It was while fleeing the civil war in South Sudan that Lual Mayen's mother gave birth to him 28 years ago. She had four children in tow and was near to the border with Uganda, in a town called Aswa. The journey was difficult; Mayen's two sisters died on the way and he became sick. No one thought he would survive. "I can't imagine what she had to go through. There was no food, no water, nothing," says Mayen. "I remember she said she was not the only woman who gave birth on the way. Other women abandoned their children because they didn't want them to suffer. But my mother thought: "He is a gift for me, I have to keep him."' Mayen's mother made it to northern Uganda with her newborn son and reunited with her husband in a refugee camp that remained their home for the next 22 years. Mayen grew up there, and although life was a struggle, he was happy and grateful for what he had. There wasn't much to do but Mayen says he found creative ways to keep himself entertained. Then, one day he had the chance to play the video game Grand Theft Auto, which mostly revolves around driving and shooting. "While I was playing, this thought came into my mind," he remembers. "In South Sudan, most of the population is under 30.

What comes to mind when we imagine a cutting-edge, tech-savvy workplace? But recent advances in technology, especially Artificial Intelligence, have attracted them too. AI-based tools have the ability to parse an unlimited amount of data, recognise patterns and apply them to new information. This allows legislators to have a dialogue with large constituents, analyse diverse opinions, participate remotely in plenary and committee meetings, and reduce paperwork through digitisation. Where is India in this picture?

AI and big data analytics are allowing healthcare providers in the Middle East to make faster, more cost-effective diagnostics, according to a broad cross-section of healthcare professionals. Along with the increasing use of AI and big data, though, security concerns about data privacy are also growing. AI is one of the fastest growing segments of the global healthcare market today. According to Frost & Sullivan forecasts, it will reach US$6.6 billion by the end of this year. Such growth rates are possible thanks to the huge amounts of data generated by a wide variety of devices, which can be analysed and acted on.

Recently, non-autoregressive (NAT) models predict outputs in parallel, achieving substantial improvements in generation speed compared to autoregressive (AT) models. While performing worse on raw data, most NAT models are trained as student models on distilled data generated by AT teacher models, which is known as sequence-level Knowledge Distillation. An effective training strategy to improve the performance of AT models is Self-Distillation Mixup (SDM) Training, which pre-trains a model on raw data, generates distilled data by the pre-trained model itself and finally re-trains a model on the combination of raw data and distilled data. In this work, we aim to view SDM for NAT models, but find directly adopting SDM to NAT models gains no improvements in terms of translation quality. Through careful analysis, we observe the invalidation is correlated to Modeling Diversity and Confirmation Bias between the AT teacher model and the NAT student models. Based on these findings, we propose an enhanced strategy named SDMRT by adding two stages to classic SDM: one is Pre-Rerank on self-distilled data, the other is Fine-Tune on Filtered teacher-distilled data. Our results outperform baselines by 0.6 to 1.2 BLEU on multiple NAT models. As another bonus, for Iterative Refinement NAT models, our methods can outperform baselines within half iteration number, which means 2X acceleration.

Deep encoders have been proven to be effective in improving neural machine translation (NMT) systems, but it reaches the upper bound of translation quality when the number of encoder layers exceeds 18. Worse still, deeper networks consume a lot of memory, making it impossible to train efficiently. In this paper, we present Symbiosis Networks, which include a full network as the Symbiosis Main Network (M-Net) and another shared sub-network with the same structure but less layers as the Symbiotic Sub Network (S-Net). We adopt Symbiosis Networks on Transformer-deep (m-n) architecture and define a particular regularization loss $\mathcal{L}_{\tau}$ between the M-Net and S-Net in NMT. We apply joint-training on the Symbiosis Networks and aim to improve the M-Net performance. Our proposed training strategy improves Transformer-deep (12-6) by 0.61, 0.49 and 0.69 BLEU over the baselines under classic training on WMT'14 EN->DE, DE->EN and EN->FR tasks. Furthermore, our Transformer-deep (12-6) even outperforms classic Transformer-deep (18-6).

