April fools bring May hallucinations – by Gary Marcus

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Humans are (usually, if well-motivated) smart enough to sort things like jokes and fiction from reality, but current AI doesn’t have a clue. All it does is absorb and mimic data, without ever reflecting critically about it.

From: April fools bring May hallucinations – by Gary Marcus.

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Software-defined warfare: A blueprint for sustaining a competitive military edge – Atlantic Council

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China and Russia are adapting to rapid advances in modern technology that could shift military power dynamics. While the United States leads in cutting-edge commercial technology—like low-cost drones, autonomy, and artificial intelligence—its military and defense institutions are not built to adopt these innovations quickly. To stay ahead of adversaries in an era driven by software and emerging technologies, the Department of Defense (DoD) must modernize its approach to capability development and procurement.
This new report presents a software-defined warfare approach, offering recommendations for the DoD to adopt modern software practices and seamlessly integrate them into existing platforms to enhance and strengthen defense strategies.

From: Software-defined warfare: A blueprint for sustaining a competitive military edge – Atlantic Council.

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Anthropic can now track the bizarre inner workings of a large language model | MIT Technology Review

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Anthropic also looked at how Claude solved simple math problems. The team found that the model seems to have developed its own internal strategies that are unlike those it will have seen in its training data. Ask Claude to add 36 and 59 and the model will go through a series of odd steps, including first adding a selection of approximate values (add 40ish and 60ish, add 57ish and 36ish). Towards the end of its process, it comes up with the value 92ish. Meanwhile, another sequence of steps focuses on the last digits, 6 and 9, and determines that the answer must end in a 5. Putting that together with 92ish gives the correct answer of 95.

And yet if you then ask Claude how it worked that out, it will say something like: “I added the ones (6+9=15), carried the 1, then added the 10s (3+5+1=9), resulting in 95.” In other words, it gives you a common approach found everywhere online rather than what it actually did. Yep! LLMs are weird. (And not to be trusted.)

The steps that Claude 3.5 Haiku used to solve a simple math problem were not what Anthropic expected—and they’re not the steps that Claude claimed it took either.
ANTHROPIC
This is clear evidence that large language models will give reasons for what they do that do not necessarily reflect what they actually did. But this is true for people too

From: Anthropic can now track the bizarre inner workings of a large language model | MIT Technology Review.

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As AI Takes His Readers, A Leading History Publisher Wonders What’s Next

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the migration of readers from the web to AI summaries will likely continue. The internet has always favored ease. And using generative AI to find the most valuable parts of the web’s evergreen content — like recipes, personal finance, and history content — can be a better experience than poking through sites one by one. Along the way, these systems will likely rewrite the economics of the web, and perhaps reshape the internet itself.

From: As AI Takes His Readers, A Leading History Publisher Wonders What’s Next.

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India is obsessed with giving its people “unique IDs”

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Meanwhile, constant demands from banks and mobile operators to comply with government-mandated “know your customer” regulations have been a boon for cyber fraudsters. Repeated requests for personal data to set up new ids are similarly conditioning Indians to hand over information to anyone who asks.

From: India is obsessed with giving its people “unique IDs”.

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