Class incremental learning (CIL) trains a network on sequential tasks with separated categories in each task but suffers from catastrophic forgetting, where models quickly lose previously learned knowledge when acquiring new tasks. The generalized CIL (GCIL) aims to address the CIL problem in a more real-world scenario, where incoming data have mixed data categories and unknown sample size distribution. Existing attempts for the GCIL either have poor performance or invade data privacy by saving exemplars. In this paper, we propose a new exemplar-free GCIL technique named generalized analytic continual learning (GACL). The GACL adopts analytic learning (a gradient-free training technique) and delivers an analytical (i.e., closed-form) solution to the GCIL scenario. This solution is derived via decomposing the incoming data into exposed and unexposed classes, thereby attaining a weight-invariant property, a rare yet valuable property supporting an equivalence between incremental learning and its joint training. Such an equivalence is crucial in GCIL settings as data distributions among different tasks no longer pose challenges to adopting our GACL. Theoretically, this equivalence property is validated through matrix analysis tools. Empirically, we conduct extensive experiments where, compared with existing GCIL methods, our GACL exhibits a consistently leading performance across various datasets and GCIL settings.

Contemporary database systems, while effective, suffer severe issues related to complexity and usability, especially among individuals who lack technical expertise but are unfamiliar with query languages like Structured Query Language (SQL). This paper presents a new database system supported by Artificial Intelligence (AI), which is intended to improve the management of data using natural language processing (NLP) - based intuitive interfaces, and automatic creation of structured queries and semi-structured data formats like yet another markup language (YAML), java script object notation (JSON), and application program interface (API) documentation. The system is intended to strengthen the potential of databases through the integration of Large Language Models (LLMs) and advanced machine learning algorithms. The integration is purposed to allow the automation of fundamental tasks such as data modeling, schema creation, query comprehension, and performance optimization. We present in this paper a system that aims to alleviate the main problems with current database technologies. It is meant to reduce the need for technical skills, manual tuning for better performance, and the potential for human error. The AI database employs generative schema inference and format selection to build its schema models and execution formats.

Class incremental learning (CIL) trains a network on sequential tasks with separated categories in each task but suffers from catastrophic forgetting, where models quickly lose previously learned knowledge when acquiring new tasks. The generalized CIL (GCIL) aims to address the CIL problem in a more real-world scenario, where incoming data have mixed data categories and unknown sample size distribution. Existing attempts for the GCIL either have poor performance or invade data privacy by saving exemplars. In this paper, we propose a new exemplar-free GCIL technique named generalized analytic continual learning (GACL). The GACL adopts analytic learning (a gradient-free training technique) and delivers an analytical (i.e., closed-form) solution to the GCIL scenario.

The rapid growth of video-text data presents challenges in storage and computation during training. Online learning, which processes streaming data in real-time, offers a promising solution to these issues while also allowing swift adaptations in scenarios demanding real-time responsiveness. One strategy to enhance the efficiency and effectiveness of learning involves identifying and prioritizing data that enhances performance on target downstream tasks. We propose Relevance and Specificity-based online filtering framework (ReSpec) that selects data based on four criteria: (i) modality alignment for clean data, (ii) task relevance for target focused data, (iii) specificity for informative and detailed data, and (iv) efficiency for low-latency processing. Relevance is determined by the probabilistic alignment of incoming data with downstream tasks, while specificity employs the distance to a root embedding representing the least specific data as an efficient proxy for informativeness. By establishing reference points from target task data, ReSpec filters incoming data in real-time, eliminating the need for extensive storage and compute. Evaluating on large-scale datasets WebVid2M and VideoCC3M, ReSpec attains state-of-the-art performance on five zeroshot video retrieval tasks, using as little as 5% of the data while incurring minimal compute. The source code is available at https://github.com/cdjkim/ReSpec.

