❗️Neuralink announced the approval and launch of a new feasibility trial to extend BCI control using the N1 Implant to an investigational assistive robotic arm.
This builds on their ongoing PRIME Study, which helps people with quadriplegia control digital devices through thought alone.
Now, they're taking a crucial step forward - from digital to physical control. Participants who are already enrolled in PRIME will have the opportunity to join CONVOY and potentially control an assistive robotic arm using just their thoughts.
This feasibility trial marks a pivotal moment in brain-computer interface technology. While PRIME focuses on restoring "digital freedom" - letting users control computers and smartphones mentally - CONVOY aims to restore physical independence through robotic assistance.
This builds on their ongoing PRIME Study, which helps people with quadriplegia control digital devices through thought alone.
Now, they're taking a crucial step forward - from digital to physical control. Participants who are already enrolled in PRIME will have the opportunity to join CONVOY and potentially control an assistive robotic arm using just their thoughts.
This feasibility trial marks a pivotal moment in brain-computer interface technology. While PRIME focuses on restoring "digital freedom" - letting users control computers and smartphones mentally - CONVOY aims to restore physical independence through robotic assistance.
Neuralink
Clinical Trials | Neuralink
Connect with us and learn more about Neuralink clinical trials.
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Andrew Ng announced new open-source Python package: aisuite
This makes it easy for developers to use large language models from multiple providers.
Open-source code with instructions.
This makes it easy for developers to use large language models from multiple providers.
Open-source code with instructions.
GitHub
GitHub - andrewyng/aisuite: Simple, unified interface to multiple Generative AI providers
Simple, unified interface to multiple Generative AI providers - andrewyng/aisuite
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You can connect Claude to an internet search engine using MCP
Here's how you can do it too in under 5 minutes:
1. U will need to download the latest version of our Claude desktop app here. To use Brave Web Search specifically, you will need to sign up for a free API key here.
2. Open up your Claude Desktop configuration file:
- macOS: ~/Library/Application Support/Claude/claude_desktop_config.json - Windows: %APPDATA%\Claude\claude_desktop_config.json
3. Then, just add this to that file and save it:
{
"mcpServers": {
"brave-search": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-brave-search"],
"env": {
"BRAVE_API_KEY": "ADD_YOUR_API_KEY_HERE"
}
}
}
}
4. Restart your Claude desktop app for the changes to load.
You can check that the server has been configured properly if you navigate to "Claude" > "Settings" in the top bar and check the Developer tab in this window that pops up.
5. After that, just ask Claude to make a web search for you!
The server tools automatically get loaded in to the system prompt so that Claude knows it has access to them.
If you want to improve on this server, explore more servers, or make your own integrations, go check out GitHub repository.
Here's how you can do it too in under 5 minutes:
1. U will need to download the latest version of our Claude desktop app here. To use Brave Web Search specifically, you will need to sign up for a free API key here.
2. Open up your Claude Desktop configuration file:
- macOS: ~/Library/Application Support/Claude/claude_desktop_config.json - Windows: %APPDATA%\Claude\claude_desktop_config.json
3. Then, just add this to that file and save it:
{
"mcpServers": {
"brave-search": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-brave-search"],
"env": {
"BRAVE_API_KEY": "ADD_YOUR_API_KEY_HERE"
}
}
}
}
4. Restart your Claude desktop app for the changes to load.
You can check that the server has been configured properly if you navigate to "Claude" > "Settings" in the top bar and check the Developer tab in this window that pops up.
5. After that, just ask Claude to make a web search for you!
The server tools automatically get loaded in to the system prompt so that Claude knows it has access to them.
If you want to improve on this server, explore more servers, or make your own integrations, go check out GitHub repository.
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OpneAI’s long awaited Sora video model may have leaked on HuggingFace.
huggingface.co
PR Puppet Sora - a Hugging Face Space by PR-Puppets
Enter a description of the scene you want, pick the resolution and duration, and the app creates a short video using OpenAI’s Sora model. The video is downloaded, saved locally, and its prompt is r...
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Google introduced Health AI Developer Foundations is a game-changer for healthcare AI innovation
It provides open-weight models and resources to help developers build healthcare AI tools faster and more efficiently.
The initial focus is on imaging applications in radiology, dermatology, and pathology, with models pre-trained on large, diverse datasets for powerful performance.
