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Company Spotlight
NVIDIA
From Radiology to Drug Discovery, Survey Reveals AI Is Delivering Clear Return on Investment in Healthcare
Meta
Llama 3, PyTorch, AR/VR
See the whole picture and find the look with Circle to Search
Microsoft
A conversation with Kevin Scott: What’s next in AI
OpenAI
GPT-4, ChatGPT, DALL-E
Anthropic
Claude 3.5, Constitutional AI
Latest Signals
Qwen3.5-35B-A3B momentum +20%
Qwen • Qwen3.5-35B-A3B
Qwen3.5-397B-A17B momentum +2%
Qwen • Qwen3.5-397B-A17B
Qwen3.5-27B momentum +30%
Qwen • Qwen3.5-27B
GLM-5 momentum +9%
zai-org • GLM-5
InMotion Hosting Launches March Server Madness as Demand Grows for Predictable Infrastructure
Pr Newswire • AI News
Signing Day Sports Announces BlockchAIn's AI-Focused Initiative for Next Generation Power-Advantaged Digital Infrastructure Platform
Benzinga • AI News
AI at the Crossroads: Pentagon–Anthropic Standoff Signals New Era for Military Technology and Ethics
Abacus News • AI News
Jeffs’ Brands: KeepZone AI Enters Advanced Counter-Drone Solutions Market Amid Surging Global Demand for Airspace Security
The Manila Times • AI News
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Funding Radar
View allxAI
$6BSeries C • Foundation Models
Databricks
$10BSeries J • AI Infrastructure
Perplexity
$500MSeries B • AI Applications
Physical Intelligence
$400MSeries A • Robotics
AI Education
Keep sharpening fundamentals with 15 lessons and 126+ terms.
Term of the day: World Model
AI Stock Pulse
Microsoft
Meta
Apple
NVIDIA
Market data delayed. For informational purposes only.
Latest Research
View allTest-Time Training with KV Binding Is Secretly Linear Attention
Test-time training (TTT) with KV binding as sequence modeling layer is commonly interpreted as a form of online meta-learning that memorizes a key-value mapping at test time. However, our analysis reveals multiple phenomena that contradict this memorization-based interpretation. Motivated by these f...
arXivSquint: Fast Visual Reinforcement Learning for Sim-to-Real Robotics
Visual reinforcement learning is appealing for robotics but expensive -- off-policy methods are sample-efficient yet slow; on-policy methods parallelize well but waste samples. Recent work has shown that off-policy methods can train faster than on-policy methods in wall-clock time for state-based co...