Supervised Deep Learning is similar to concept learning in humans and animals, the difference being that the student in the former case is a computational network. Supervised deep learning frameworks are trained using well-labelled data. It teaches the learning algorithm to generalise from the training data and to implement in unseen situations. After completing the training process, the model is tested on a subset of the testing set to predict the output. Thus, datasets containing inputs and correct outputs become critical as they help the model learn faster. Regression and classification are two subfields of supervised machine learning.

Now, any biochemist can speed their work in much the same way. On Thursday, DeepMind released the predicted shapes of more than 350,000 proteins -- the microscopic mechanisms that drive the behavior of bacteria, viruses, the human body and all other living things. This new database includes the 3D structures for all proteins expressed by the human genome, as well as those for proteins that appear in 20 other organisms, including the mouse, fruit fly and E. coli bacterium.

A grieving Canadian man used pioneering artificial intelligence software to have life-like online "chats" with his girlfriend -- eight years after she died. Joshua Barbeau, 33, told the San Francisco Chronicle how he paid just $5 to use a beta test of GPT-3, AI software first developed by a research group co-founded by Elon Musk. Still overwhelmed by grief after losing 23-year-old girlfriend Jessica Pereira in 2012, Barbeau said he used her old text messages and Facebook posts to help the chatbot mimic his late lover's writing voice. In scenes from an episode of "Black Mirror" or the movie "Her," Barbeau broke down in tears during a 10-hour, all-night chat in September that at times eerily mimicked the woman he had lost to liver disease. "Of course it is me!" the chatbot he named after Pereira told him at the start of their talk, according to transcripts the freelance Dungeons & Dragons writer from Bradford, Ontario gave the California newspaper.

The aim of brain-computer interfaces (BCIs), also called brain-machine interfaces (BMIs), is to improve the quality of life and restore capabilities to those who are physically disabled. Last week, researchers at the Georgia Institute of Technology and their global collaborators published a new study in Advanced Science that shows a wireless brain-computer interface that uses virtual reality (VR) and artificial intelligence (AI) deep learning to convert brain imagery into actions. The brain-computer interface industry is expected to reach USD 3.7 billion by 2027 with a compound annual growth rate of 15.5 percent during 2020-2027 according to Grandview Research. "Motor imagery offers an excellent opportunity as a stimulus-free paradigm for brain–machine interfaces," wrote Woon-Hong Yeo at the Georgia Institute of Technology whose laboratory led the study in collaboration with the University of Kent in the United Kingdom and Yonsei University in the Republic of Korea. The AI, VR with BCI system was assessed on four able-bodied human participants according to a statement released on Tuesday by the Georgia Institute of Technology.

With the development of end-to-end control based on deep learning, it is important to study new system modeling techniques to realize dynamics modeling with high-dimensional inputs. In this paper, a novel Koopman-based deep convolutional network, called CKNet, is proposed to identify latent dynamics from raw pixels. CKNet learns an encoder and decoder to play the role of the Koopman eigenfunctions and modes, respectively. The Koopman eigenvalues can be approximated by eigenvalues of the learned state transition matrix. The deterministic convolutional Koopman network (DCKNet) and the variational convolutional Koopman network (VCKNet) are proposed to span some subspace for approximating the Koopman operator respectively. Because CKNet is trained under the constraints of the Koopman theory, the identified latent dynamics is in a linear form and has good interpretability. Besides, the state transition and control matrices are trained as trainable tensors so that the identified dynamics is also time-invariant. We also design an auxiliary weight term for reducing multi-step linearity and prediction losses. Experiments were conducted on two offline trained and four online trained nonlinear forced dynamical systems with continuous action spaces in Gym and Mujoco environment respectively, and the results show that identified dynamics are adequate for approximating the latent dynamics and generating clear images. Especially for offline trained cases, this work confirms CKNet from a novel perspective that we visualize the evolutionary processes of the latent states and the Koopman eigenfunctions with DCKNet and VCKNet separately to each task based on the same episode and results demonstrate that different approaches learn similar features in shapes.