Optical devices lie at the heart of most of the technology we see around us. When one actually wants to make such an optical device, one can predict its optical behavior using computational simulations of Maxwell's equations. If one then asks what the optimal design would be in order to obtain a certain optical behavior, the only way to go further would be to try out all of the possible designs and compute the electromagnetic spectrum they produce. When there are many design parameters, this brute force approach quickly becomes too computationally expensive. We therefore need other methods to create optimal optical devices. An alternative to the brute force approach is inverse design. In this paradigm, one starts from the desired optical response of a material and then determines the design parameters that are needed to obtain this optical response. There are many algorithms known in the literature that implement this inverse design. Some of the best performing, recent approaches are based on Deep Learning. The central idea is to train a neural network to predict the optical response for given design parameters. Since neural networks are completely differentiable, we can compute gradients of the response with respect to the design parameters. We can use these gradients to update the design parameters and get an optical response closer to the one we want. This allows us to obtain an optimal design much faster compared to the brute force approach. In my thesis, I use Deep Learning for the inverse design of the Fabry-P\'erot resonator. This system can be described fully analytically and is therefore ideal to study.

A Bayesian Network is a directed acyclic graph (DAG) on a set of $n$ random variables (identified with the vertices); a Bayesian Network Distribution (BND) is a probability distribution on the rv's that is Markovian on the graph. A finite mixture of such models is the projection on these variables of a BND on the larger graph which has an additional "hidden" (or "latent") random variable $U$, ranging in $\{1,\ldots,k\}$, and a directed edge from $U$ to every other vertex. Models of this type are fundamental to research in Causal Inference, where $U$ models a confounding effect. One extremely special case has been of longstanding interest in the theory literature: the empty graph. Such a distribution is simply a mixture of $k$ product distributions. A longstanding problem has been, given the joint distribution of a mixture of $k$ product distributions, to identify each of the product distributions, and their mixture weights. Our results are: (1) We improve the sample complexity (and runtime) for identifying mixtures of $k$ product distributions from $\exp(O(k^2))$ to $\exp(O(k \log k))$. This is almost best possible in view of a known $\exp(\Omega(k))$ lower bound. (2) We give the first algorithm for the case of non-empty graphs. The complexity for a graph of maximum degree $\Delta$ is $\exp(O(k(\Delta^2 + \log k)))$. (The above complexities are approximate and suppress dependence on secondary parameters.)

Predicting the future behavior of road users is one of the most challenging and important problems in autonomous driving. Applying deep learning to this problem requires fusing heterogeneous world state in the form of rich perception signals and map information, and inferring highly multi-modal distributions over possible futures. In this paper, we present MultiPath++, a future prediction model that achieves state-of-the-art performance on popular benchmarks. MultiPath++ improves the MultiPath architecture by revisiting many design choices. The first key design difference is a departure from dense image-based encoding of the input world state in favor of a sparse encoding of heterogeneous scene elements: MultiPath++ consumes compact and efficient polylines to describe road features, and raw agent state information directly (e.g., position, velocity, acceleration). We propose a context-aware fusion of these elements and develop a reusable multi-context gating fusion component. Second, we reconsider the choice of pre-defined, static anchors, and develop a way to learn latent anchor embeddings end-to-end in the model. Lastly, we explore ensembling and output aggregation techniques -- common in other ML domains -- and find effective variants for our probabilistic multimodal output representation. We perform an extensive ablation on these design choices, and show that our proposed model achieves state-of-the-art performance on the Argoverse Motion Forecasting Competition and the Waymo Open Dataset Motion Prediction Challenge.

Dec 20 (Thomson Reuters Foundation) -Two days after it was sued by Rohingya refugees from Myanmar over allegations that it did not take action against hate speech, social media company Meta, formerly known as Facebook, announced a new artificial intelligence system to tackle harmful content. Machine learning tools have increasingly become the go-to solution for tech firms to police their platforms, but questions have been raised about their accuracy and their potential threat to freedom of speech. WHY ARE SOCIAL MEDIA FIRMS UNDER FIRE OVER CONTENT MODERATION? The $150 billion Rohingya class-action lawsuit filed this month came at the end of a tumultuous period for social media giants, which have been criticised for failing to effectively tackle hate speech online and increasing polarization. The complaint argues that calls for violence shared on Facebook contributed to real-world violence against the Rohingya community, which suffered a military crackdown in 2017 that refugees said included mass killings and rape.

Pune, Dec. 20, 2021 (GLOBE NEWSWIRE) -- Global Artificial Intelligence Consulting Service Market research report study covers the global and regional market with an in-depth analysis of the overall growth prospects in the market. Artificial Intelligence Consulting Service Market Research Report identifies various key manufacturers of the market. It helps the reader understand the strategies and collaborations that players are focusing on combatting competition in the market. The researchers used advanced primary and secondary research methodologies and tools for preparing this report on the Artificial Intelligence Consulting Service market. In 2021, the global Artificial Intelligence Consulting Service market size will be USD million and it is expected to reach USD million by the end of 2027, with a CAGR of % during 2021-2027.