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A study of review reports identifies dozens of adjectives that could indicate text written with the help of chatbots. A study of review reports identifies dozens of adjectives that could indicate text written with the help of chatbots.
Large language models combined with confidence scores help them recognize uncertainty. That could be key to making robots safe and trustworthy.
<p>Engineers at Princeton University and Google have come up with a new way to teach robots to know when they don’t know. The technique involves quantifying the fuzziness of human language and using that measurement to tell robots when to ask for further directions. Telling a robot to pick up a bowl from a table with only one bowl is fairly clear. But telling a robot to pick up a bowl when there are five bowls on the table generates a much higher degree of uncertainty — and triggers the robot to ask for clarification.</p>
A new technique enables an AI agent to be guided by data crowdsourced asynchronously from nonexpert human users as it learns to complete a task through reinforcement learning. The method trains the robot faster and better than other approaches.
The answer is, of course, robot learning. Walk into nearly any robotics research lab these days and you will find teams working on tackling the issue. Rather, building more complex and capable systems will almost certainly involve a combination of solutions.