Non-negative matrix factorization (NMF) has become a popular method for representing meaningful data by extracting a non-negative basis feature from an observed non-negative data matrix. Some of the unique features of this method in identifying hidden data put this method amongst the powerful methods in the machine learning area. The NMF is a known non-convex optimization problem and the initial point has a significant effect on finding an efficient local solution. In this paper, we investigate the most popular initialization procedures proposed for NMF so far. We describe each method and present some of their advantages and disadvantages. Finally, some numerical results to illustrate the performance of each algorithm are presented.

Multilingual neural machine translation (MNMT) learns to translate multiple language pairs with a single model, potentially improving both the accuracy and the memory-efficiency of deployed models. However, the heavy data imbalance between languages hinders the model from performing uniformly across language pairs. In this paper, we propose a new learning objective for MNMT based on distributionally robust optimization, which minimizes the worst-case expected loss over the set of language pairs. We further show how to practically optimize this objective for large translation corpora using an iterated best response scheme, which is both effective and incurs negligible additional computational cost compared to standard empirical risk minimization. We perform extensive experiments on three sets of languages from two datasets and show that our method consistently outperforms strong baseline methods in terms of average and per-language performance under both many-to-one and one-to-many translation settings.

Robotic touch, particularly when using soft optical tactile sensors, suffers from distortion caused by motion-dependent shear. The manner in which the sensor contacts a stimulus is entangled with the tactile information about the geometry of the stimulus. In this work, we propose a supervised convolutional deep neural network model that learns to disentangle, in the latent space, the components of sensor deformations caused by contact geometry from those due to sliding-induced shear. The approach is validated by reconstructing unsheared tactile images from sheared images and showing they match unsheared tactile images collected with no sliding motion. In addition, the unsheared tactile images give a faithful reconstruction of the contact geometry that is not possible from the sheared data, and robust estimation of the contact pose that can be used for servo control sliding around various 2D shapes. Finally, the contact geometry reconstruction in conjunction with servo control sliding were used for faithful full object reconstruction of various 2D shapes. The methods have broad applicability to deep learning models for robots with a shear-sensitive sense of touch.

The field of artificial intelligence (AI), regarded as one of the most enigmatic areas of science, has witnessed exponential growth in the past decade including a remarkably wide array of applications, having already impacted our everyday lives. Advances in computing power and the design of sophisticated AI algorithms have enabled computers to outperform humans in a variety of tasks, especially in the areas of computer vision and speech recognition. Yet, AI's path has never been smooth, having essentially fallen apart twice in its lifetime ('winters' of AI), both after periods of popular success ('summers' of AI). We provide a brief rundown of AI's evolution over the course of decades, highlighting its crucial moments and major turning points from inception to the present. In doing so, we attempt to learn, anticipate the future, and discuss what steps may be taken to prevent another 'winter'.

Generative approaches have been recently shown to be effective for both Entity Disambiguation and Entity Linking (i.e., joint mention detection and disambiguation). However, the previously proposed autoregressive formulation for EL suffers from i) high computational cost due to a complex (deep) decoder, ii) non-parallelizable decoding that scales with the source sequence length, and iii) the need for training on a large amount of data. In this work, we propose a very efficient approach that parallelizes autoregressive linking across all potential mentions and relies on a shallow and efficient decoder. Moreover, we augment the generative objective with an extra discriminative component, i.e., a correction term which lets us directly optimize the generator's ranking. When taken together, these techniques tackle all the above issues: our model is >70 times faster and more accurate than the previous generative method, outperforming state-of-the-art approaches on the standard English dataset AIDA-CoNLL. Source code available at https://github.com/nicola-decao/efficient-autoregressive-EL

Major disruptive technologies such as artificial intelligence, machine learning, computer vision, IoT, and many other have helped tech companies to offer a wide range of technical products and services across the world. This has increased the demand for tech stocks among investors in these recent years. Some tech stocks are established names whereas some are rising high gradually in Industry 4.0. Analytics Insight provides a list of the top 5 tech stocks, according to Yahoo Finance. Fiverr International Ltd. is an Israel-based tech company focused on offering a platform to allow sellers and buyers in exchanging products and services.

Palindrome Data, a data science implementer that specialises in alternative data and machine learning tools for community development, has created what it believes to be South Africa's first suite of digital and paper-based HIV tools backed by machine learning, designed to help frontline healthcare workers triage at-risk patients. The solution, leveraging machine learning and multiple data sources, is designed to be used in both digital and paper-based environments so that healthcare workers can identify and manage high-risk patients and relevant interventions to increase HIV treatment retention and mitigate the risk of loss to follow-up (LTFU). The solution can correctly predict a patient's viral load (suppressed versus unsuppressed) for three out of four patients; and can anticipate two out of three times when a patient will drop out of care. "The biggest obstacle facing HIV patients is dealing with an overburdened healthcare system that can't afford to take the time to deal with their unique challenges," says Lucien De Voux, director of market strategy at Palindrome Data. "There is a need to retain and engage patients in a relevant way.

A new article published last week in the European Heart Journal discusses the use of drones for delivering life-saving automated external defibrillators (AED) to out-of-hospital cardiac arrest (OHCA) patients. As the study describes, "Early treatment in line with the'chain-of-survival' concept such as cardiopulmonary resuscitation (CPR) and defibrillation by an automated external defibrillator (AED) prior to ambulance arrival is associated with increased survival. Use of AEDs in the early-cardiac-arrest electrical phase can increase survival rates to up to 50–70%. Although hundreds of thousands of AEDs are available in high-income countries, their accessibility and use are still low." Thus, the investigators of the study designed a system to deploy drones to real-life suspected OHCA patients in order to determine whether this was a viable solution to the accessibility problem.

The US District Court for the Eastern District of Virginia on Wednesday ruled that an artificial intelligence (AI) machine cannot be an inventor under the Patent Act. The action was a motion for summary judgement concerning two patent applications filed by Stephen Thaler for an AI machine called DABUS. DABUS was listed as the inventor for Neural Flame--a light beacon that flashes in a new and inventive manner to attract attention--and Fractal Container--a beverage container based on fractal geometry. Thaler's patent applications were rejected by the US Patent and Trademarks Office (USPTO) and he challenged this refusal as "arbitrary, capricious, an abuse of direction and not in accordance with the law". He filed this action seeking a declaration that a patent application should not be rejected only on grounds that there is no natural person identified as the inventor and that a patent application for an invention by AI should list the AI as the inventor when the criteria for inventorship has been fulfilled by the AI. The court rejected Thaler's contentions, holding that the definitions provided by Congress for "inventor" within the Patent Act reference an "individual" whose ordinary dictionary and statutory meaning is a natural person or a human being.

Summary: A new AI algorithm can predict the onset of Alzheimer's disease with an accuracy of over 99% by analyzing fMRI brain scans. Researchers from Kaunas University, Lithuania developed a deep learning-based method that can predict the possible onset of Alzheimer's disease from brain images with an accuracy of over 99 percent. The method was developed while analyzing functional MRI images obtained from 138 subjects and performed better in terms of accuracy, sensitivity, and specificity than previously developed methods. According to World Health Organisation, Alzheimer's disease is the most frequent cause of dementia, contributing to up to 70 percent of dementia cases. Worldwide, approximately 24 million people are affected, and this number is expected to double every 20 years.