Upon deployment to edge devices, it is often desirable for a model to further learn from streaming data to improve accuracy. However, extracting representative features from such data is challenging because it is typically unlabeled, non-independent and identically distributed (non-i.i.d), and is seen only once. To mitigate this issue, a common strategy is to maintain a small data buffer on the edge device to hold the most representative data for further learning. As most data is either never stored or quickly discarded, identifying the most representative data to avoid significant information loss becomes critical. In this paper, we propose an on-device framework that addresses this issue by condensing incoming data into more informative samples. Specifically, to effectively handle unlabeled incoming data, we propose a pseudo-labeling technique designed for unlabeled on-device learning environments. Additionally, we develop a dataset condensation technique that only requires little computation resources. To counteract the effects of noisy labels during the condensation process, we further utilize a contrastive learning objective to improve the purity of class data within the buffer. Our empirical results indicate substantial improvements over existing methods, particularly when buffer capacity is severely restricted. For instance, with a buffer capacity of just one sample per class, our method achieves an accuracy that outperforms the best existing baseline by 58.4% on the CIFAR-10 dataset.

The future success of the Navy will depend, in part, on artificial intelligence. In practice, many artificially intelligent algorithms, and in particular deep learning models, rely on continual learning to maintain performance in dynamic environments. The software requires adaptation to maintain its initial level of performance in unseen situations. However, if not monitored properly, continual learning may lead to several issues including catastrophic forgetting in which a trained model forgets previously learned tasks when being retrained on new data. The authors created a new framework for safely performing continual learning with the goal of pairing this safety framework with a deep learning computer vision algorithm to allow for safe and high-performing automatic deck tracking on carriers and amphibious assault ships. The safety framework includes several features, such as an ensemble of convolutional neural networks to perform image classification, a manager to record confidences and determine the best answer from the ensemble, a model of the environment to predict when the system may fail to meet minimum performance metrics, a performance monitor to log system and domain performance and check against requirements, and a retraining component to update the ensemble and manager to maintain performance. The authors validated the proposed method using extensive simulation studies based on dynamic image classification. The authors showed the safety framework could probabilistically detect out of distribution data. The results also show the framework can detect when the system is no longer performing safely and can significantly extend the working envelope of an image classifier.

LGBTQ advocate Heather Hester scolded Target's "rainbow capitalism" after the retailer dialed back Pride displays (Reuters) Subscribe now to get Fox News First in your email. And here's what you need to know to start your day ... EYE ON THE TARGET - Retailer's $15B loss in'stunning collapse' should serve as warning to CEOs, 'Shark Tank' star says. 'UNDENIABLE FACTS': - Tim Scott earns praise after leaving liberal'View' host'speechless.' TARMAC TROUBLE - Deputies remove handcuff and remove unruly passenger from Southwest plane before takeoff. SCIENTOLOGY SPOTLIGHT - Danny Masterson, Tom Cruise and Leah Remini illuminate Hollywood church drama.

Imagine if your smart speaker could be trained to recognize your accent, or if a pair of running shoes could alert you in real time if your gait changed, indicating fatigue. Or if, in the industrial world, sensors could parse vibration information from a machine that changed location and function often in real time, halting the machine if that information suggested there was a problem. We often write about the value of on-device machine learning (ML), but what we're generally discussing is running existing models on a device and matching incoming data against the established model. This is known as inference. So when you say the name "Alexa," your smart speaker matches the pattern and wakes up.

This post was originally published in Harvard Business Review. Despite rising investments in artificial intelligence (AI) by today's enterprises, trust in the insights delivered by AI can be a hit or a miss with the C-suite. Are executives just resisting a new, unknown, and still unproven technology, or their hesitancy is rooted in something deeper? Executives have long resisted data analytics for higher-level decision-making, and have always preferred to rely on gut-level decision-making based on field experience to AI-assisted decisions. AI has been adopted widely for tactical, lower-level decision-making in many industries -- credit scoring, upselling recommendations, chatbots, or managing machine performance are examples where it is being successfully deployed.

When building a Machine Learning Model for your company, for your portfolio, or for fun, there are some steps to take. And there are some other things you should avoid to increase your model accuracy. In this article, I try to warn you about 4 Common Pitfalls, when building a machine learning model. Although tons of cautions, you should take, while applying Machine Learning Model, when you avoid doing these steps, your model will be okay. These days, when building machine learning, it is common to find sources online.