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Amazon is offering the SiriusXM Roady BT In-Vehicle Satellite Radio Kit for $69.99 shipped. Down 30% from its normal going rate at Amazon, today’s deal marks a new low that we’ve tracked there and is also the first discount all-time at the retailer. Designed to deliver in-vehicle entertainment, the Roady BT satellite radio installs in your car and connects to your stereo through Bluetooth, 3.5mm aux, or over a built-in FM transmitter. You can choose to mount it via a magnetic vent or dash adapter and there’s an additional mounting system that’s sold separately should you need it. Plus, it comes with a three month free trial of Sirius XM or you could opt for 12 months of the brand’s Platinum Programming Package for $99. Keep reading for more.

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The post SiriusXM Roady BT in-car satellite radio kit lets you tune in anywhere for $70 (First sale) appeared first on 9to5Toys.

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Telling AI model to “take a deep breath” causes math scores to soar in study

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A worried-looking tin toy robot.

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Google DeepMind researchers recently developed a technique to improve math ability in AI language models like ChatGPT by using other AI models to improve prompting—the written instructions that tell the AI model what to do. It found that using human-style encouragement improved math skills dramatically, in line with earlier results.

In a paper called "Large Language Models as Optimizers" listed this month on arXiv, DeepMind scientists introduced Optimization by PROmpting (OPRO), a method to improve the performance of large language models (LLMs) such as OpenAI’s ChatGPT and Google’s PaLM 2. This new approach sidesteps the limitations of traditional math-based optimizers by using natural language to guide LLMs in problem-solving. "Natural language" is a fancy way of saying everyday human speech.

"Instead of formally defining the optimization problem and deriving the update step with a programmed solver," the researchers write, "we describe the optimization problem in natural language, then instruct the LLM to iteratively generate new solutions based on the problem description and the previously found solutions."

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