NLP impacts how firms provide products to users, content individuals receive through search and social media, and how While artificial intelligence provides the backbone for many tools individuals interact with news and emails. Despite the growing people use around the world, recent work has brought to attention importance of NLP algorithms in shaping our lives, recently scholars, that the algorithms powering AI are not free of politics, stereotypes, policymakers, and the business community have raised the and bias. While most work in this area has focused on the ways alarm of how gender and racial biases may be baked into these algorithms. in which AI can exacerbate existing inequalities and discrimination, Because they are trained on human data, the algorithms very little work has studied how governments actively shape themselves can replicate implicit and explicit human biases and training data. We describe how censorship has affected the development aggravate discrimination [6, 8, 39]. Additionally, training data that of Wikipedia corpuses, text data which are regularly used over-represents a subset of the population may do a worse job for pre-trained inputs into NLP algorithms. We show that word embeddings at predicting outcomes for other groups in the population [13].

In retrospect, Nisos experts made the right forecast. However, this was a clear minority opinion. Before and after their report, dozens of politicians and institutions drew considerable attention to the approaching danger: 'imagine a scenario where, on the eve of next year's presidential election, the Democratic nominee appears in a video where he or she endorses President Trump. Now, imagine it the other way around.' (Sprangler, 2019). It is fair to say that deepfakes' high potential for disinformation was noticed long before these hypothetical consequences were evoked, mainly because they were revealed to be highly credible. Two examples: 'In an online quiz, 49 percent of people who visited our site said they incorrectly believed Nixon's synthetically altered face was real and 65 percent thought his voice was real' (Panetta et al, 2020), or'Two-thirds of participants believed that one day it would be impossible to discern a real video from a fake one.

Vulnerable elephant populations are now being tracked from space using Earth-observation satellites and a type of artificial intelligence (AI) called machine learning. As part of an international project, researchers are using satellite images processed with computer algorithms, which are trained with more than 1,000 images of elephants to help spot the creatures. With machine learning, the algorithms can count elephants even on'complex geographical landscapes', such as those dotted with trees and shrubs. Researchers say this method is a promising new tool for surveying endangered wildlife and can detect animals with the same accuracy as humans. Elephants in woodland as seen from space.

In general-purpose particle detectors, the particle flow algorithm may be used to reconstruct a coherent particle-level view of the event by combining information from the calorimeters and the trackers, significantly improving the detector resolution for jets and the missing transverse momentum. In view of the planned high-luminosity upgrade of the CERN Large Hadron Collider, it is necessary to revisit existing reconstruction algorithms and ensure that both the physics and computational performance are sufficient in a high-pileup environment. Recent developments in machine learning may offer a prospect for efficient event reconstruction based on parametric models. We introduce MLPF, an end-to-end trainable machine-learned particle flow algorithm for reconstructing particle flow candidates based on parallelizable, computationally efficient, scalable graph neural networks and a multi-task objective. We report the physics and computational performance of the MLPF algorithm on on a synthetic dataset of ttbar events in HL-LHC running conditions, including the simulation of multiple interaction effects, and discuss potential next steps and considerations towards ML-based reconstruction in a general purpose particle detector.

Geometry of neural network loss landscape has been studied via the analysis of the global optimality of local minima [1, 2, 5, 8, 9, 14], the existence of a continuous descending path to a global optimum [4, 7, 10, 11, 13], the connectivity of dropout-stable solutions [6, 12], and the topology of sublevel sets [7, 13]. In this paper, we improve the result of [7]. In particular, [7] shows that for a general class of convex loss functions (e.g.

