🤥Anthropic Catches Claude Cheating

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• Anthropic just blew AI’s cover—New research uncovers how AI thinks ahead, reasons through problems, and sometimes fakes its answers

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• A new AI chip contender—Extropic’s energy-efficient chip could disrupt Nvidia’s dominance in AI computing

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• In other AI news today — Google expands vacation-planning tools in Search, Maps, and Gemini, NASA explores AI-driven spaceship design, OpenAI limits ChatGPT image generation due to GPU strain, and SoftBank considers a $1 trillion investment in AI industrial parks across the US

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Anthropic research proves that AI plans, reasons, and sometimes cheats

Key Points 

• Anthropic’s new research reveals that AI models plan ahead, reason through problems, and sometimes work backward to justify answers.

• The study provides insights into AI hallucinations and could help make models more transparent, reliable, and safer to use.

👨‍💻News - Anthropic has introduced a new method for understanding how AI models process information, providing the clearest look yet into their internal reasoning. 

Their research, published today, suggests that models like Claude don’t just predict words. They plan ahead, reason through problems, and sometimes even work backward from a conclusion to justify an answer.

🕵️‍♂️What did they find? Using neuroscience-inspired techniques called circuit tracing and attribution graphs, researchers mapped out how Claude generates responses. They discovered that when writing poetry, the model selects rhyming words before composing a line, showing a level of foresight similar to human creativity.

Claude also demonstrated multi-step reasoning. When asked, “The capital of the state containing Dallas is…,” it first identified Texas before determining Austin as the correct answer. By altering internal representations, swapping “Texas” for “California,” for instance, researchers could make it output “Sacramento,” confirming that the model isn’t just recalling memorized facts.

However, the study also revealed concerning behavior. When given difficult math problems, Claude sometimes reverse-engineers a justification rather than calculating from scratch. This sheds light on why AI models sometimes “hallucinate” incorrect information.

🤓Why this matters - AI has long operated as a black box, with even its creators unsure of how it reaches conclusions. Anthropic’s research could help make these systems more transparent, allowing researchers to better detect biases, improve reliability, and reduce the risk of misleading outputs.

Read the full story here. 

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Nvidia’s reign in AI chips faces a new challenger

Key Points 

• Extropic is developing a chip that uses thermodynamic fluctuations for efficient probabilistic computing, challenging Nvidia’s AI hardware dominance.

• The company’s probabilistic bits, or p-bits, can perform Monte Carlo simulations, crucial for AI, finance, and scientific modeling.

• With AI’s growing energy demands, Extropic believes its energy-efficient chip could be a viable alternative to Nvidia’s GPUs.

♨️News - Extropic, a new player in the AI hardware space, is betting on a radical approach to computing that could put it in competition with Nvidia, the undisputed leader in AI chips. 

👀Here's the lowdown - The company is developing a new type of processor that uses thermodynamic fluctuations, which are normally considered an obstacle in electronics, to perform highly efficient probabilistic calculations.

With AI companies burning through massive amounts of computing power to train ever-larger models and datacenters now being built next to nuclear plants to keep up, Extropic believes the time is right for a different kind of chip.

At the heart of Extropic’s technology is the probabilistic bit, or p-bit, which doesn’t operate like a traditional 0 or 1 computer bit. Instead, it fluctuates between states, with Extropic able to control the probability of each. CEO Guillaume Verdon describes this as the foundation for a new class of energy-efficient computing.

“This may look like a simple signal on an oscilloscope, but it represents the birth of the world’s first scalable, mass-manufacturable, and energy-efficient probabilistic computing platform,” says Verdon, who is also known for his online persona, Based Beff Jezos.

⚖️The odds of success - Extropic’s chips are optimized for Monte Carlo simulations, which are computations widely used in AI, finance, and scientific modeling. But going up against Nvidia is a tall order. Nvidia’s GPUs remain the gold standard for AI training, and switching to an entirely new architecture isn’t easy.

Still, Extropic argues that with AI’s insatiable demand for computing power, the industry is ripe for disruption. As Verdon puts it, “The reality is that the most computationally-hungry workloads are Monte Carlo simulations.” Given the pressure on power grids, the soaring costs of AI, and the growing need for efficiency, perhaps trying to reinvent computing isn’t so far-fetched after all.

Read the full story here. 

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