In this paper, we investigate representation learning for low-resource keyword spotting (KWS). The main challenges of KWS are limited labeled data and limited available device resources. To address those challenges, we explore representation learning for KWS by self-supervised contrastive learning and self-training with pretrained model. First, local-global contrastive siamese networks (LGCSiam) are designed to learn similar utterance-level representations for similar audio samplers by proposed local-global contrastive loss without requiring ground-truth. Second, a self-supervised pretrained Wav2Vec 2.0 model is applied as a constraint module (WVC) to force the KWS model to learn frame-level acoustic representations. By the LGCSiam and WVC modules, the proposed small-footprint KWS model can be pretrained with unlabeled data. Experiments on speech commands dataset show that the self-training WVC module and the self-supervised LGCSiam module significantly improve accuracy, especially in the case of training on a small labeled dataset.

We investigate a family of bilevel imaging learning problems where the lower-level instance corresponds to a convex variational model involving first- and second-order nonsmooth sparsity-based regularizers. By using geometric properties of the primal-dual reformulation of the lower-level problem and introducing suitable auxiliar variables, we are able to reformulate the original bilevel problems as Mathematical Programs with Complementarity Constraints (MPCC). For the latter, we prove tight constraint qualification conditions (MPCC-RCPLD and partial MPCC-LICQ) and derive Mordukhovich (M-) and Strong (S-) stationarity conditions. The stationarity systems for the MPCC turn also into stationarity conditions for the original formulation. Second-order sufficient optimality conditions are derived as well, together with a local uniqueness result for stationary points. The proposed reformulation may be extended to problems in function spaces, leading to MPCC's with constraints on the gradient of the state. The MPCC reformulation also leads to the efficient use of available large-scale nonlinear programming solvers, as shown in a companion paper, where different imaging applications are studied.

Counterfactual fairness is an approach to AI fairness that tries to make decisions based on the outcomes that an individual with some kind of sensitive status would have had without this status. This paper proposes Double Machine Learning (DML) Fairness which analogises this problem of counterfactual fairness in regression problems to that of estimating counterfactual outcomes in causal inference under the Potential Outcomes framework. It uses arbitrary machine learning methods to partial out the effect of sensitive variables on nonsensitive variables and outcomes. Assuming that the effects of the two sets of variables are additively separable, outcomes will be approximately equalised and individual-level outcomes will be counterfactually fair. This paper demonstrates the approach in a simulation study pertaining to discrimination in workplace hiring and an application on real data estimating the GPAs of law school students. It then discusses when it is appropriate to apply such a method to problems of real-world discrimination where constructs are conceptually complex and finally, whether DML Fairness can achieve justice in these settings.

Quantum Machine Learning has the potential to improve traditional machine learning methods and overcome some of the main limitations imposed by the classical computing paradigm. However, the practical advantages of using quantum resources to solve pattern recognition tasks are still to be demonstrated. This work proposes a universal, efficient framework that can reproduce the output of a plethora of classical supervised machine learning algorithms exploiting quantum computation's advantages. The proposed framework is named Multiple Aggregator Quantum Algorithm (MAQA) due to its capability to combine multiple and diverse functions to solve typical supervised learning problems. In its general formulation, MAQA can be potentially adopted as the quantum counterpart of all those models falling into the scheme of aggregation of multiple functions, such as ensemble algorithms and neural networks. From a computational point of view, the proposed framework allows generating an exponentially large number of different transformations of the input at the cost of increasing the depth of the corresponding quantum circuit linearly. Thus, MAQA produces a model with substantial descriptive power to broaden the horizon of possible applications of quantum machine learning with a computational advantage over classical methods. As a second meaningful addition, we discuss the adoption of the proposed framework as hybrid quantum-classical and fault-tolerant quantum algorithm.

Machine learning has changed the way businesses plan, work and breathe! It's been here for quite some time now, and the estimated boost in productivity with its implementation has already touched 54%. While it ostensibly risks many jobs, it is here to give. Machine learning and automation are helping industries (healthcare, logistics, and more) gear up for digital transformation more enthusiastically than ever – and it still looks like the beginning. HR automation is one of the buzzwords in the business world that's been headlining with machine learning for quite some time now.

Recently, OpenAI, an AI research laboratory based in San Francisco, announced the launch of its latest AI chatbot, GPT-4. This advanced chatbot has the capability of handling both text and image input, making it more technologically advanced than its predecessor, GPT-3.5. The launch of GPT-4 is expected to usher in a new era of artificial intelligence and its impact on the world. According to OpenAI, GPT-4 is more creative and collaborative than its predecessor, ChatGPT, which was released in 2022. It can handle multiple tasks, such as generating, editing, and collaborating with users on various technical and creative writing tasks, including composing songs, creating screenplays, and analyzing the writing style of a user.

Andrew Ng is the Founder of DeepLearning.AI, Founder and CEO of Landing AI, Managing General Partner at AI Fund, Chairman and Co-Founder of Coursera, and an Adjunct Professor at Stanford University. As a pioneer both in machine learning and online education, Dr. Ng has changed countless lives through his work in AI, authoring or co-authoring over 200 research papers in machine learning, robotics, and related fields. He was also the founding lead of the Google Brain team, and Chief Scientist at Baidu, and through this work built the teams that led the AI transformation of two leading internet companies. He is also co-founder and Chairman of Coursera, which had started with his machine learning course. Dr. Ng now focuses his time primarily on his entrepreneurial ventures, looking for the best ways to accelerate responsible AI practices in the larger global economy.

When classrooms and conference rooms abruptly moved online three years ago, we all experienced moments of technical frustration. Whether dealing with connectivity issues or clumsy virtual interactions, which were sometimes accompanied by awkward background noises, we persisted. Fortunately, the education sector had time to smooth out some of these wrinkles, especially with improved connectivity and advancing technology such as artificial intelligence (AI). Having seen such positive changes firsthand, Elliott Levine, director of worldwide public sector and education at Qualcomm Technologies, Inc. is excited about the newest technologies and their impact on the learning experience. Before transitioning to EdTech, Levine enjoyed 30 years working in various positions in K-12 and higher ed.

An academic paper entitled Chatting and Cheating: Ensuring Academic Integrity in the Era of ChatGPT was published this month in an education journal, describing how artificial intelligence (AI) tools "raise a number of challenges and concerns, particularly in relation to academic honesty and plagiarism". What readers – and indeed the peer reviewers who cleared it for publication – did not know was that the paper itself had been written by the controversial AI chatbot ChatGPT. "We wanted to show that ChatGPT is writing at a very high level," said Prof Debby Cotton, director of academic practice at Plymouth Marjon University, who pretended to be the paper's lead author. "This is an arms race," she said. "The technology is improving very fast and it's going to be difficult for universities to outrun it."

Learning a new language can be a daunting task. Traditional methods often involve spending countless hours practicing vocabulary and grammar rules, and it can take months or even years to become proficient. But what if there was a way to learn a language naturally over time, without any practice at all? It might sound too good to be true, but that's exactly what I set out to create with my latest project: a browser extension that uses the power of natural acquisition to help people learn a new language quickly and easily. My extension, Sponge, is a clone of a VC-backed startup that was based around browser extensions that would help you learn a new language by integrating the new language in your everyday reading.