Whether it's chest X-rays, skin conditions, or pathology slides, these models offer a robust starting point for creating AI solutions with minimal additional data and compute.
This suite builds on community feedback and is available for download via platforms like Vertex AI Model Garden and Hugging Face.
This initiative could democratize AI development, enabling creators to innovate for real-world healthcare challenges—improving diagnostics, broadening access, and easing clinician workloads.
It provides open-weight models and resources to help developers build healthcare AI tools faster and more efficiently.
The initial focus is on imaging applications in radiology, dermatology, and pathology, with models pre-trained on large, diverse datasets for powerful performance.
Whether it's chest X-rays, skin conditions, or pathology slides, these models offer a robust starting point for creating AI solutions with minimal additional data and compute.
This suite builds on community feedback and is available for download via platforms like Vertex AI Model Garden and Hugging Face.
This initiative could democratize AI development, enabling creators to innovate for real-world healthcare challenges—improving diagnostics, broadening access, and easing clinician workloads.
Google Research
Helping everyone build AI for healthcare applications with open foundation models
Health AI Developer Foundations (HAI-DEF) is a new suite of open weight models to help developers more easily build AI models for healthcare applications. The initial launch is focused on imaging applications in radiology, dermatology and pathology.
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Web3 x AI Market Map 2024
Web3 and AI are converging to create a new "supercycle" of technological development. AI serves as the brain of the system, handling decision-making and automation, while Web3 forms the economic foundation, enabling decentralized value exchange.
Key Trends of 2024:
1. The Age of Smart Agents
- AI agents are now creating real economic value
- Agents trade with each other, manage assets, and even launch their own projects
- Platforms have emerged where anyone can create a personal AI agent in minutes.
2. Five Layers of the New Economy
🔵 Compute: Decentralized networks providing AI processing power
🔵 Data: Transparent systems for collecting and validating information
🔵 Model: Creating and training AI on blockchain
🔵 Interface: Tools for system interaction
🔵 Applications: End products for users
3. Practical Applications
- Finance: AI manages investments and analyzes risks
- Gaming: Smart characters with unique personalities
- Creativity: AI creates music and art in the metaverse
- Social Networks: New level of AI-powered interaction
Web3 and AI are converging to create a new "supercycle" of technological development. AI serves as the brain of the system, handling decision-making and automation, while Web3 forms the economic foundation, enabling decentralized value exchange.
Key Trends of 2024:
1. The Age of Smart Agents
- AI agents are now creating real economic value
- Agents trade with each other, manage assets, and even launch their own projects
- Platforms have emerged where anyone can create a personal AI agent in minutes.
2. Five Layers of the New Economy
🔵 Compute: Decentralized networks providing AI processing power
🔵 Data: Transparent systems for collecting and validating information
🔵 Model: Creating and training AI on blockchain
🔵 Interface: Tools for system interaction
🔵 Applications: End products for users
3. Practical Applications
- Finance: AI manages investments and analyzes risks
- Gaming: Smart characters with unique personalities
- Creativity: AI creates music and art in the metaverse
- Social Networks: New level of AI-powered interaction
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AI2 released OLMo 2, fully open language model to date, including a family of 7B and 13B models trained up to 5T tokens.
OLMo 2 outperforms other fully open models and competes with open-weight models like Llama 3.1 8B.
Model.
Try out an OLMo 2 demo on Playground.
OLMo 2 outperforms other fully open models and competes with open-weight models like Llama 3.1 8B.
Model.
Try out an OLMo 2 demo on Playground.
allenai.org
OLMo 2: The best fully open language model to date | Ai2
Our next generation of fully-open base and instruct models sit at the Pareto frontier of performance and training efficiency.
Check out our [paper](https://arxiv.org/abs/2501.00656) to learn more, or keep reading for a summary.
Check out our [paper](https://arxiv.org/abs/2501.00656) to learn more, or keep reading for a summary.
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Meta introduced Sparsh is the first general-purpose encoder for vision-based tactile sensing that works across many tactile sensors + many tasks.
The family of models was pre-trained on a large dataset of 460K+ tactile images using SSL.
