We thank all the reviewers for carefully checking the paper and acknowledging the "efficiency and practicality" of We will also clarify in the revised version. R1 asks for discussion of similarity and difference of technical results to [19]. Hence, training on the medoids is robust to noisy labels. Indeed, Eq. 5 finds the best subset of Fraction of clean data points in coreset. R2 asks how clean the coreset is.

AI risks entrenching racism and sexism in Australia, the human rights commissioner has warned, amid internal Labor debate about how to respond to the emerging technology. Lorraine Finlay says the pursuit of productivity gains from AI should not come at the expense of discrimination if the technology is not properly regulated. Finlay's comments follow Labor senator Michelle Ananda-Rajah breaking ranks to call for all Australian data to be "freed" to tech companies to prevent AI perpetuating overseas biases and reflect Australian life and culture. Ananda-Rajah is opposed to a dedicated AI act but believes content creators should be paid for their work. Media and arts groups have warned of "rampant theft" of intellectual property if big tech companies can take their content to train AI models.

This paper presents a topological analytics approach within the 5-level Cyber-Physical Systems (CPS) architecture for the Stream-of-Quality assessment in smart manufacturing. The proposed methodology not only enables real-time quality monitoring and predictive analytics but also discovers the hidden relationships between quality features and process parameters across different manufacturing processes. A case study in additive manufacturing was used to demonstrate the feasibility of the proposed methodology to maintain high product quality and adapt to product quality variations. This paper demonstrates how topological graph visualization can be effectively used for the real-time identification of new representative data through the Stream-of-Quality assessment.

Deep learning on large-scale data is dominant nowadays. The unprecedented scale of data has been arguably one of the most important driving forces for the success of deep learning. However, there still exist scenarios where collecting data or labels could be extremely expensive, e.g., medical imaging and robotics. To fill up this gap, this paper considers the problem of data-efficient learning from scratch using a small amount of representative data. First, we characterize this problem by active learning on homeomorphic tubes of spherical manifolds. This naturally generates feasible hypothesis class. With homologous topological properties, we identify an important connection -- finding tube manifolds is equivalent to minimizing hyperspherical energy (MHE) in physical geometry. Inspired by this connection, we propose a MHE-based active learning (MHEAL) algorithm, and provide comprehensive theoretical guarantees for MHEAL, covering convergence and generalization analysis. Finally, we demonstrate the empirical performance of MHEAL in a wide range of applications on data-efficient learning, including deep clustering, distribution matching, version space sampling and deep active learning.

In this post, we will come through some of the major challenges that you might face while developing your machine learning model. Assuming that you know what machine learning is really about, why do people use it, what are the different categories of machine learning, and how the overall workflow of development takes place. What can possibly go wrong during the development and prevent you from getting accurate predictions? So let's get started, during the development phase our focus is to select a learning algorithm and train it on some data, the two things that might be a problem are a bad algorithm or bad data, or perhaps both of them. Let's say for a child, to make him learn what an apple is, all it takes for you to point to an apple and say apple repeatedly.

ANN is a modeling technique inspired by the human nervous system that allows learning by example from representative data that describes a physical phenomenon or a decision process. A unique feature of ANN is that they are able to establish empirical relationships between independent and dependent variables, and extract subtle information and complex knowledge from representative data sets. The relationships between independent and dependent variables can be established without assumptions about any mathematical representation of the phenomena. ANN models provide certain advantages over regression-based models including its capacity to deal with noisy data. ANNs consist of a layer of input nodes and layer of output nodes, connected by one or more layers of hidden nodes.

As companies step up the use of machine learning-enabled systems in their day-to-day operations, they become increasingly reliant on those systems to help them make critical business decisions. In some cases, the machine learning systems operate autonomously, making it especially important that the automated decision-making works as intended. However, machine learning-based systems are only as good as the data that's used to train them. If there are inherent biases in the data used to feed a machine learning algorithm, the result could be systems that are untrustworthy and potentially harmful. In this article, you'll learn why bias in AI systems is a cause for concern, how to identify different types of biases and six effective methods for reducing bias in machine learning.

Last month, Microsoft announced that Teams, its competitor to Slack, Facebook's Workplace, and Google's Hangouts Chat, had passed 44 million daily active users. The milestone overshadowed its unveiling of a few new features coming "later this year." Most were straightforward: a hand-raising feature to indicate you have something to say, offline and low-bandwidth support to read chat messages and write responses even if you have poor or no internet connection, and an option to pop chats out into a separate window. But one feature, real-time noise suppression, stood out -- Microsoft demoed how the AI minimized distracting background noise during a call. How many times have you asked someone to mute themselves or to relocate from a noisy area? Real-time noise suppression will filter out someone typing on their keyboard while in a meeting, the rustling of a bag of chips (as you can see in the video above), and a vacuum cleaner running in the background.

Your firm produces data, so surely it can benefit from applying AI, right? Here are five questions to ask yourself about whether a business problem is "AI-solvable". Machine learning, the latest incarnation of artificial intelligence (AI), works by detecting complex patterns in past data and using them to predict future data. Since almost all business decisions ultimately rely on predictions (about profits, employee performance, costs, regulation etc.) it would seem obvious that machine learning (ML) could be useful whenever "big" data are available to support business decisions. The reality in most organisations is that data may be captured but it is stored haphazardly.