Apple has introduced new sharing and personalized recommendation features for Podcasts on iOS 15, all meant to help you discover new shows to listen to. Starting today, you'll find personalized recommendation sections in the Listen Now page that show you podcasts similar to the ones you enjoy. They'll be entitled "If You Like '[Show Name]'..." and then list titles in the same category or with the same theme or same format. They could also list shows from the same studio or titles other users listening to that particular podcast are also following. You'll find new sections with recommendations based the topics you usually enjoy, as well.

This is a three-part article series discussing the impact of the Personal Information Act (4 of 2013) on artificial intelligence or machine learning systems used in the context of the workplace. Although it is dependent on the type of workplace or employer regarding the degree to which the processing of personal information by artificial intelligence systems is relevant, and may appear prescient, it is reasonable to conclude that this will grow in the not too distant future as more technologies make up the workplace. From a general perspective, this article looks at what is viewed as the relevant provisions or themes of POPI and its possible relation to artificial intelligence or machine learning systems. However, the provisions of POPI discussed are not exhaustive and other areas (not covered) may also be relevant considering the specific context in each given case. In Part 1 of the series, I discuss the background to the series; the Protection of Personal Information Act (4 of 2013), providing an overview of the Act for purposes of the series, and personal versus de-identified information: the likely relationship between POPI and AI systems.

More of the creative work these days is not being done by humans at all. When Dixons Carphone wanted to push shoppers towards its Black Friday sale, the company turned to Artificial Intelligence (AI) software and got the winning line "The time is now". Saul Lopes, head of customer marketing at Dixons Carphone, thinks it worked because it didn't have the words Black Friday in it. His human copywriters had produced dozens of potentially successful sentences but they all mentioned Black Friday. It was technology that broke this chain of thought.

The Exa.TrkX project has applied geometric learning concepts such as metric learning and graph neural networks to HEP particle tracking. Exa.TrkX's tracking pipeline groups detector measurements to form track candidates and filters them. The pipeline, originally developed using the TrackML dataset (a simulation of an LHC-inspired tracking detector), has been demonstrated on other detectors, including DUNE Liquid Argon TPC and CMS High-Granularity Calorimeter. This paper documents new developments needed to study the physics and computing performance of the Exa.TrkX pipeline on the full TrackML dataset, a first step towards validating the pipeline using ATLAS and CMS data. The pipeline achieves tracking efficiency and purity similar to production tracking algorithms. Crucially for future HEP applications, the pipeline benefits significantly from GPU acceleration, and its computational requirements scale close to linearly with the number of particles in the event.

WhatsApp emerged as a major communication platform in many countries in the recent years. Despite offering only one-to-one and small group conversations, WhatsApp has been shown to enable the formation of a rich underlying network, crossing the boundaries of existing groups, and with structural properties that favor information dissemination at large. Indeed, WhatsApp has reportedly been used as a forum of misinformation campaigns with significant social, political and economic consequences in several countries. In this article, we aim at complementing recent studies on misinformation spread on WhatsApp, mostly focused on content properties and propagation dynamics, by looking into the network that connects users sharing the same piece of content. Specifically, we present a hierarchical network-oriented characterization of the users engaged in misinformation spread by focusing on three perspectives: individuals, WhatsApp groups and user communities, i.e., groupings of users who, intentionally or not, share the same content disproportionately often. By analyzing sharing and network topological properties, our study offers valuable insights into how WhatsApp users leverage the underlying network connecting different groups to gain large reach in the spread of misinformation on the platform.

Computing latent representations for graph-structured data is an ubiquitous learning task in many industrial and academic applications ranging from molecule synthetization to social network analysis and recommender systems. Knowledge graphs are among the most popular and widely used data representations related to the Semantic Web. Next to structuring factual knowledge in a machine-readable format, knowledge graphs serve as the backbone of many artificial intelligence applications and allow the ingestion of context information into various learning algorithms. Graph neural networks attempt to encode graph structures in low-dimensional vector spaces via a message passing heuristic between neighboring nodes. Over the recent years, a multitude of different graph neural network architectures demonstrated ground-breaking performances in many learning tasks. In this work, we propose a strategy to map deep graph learning architectures for knowledge graph reasoning to neuromorphic architectures. Based on the insight that randomly initialized and untrained (i.e., frozen) graph neural networks are able to preserve local graph structures, we compose a frozen neural network with shallow knowledge graph embedding models. We experimentally show that already on conventional computing hardware, this leads to a significant speedup and memory reduction while maintaining a competitive performance level. Moreover, we extend the frozen architecture to spiking neural networks, introducing a novel, event-based and highly sparse knowledge graph embedding algorithm that is suitable for implementation in neuromorphic hardware.