The family of models was pre-trained on a large dataset of 460K+ tactile images using SSL.
sparsh-ssl.github.io
Sparsh | Self-supervised touch representations for vision-based tactile sensing
Sparsh: Self-supervised touch representations for vision-based tactile sensing
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HuggingFace released SmolVLM, a smol 2B VLM built for on-device inference that outperforms all models at similar GPU RAM usage and tokens throughputs.
SmolVLM can be fine-tuned on a Google collab and be run on a laptop.
Or process millions of documents with a consumer GPU.
Demo.
Model.
Fine-tuning script.
SmolVLM can be fine-tuned on a Google collab and be run on a laptop.
Or process millions of documents with a consumer GPU.
Demo.
Model.
Fine-tuning script.
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👀Total Stablecoin Value Tops Terra-Era Peak to Near $200 Billion
The total market capitalization for stablecoins has swollen 46% this year to a record of about $190 billion on Sunday, according to DeFiLlama data.
Tether, which issues the world’s largest stablecoin USDT, has seen its token’s circulation soar to nearly $133 billion, accounting for 70% of the total stablecoin market. The company wants to broaden usage of USDT by pushing into new industries including commodities. It recently announced the funding of its first crude oil transaction in the Middle East
Meanwhile, Stripe Inc., the $70 billion fintech giant, in October said it plans to buy stablecoin startup Bridge for $1.1 billion in one of the largest-ever acquisitions of a digital-asset startup. Other traditional finance firms including PayPal Holdings Inc. have also ventured into the sector.
The recent stablecoin surge coincides with a crypto rally spurred by Donald Trump’s victory in the US presidential race. Once an industry skeptic, Trump won over the sector with pledges to put in place more favorable policies.
The overall value of the digital-assets market has risen by nearly a trillion dollars since the election, according to data from CoinGecko.
The total market capitalization for stablecoins has swollen 46% this year to a record of about $190 billion on Sunday, according to DeFiLlama data.
Tether, which issues the world’s largest stablecoin USDT, has seen its token’s circulation soar to nearly $133 billion, accounting for 70% of the total stablecoin market. The company wants to broaden usage of USDT by pushing into new industries including commodities. It recently announced the funding of its first crude oil transaction in the Middle East
Meanwhile, Stripe Inc., the $70 billion fintech giant, in October said it plans to buy stablecoin startup Bridge for $1.1 billion in one of the largest-ever acquisitions of a digital-asset startup. Other traditional finance firms including PayPal Holdings Inc. have also ventured into the sector.
The recent stablecoin surge coincides with a crypto rally spurred by Donald Trump’s victory in the US presidential race. Once an industry skeptic, Trump won over the sector with pledges to put in place more favorable policies.
The overall value of the digital-assets market has risen by nearly a trillion dollars since the election, according to data from CoinGecko.
Bloomberg.com
Total Stablecoin Value Tops Terra-Era Peak to Near $200 Billion
The value of the stablecoin market has risen to new heights after regaining ground lost in the aftermath of TerraUSD’s infamous collapse in 2022.
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Alibaba released a preview of QwQ /kwju:/ — an open model designed to advance AI reasoning capabilities.
Qwen
QwQ: Reflect Deeply on the Boundaries of the Unknown
GITHUB HUGGING FACE MODELSCOPE DEMO DISCORD
Note: This is the pronunciation of QwQ: /kwju:/ , similar to the word “quill”.
What does it mean to think, to question, to understand? These are the deep waters that QwQ (Qwen with Questions) wades into. Like an…
Note: This is the pronunciation of QwQ: /kwju:/ , similar to the word “quill”.
What does it mean to think, to question, to understand? These are the deep waters that QwQ (Qwen with Questions) wades into. Like an…
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LLM-brained GUI Agents
Great survey on LLM-brained GUI agents, including techniques and applications.
Great survey on LLM-brained GUI agents, including techniques and applications.
arXiv.org
Large Language Model-Brained GUI Agents: A Survey
GUIs have long been central to human-computer interaction, providing an intuitive and visually-driven way to access and interact with digital systems. The advent of LLMs, particularly multimodal...
Mistral announced Startup Program: Mistralship
Become part of a 6-month-cohort of 10 startups to receive:
- 30K credits for La Plateforme
- Dedicated 1:1 support from our Solutions & Science team
- Early access to new models & products
Apply here before January 5th.
