Research into humanoid robots is a rapidly advancing field, with companies around the world striving to produce robots that look and act more like us. But what is it about recreating ourselves in robot form that we find so captivating? Why do humanoid robots both enthral and terrify us? And is our obsession with robotic humans just vanity, or could they play valuable roles in our future society? In this special live recording at Imperial College London as part of the Great Exhibition Road Festival, Claire chatted to Ben Russell (Science Museum), Maryam Banitalebi Dehkordi (University of Hertfordshire) and Petar Kormushev (Imperial College London) about humanoid robotics.

After Julia Dixon graduated from the University of Michigan in 2014, her family and friends asked for her help with the college application process. Dixon was happy to share her recently earned expertise about the world of higher education but soon realized how many parents and students in her community needed help and how hard it was for them to access that support. The ratio of college counselors to students in the US, according to the American School Counselor Association, is one for every 376 students. Many students don't have proper access to a college counselor to help them with admissions or pick which schools and areas of study might suit them best. Hiring a private college counselor is an option, but that can cost thousands of dollars.

Thousands of university students in the UK have been caught misusing ChatGPT and other artificial intelligence tools in recent years, while traditional forms of plagiarism show a marked decline, a Guardian investigation can reveal. A survey of academic integrity violations found almost 7,000 proven cases of cheating using AI tools in 2023-24, equivalent to 5.1 for every 1,000 students. That was up from 1.6 cases per 1,000 in 2022-23. Figures up to May suggest that number will increase again this year to about 7.5 proven cases per 1,000 students – but recorded cases represent only the tip of the iceberg, according to experts. The data highlights a rapidly evolving challenge for universities: trying to adapt assessment methods to the advent of technologies such as ChatGPT and other AI-powered writing tools.

Leo Goldsmith, an assistant professor of screen studies at the New School, can tell when you use AI to cheat on an assignment. There's just no good way for him to prove it. "I know a lot of examples where educators, and I've had this experience too, where they receive an assignment from a student, they're like, 'This is gotta be AI,' and then they don't have" any simple way of proving that, Goldsmith told me. "This is true with all kinds of cheating: The process itself is quite a lot of work, and if the goal of that process is to get an undergraduate, for example, kicked out of school, very few people want to do this." This is the underlying hum AI has created in academia: my students are using AI to cheat, and there's not much I can do about it.

These free online courses don't include certificates of completion or direct instructor messaging, but you still get unrestricted access to all the video content. So there's no unpleasant catch to worry about. Find the best free AI courses on Udemy.

The catch with these free courses is that they don't include certificate of completion or graded assignments and exams. But you can still enroll at any time and start learning at your own pace. Find the best free online courses from Harvard University with edX.

Good Job is Slate's advice column on work. Have a workplace problem big or small? I have been an eighth-grade teacher for seven years now and am beginning to think I made a terrible mistake in terms of choosing my profession. The kids I teach are rude and feral. They refuse to read or treat others with the slightest bit of decency, give up at the first sign of difficulty, and possess the attention span of goldfish.

The goal of minimizing misclassification error on a training set is often just one of several real-world goals that might be defined on different datasets. For example, one may require a classifier to also make positive predictions at some specified rate for some subpopulation (fairness), or to achieve a specified empirical recall. Other real-world goals include reducing churn with respect to a previously deployed model, or stabilizing online training. In this paper we propose handling multiple goals on multiple datasets by training with dataset constraints, using the ramp penalty to accurately quantify costs, and present an efficient algorithm to approximately optimize the resulting non-convex constrained optimization problem. Experiments on both benchmark and real-world industry datasets demonstrate the effectiveness of our approach.

Out-of-distribution (OOD) detection is critical when deploying machine learning models in the real world. Outlier exposure methods, which incorporate auxiliary outlier data in the training process, can drastically improve OOD detection performance compared to approaches without advanced training strategies. We introduce Hopfield Boosting, a boosting approach, which leverages modern Hopfield energy to sharpen the decision boundary between the in-distribution and OOD data. Hopfield Boosting encourages the model to focus on hard-to-distinguish auxiliary outlier examples that lie close to the decision boundary between in-distribution and auxiliary outlier data. Our method achieves a new state-of-the-art in OOD detection with outlier exposure, improving the FPR95 from 2.28 to 0.92 on CIFAR-10, from 11.76 to 7.94 on CIFAR-100, and from 50.74 to 36.60 on ImageNet-1K.

We develop a method to generate prediction sets with a guaranteed coverage rate that is robust to corruptions in the training data, such as missing or noisy variables. Our approach builds on conformal prediction, a powerful framework to construct prediction sets that are valid under the i.i.d assumption. Importantly, naively applying conformal prediction does not provide reliable predictions in this setting, due to the distribution shift induced by the corruptions. To account for the distribution shift, we assume access to privileged information (PI). The PI is formulated as additional features that explain the distribution shift, however, they are only available during training and absent at test time. We approach this problem by introducing a novel generalization of weighted conformal prediction and support our method with theoretical coverage guarantees. Empirical experiments on both real and synthetic datasets indicate that our approach achieves a valid coverage rate and constructs more informative predictions compared to existing methods, which are not supported by theoretical guarantees.