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Nimrat Kaur Opens Up on Playing The Villain in The Family Man Season 3_我的网站

A |     The third season of The Family Man has been released with the introduction of newer characters. Jaideep Ahlawat, Nimrat Kaur and more have joined the series. Opening up about her villainous role, Nimrat Kaur shared how she tried to focus on playing the self-assured character who operates ruthlessly in a male-dominated sphere.          Speaking about the nitty gritties of playing such a role, she noted, "I think it's fascinating that the writer's room is all men. We are meeting a woman in a man's world who is running a show like a man and is written by men. I find that fascinating and it is so empowering to take that position and then fly with it and then look at your own self. And many times I used to actually think about my own self and I used to be like - it would be kind of cool to be this self assured about so many situations which otherwise I would just you know be under a quilt about."Nimrat acknowledged the gap between the character's self-assuredness and her own personal life. She added, "It is so interesting that what I'm trying to sell as an idea or as a character on screen, I'm not sure if I can pull it off in real life. I really don't know if I have it in me with my conditioning, my upbringing, all of that. This is the freedom of being an actor."Apart from Nimrat and Jaideep, the series marks the return of fan-favourite actors like Manoj Bajpayee (Srikant Tiwari), Sharib Hashmi (JK) and Priyamani (Suchitra).          Opening up on the season, our reviewer noted, "Season 3 may not be the franchise’s most cohesive outing, but it remains gripping, emotionally alert, and rich with possibility. Its excesses suggest a universe ready to grow, what it now needs is not less ambition, but sharper focus."Also Read: The Lunchbox: Love and Longing Served With Melancholy and Measure。    “Sim2Real”是描述人工智能创造过程的一种方式，其中机器学习模型被教导在虚拟环境或模拟中做什么，然后在现实世界中应用这些知识。当需要多年的试验和错误才能得出一个有效的模型时，这是很有必要的--在模拟中进行，可以在几分钟或几小时内完成多年的实时训练。但是，在模拟中做一些事情并不总是可能的；例如，如果一个机器人需要与人互动呢？这不是那么容易模拟的，所以你需要真实世界的数据来开始。你最终会遇到一个鸡和蛋的问题：你没有人类的数据，因为你需要它来制造人类将与之互动的机器人，并首先产生这些数据。Google的研究人员通过简单的开始和制造一个反馈回路来解决这个难题。[i-Sim2Real]使用一个简单的人类行为模型作为近似的起点，在模拟训练和在现实世界中部署之间交替进行。在每次迭代中，人类行为模型和政策都会得到完善。从人类行为的近似值开始是可以的，因为机器人也只是刚刚开始学习。每场比赛都会收集到更多真实的人类数据，从而提高准确性，让人工智能学习更多。这种方法足够成功，该团队的乒乓球机器人已能够连续对打340次。它还能够将球送回不同的区域，当然不是准确的数学精度，但是好到可以开始执行策略。该团队还尝试了一种不同的方法，以实现更多的目标行为，比如从不同的位置将球返回到一个非常具体的地方。

B | 同样，这并不是要创造终极乒乓球机（尽管这很可能是一个结果），而是要找到有效训练人类互动的方法，而不是让人们重复成千上万次的相同动作。

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