Become part of a 6-month-cohort of 10 startups to receive:
- 30K credits for La Plateforme
- Dedicated 1:1 support from our Solutions & Science team
- Early access to new models & products
Apply here before January 5th.
First open source reasoning model from Alibaba
Qwen QwQ 32B is in preview, does reasoning like o1 and Deepseek R1 but y'know, on your local machine.
Demo
Model
Qwen QwQ 32B is in preview, does reasoning like o1 and Deepseek R1 but y'know, on your local machine.
Demo
Model
Qwen
QwQ: Reflect Deeply on the Boundaries of the Unknown
GITHUB HUGGING FACE MODELSCOPE DEMO DISCORD
Note: This is the pronunciation of QwQ: /kwju:/ , similar to the word “quill”.
What does it mean to think, to question, to understand? These are the deep waters that QwQ (Qwen with Questions) wades into. Like an…
Note: This is the pronunciation of QwQ: /kwju:/ , similar to the word “quill”.
What does it mean to think, to question, to understand? These are the deep waters that QwQ (Qwen with Questions) wades into. Like an…
Scientists at Yale University have developed BrainLM, the first foundation model for analyzing functional MRI brain recordings.
Here's what makes it revolutionary:
- Trained on 6,700 hours of brain activity recordings
- Uses self-supervised masked-prediction training
- Processes data from 77,298 fMRI samples
- Analyzes 424 brain regions simultaneously
Capabilities:
- Accurately predicts clinical variables like age, anxiety, and PTSD
- Forecasts future brain states
- Identifies functional networks without supervision
- Generates interpretable representations of brain activity patterns
What Sets It Apart:
- Generalizes well to new patients and external datasets
- Outperforms baseline models in clinical predictions
- Serves as a powerful "lens" for analyzing massive fMRI repositories
- Creates meaningful insights about brain organization
Potential Applications:
- Non-invasive assessment of cognitive health
- Early detection of psychiatric disorders
- Research tool for understanding brain dynamics
- Biomarker discovery for mental health conditions
Technical Implementation:
- Based on Transformer architecture
- Trained on UK Biobank and Human Connectome Project data
- Uses advanced preprocessing and brain parcellation techniques
- Employs state-of-the-art deep learning methods
Here's what makes it revolutionary:
- Trained on 6,700 hours of brain activity recordings
- Uses self-supervised masked-prediction training
- Processes data from 77,298 fMRI samples
- Analyzes 424 brain regions simultaneously
Capabilities:
- Accurately predicts clinical variables like age, anxiety, and PTSD
- Forecasts future brain states
- Identifies functional networks without supervision
- Generates interpretable representations of brain activity patterns
What Sets It Apart:
- Generalizes well to new patients and external datasets
- Outperforms baseline models in clinical predictions
- Serves as a powerful "lens" for analyzing massive fMRI repositories
- Creates meaningful insights about brain organization
Potential Applications:
- Non-invasive assessment of cognitive health
- Early detection of psychiatric disorders
- Research tool for understanding brain dynamics
- Biomarker discovery for mental health conditions
Technical Implementation:
- Based on Transformer architecture
- Trained on UK Biobank and Human Connectome Project data
- Uses advanced preprocessing and brain parcellation techniques
- Employs state-of-the-art deep learning methods
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PrimeIntellect released INTELLECT-1: open-sourcing the first decentralized trained 10B model
INTELLECT-1 base model & intermediate checkpoints
- Pre-training dataset
- Post-trained instruct models by Arcee ai
- PRIME training framework
- Technical paper with all details.
Decentralized training is one of the most important things to track from an AI policy POV - most efforts to control compute rest on the assumption that strategic AI compute needs to be centralized.
HuggingFace.
Pre-training Dataset.
Post-training datasets by Arcee ai: 1 , 2.
Try out Intellect-1 on.
INTELLECT-1 base model & intermediate checkpoints
- Pre-training dataset
- Post-trained instruct models by Arcee ai
- PRIME training framework
- Technical paper with all details.
Decentralized training is one of the most important things to track from an AI policy POV - most efforts to control compute rest on the assumption that strategic AI compute needs to be centralized.
HuggingFace.
Pre-training Dataset.
Post-training datasets by Arcee ai: 1 , 2.
