正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

Introducing Microsoft-Decision-1, our new model for fast decision-making. It delivers top performance on structured decision tasks, outperforming both LLMs and other decision models in latency and quality. We’re already testing it across Microsoft for everything from incident response and quality control to scientific discovery.

Satya Nadella

9,615,015 subscribers

1,705,059 次观看 • 20 天前 •via X (Twitter)

新闻政治教育科学技术

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

Laya is now available on Vercel, served by Boundless and free to use. An open-weight decision model for fast, structured decisions in agents and workflows.

Laya is now available on Vercel, served by Boundless and free to use. An open-weight decision model for fast, structured decisions in agents and workflows.

Boundless

11,359 次观看 • 28 天前

Introducing Slingshots // FOUR: 14 projects across eight top institutions. Self-improving agent systems and harnesses, maximally-open models and training data, applications for research and decision-making – plus kernels and more. Say hello to the batch 🧵

Introducing Slingshots // FOUR: 14 projects across eight top institutions. Self-improving agent systems and harnesses, maximally-open models and training data, applications for research and decision-making – plus kernels and more. Say hello to the batch 🧵

Laude Institute

16,434 次观看 • 9 天前

Introducing Inversion, our family of structured LLMs. Our first generation models excel in structured tasks, offering unmatched speed, latency, reliability, and efficiency, with the most comprehensive typed JSON output support available anywhere.

Introducing Inversion, our family of structured LLMs. Our first generation models excel in structured tasks, offering unmatched speed, latency, reliability, and efficiency, with the most comprehensive typed JSON output support available anywhere.

John

213,103 次观看 • 2 年前

New Generation Model! 🚨 We're introducing the Mistral model to our expanding lineup of generation models. Mistral brings efficient performance and strong language understanding capabilities to our platform. Initial testing shows promising results in code comprehension and generation tasks, making it a valuable addition to development workflows. While we continue to optimize its implementation, early benchmarks demonstrate consistent and reliable outputs across various programming tasks.

New Generation Model! 🚨 We're introducing the Mistral model to our expanding lineup of generation models. Mistral brings efficient performance and strong language understanding capabilities to our platform. Initial testing shows promising results in code comprehension and generation tasks, making it a valuable addition to development workflows. While we continue to optimize its implementation, early benchmarks demonstrate consistent and reliable outputs across various programming tasks.

ALCHEMIST AI 🔮

16,438 次观看 • 1 年前

Microsoft Asia just dropped Mage-Flow on Hugging Face a smol 4B model for image generation and editing that matches much larger models in quality and it's fast! 4 steps in < 1s at 1024x1024 (model goes up to 4K) try out on spaces

Microsoft Asia just dropped Mage-Flow on Hugging Face a smol 4B model for image generation and editing that matches much larger models in quality and it's fast! 4 steps in < 1s at 1024x1024 (model goes up to 4K) try out on spaces

Hugging Apps

48,598 次观看 • 3 个月前

Introducing Span-02: anytime reasoning for decisions. Reasoning models make you pick an effort level and wait. Span-02 takes a deadline and always returns its best decision so far. It gets better the longer it thinks. With reasoning off, it beats Jev at 1/4 of the price, and runs at 1/3 the latency of decision models at its level. It can: - Answer with numbers and scores, not just probabilities and choices - See images and screens (multimodal) - Take a configurable reasoning time And it’s fully open weight!

Introducing Span-02: anytime reasoning for decisions. Reasoning models make you pick an effort level and wait. Span-02 takes a deadline and always returns its best decision so far. It gets better the longer it thinks. With reasoning off, it beats Jev at 1/4 of the price, and runs at 1/3 the latency of decision models at its level. It can: - Answer with numbers and scores, not just probabilities and choices - See images and screens (multimodal) - Take a configurable reasoning time And it’s fully open weight!

Respan

217,916 次观看 • 14 天前

Dan Campbell breaks down his decision to go for it on 4th-and-1 in Lions territory coming down the stretch.

Dan Campbell breaks down his decision to go for it on 4th-and-1 in Lions territory coming down the stretch.

The Lions Wire

11,927 次观看 • 4 天前

Every leverage decision is a sizing decision. Kelly answered it in 1956. Too little and you leave growth on the table. Too much and volatility eats it. For BTC-Jr, the math landed on 1.33x.

Every leverage decision is a sizing decision. Kelly answered it in 1956. Too little and you leave growth on the table. Too much and volatility eats it. For BTC-Jr, the math landed on 1.33x.