COVID-19, as a global health crisis, has triggered the fear emotion with unprecedented intensity. Besides the fear of getting infected, the outbreak of COVID-19 also created significant disruptions in people's daily life and thus evoked intensive psychological responses indirect to COVID-19 infections. Here, we construct an expressed fear database using 16 million social media posts generated by 536 thousand users between January 1st, 2019 and August 31st, 2020 in China. We employ deep learning techniques to detect the fear emotion within each post and apply topic models to extract the central fear topics. Based on this database, we find that sleep disorders ("nightmare" and "insomnia") take up the largest share of fear-labeled posts in the pre-pandemic period (January 2019-December 2019), and significantly increase during the COVID-19. We identify health and work-related concerns are the two major sources of fear induced by the COVID-19. We also detect gender differences, with females generating more posts containing the daily-life fear sources during the COVID-19 period. This research adopts a data-driven approach to trace back public emotion, which can be used to complement traditional surveys to achieve real-time emotion monitoring to discern societal concerns and support policy decision-making.

Recent advances in the areas of multimodal machine learning and artificial intelligence (AI) have led to the development of challenging tasks at the intersection of Computer Vision, Natural Language Processing, and Embodied AI. Whereas many approaches and previous survey pursuits have characterised one or two of these dimensions, there has not been a holistic analysis at the center of all three. Moreover, even when combinations of these topics are considered, more focus is placed on describing, e.g., current architectural methods, as opposed to also illustrating high-level challenges and opportunities for the field. In this survey paper, we discuss Embodied Vision-Language Planning (EVLP) tasks, a family of prominent embodied navigation and manipulation problems that jointly use computer vision and natural language. We propose a taxonomy to unify these tasks and provide an in-depth analysis and comparison of the new and current algorithmic approaches, metrics, simulated environments, as well as the datasets used for EVLP tasks. Finally, we present the core challenges that we believe new EVLP works should seek to address, and we advocate for task construction that enables model generalizability and furthers real-world deployment.

Accurately forecasting Arctic sea ice from subseasonal to seasonal scales has been a major scientific effort with fundamental challenges at play. In addition to physics-based earth system models, researchers have been applying multiple statistical and machine learning models for sea ice forecasting. Looking at the potential of data-driven sea ice forecasting, we propose an attention-based Long Short Term Memory (LSTM) ensemble method to predict monthly sea ice extent up to 1 month ahead. Using daily and monthly satellite retrieved sea ice data from NSIDC and atmospheric and oceanic variables from ERA5 reanalysis product for 39 years, we show that our multi-temporal ensemble method outperforms several baseline and recently proposed deep learning models. This will substantially improve our ability in predicting future Arctic sea ice changes, which is fundamental for forecasting transporting routes, resource development, coastal erosion, threats to Arctic coastal communities and wildlife.

State-of-the-art neural network architectures continue to scale in size and deliver impressive generalization results, although this comes at the expense of limited interpretability. In particular, a key challenge is to determine when to stop training the model, as this has a significant impact on generalization. Convolutional neural networks (ConvNets) comprise high-dimensional feature spaces formed by the aggregation of multiple channels, where analyzing intermediate data representations and the model's evolution can be challenging owing to the curse of dimensionality. We present channel-wise DeepNNK (CW-DeepNNK), a novel channel-wise generalization estimate based on non-negative kernel regression (NNK) graphs with which we perform local polytope interpolation on low-dimensional channels. This method leads to instance-based interpretability of both the learned data representations and the relationship between channels. Motivated by our observations, we use CW-DeepNNK to propose a novel early stopping criterion that (i) does not require a validation set, (ii) is based on a task performance metric, and (iii) allows stopping to be reached at different points for each channel. Our experiments demonstrate that our proposed method has advantages as compared to the standard criterion based on validation set performance.

Validating the safety of autonomous systems generally requires the use of high-fidelity simulators that adequately capture the variability of real-world scenarios. However, it is generally not feasible to exhaustively search the space of simulation scenarios for failures. Adaptive stress testing (AST) is a method that uses reinforcement learning to find the most likely failure of a system. AST with a deep reinforcement learning solver has been shown to be effective in finding failures across a range of different systems. This approach generally involves running many simulations, which can be very expensive when using a high-fidelity simulator. To improve efficiency, we present a method that first finds failures in a low-fidelity simulator. It then uses the backward algorithm, which trains a deep neural network policy using a single expert demonstration, to adapt the low-fidelity failures to high-fidelity. We have created a series of autonomous vehicle validation case studies that represent some of the ways low-fidelity and high-fidelity simulators can differ, such as time discretization. We demonstrate in a variety of case studies that this new AST approach is able to find failures with significantly fewer high-fidelity simulation steps than are needed when just running AST directly in high-fidelity. As a proof of concept, we also demonstrate AST on NVIDIA's DriveSim simulator, an industry state-of-the-art high-fidelity simulator for finding failures in autonomous vehicles.