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👉 Researchers at the Shanghai Artificial Intelligence Laboratory are combining the Monte Carlo Tree Search (MCTS) algorithm with large language models to improve its ability to solve complex mathematical problems.

Integrating the Monte Carlo Tree Search (MCTS) algorithm into large language models could significantly enhance their ability to solve complex mathematical problems. Initial experiments show promising results.

While large language models like GPT-4 have made remarkable progress in language processing, they still struggle with tasks requiring strategic and logical thinking. Particularly in mathematics, the models tend to produce plausible-sounding but factually incorrect answers.

In a new paper, researchers from the Shanghai Artificial Intelligence Laboratory propose combining language models with the Monte Carlo Tree Search (MCTS) algorithm. MCTS is a decision-making tool used in artificial intelligence for scenarios that require strategic planning, such as games and complex problem-solving. One of the most well-known applications is AlphaGo and its successor systems like AlphaZero, which have consistently beaten humans in board games. The combination of language models and MCTS has long been considered promising and is being studied by many labs — likely including OpenAI with Q*.

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Researchers in the Netherlands are developing ‘virtually painless’ injections without needles in what they hope is a breakthrough that will ease fear and encourage vaccinations.

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Sinonus uses technology developed at Chalmers University of Technology in Gothenburg, where researchers have been studying the concept of a structural battery using carbon fibre for years.

Massless batteries have been something of a holy grail for energy storage since 2007, because the weight of the battery effectively disappears once it is part of the load-bearing structure. The Chalmers team, led by professor Leif Asp, is one of the few to find a material that works.

Carbon fibre is known for its strength versus weight.

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Science and Technology: Some robots could be “eaten” so they could walk around inside the body and perform tests or surgeries from the inside out; or administer medications.

Robots made of several nanorobots joined together could assemble and reassemble themselves inside the body even after being


Robots and food have long been distant worlds: Robots are inorganic, bulky, and non-disposable; food is organic, soft, and biodegradable. Yet, research that develops edible robots has progressed recently and promises positive impacts: Robotic food could reduce , help deliver nutrition and medicines to people and animals in need, monitor health, and even pave the way to novel gastronomical experiences.

But how far are we from having a fully edible robot for lunch or dessert? And what are the challenges? Scientists from the RoboFood project, based at EPFL, address these and other questions in a perspective article in the journal Nature Reviews Materials.

“Bringing robots and food together is a fascinating challenge,” says Dario Floreano, director of the Laboratory of Intelligent Systems at EPFL and first author of the article. In 2021, Floreano joined forces with Remko Boom from Wageningen University, The Netherlands, Jonathan Rossiter from the University of Bristol, UK, and Mario Caironi from the Italian Institute of Technology, to launch the project RoboFood.

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