A fundamental challenge to providing edge-AI services is the need for a machine learning (ML) model that achieves personalization (i.e., to individual clients) and generalization (i.e., to unseen data) properties concurrently. Existing techniques in federated learning (FL) have encountered a steep tradeoff between these objectives and impose large computational requirements on edge devices during training and inference. In this paper, we propose SplitGP, a new split learning solution that can simultaneously capture generalization and personalization capabilities for efficient inference across resource-constrained clients (e.g., mobile/IoT devices). Our key idea is to split the full ML model into client-side and server-side components, and impose different roles to them: the client-side model is trained to have strong personalization capability optimized to each client's main task, while the server-side model is trained to have strong generalization capability for handling all clients' out-of-distribution tasks. We analytically characterize the convergence behavior of SplitGP, revealing that all client models approach stationary points asymptotically. Further, we analyze the inference time in SplitGP and provide bounds for determining model split ratios. Experimental results show that SplitGP outperforms existing baselines by wide margins in inference time and test accuracy for varying amounts of out-of-distribution samples.

For unsupervised pretraining, mask-reconstruction pretraining (MRP) approaches, e.g. MAE and data2vec, randomly mask input patches and then reconstruct the pixels or semantic features of these masked patches via an auto-encoder. Then for a downstream task, supervised fine-tuning the pretrained encoder remarkably surpasses the conventional ``supervised learning'' (SL) trained from scratch. However, it is still unclear 1) how MRP performs semantic feature learning in the pretraining phase and 2) why it helps in downstream tasks. To solve these problems, we first theoretically show that on an auto-encoder of a two/one-layered convolution encoder/decoder, MRP can capture all discriminative features of each potential semantic class in the pretraining dataset. Then considering the fact that the pretraining dataset is of huge size and high diversity and thus covers most features in downstream dataset, in fine-tuning phase, the pretrained encoder can capture as much features as it can in downstream datasets, and would not lost these features with theoretical guarantees. In contrast, SL only randomly captures some features due to lottery ticket hypothesis. So MRP provably achieves better performance than SL on the classification tasks. Experimental results testify to our data assumptions and also our theoretical implications.

Web 3.0 and the metaverse are two emerging technologies that are expected to revolutionize how we do business in the coming years. Web 3.0, also known as the semantic web, is a next-generation version of the internet that aims to make it more intelligent and intuitive for users. It does this by using artificial intelligence (AI) and machine learning algorithms to understand the context and meaning of the data that is being shared, making it easier for users to find the information they are looking for. The metaverse, on the other hand, is a virtual world that is created and maintained by users. It allows people to interact with each other and with virtual objects in a fully immersive and interactive environment.

Data scientist Sahar Nasiri originally went to college to study industrial engineering. After taking Andrew Ng's Machine Learning course on a professor's recommendation, however, she knew she wanted her future to be in AI. Now she uses AI to help Delta Airlines keep its planes in top operating condition. She spoke with us about her early interview struggles, how she landed her first job, and the value of truly understanding statistics. Can you tell me about your current role? When did you start, what is your title, and what are your primary responsibilities?

If so, then this course is for you! With DALL•E, you'll learn how to create impressive AI art in minutes. You'll learn how to use DALL•E to create art; we'll go through all of the tools available so you can master this revolutionary new medium. Whether you're a seasoned artist or a complete beginner, this course will give you the skills you need to get started in the exciting world of AI art. This course takes you step-by-step through the process of becoming a professional AI artist.

Studies at the molecular level demonstrate that dietary amino acid intake produces substantial effects on health and disease by modulating metabolism. However, how these effects may manifest in human food consumption and dietary patterns is unknown. Here, we develop a series of algorithms to map, characterize and model the landscape of amino acid content in human food, dietary patterns, and individual consumption including relations to health status, covering over 2,000 foods, ten dietary patterns, and over 30,000 dietary profiles. We find that the type of amino acids contained in foods and human consumption is highly dynamic with variability far exceeding that of fat and carbohydrate. Some amino acids positively associate with conditions such as obesity while others contained in the same food negatively link to disease. Using linear programming and machine learning, we show that these health trade-offs can be accounted for to satisfy biochemical constraints in food and human eating patterns to construct a Pareto front in dietary practice, a means of achieving optimality in the face of trade-offs that are commonly considered in economic and evolutionary theories. Thus this study may enable the design of human protein quality intake guidelines based on a quantitative framework. Amino acids are important components in a variety of human foods and diets. Here, the authors show trade-offs linking dietary intake of amino acids to human health and develop amino acid intake guidelines based on them.

Created by Raymond Davey 4.5 hours on-demand video course Ever since OpenAI arrived on the scene, access to a trained AI has become accessible to everyone. GPT allows you to ask a chatbot to complete tasks, and to answer questions. Fine-tuning allows you to change the way the AI responds. Embedding allows you to use your own knowledge base. Dall-E allows you to generate images from text.

From 2001: A Space Odyssey and Ex Machina to Wall-E and Her, artificial intelligence has reliably been a subject of fascination in modern culture. But AI is no longer a thing of imagination, books or film scripts – it is already playing a pivotal role in both our professional and personal lives. And when it comes to the capability of this next-generation technology, we are now on the precipice of an exponential leap. The potential impact of AI on our lives cannot be understated, so the growing AI skills gap must be addressed if we are to ensure that businesses are prepared to take this jump. AI has already transformed the way we interact with banks, how we shop and how we manufacture.

This course gives you a comprehensive introduction to both the theory and practice of machine learning. You will learn to use Python along with industry-standard libraries and tools, including Pandas, Scikit-learn, and Tensorflow, to ingest, explore, and prepare data for modeling and then train and evaluate models using a wide variety of techniques. Those techniques include linear regression with ordinary least squares, logistic regression, support vector machines, decision trees and ensembles, clustering, principal component analysis, hidden Markov models, and deep learning. A key feature of this course is that you not only learn how to apply these techniques, you also learn the conceptual basis underlying them so that you understand how they work, why you are doing what you are doing, and what your results mean. The course also features real-world datasets, drawn primarily from the realm of public policy.

As AI models continue to advance into many real-life applications, their ability to maintain reliable quality over time becomes increasingly important. The principal challenge in this task stems from the very nature of current machine learning models, dependent on the data as it was at the time of training. In this study, we present the first analysis of AI “aging”: the complex, multifaceted phenomenon of AI model quality degradation as more time passes since the last model training cycle. Using datasets from four different industries (healthcare operations, transportation, finance, and weather) and four standard machine learning models, we identify and describe the main temporal degradation patterns. We also demonstrate the principal differences between temporal model degradation and related concepts that have been explored previously, such as data concept drift and continuous learning. Finally, we indicate potential causes of temporal degradation, and suggest approaches to detecting aging and reducing its impact.