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🛡️ Ethics vs Weapons: Why did Anthropic say "no" to the U.S. Department of Defense?
A serious divergence has erupted between the AI community and government contracts! 💥 Anthropic's negotiations with the U.S. Department of Defense (Pentagon) regarding a $200 million contract have reached an impasse. The reason is simple: Claude's developers do not want their AI to become a "Terminator."
Core conflict:
The Pentagon wants complete autonomy in military operations, while Anthropic has drawn clear "red lines":
❌ Prohibition of automatic targeting systems for weapons.
❌ Prohibition of internal surveillance on U.S. citizens.
❌ Human-in-the-loop must be maintained, independent decision-making by AI is strictly prohibited.
Military stance:
The Pentagon believes that private companies should not meddle in national security issues. They argue that the use of AI should only be restricted by federal law, and ethical constraints from corporations will hinder the enforcement efficiency of departments like the FBI and ICE.
Why should investors pay attention?
Industry precedent: If Anthropic ultimately compromises, it will mean that the ethical guidelines of AI companies may become meaningless in the face of huge contracts. Competitive landscape: While Anthropic hesitates, competitors like Microsoft/OpenAI or Palantir may seize the market with more "flexible" terms. Regulatory trends: This conflict will accelerate the legislative process for military AI, directly affecting the stock prices of tech giants and the related tokens in the decentralized AI sector.
Anthropic is trying to maintain its "safe AI" brand image, but how long can it hold out against the temptations of the national apparatus and hundreds of millions of dollars?
Do you think AI should have the right to refuse to execute military orders from the state? Feel free to discuss in the comments! 👇
#Aİ #Anthropic #Claude #加密新闻 #国家安全
A serious divergence has erupted between the AI community and government contracts! 💥 Anthropic's negotiations with the U.S. Department of Defense (Pentagon) regarding a $200 million contract have reached an impasse. The reason is simple: Claude's developers do not want their AI to become a "Terminator."
Core conflict:
The Pentagon wants complete autonomy in military operations, while Anthropic has drawn clear "red lines":
❌ Prohibition of automatic targeting systems for weapons.
❌ Prohibition of internal surveillance on U.S. citizens.
❌ Human-in-the-loop must be maintained, independent decision-making by AI is strictly prohibited.
Military stance:
The Pentagon believes that private companies should not meddle in national security issues. They argue that the use of AI should only be restricted by federal law, and ethical constraints from corporations will hinder the enforcement efficiency of departments like the FBI and ICE.
Why should investors pay attention?
Industry precedent: If Anthropic ultimately compromises, it will mean that the ethical guidelines of AI companies may become meaningless in the face of huge contracts. Competitive landscape: While Anthropic hesitates, competitors like Microsoft/OpenAI or Palantir may seize the market with more "flexible" terms. Regulatory trends: This conflict will accelerate the legislative process for military AI, directly affecting the stock prices of tech giants and the related tokens in the decentralized AI sector.
Anthropic is trying to maintain its "safe AI" brand image, but how long can it hold out against the temptations of the national apparatus and hundreds of millions of dollars?
Do you think AI should have the right to refuse to execute military orders from the state? Feel free to discuss in the comments! 👇
#Aİ #Anthropic #Claude #加密新闻 #国家安全
🤖 Polymarket in the AI Era: Claude Helps Ordinary Traders Earn Up to $800 Daily
The prediction market is undergoing a transformation right before our eyes. If creating trading bots was once a privilege of programmers, now, thanks to artificial intelligence, the barrier has been significantly lowered.
Expert frostikk reveals how the combination of neural network Claude and Polymarket changes the game. The main points are as follows:
🔹 Code is no longer a barrier. Traders without a technical background are using Claude to write Python scripts. Simply describe the strategy in natural language, and the AI will provide ready-made logic with API connections.
🔹 Popular strategies:
Arbitrage: Searching for price differences between Polymarket and other platforms. Following 'whales': Automatically copying the trades of large holders. Micro scalping: Participating in the 15-minute BTC market.
💰 Impressive numbers:
A simple arbitrage bot can earn about $25 per hour. Advanced market-making strategies can yield between $500 and $800 daily. Monthly case: Through high-frequency small trades, capital increased from $20,000 to $215,000 in 30 days.
⚠️ Important reminder: Automation is not a "printing press." Success depends on discipline and the correctness of logic. Errors in the bot's code can drain your account faster than you can hit the "stop" button.
The future of prediction markets lies in algorithms. The only question is whose bot is smarter. 🚀
#Polymarket #AI #Claude #CryptoNews
#Arbitrage
The prediction market is undergoing a transformation right before our eyes. If creating trading bots was once a privilege of programmers, now, thanks to artificial intelligence, the barrier has been significantly lowered.
