Learning from a sequence of tasks for a lifetime is essential for an agent towards artificial general intelligence. This requires the agent to continuously learn and memorize new knowledge without interference. This paper first demonstrates a fundamental issue of lifelong learning using neural networks, named anterograde forgetting, i.e., preserving and transferring memory may inhibit the learning of new knowledge. This is attributed to the fact that the learning capacity of a neural network will be reduced as it keeps memorizing historical knowledge, and the fact that conceptual confusion may occur as it transfers irrelevant old knowledge to the current task. This work proposes a general framework named Cycled Memory Networks (CMN) to address the anterograde forgetting in neural networks for lifelong learning. The CMN consists of two individual memory networks to store short-term and long-term memories to avoid capacity shrinkage. A transfer cell is designed to connect these two memory networks, enabling knowledge transfer from the long-term memory network to the short-term memory network to mitigate the conceptual confusion, and a memory consolidation mechanism is developed to integrate short-term knowledge into the long-term memory network for knowledge accumulation. Experimental results demonstrate that the CMN can effectively address the anterograde forgetting on several task-related, task-conflict, class-incremental and cross-domain benchmarks.

Cold-start problem is a fundamental challenge for recommendation tasks. The recent self-supervised learning (SSL) on Graph Neural Networks (GNNs) model, PT-GNN, pre-trains the GNN model to reconstruct the cold-start embeddings and has shown great potential for cold-start recommendation. However, due to the over-smoothing problem, PT-GNN can only capture up to 3-order relation, which can not provide much useful auxiliary information to depict the target cold-start user or item. Besides, the embedding reconstruction task only considers the intra-correlations within the subgraph of users and items, while ignoring the inter-correlations across different subgraphs. To solve the above challenges, we propose a multi-strategy based pre-training method for cold-start recommendation (MPT), which extends PT-GNN from the perspective of model architecture and pretext tasks to improve the cold-start recommendation performance. Specifically, in terms of the model architecture, in addition to the short-range dependencies of users and items captured by the GNN encoder, we introduce a Transformer encoder to capture long-range dependencies. In terms of the pretext task, in addition to considering the intra-correlations of users and items by the embedding reconstruction task, we add embedding contrastive learning task to capture inter-correlations of users and items. We train the GNN and Transformer encoders on these pretext tasks under the meta-learning setting to simulate the real cold-start scenario, making the model easily and rapidly being adapted to new cold-start users and items. Experiments on three public recommendation datasets show the superiority of the proposed MPT model against the vanilla GNN models, the pre-training GNN model on user/item embedding inference and the recommendation task.

With the great success of pre-trained models, the pretrain-then-finetune paradigm has been widely adopted on downstream tasks for source code understanding. However, compared to costly training a large-scale model from scratch, how to effectively adapt pre-trained models to a new task has not been fully explored. In this paper, we propose an approach to bridge pre-trained models and code-related tasks. We exploit semantic-preserving transformation to enrich downstream data diversity, and help pre-trained models learn semantic features invariant to these semantically equivalent transformations. Further, we introduce curriculum learning to organize the transformed data in an easy-to-hard manner to fine-tune existing pre-trained models. We apply our approach to a range of pre-trained models, and they significantly outperform the state-of-the-art models on tasks for source code understanding, such as algorithm classification, code clone detection, and code search. Our experiments even show that without heavy pre-training on code data, natural language pre-trained model RoBERTa fine-tuned with our lightweight approach could outperform or rival existing code pre-trained models fine-tuned on the above tasks, such as CodeBERT and GraphCodeBERT. This finding suggests that there is still much room for improvement in code pre-trained models.

The existence of a universal learning architecture in human cognition is a widely spread conjecture supported by experimental findings from neuroscience. While no low-level implementation can be specified yet, an abstract outline of human perception and learning is believed to entail three basic properties: (a) hierarchical attention and processing, (b) memory-based knowledge representation, and (c) progressive learning and knowledge compaction. We approach the design of such a learning architecture from a system-theoretic viewpoint, developing a closed-loop system with three main components: (i) a multi-resolution analysis pre-processor, (ii) a group-invariant feature extractor, and (iii) a progressive knowledge-based learning module. Multi-resolution feedback loops are used for learning, i.e., for adapting the system parameters to online observations. To design (i) and (ii), we build upon the established theory of wavelet-based multi-resolution analysis and the properties of group convolution operators. Regarding (iii), we introduce a novel learning algorithm that constructs progressively growing knowledge representations in multiple resolutions. The proposed algorithm is an extension of the Online Deterministic Annealing (ODA) algorithm based on annealing optimization, solved using gradient-free stochastic approximation. ODA has inherent robustness and regularization properties and provides a means to progressively increase the complexity of the learning model i.e. the number of the neurons, as needed, through an intuitive bifurcation phenomenon. The proposed multi-resolution approach is hierarchical, progressive, knowledge-based, and interpretable. We illustrate the properties of the proposed architecture in the context of the state-of-the-art learning algorithms and deep learning methods.

Description Build Your own Self Driving Car Deep Learning, OpenCV, C is an IoT training course focused on self-driving cars published by Yodemi Academy. In this course, you will use various technologies such as Raspberry Pi computer boards, Arduino UNO board, image processing technology, virtual neural networks, machine learning techniques, etc., and are familiar with the use of each of these tools in the world of the Internet of Things. Machine learning and artificial intelligence are two modern technologies that will have many job opportunities in the near future. The development of IoT-based systems has specific and separate steps and processes that you will learn about in all of these processes. Among the most important topics covered in this course are hardware design, initial installation of Raspberry Pi and Arduino boards, establishing communication links between devices and different parts of the car, image processing with OpenCV4, various techniques Machine learning and… pointed out.

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AZEEM AZHAR: Welcome to The Exponential View podcast where multidisciplinary conversations about the near future happen every week. Now, as an entrepreneur, investor, and analyst I've been inside the technology industry for over 20 years. During that time, I've observed that exponentially developing technologies are changing the face of our economies, business models, and culture in unexpected ways. Now, I return to this question every week in my newsletter Exponential View, in this podcast, as well as in my recent book The Exponential Age. So, in today's edition I wanted to look back and forward on one of the key technologies of the exponential age, artificial intelligence. We're about a decade into the current industrial boom in AI and I thought it was time to take a scorecard, look at what we've achieved, and how and perhaps what we didn't on which milestones have surprised us. To help me I called on a great experts Murray Shanahan, a senior research scientist at London's DeepMind, as well as a professor of cognitive robotics at Imperial College in London. Murray works on machine learning, consciousness, the impacts of artificial intelligence. He and I have known each other for a few years and have indeed done a podcast together previously. We appeared as guests on a show hosted by a technology investor. So, my challenge to Murray today was not simply to access the last 10 years of development, but to look forward to the next 10. It's a bold challenge and we did our best to look forward as well as back. MURRAY SHANAHAN: It's very nice to be here.

Amazon Web Services Inc. says it's trying to break down accessibility barriers around machine learning technology and make it possible for anyone who's interested to become an expert in the field. To that end, the company is launching an artificial intelligence and machine learning education and scholarship program that's specifically aimed at underrepresented and underserved students around the world. The company is also boosting access to machine learning with the launch of a free version of its Amazon SageMaker service that's used by enterprises to build, train and deploy machine learning models in production. The AWS AI & ML Scholarship, announced today during Amazon's annual re:Invent, which it styles as a "education" conference, provides educational content centered on machine learning basics. What's more interesting, though, is that students will be able to use the AWS DeepRacer service to to put their new-found machine learning skills into action.

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