#anthropic
Imagínate despertar y que el mundo digital haya colapsado. Las agencias de inteligencia lo advierten: La IA avanza tan rápido que podría superar muchas de las defensas de ciberseguridad actuales en cuestión de meses. #dwdigital #toptecheditorschoice #ia #anthropic #openia #mythos #fable #dwmagacines
DW Español12,323 Aufrufe • vor 8 Tagen
Anthropic...क्यों इस कंपनी की वजह से मचा हाहाकार⁉️🤯 क्या है ये मामला जिससे IT Shares हुए बेजार...⁉️ देखिए 2026 के लिए IT Shares पर क्या है मेरी राय...👇 बताइए क्या आप IT में करेंगे BUY या TATA BYE BYE...⁉️ #ITStocks #TechCrash #Anthropic #StockMarket #NiftyIT Zee Business
CA Anil Singhvi Zee Business79,207 Aufrufe • vor 5 Monaten
#Donald_Trump acaba de castigar a la empresa #Anthropic fabricante de IA que se negó a fabricar robots asesinos para el Pentágono, mientras tanto #OpenAI aprovechó para colarse por la puerta trasera aceptando las condiciones del pentagono ¿A donde nos llevará esta decisión en el futuro inmediato? Los leo #UrgenteCOMPARTAN #RTMasivo #IranIsraelWar #EEUUTerroristState 👇🏼👇🏼👇🏼👇🏼👇🏼👇🏼👇🏼👇🏼👇🏼
Valy 🎩🎭21,605 Aufrufe • vor 4 Monaten
🔎 🖥️ The #US military reportedly used #Anthropic's #Claude #AI chatbot to analyse data from its operations in #Iran and #Venezuela. The Trump administration has since ended its partnership with Anthropic after the company said its tech should not be used for autonomous weapons.
FRANCE 24 English19,506 Aufrufe • vor 3 Monaten
![[RLHF] by Hand ✍️ Yesterday, Jan Leike (Jan Leike) announced he is joining #Anthropic to lead their "super-alignment" mission. He is the co-inventor of Reinforcement Learning with Human Feedback (#RLHF). How does RLHF work? [1] Given ↳ Reward Model (RM) ↳ Large Language Model (LLM) ↳ Two (Prompt, Next) Pairs 🟪 TRAIN RM Goal: Learn to give higher rewards to winners [2] Preferences ↳ A human reviews the two pairs and picks a "winner" ↳ (doc is, him) Embeddings ↳ This prompt has never received human feedback directly ↳ [S] is the special start symbol [11] Transformer ↳ Attention (yellow) ↳ Feed Forward (4x2 weight and bias matrix) ↳ Output: 3 "transformed" feature vector, one per position ↳ More details in my previous post 8. Transformer [] [12] Output Probabilities ↳ Apply a linear layer to map each transformed feature vector to a probability distribution over the vocabulary. [13] Sample ↳ Apply the greedy method, which is to pick the word with the highest score ↳ For output 1 and 2, the model accurately predicts the next word ↳ For 3rd output position, the model's predicts "him" [14] Reward Model ↳ The new pair (CEO is, him) is fed to the reward model ↳ The process is same as [3]-[6] ↳ Output: Reward = 3 [15] Loss Gradient ↳ We set the loss as the negative of the reward. ↳ The loss gradient is simply a constant -1. ↳ Run backpropagation and gradient descent to update LLM's weights and biases (red border)](https://image.24vids.com/tw-1795803665845899511/ext_tw_video_thumb/1795803020149534720/pu/img/jb8SBLT_1drHtYd_.jpg)
[RLHF] by Hand ✍️ Yesterday, Jan Leike (Jan Leike) announced he is joining #Anthropic to lead their "super-alignment" mission. He is the co-inventor of Reinforcement Learning with Human Feedback (#RLHF). How does RLHF work? [1] Given ↳ Reward Model (RM) ↳ Large Language Model (LLM) ↳ Two (Prompt, Next) Pairs 🟪 TRAIN RM Goal: Learn to give higher rewards to winners [2] Preferences ↳ A human reviews the two pairs and picks a "winner" ↳ (doc is, him) Embeddings ↳ This prompt has never received human feedback directly ↳ [S] is the special start symbol [11] Transformer ↳ Attention (yellow) ↳ Feed Forward (4x2 weight and bias matrix) ↳ Output: 3 "transformed" feature vector, one per position ↳ More details in my previous post 8. Transformer [] [12] Output Probabilities ↳ Apply a linear layer to map each transformed feature vector to a probability distribution over the vocabulary. [13] Sample ↳ Apply the greedy method, which is to pick the word with the highest score ↳ For output 1 and 2, the model accurately predicts the next word ↳ For 3rd output position, the model's predicts "him" [14] Reward Model ↳ The new pair (CEO is, him) is fed to the reward model ↳ The process is same as [3]-[6] ↳ Output: Reward = 3 [15] Loss Gradient ↳ We set the loss as the negative of the reward. ↳ The loss gradient is simply a constant -1. ↳ Run backpropagation and gradient descent to update LLM's weights and biases (red border)
Tom Yeh79,758 Aufrufe • vor 2 Jahren
72小时！五角大楼要'征用'AI，Anthropic撑得住吗？马斯克补刀：扒开AI军备竞赛最残酷的底裤 #anthropic
⧫ Alan Chen ⧫(alanchentsla.eth）14,083 Aufrufe • vor 4 Monaten
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