2FactorFinance

43,119 次观看 • 28 天前

Introducing Amos 1, four open-weight decision models from 0.5B to 27B. > 94.9% on Decision Bench, ahead of Perplexity, Gemini and Jev > beats Jev by 10 points on JevBench hard > reads photos and answers with boxes > decisions in 27 to 122 ms, free API, Apache 2.0

Introducing Amos 1, four open-weight decision models from 0.5B to 27B. > 94.9% on Decision Bench, ahead of Perplexity, Gemini and Jev > beats Jev by 10 points on JevBench hard > reads photos and answers with boxes > decisions in 27 to 122 ms, free API, Apache 2.0

Zaid

35,135 次观看 • 16 天前

Prime Intellect has acquired Moondream We’re bringing Moondream’s open vision models and inference optimizations into our stack Train and deploy multimodal models across vision, video, voice, decision models and computer use

Prime Intellect has acquired Moondream We’re bringing Moondream’s open vision models and inference optimizations into our stack Train and deploy multimodal models across vision, video, voice, decision models and computer use

Prime Intellect

145,995 次观看 • 16 天前

$0.04 per 1M input tokens 128K context #1 on the Decision Index sub-second decisions Drex is looking less like “another small model” and more like an actual decision layer for agents and LLMs especially if you don’t want to burn a huge model’s tokens just deciding what should happen next

$0.04 per 1M input tokens 128K context #1 on the Decision Index sub-second decisions Drex is looking less like “another small model” and more like an actual decision layer for agents and LLMs especially if you don’t want to burn a huge model’s tokens just deciding what should happen next

draven

27,308 次观看 • 1 个月前

Jev + SERV is actually insane. We already showed you can increase Jev's performance with SERV Reasoning. Now we're taking it further, bringing Jev-powered Decision nodes into Graph Sharding with the upcoming SERV v3. Here's a breakdown of how it works: Jev is a decision-making model. Given a task and a set of options, it predicts which path is more likely. Think of the octopus that predicted World Cup results. Jev does that for your business, except it's not luck. It weighs every option and tells you how sure it is. It does this by assigning probabilities to outcomes. It doesn't generate text on its own, so you can't expect it to create a new outcome for you. But that's also what enables it to be lightning fast and dirt cheap. For example, in customer service you can ask Jev how to triage an incoming query and route it to the correct department. It can only select from the list of departments you provide it. This also means it can't hallucinate a new outcome outside the options it's given, which makes it incredibly interesting for OpenServ. In Graph Sharding, we take a single system prompt and break it down into multiple LLM steps with deterministic input and output shapes. Some of these steps require an LLM to produce new output, while others are simply decision routers that determine the next possible path. Traditionally, LLMs are slow and expensive. Breaking a single prompt into multiple steps increases accuracy and reliability by a ton, but it also introduces latency. Jev takes on those decision nodes, which are the backbone of a business process and therefore SERV graphs, and makes them super consistent and lightning fast, lowering the overall cost and latency of graph execution. SERV Reasoning on its own is a great force multiplier for Jev because, like all other models, it works by interpreting input instructions. The clearer those instructions are, the better the model performs. That's where SERV Reasoning comes into play. Just like amplifying any other model, we also amplify the accuracy and consistency of Jev's responses. And now we're bringing Jev-powered Decision nodes into Graph Sharding with SERV v3.

Jev + SERV is actually insane. We already showed you can increase Jev's performance with SERV Reasoning. Now we're taking it further, bringing Jev-powered Decision nodes into Graph Sharding with the upcoming SERV v3. Here's a breakdown of how it works: Jev is a decision-making model. Given a task and a set of options, it predicts which path is more likely. Think of the octopus that predicted World Cup results. Jev does that for your business, except it's not luck. It weighs every option and tells you how sure it is. It does this by assigning probabilities to outcomes. It doesn't generate text on its own, so you can't expect it to create a new outcome for you. But that's also what enables it to be lightning fast and dirt cheap. For example, in customer service you can ask Jev how to triage an incoming query and route it to the correct department. It can only select from the list of departments you provide it. This also means it can't hallucinate a new outcome outside the options it's given, which makes it incredibly interesting for OpenServ. In Graph Sharding, we take a single system prompt and break it down into multiple LLM steps with deterministic input and output shapes. Some of these steps require an LLM to produce new output, while others are simply decision routers that determine the next possible path. Traditionally, LLMs are slow and expensive. Breaking a single prompt into multiple steps increases accuracy and reliability by a ton, but it also introduces latency. Jev takes on those decision nodes, which are the backbone of a business process and therefore SERV graphs, and makes them super consistent and lightning fast, lowering the overall cost and latency of graph execution. SERV Reasoning on its own is a great force multiplier for Jev because, like all other models, it works by interpreting input instructions. The clearer those instructions are, the better the model performs. That's where SERV Reasoning comes into play. Just like amplifying any other model, we also amplify the accuracy and consistency of Jev's responses. And now we're bringing Jev-powered Decision nodes into Graph Sharding with SERV v3.