Expert frostikk reveals how the combination of neural network Claude and Polymarket changes the game. The main points are as follows:
🔹 Code is no longer a barrier. Traders without a technical background are using Claude to write Python scripts. Simply describe the strategy in natural language, and the AI will provide ready-made logic with API connections.
🔹 Popular strategies:
Arbitrage: Searching for price differences between Polymarket and other platforms. Following 'whales': Automatically copying the trades of large holders. Micro scalping: Participating in the 15-minute BTC market.
💰 Impressive numbers:
A simple arbitrage bot can earn about $25 per hour. Advanced market-making strategies can yield between $500 and $800 daily. Monthly case: Through high-frequency small trades, capital increased from $20,000 to $215,000 in 30 days.
⚠️ Important reminder: Automation is not a "printing press." Success depends on discipline and the correctness of logic. Errors in the bot's code can drain your account faster than you can hit the "stop" button.
The future of prediction markets lies in algorithms. The only question is whose bot is smarter. 🚀
#Polymarket #AI #Claude #CryptoNews
#Arbitrage
Grok-3: Is Elon Musk's New AI Worth Comparing to ChatGPT, Claude, Gemini?
Elon Musk caused a stir again with the launch of #grok3 , the latest AI product from xAI, aiming to compete with giants like GPT-4o, Claude 3.5 Sonnet, Gemini, and DeepSeek. But how powerful is Grok-3 really? Let's compare!
1. Creation: Grok-3 beats Claude
Grok-3 excels in storytelling, surpassing #Claude 3.5 Sonnet with rich content, quality characters, and engaging storytelling. However, the content is still not perfect, with some details that may feel restrictive to the reader.
1. Creation: Grok-3 beats Claude
Grok-3 excels in storytelling, surpassing #Claude 3.5 Sonnet with rich content, quality characters, and engaging storytelling. However, the content is still not perfect, with some details that may feel restrictive to the reader.
🤖 The #AI race is heating up: #ChatGPT , #Claude , #Grok , #Cursor ... and dozens more. Just 3 years ago, 90% of this stuff didn’t exist.
Now it's not about whether AI works; it's which AI works best for what.
Some write like copywriters, some code like senior developers, and some organize like ops leads.
What’s in your stack? And what’s still missing from the perfect AI toolkit?
Now it's not about whether AI works; it's which AI works best for what.
Some write like copywriters, some code like senior developers, and some organize like ops leads.
What’s in your stack? And what’s still missing from the perfect AI toolkit?
📛 Anthropic have created a "microscope" for LLM - now you can see how AI thinks
Anthropic have been working on the interpretability of neural networks for a long time. Their past SAE (Sparse Autoencoder) method has already been adopted by OpenAI and Google, and now they offer a new way to "parse" AI into thoughts - Circuit Tracing.
🟢 How does it work?
🍒 They take an off-the-shelf language model and select a task.
😘 Replace some components of the model with simple linear models (Cross-Layer Transcoder).
😘 Train these replaced parts to mimic the original model, minimizing the difference in output.
🍒 Now you can see how information "flows" through all the layers of the model.
😘 Based on this data, an attribution graph is built - it shows which attributes influence each other and form the final answer.
🟢 What interesting things were discovered in Claude's brain?
🟠 The LLM "thinks ahead." For example, when she writes a poem, she plans the rhyme scheme in advance, even before she starts a new line.
🟠 Math is not just about memorization. Turns out the model is actually calculating, not just retrieving memorized answers.
🟠 Hallucinations have a cause. A specific "answer is known" trigger is found. If it is triggered in error - the model starts making things up.
🟠 Fun fact: if you tell the model the answer to a problem right away, it will think backwards - come up with a plausible path to that answer.
#claude #AI
🟢 How does it work?
🍒 They take an off-the-shelf language model and select a task.
😘 Replace some components of the model with simple linear models (Cross-Layer Transcoder).
😘 Train these replaced parts to mimic the original model, minimizing the difference in output.
🍒 Now you can see how information "flows" through all the layers of the model.
😘 Based on this data, an attribution graph is built - it shows which attributes influence each other and form the final answer.
🟢 What interesting things were discovered in Claude's brain?
🟠 The LLM "thinks ahead." For example, when she writes a poem, she plans the rhyme scheme in advance, even before she starts a new line.
🟠 Math is not just about memorization. Turns out the model is actually calculating, not just retrieving memorized answers.
🟠 Hallucinations have a cause. A specific "answer is known" trigger is found. If it is triggered in error - the model starts making things up.
🟠 Fun fact: if you tell the model the answer to a problem right away, it will think backwards - come up with a plausible path to that answer.
#claude #AI
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