This thesis is a proof of concept for the potential of Variational Auto-Encoder (VAE) on representation learning of real-world Knowledge Graphs (KG). Inspired by successful approaches to the generation of molecular graphs, we evaluate the capabilities of our model, the Relational Graph Variational Auto-Encoder (RGVAE). The impact of the modular hyperparameter choices, encoding through graph convolutions, graph matching and latent space prior, is compared. The RGVAE is first evaluated on link prediction. The mean reciprocal rank (MRR) scores on the two datasets FB15K-237 and WN18RR are compared to the embedding-based model DistMult. A variational DistMult and a RGVAE without latent space prior constraint are implemented as control models. The results show that between different settings, the RGVAE with relaxed latent space, scores highest on both datasets, yet does not outperform the DistMult. Further, we investigate the latent space in a twofold experiment: first, linear interpolation between the latent representation of two triples, then the exploration of each latent dimension in a $95\%$ confidence interval. Both interpolations show that the RGVAE learns to reconstruct the adjacency matrix but fails to disentangle. For the last experiment we introduce a new validation method for the FB15K-237 data set. The relation type-constrains of generated triples are filtered and matched with entity types. The observed rate of valid generated triples is insignificantly higher than the random threshold. All generated and valid triples are unseen. A comparison between different latent space priors, using the $\delta$-VAE method, reveals a decoder collapse. Finally we analyze the limiting factors of our approach compared to molecule generation and propose solutions for the decoder collapse and successful representation learning of multi-relational KGs.

Ensemble learning methods are designed to benefit from multiple learning algorithms for better predictive performance. The tradeoff of this improved performance is slower speed and larger size of ensemble learning systems compared to single learning systems. In this paper, we present a novel approach to deal with this problem in Random Forest (RF) as one of the most powerful ensemble methods. The method is based on crossbreeding of the best tree branches to increase the performance of RF in space and speed while keeping the performance in the classification measures. The proposed approach has been tested on a group of synthetic and real datasets and compared to the standard RF approach. Several evaluations have been conducted to determine the effects of the Crossbred RF (CRF) on the accuracy and the number of trees in a forest. The results show better performance of CRF compared to RF.

Alan Kalton, Vice President and General Manager of Aktana Europe, is a leader in data analytics and manages all new Contextual Intelligence implementations and developments across Europe. He comes to Aktana from Cape Town, South Africa where he led a data analytics venture called BroadReach and prior was the Analytics Leader of EY in South Africa. He also held prominent executive leadership positions in data analytics at IBM, Elsevier, Cognizant, Steris, Novartis, GSK, and ZS Associates. He graduated with a BS and MSc of industrial and operations engineering from the University of Michigan. Kalton can be reached at alan.kalton@aktana.com.

Convolutional neural networks are able to learn realistic image priors from numerous training samples in low-level image generation and restoration. We show that, for high-level image recognition tasks, we can further reconstruct "realistic" images of each category by leveraging intrinsic Batch Normalization (BN) statistics without any training data. Inspired by the popular VAE/GAN methods, we regard the zero-shot optimization process of synthetic images as generative modeling to match the distribution of BN statistics. The generated images serve as a calibration set for the following zero-shot network quantizations. Our method meets the needs for quantizing models based on sensitive information, \textit{e.g.,} due to privacy concerns, no data is available. Extensive experiments on benchmark datasets show that, with the help of generated data, our approach consistently outperforms existing data-free quantization methods.

Logical theories in the form of ontologies and similar artefacts in computing and IT are used for structuring, annotating, and querying data, among others, and therewith influence data analytics regarding what is fed into the algorithms. Algorithmic bias is a well-known notion, but what does bias mean in the context of ontologies that provide a structuring mechanism for an algorithm's input? What are the sources of bias there and how would they manifest themselves in ontologies? We examine and enumerate types of bias relevant for ontologies, and whether they are explicit or implicit. These eight types are illustrated with examples from extant production-level ontologies and samples from the literature. We then assessed three concurrently developed COVID-19 ontologies on bias and detected different subsets of types of bias in each one, to a greater or lesser extent. This first characterisation aims contribute to a sensitisation of ethical aspects of ontologies primarily regarding representation of information and knowledge.