On Thursday, Anthropic announced Citations , a new API feature that helps Claude models avoid confabulations (also called hallucinations) by linking their responses directly to source documents. The feature lets developers add documents to Claude's context window, enabling the model to automatically cite specific passages it uses to generate answers.

"When Citations is enabled, the API processes user-provided source documents (PDF documents and plaintext files) by chunking them into sentences," Anthropic says. "These chunked sentences, along with user-provided context, are then passed to the model with the user's query."

The company describes several potential uses for Citations, including summarizing case files with source-linked key points, answering questions across financial documents with traced references, and powering support systems that cite specific product documentation.

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On Thursday, Anthropic announced Citations , a new API feature that helps Claude models avoid confabulations (also called hallucinations) by linking their responses directly to source documents. The feature lets developers add documents to Claude's context window, enabling the model to automatically cite specific passages it uses to generate answers.

"When Citations is enabled, the API processes user-provided source documents (PDF documents and plaintext files) by chunking them into sentences," Anthropic says. "These chunked sentences, along with user-provided context, are then passed to the model with the user's query."

The company describes several potential uses for Citations, including summarizing case files with source-linked key points, answering questions across financial documents with traced references, and powering support systems that cite specific product documentation.

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On Thursday, Anthropic announced Citations , a new API feature that helps Claude models avoid confabulations (also called hallucinations) by linking their responses directly to source documents. The feature lets developers add documents to Claude's context window, enabling the model to automatically cite specific passages it uses to generate answers.

"When Citations is enabled, the API processes user-provided source documents (PDF documents and plaintext files) by chunking them into sentences," Anthropic says. "These chunked sentences, along with user-provided context, are then passed to the model with the user's query."

The company describes several potential uses for Citations, including summarizing case files with source-linked key points, answering questions across financial documents with traced references, and powering support systems that cite specific product documentation.

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The founder of an AI startup in San Francisco was indicted this week for allegedly conspiring with his wife for six years to defraud investors out of $60 million.

According to a press release from the US Attorney's Office in the Northern District of California, Alexander Beckman—founder of GameOn Technology (now known as ON Platform)—and Valerie Lau Beckman—an attorney hired by GameOn who later became his wife—were charged with 25 counts, including conspiracy, wire fraud, securities fraud, identity theft, and other offenses. Lau also faces one charge of obstruction of justice after allegedly deleting evidence.

If convicted, the maximum penalties for Beckman, 41, could exceed 60 years and for Lau, 38, potentially 80 years.

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The founder of an AI startup in San Francisco was indicted this week for allegedly conspiring with his wife for six years to defraud investors out of $60 million.

According to a press release from the US Attorney's Office in the Northern District of California, Alexander Beckman—founder of GameOn Technology (now known as ON Platform)—and Valerie Lau Beckman—an attorney hired by GameOn who later became his wife—were charged with 25 counts, including conspiracy, wire fraud, securities fraud, identity theft, and other offenses. Lau also faces one charge of obstruction of justice after allegedly deleting evidence.

If convicted, the maximum penalties for Beckman, 41, could exceed 60 years and for Lau, 38, potentially 80 years.

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The founder of an AI startup in San Francisco was indicted this week for allegedly conspiring with his wife for six years to defraud investors out of $60 million.

According to a press release from the US Attorney's Office in the Northern District of California, Alexander Beckman—founder of GameOn Technology (now known as ON Platform)—and Valerie Lau Beckman—an attorney hired by GameOn who later became his wife—were charged with 25 counts, including conspiracy, wire fraud, securities fraud, identity theft, and other offenses. Lau also faces one charge of obstruction of justice after allegedly deleting evidence.

If convicted, the maximum penalties for Beckman, 41, could exceed 60 years and for Lau, 38, potentially 80 years.

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The game of chess has long been central to computer science and AI-related research, most notably in IBM's Deep Blue in the 1990s and, more recently, AlphaZero. But the game is about more than algorithms, according to Marc Barthelemy , a physicist at the Paris-Saclay University in France, with layers of depth arising from the psychological complexity conferred by player strategies.

Now, Barthelmey has taken things one step further by publishing a new paper in the journal Physical Review E that treats chess as a complex system, producing a handy metric that can help predict the proverbial "tipping points" in chess matches.

In his paper, Barthelemy cites Richard Reti , an early 20th-century chess master who gave a series of lectures in the 1920s on developing a scientific understanding of chess. It was an ambitious program involving collecting empirical data, constructing typologies, and devising laws based on those typologies, but Reti's insights fell by the wayside as advances in computer science came to dominate the field. That's understandable. "With its simple rules yet vast strategic depth, chess provides an ideal platform for developing and testing algorithms in AI, machine learning, and decision theory," Barthelemy writes.

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The game of chess has long been central to computer science and AI-related research, most notably in IBM's Deep Blue in the 1990s and, more recently, AlphaZero. But the game is about more than algorithms, according to Marc Barthelemy , a physicist at the Paris-Saclay University in France, with layers of depth arising from the psychological complexity conferred by player strategies.

Now, Barthelmey has taken things one step further by publishing a new paper in the journal Physical Review E that treats chess as a complex system, producing a handy metric that can help predict the proverbial "tipping points" in chess matches.

In his paper, Barthelemy cites Richard Reti , an early 20th-century chess master who gave a series of lectures in the 1920s on developing a scientific understanding of chess. It was an ambitious program involving collecting empirical data, constructing typologies, and devising laws based on those typologies, but Reti's insights fell by the wayside as advances in computer science came to dominate the field. That's understandable. "With its simple rules yet vast strategic depth, chess provides an ideal platform for developing and testing algorithms in AI, machine learning, and decision theory," Barthelemy writes.

Read full article

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The game of chess has long been central to computer science and AI-related research, most notably in IBM's Deep Blue in the 1990s and, more recently, AlphaZero. But the game is about more than algorithms, according to Marc Barthelemy , a physicist at the Paris-Saclay University in France, with layers of depth arising from the psychological complexity conferred by player strategies.

Now, Barthelmey has taken things one step further by publishing a new paper in the journal Physical Review E that treats chess as a complex system, producing a handy metric that can help predict the proverbial "tipping points" in chess matches.

In his paper, Barthelemy cites Richard Reti , an early 20th-century chess master who gave a series of lectures in the 1920s on developing a scientific understanding of chess. It was an ambitious program involving collecting empirical data, constructing typologies, and devising laws based on those typologies, but Reti's insights fell by the wayside as advances in computer science came to dominate the field. That's understandable. "With its simple rules yet vast strategic depth, chess provides an ideal platform for developing and testing algorithms in AI, machine learning, and decision theory," Barthelemy writes.

Read full article

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