Armagan Amcalar

366,601 次观看 • 1 个月前

Nace shows Drex playing a strategy game. i gave it a harder opponent: my timeline Spoiler Guard sends each post to the new decision model and blurs the spoilers, 352 post checked in one session with sub-second calls → 128K context window, per Nace the first and only decision model at that size → #1 on the Decision Index, a Jev rival for $0.04 per 1M input tokens → no text generated, just a probability per post try it with 250M free tokens from Nace AI:

Nace shows Drex playing a strategy game. i gave it a harder opponent: my timeline Spoiler Guard sends each post to the new decision model and blurs the spoilers, 352 post checked in one session with sub-second calls → 128K context window, per Nace the first and only decision model at that size → #1 on the Decision Index, a Jev rival for $0.04 per 1M input tokens → no text generated, just a probability per post try it with 250M free tokens from Nace AI:

Israfil

12,386 次观看 • 29 天前

Decision models are moving into enterprise workflows. That company data must stay private. Now it can. Introducing the first private decision model, inside a TEE. Sage is now live on NEAR AI’s confidential infrastructure. Private decisions, at machine speed.

Decision models are moving into enterprise workflows. That company data must stay private. Now it can. Introducing the first private decision model, inside a TEE. Sage is now live on NEAR AI’s confidential infrastructure. Private decisions, at machine speed.

Levanto Labs

19,368 次观看 • 3 天前

instead of generating text, jev from TypeSafe AI generates structured output this makes it great for classification tasks like model routing, tool selection/search, and guardrails of many forms! it's also ridiculously fast and cheap compared to LLMs doing the same tasks

instead of generating text, jev from TypeSafe AI generates structured output this makes it great for classification tasks like model routing, tool selection/search, and guardrails of many forms! it's also ridiculously fast and cheap compared to LLMs doing the same tasks

Sydney Runkle

52,876 次观看 • 1 个月前

Local Laya blitzed other models on RTX 4090 ⚡️ Four local decision models raced down Wikipedia picking out every word that names a centipede and Laya took 3.9 ms a word so it ended up being the fastest overall Run decision models locally ->

Local Laya blitzed other models on RTX 4090 ⚡️ Four local decision models raced down Wikipedia picking out every word that names a centipede and Laya took 3.9 ms a word so it ended up being the fastest overall Run decision models locally ->

atomic.chat

26,773 次观看 • 21 天前

"What does knowledge from a scientific worldview offer? At a minimum, it cultivates critical thinking and bolsters psychological resilience, which minimizes decision-making errors and increases productivity." - Zafar Mirzo Zafar Mirzo

"What does knowledge from a scientific worldview offer? At a minimum, it cultivates critical thinking and bolsters psychological resilience, which minimizes decision-making errors and increases productivity." - Zafar Mirzo Zafar Mirzo

Zafar Mirzo | Quotes

1,731,773 次观看 • 1 年前

🇺🇸🇮🇷 U.S. Treasury Secretary Scott Bessent: “It is now time for world leaders to make the decision between America and Iran.” This seems like an easy decision.

🇺🇸🇮🇷 U.S. Treasury Secretary Scott Bessent: “It is now time for world leaders to make the decision between America and Iran.” This seems like an easy decision.

DD Geopolitics

68,392 次观看 • 2 个月前

Pelosi on Biden exiting the presidential race: “It was his decision." Pelosi on Swalwell exiting the governor’s race and resigning from Congress: “It was his decision." Nancy never has anything to do with anything.🤣

Pelosi on Biden exiting the presidential race: “It was his decision." Pelosi on Swalwell exiting the governor’s race and resigning from Congress: “It was his decision." Nancy never has anything to do with anything.🤣

Western Lensman

44,064 次观看 • 6 个月前