Language Models (LMs) have been ubiquitously leveraged in various tasks including spoken language understanding (SLU). Spoken language requires careful understanding of speaker interactions, dialog states and speech induced multimodal behaviors to generate a meaningful representation of the conversation. In this work, we propose to dissect SLU into three representative properties:conversational (disfluency, pause, overtalk), channel (speaker-type, turn-tasks) and ASR (insertion, deletion,substitution). We probe BERT based language models (BERT, RoBERTa) trained on spoken transcripts to investigate its ability to understand multifarious properties in absence of any speech cues. Empirical results indicate that LM is surprisingly good at capturing conversational properties such as pause prediction and overtalk detection from lexical tokens. On the downsides, the LM scores low on turn-tasks and ASR errors predictions. Additionally, pre-training the LM on spoken transcripts restrain its linguistic understanding. Finally, we establish the efficacy and transferability of the mentioned properties on two benchmark datasets: Switchboard Dialog Act and Disfluency datasets.

Web search is fundamentally multimodal and multihop. Often, even before asking a question we choose to go directly to image search to find our answers. Further, rarely do we find an answer from a single source but aggregate information and reason through implications. Despite the frequency of this everyday occurrence, at present, there is no unified question answering benchmark that requires a single model to answer long-form natural language questions from text and open-ended visual sources -- akin to a human's experience. We propose to bridge this gap between the natural language and computer vision communities with WebQA. We show that A. our multihop text queries are difficult for a large-scale transformer model, and B. existing multi-modal transformers and visual representations do not perform well on open-domain visual queries. Our challenge for the community is to create a unified multimodal reasoning model that seamlessly transitions and reasons regardless of the source modality.

We develop a subseasonal forecasting toolkit of simple learned benchmark models that outperform both operational practice and state-of-the-art machine learning and deep learning methods. Our new models include (a) Climatology++, an adaptive alternative to climatology that, for precipitation, is 9% more accurate and 250% more skillful than the United States operational Climate Forecasting System (CFSv2); (b) CFSv2++, a learned CFSv2 correction that improves temperature and precipitation accuracy by 7-8% and skill by 50-275%; and (c) Persistence++, an augmented persistence model that combines CFSv2 forecasts with lagged measurements to improve temperature and precipitation accuracy by 6-9% and skill by 40-130%. Across the contiguous U.S., our Climatology++, CFSv2++, and Persistence++ toolkit consistently outperforms standard meteorological baselines, state-of-the-art machine and deep learning methods, and the European Centre for Medium-Range Weather Forecasts ensemble. Overall, we find that augmenting traditional forecasting approaches with learned enhancements yields an effective and computationally inexpensive strategy for building the next generation of subseasonal forecasting benchmarks.

In this paper, we consider the problem of automatically designing a Rectified Linear Unit (ReLU) Neural Network (NN) architecture (number of layers and number of neurons per layer) with the assurance that it is sufficiently parametrized to control a nonlinear system; i.e. control the system to satisfy a given formal specification. This is unlike current techniques, which provide no assurances on the resultant architecture. Moreover, our approach requires only limited knowledge of the underlying nonlinear system and specification. We assume only that the specification can be satisfied by a Lipschitz-continuous controller with a known bound on its Lipschitz constant; the specific controller need not be known. From this assumption, we bound the number of affine functions needed to construct a Continuous Piecewise Affine (CPWA) function that can approximate any Lipschitz-continuous controller that satisfies the specification. Then we connect this CPWA to a NN architecture using the authors' recent results on the Two-Level Lattice (TLL) NN architecture; the TLL architecture was shown to be parameterized by the number of affine functions present in the CPWA function it realizes.