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Research

Surprisingly, large language models utilize a fairly straightforward method to access stored information.

Large language models (LLMs), such as those used in AI chatbots, are complex, and scientists are still trying to understand how they function. Researchers from MIT and other institutions conducted a study to understand how these models retrieve stored knowledge. They found that LLMs usually use a simple linear function to recover and decode information.…

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Reprogramming domestic robots to possess a certain degree of common sense.

Robots are becoming increasingly adept at handling complex household tasks, from cleaning messes to serving meals. However, their ability to handle unexpected disturbances or difficulties during these tasks has been a challenge. Common scenarios like a nudge or a slight mistake that deviates the robot from its expected path can cause the robot to restart…

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AI speeds up solution-finding in intricate situations.

Efficiently routing holiday packages is an intricate computational problem for delivery companies such as FedEx. So complex is the problem that companies often implement specialized software, termed a mixed-integer linear programming (MILP) solver. Yet, the solver may take prolonged times to offer a solution, leading companies to conclude midway, settling for suboptimal solutions bounded by…

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AI expedites the resolution of issues in intricate situations.

Companies like FedEx utilize intricate software to efficiently deliver holiday parcels, but these complex processes can often take hours or even days to complete. The software, known as a mixed-integer linear programming (MILP) solver, is often halted partway through by firms, accepting the best solution that can be gleaned in a particular timeframe, even if…

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AI enhances the speed of resolving issues in complicated situations.

Efficiently routing packages during the holiday season is a complex problem for companies like FedEx, a task often tackled with specialized software, known as mixed-integer linear programming (MILP) solvers. Although they break down the problem into smaller parts and use generic algorithms to find solutions, they could still take hours or days to complete. MIT and…

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AI enhances the speed of resolving issues in intricate situations.

While delivering holiday presents may seem straightforward for fictional characters like Santa Claus, for companies like FedEx, the task represents a complex optimization problem. To solve it, these companies usually utilize specialized software known as mixed-integer linear programming (MILP) solvers. These solvers break down large optimization problems into smaller pieces, utilizing generic algorithms to identify…

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AI enhances the resolution of issues in intricate situations.

Santa Claus delivers presents with the help of magic, but delivering holiday packages isn't quite as simple for companies like FedEx. These businesses often rely on advanced software called mixed-integer linear programming (MILP) solvers to route their deliveries. Yet, while these solvers break down complex problems into smaller, more manageable segments, it can still take…

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AI enhances the speed of solving issues in intricate situations.

The logistics of delivering holiday packages by companies such as FedEx requires specialized software for efficient routing, given the immense complexity of the optimization problem. The software currently in use, known as a mixed-integer linear programming (MILP) solver, often takes days to arrive at a solution, and even then, the companies have to accept solutions…

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A single step allows AI to produce high-grade images at a speed 30 times quicker.

In the age of artificial intelligence, computers can generate "art" using diffusion models. However, this often involves a complex, time-consuming process requiring multiple iterations for the algorithm to perfect the image. MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers have now launched a new technique that simplifies this process into a single step using…

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A fresh approach relies on collective input from the public to assist in the education of robots.

A team of researchers from MIT, Harvard University, and the University of Washington have developed a novel reinforcement learning technique using crowdsourced feedback. The technique allows AI to learn complex tasks more quickly and without relying on an expertly designed reward function. The conventional reward function designed by dedicated human experts has been replaced by…

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A novel approach employs collective public input to assist in educating robots.

Reinforcement learning, which involves teaching an AI agent a new task using a trial and error methodology, often requires the assistance of a human expert to create and modify the reward function. However, this can be time-consuming, inefficient and difficult to upscale, particularly when the task is highly complex and involves several stages. In response…

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A novel approach incorporates feedback from the public to assist in teaching robots.

Researchers from MIT, Harvard, and the University of Washington have developed a new method for training AI agents using reinforcement learning. Their approach replaces a process often involving a time-consuming design of a reward function by a human expert with feedback crowdsourced from non-expert users. Traditionally, AI reinforcement learning has used a reward function, designed by…

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