"Learning about the social implications of the technology you're working on is really important," says senior Austen Roberson. Austen Roberson's favorite class at MIT is 2.S007 (Design and Manufacturing I-Autonomous Machines), in which students design, build, and program a fully autonomous robot to accomplish tasks laid out on a themed game board. "The best thing about that class is everyone had a different idea," says Roberson. "We all had the same game board and the same instructions given to us, but the robots that came out of people's minds were so different." The game board was Mars-themed, with a model shuttle that could be lifted to score points.

Here at Hugging Face, we're on a journey to advance good Machine Learning and make it more accessible. Along the way, we contribute to the development of technology for the better. We have built the fastest-growing, open-source library of pre-trained models in the world. With over 100M installs and 65K stars on GitHub, over 10 thousand companies are using HF technology in production, including leading AI organizations such as Google, Elastic, Salesforce, Algolia, and Grammarly. As an intern on the open-source team, you will work to improve the open-source machine learning ecosystem.

More than 1,000 Airbus employees have graduated from Udacity's Data Analytics and Artificial Intelligence/Machine Learning Nanodegree programs.

Former English teacher, Peter Laffin, predicts OpenAI's new artificial intelligence chatbot will lead to a learning crisis and force teachers to rethink education. The emergence of artificial intelligence chatbots that can complete students' assignments will lead to a crisis in learning, forcing educators to rethink schooling entirely, a former teacher said. "The introduction of new artificial intelligence technologies into schools that enables students to auto-generate essays has the capacity to blow up our entire writing education curriculum," Peter Laffin, founder of Crush the College Essay and writing coach, told Fox News. "It may make us have to rethink it from the ground up, and that might ultimately be a good thing." Last week, tech company OpenAI unveiled an AI chatbot, ChatGPT, which has stunned users with its advanced functions.

A few weeks ago, Wharton professor Ethan Mollick told his MBA students to play around with GPT, an artificial intelligence model, and see if the technology could write an essay based on one of the topics discussed in his course. The assignment was, admittedly, mostly a gimmick meant to illustrate the power of the technology. Still, the algorithmically generated essays -- although not perfect and a tad over-reliant on the passive voice -- were at least reasonable, Mollick recalled. They also passed another critical test: a screening by Turnitin, a popular anti-plagiarism software. AI, it seems, had suddenly gotten pretty good.

The ability to learn from human demonstration endows robots with the ability to automate various tasks. However, directly learning from human demonstration is challenging since the structure of the human hand can be very different from the desired robot gripper. In this work, we show that manipulation skills can be transferred from a human to a robot through the use of micro-evolutionary reinforcement learning, where a five-finger human dexterous hand robot gradually evolves into a commercial robot, while repeated interacting in a physics simulator to continuously update the policy that is first learned from human demonstration. To deal with the high dimensions of robot parameters, we propose an algorithm for multi-dimensional evolution path searching that allows joint optimization of both the robot evolution path and the policy. Through experiments on human object manipulation datasets, we show that our framework can efficiently transfer the expert human agent policy trained from human demonstrations in diverse modalities to target commercial robots.

Deep operator network (DeepONet) has demonstrated great success in various learning tasks, including learning solution operators of partial differential equations. In particular, it provides an efficient approach to predict the evolution equations in a finite time horizon. Nevertheless, the vanilla DeepONet suffers from the issue of stability degradation in the long-time prediction. This paper proposes a {\em transfer-learning} aided DeepONet to enhance the stability. Our idea is to use transfer learning to sequentially update the DeepONets as the surrogates for propagators learned in different time frames. The evolving DeepONets can better track the varying complexities of the evolution equations, while only need to be updated by efficient training of a tiny fraction of the operator networks. Through systematic experiments, we show that the proposed method not only improves the long-time accuracy of DeepONet while maintaining similar computational cost but also substantially reduces the sample size of the training set.

Unsupervised source-free domain adaptation methods aim to train a model to be used in the target domain utilizing the pretrained source-domain model and unlabeled target-domain data, where the source data may not be accessible due to intellectual property or privacy issues. These methods frequently utilize self-training with pseudo-labeling thresholded by prediction confidence. In a source-free scenario, only supervision comes from target data, and thresholding limits the contribution of the self-training. In this study, we utilize self-training with a mean-teacher approach. The student network is trained with all predictions of the teacher network. Instead of thresholding the predictions, the gradients calculated from the pseudo-labels are weighted based on the reliability of the teacher's predictions. We propose a novel method that uses proxy-based metric learning to estimate reliability. We train a metric network on the encoder features of the teacher network. Since the teacher is updated with the moving average, the encoder feature space is slowly changing. Therefore, the metric network can be updated in training time, which enables end-to-end training. We also propose a metric-based online ClassMix method to augment the input of the student network where the patches to be mixed are decided based on the metric reliability. We evaluated our method in synthetic-to-real and cross-city scenarios. The benchmarks show that our method significantly outperforms the existing state-of-the-art methods.

As Artificial and Robotic Systems are increasingly deployed and relied upon for real-world applications, it is important that they exhibit the ability to continually learn and adapt in dynamically-changing environments, becoming Lifelong Learning Machines. Continual/lifelong learning (LL) involves minimizing catastrophic forgetting of old tasks while maximizing a model's capability to learn new tasks. This paper addresses the challenging lifelong reinforcement learning (L2RL) setting. Pushing the state-of-the-art forward in L2RL and making L2RL useful for practical applications requires more than developing individual L2RL algorithms; it requires making progress at the systems-level, especially research into the non-trivial problem of how to integrate multiple L2RL algorithms into a common framework. In this paper, we introduce the Lifelong Reinforcement Learning Components Framework (L2RLCF), which standardizes L2RL systems and assimilates different continual learning components (each addressing different aspects of the lifelong learning problem) into a unified system. As an instantiation of L2RLCF, we develop a standard API allowing easy integration of novel lifelong learning components. We describe a case study that demonstrates how multiple independently-developed LL components can be integrated into a single realized system. We also introduce an evaluation environment in order to measure the effect of combining various system components. Our evaluation environment employs different LL scenarios (sequences of tasks) consisting of Starcraft-2 minigames and allows for the fair, comprehensive, and quantitative comparison of different combinations of components within a challenging common evaluation environment.

Moreover, with the ever-increasing transmission bandwidth, nonlinearity becomes even more important [1]. Various digital signal processing (DSP) techniques have been proposed to minimize nonlinear effects [2]. Due to the universal approximation capability of neural networks (NNs), the NNs have recently been intensively studied for the optical channel post-equalization, because they can approximate the inverse optical channel transfer function with good accuracy and revert the nonlinear distortions. In particular, recurrent NNs (RNN) based equalizers have shown the best capability in equalizing nonlinear impairments as compared to the feed-forward NN types [3-5]. However, since the RNN structure has a feedback loop, it is not easily parallelizable.