents become just one component of a larger system.
Think of it as a construction set where each part plays its role:
- Traditional databases for information storage
- Classical algorithms where they're more efficient
- AI agents for tasks requiring "intelligent" analysis
- Standard interfaces for component interaction
Core Benefits
1. Flexibility. Mix and match components based on specific needs
2. Cost-effectiveness. Use expensive AI components only where necessary
3. Risk management. Not putting all eggs in one basket
4. Future-proofing. Easier to adopt new technologies as they emerge
The era of standalone AI agents is coming to an end. The future belongs to integrated solutions where artificial intelligence is an important, but not the only component of the system. This isn't a step backward, but rather a natural evolution of technology, making it truly applicable in the real world.
By Dr. Egor Kraev, Head of AI at Wise
Over the past year, we've witnessed a boom in AI agents. Many frameworks have emerged - Langchain, Autogen, CrewAI, and others. New solutions and approaches appear weekly. But what's next? Where is this technology heading?
Why Agents Alone Are Not Enough
The current approach of trying to solve everything with "smart agents" is like trying to build a house using only a hammer. Yes, a hammer is a great tool, but for quality results, you need a whole set of tools.
The Future Lies in Compound Systems
The next stage of development is compound systems, where AI agents become just one component of a larger system.
Think of it as a construction set where each part plays its role:
- Traditional databases for information storage
- Classical algorithms where they're more efficient
- AI agents for tasks requiring "intelligent" analysis
- Standard interfaces for component interaction
Core Benefits
1. Flexibility. Mix and match components based on specific needs
2. Cost-effectiveness. Use expensive AI components only where necessary
3. Risk management. Not putting all eggs in one basket
4. Future-proofing. Easier to adopt new technologies as they emerge
The era of standalone AI agents is coming to an end. The future belongs to integrated solutions where artificial intelligence is an important, but not the only component of the system. This isn't a step backward, but rather a natural evolution of technology, making it truly applicable in the real world.
👍5
Streamline AI Agent Evaluation with Synthetic Data
Databricks has introduced a new synthetic data generation API to streamline the testing and improvement of AI agents. This feature significantly reduces the time and cost associated with evaluating agent performance.
The synthetic data generation API automates the evaluation process by:
- Automatically generating test questions and answers from company documents
- Creating comprehensive evaluation datasets in minutes
- Enabling rapid quality assessment without SME bottlenecks
- Providing immediate feedback on agent performance
Databricks has introduced a new synthetic data generation API to streamline the testing and improvement of AI agents. This feature significantly reduces the time and cost associated with evaluating agent performance.
The synthetic data generation API automates the evaluation process by:
- Automatically generating test questions and answers from company documents
- Creating comprehensive evaluation datasets in minutes
- Enabling rapid quality assessment without SME bottlenecks
- Providing immediate feedback on agent performance
Databricks
Streamline AI Agent Evaluation with New Synthetic Data Capabilities | Databricks Blog
2025 Crypto Trends: a16z's Vision for Building a Resilient Future
Key takeaway: While these trends showcase technological advancement, they also represent a strategic shift toward building financial infrastructure that's more resilient to centralized control.
Leading VC firm a16z crypto shared their vision for 2025, and it's fascinating how these predictions align with addressing current challenges in the financial system:
1. AI Agents with Wallets
Imagine AI chatbots that can independently own and transact in crypto. This isn't just about technology - it's about creating autonomous financial actors that operate outside traditional banking constraints.
2. Proof of Personhood
In a world of deepfakes and AI-generated content, verifying real human identity becomes crucial. The industry is working on new ways to prove humanity while preserving privacy.
3. Enterprise Stablecoins
Major companies are expected to adopt stablecoins for payments - not just because they're the cheapest way to send dollars globally, but as a hedge against potential banking restrictions.
4. User Experience Revolution
Crypto is finally prioritizing accessibility over infrastructure. New crypto-native app stores will emerge, similar to the App Store but without centralized control.
5. Asset Tokenization
From government bonds to biometric data - more assets will move to blockchain. This creates a parallel financial system less dependent on traditional banking.
Interesting times ahead!
Key takeaway: While these trends showcase technological advancement, they also represent a strategic shift toward building financial infrastructure that's more resilient to centralized control.
Leading VC firm a16z crypto shared their vision for 2025, and it's fascinating how these predictions align with addressing current challenges in the financial system:
1. AI Agents with Wallets
Imagine AI chatbots that can independently own and transact in crypto. This isn't just about technology - it's about creating autonomous financial actors that operate outside traditional banking constraints.
2. Proof of Personhood
In a world of deepfakes and AI-generated content, verifying real human identity becomes crucial. The industry is working on new ways to prove humanity while preserving privacy.
3. Enterprise Stablecoins
Major companies are expected to adopt stablecoins for payments - not just because they're the cheapest way to send dollars globally, but as a hedge against potential banking restrictions.
4. User Experience Revolution
Crypto is finally prioritizing accessibility over infrastructure. New crypto-native app stores will emerge, similar to the App Store but without centralized control.
5. Asset Tokenization
From government bonds to biometric data - more assets will move to blockchain. This creates a parallel financial system less dependent on traditional banking.
Interesting times ahead!
a16z crypto
A few of the things we’re excited about in crypto (2025) - a16z crypto
A list of crypto, blockchains, web3 trends for 2025 -- including AI x crypto, prediction markets, stablecoins, voting, and much more
OpenAI just officially released its Sora AI video generation model
Sora has its own interface, where users can:
— Organize and view their generated videos
— See other users’ prompts and featured content
Much like Midjourney’s web UI, this feed style will lead to some awesome inspiration and discoverability of effective prompts.
Sora can generate up to 20-sec videos, in several different aspect ratios.
Generation time was a previous concern with early Sora versions, and it appears OpenAI has gotten it down significantly.
Sora will be available today to Plus subscribers, with Pro users getting 10x usage and higher resolution.
The rollout looks to exclude the EU, UK, China at launch.
Sora has its own interface, where users can:
— Organize and view their generated videos
— See other users’ prompts and featured content
Much like Midjourney’s web UI, this feed style will lead to some awesome inspiration and discoverability of effective prompts.
Sora can generate up to 20-sec videos, in several different aspect ratios.
Generation time was a previous concern with early Sora versions, and it appears OpenAI has gotten it down significantly.
Sora will be available today to Plus subscribers, with Pro users getting 10x usage and higher resolution.
The rollout looks to exclude the EU, UK, China at launch.
Sora
Transform text and images into immersive videos. Animate stories, visualize ideas, and bring your concepts to life.
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The Willow chip breakthrough is cool!
Google announced that Willow has reached 105 qubits with improved error rates.
This Google’s roadmap from the blog is even more exciting.
A few more years of progress can lead to commercially relevant applications for a quantum computer and maybe even applications we haven't conceived yet.
Google announced that Willow has reached 105 qubits with improved error rates.
This Google’s roadmap from the blog is even more exciting.
A few more years of progress can lead to commercially relevant applications for a quantum computer and maybe even applications we haven't conceived yet.