Can AI be used ethically for school work? It depends upon who you ask -- quite literally. That's because less than two years after ChatGPT was originally released in November 2022, the attitudes towards AI in the classroom still vary widely. High schools have viewed AI as a crutch at best, and at worst as a tool for cheating. But several universities leave generative AI use entirely up to the discretion of the person teaching the course.

Swimming microrobots are increasingly developed with complex materials and dynamic shapes and are expected to operate in complex environments in which the system dynamics are difficult to model and positional control of the microrobot is not straightforward to achieve. Deep reinforcement learning is a promising method of autonomously developing robust controllers for creating smart microrobots, which can adapt their behavior to operate in uncharacterized environments without the need to model the system dynamics. Here, we report the development of a smart helical magnetic hydrogel microrobot that used the soft actor critic reinforcement learning algorithm to autonomously derive a control policy which allowed the microrobot to swim through an uncharacterized biomimetic fluidic environment under control of a time varying magnetic field generated from a three-axis array of electromagnets. The reinforcement learning agent learned successful control policies with fewer than 100,000 training steps, demonstrating sample efficiency for fast learning. We also demonstrate that we can fine tune the control policies learned by the reinforcement learning agent by fitting mathematical functions to the learned policy's action distribution via regression. Deep reinforcement learning applied to microrobot control is likely to significantly expand the capabilities of the next generation of microrobots.

Unless one is having a medical emergency, going to an ER, in general, is often not the best way for a patient to receive care for more minor ailments. It is expensive, especially for the uninsured, but increasingly for the insured with a high deductible as well. It's also incredibly time-consuming; ERs by nature of what they are triage patients in order of need, meaning if one is not profusely bleeding, unconscious, actively having a heart attack or a baby, the wait can be long. And that is just the baseline. In a world where the COVID-19 virus is spreading rapidly, the government, health organizations and medical community have spoken very clearly and in a single voice on this: They do not want patients with non-emergency patients heading to ER, on the fear that the highly contagious virus will spread rapidly through hospitals and vulnerable patient populations.

Gather student data, make predictions about their learning--and perhaps their future. For years education companies have tried to apply technologies to better understand students and tailor their learning experiences, or support instructors who can intervene when human help is needed. Today the latest buzz revolves around machine learning, which education technologists claim can support more precise tools. And what it takes to make these products effective, and how to boost student learning equitably and ethically, remains an ongoing debate. Speakers quickly contextualized the technology with the shift in how widely available data is today.

Google scientists have developed the first computer program capable of learning a wide variety of tasks independently, in what has been hailed as a significant step towards true artificial intelligence. The same program, or "agent" as its creators call it, learnt to play 49 different retro computer games, and came up with its own strategies for winning. In the future, the same approach could be used to power self-driving cars, personal assistants in smartphones or conduct scientific research in fields from climate change to cosmology. The research was carried out by DeepMind, the British company bought by Google last year for £400m, whose stated aim is to build "smart machines". Demis Hassabis, the company's founder said: "This is the first significant rung of the ladder towards proving a general learning system can work. It can work on a challenging task that even humans find difficult. The work is seen as a fundamental departure from previous attempts to create AI, such as the program Deep Blue, which famously beat Gary Kasparov at chess in 1997 or IBM's Watson, which won the quiz show Jeopardy! in 2011. In both these cases, computers were pre-programmed with the rules of the game and specific strategies and overcame human performance through sheer number-crunching power. "With Deep Blue, it was team of programmers and grand masters that distilled the knowledge into a program," said Hassabis. "We've built algorithms that learn from the ground up." The DeepMind agent is simply given a raw input, in this case the pixels making up the display on Atari games, and provided with a running score. When the agent begins to play, it simply watches the frames of the game and makes random button presses to see what happens. "A bit like a baby opening their eyes and seeing the world for the first time," said Hassabis. The agent uses a method called "deep learning" to turn the basic visual input into meaningful concepts, mirroring the way the human brain takes raw sensory information and transforms it into a rich understanding of the world. The agent is programmed to work out what is meaningful through "reinforcement learning", the basic notion that scoring points is good and losing them is bad. Tim Behrens, a professor of cognitive neuroscience at University College London, said: "What they've done is really impressive, there's no question.