Try out Intellect-1 on.
www.primeintellect.ai
INTELLECT-1 Release The First Globally Trained 10B Parameter Model
We're excited to release INTELLECT-1, the first 10B parameter language model collaboratively trained across the globe. This represents a 10× scale-up from our previous research and demonstrates that large-scale model training is no longer confined to large…
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Tesla unveiled an upgraded hand system for its Optimus humanoid robot, featuring 22 total degrees of freedom
The robot teleoperated by a human, can now catch a ball, with tactile sensing and fine tendon control improvements coming by the end of the year.
The robot teleoperated by a human, can now catch a ball, with tactile sensing and fine tendon control improvements coming by the end of the year.
BCG has released its insightful AI Maturity Matrix report.
Key findings :
1. The report categorizes economies into three groups: AI Emergents, AI Practitioners & Contenders, and AI Pioneers.
2. AI Emergents are economies in the early stages of AI adoption, requiring strategies to build competitiveness.
3. AI Practitioners & Contenders are economies with gradual or exposed AI adoption, including countries from East Asia, Eastern Europe, and South America.
4. AI Pioneers are the top 5 economies (out of 73) that excel in AI integration, R&D, infrastructure, and talent.
5. The report notes a substantial gap in AI preparedness, with over 70% of nations scoring below the halfway mark in areas like skills, research, and ecosystem development.
6. Global AI expenditure is expected to double, reaching $632 billion by 2028, reflecting technology's central role in future economic strategies.
7. The AI Maturity Index is measured by two indices: AI exposure and AI readiness, evaluated across six dimensions (ASPIRE framework).
8. Economies like Luxembourg and Singapore lead in AI readiness due to their reliance on financial and business services.
9. Developing economies like India can benefit from AI applications in 'agritech' and industrial optimization.
10. The report concludes with recommendations for each economy archetype, urging pioneers to drive global standards, contenders to expand AI applications, and emergents to build foundational infrastructure.
Key findings :
1. The report categorizes economies into three groups: AI Emergents, AI Practitioners & Contenders, and AI Pioneers.
2. AI Emergents are economies in the early stages of AI adoption, requiring strategies to build competitiveness.
3. AI Practitioners & Contenders are economies with gradual or exposed AI adoption, including countries from East Asia, Eastern Europe, and South America.
4. AI Pioneers are the top 5 economies (out of 73) that excel in AI integration, R&D, infrastructure, and talent.
5. The report notes a substantial gap in AI preparedness, with over 70% of nations scoring below the halfway mark in areas like skills, research, and ecosystem development.
6. Global AI expenditure is expected to double, reaching $632 billion by 2028, reflecting technology's central role in future economic strategies.
7. The AI Maturity Index is measured by two indices: AI exposure and AI readiness, evaluated across six dimensions (ASPIRE framework).
8. Economies like Luxembourg and Singapore lead in AI readiness due to their reliance on financial and business services.
9. Developing economies like India can benefit from AI applications in 'agritech' and industrial optimization.
10. The report concludes with recommendations for each economy archetype, urging pioneers to drive global standards, contenders to expand AI applications, and emergents to build foundational infrastructure.
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⚡️Intel announced retirement of CEO Pat Gelsinger
David Zinsner and Michelle Johnston Holthaus named interim Co-CEOs.
Holthaus also appointed to the newly created position of CEO of Intel Products. Frank Yeary named interim executive chair.
David Zinsner and Michelle Johnston Holthaus named interim Co-CEOs.
Holthaus also appointed to the newly created position of CEO of Intel Products. Frank Yeary named interim executive chair.
Intel Corporation
Intel Announces Retirement of CEO Pat Gelsinger
Reverse Thinking Makes LLMs Stronger Reasoners
Shows that training LLMs to learn "reverse thinking" helps to improve performance in commonsense, math, and logical reasoning tasks.
It claims to outperform a standard fine-tuning method trained on 10x more forward reasoning.
Shows that training LLMs to learn "reverse thinking" helps to improve performance in commonsense, math, and logical reasoning tasks.
It claims to outperform a standard fine-tuning method trained on 10x more forward reasoning.
arXiv.org
Reverse Thinking Makes LLMs Stronger Reasoners
Reverse thinking plays a crucial role in human reasoning. Humans can reason not only from a problem to a solution but also in reverse, i.e., start from the solution and reason towards the problem....
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