If you have already looked at how to invest in robotics stock and perused a list of the biggest robotics stocks, it's now time to take a look at five stocks that give investors the best way to play the theme of rising adoption of robotics automation. Let's take a look at five of the top companies playing the field of robotics and why their stocks are attractive for investors. Deere (NYSE:DE), an agricultural and construction machinery equipment manufacturer, might not be the first name that springs to mind when looking at robotics automation stocks; however, the Internet of Things (IoT) and the increasing use of automation will be key drivers of Deere's growth in the future. The company's core business is agricultural machinery. To be clear, a stock that operates in the farming sector will always be susceptible to the vagaries of the industry.

With automation looming, Amazon is investing $700 million to retrain 100,000 U.S.-based workers for new jobs. The goal is to shift one-third of Amazon's workforce by 2025 into in-demand jobs at the company that focus on software engineering, data sciences, robotics, and coordinating logistics to ship products. Still, the company says its free retraining programs will help employees get "highly skilled roles within or outside of Amazon." "The American workforce is changing. There is a greater need for technical skills in the workplace than ever before. Amazon is no exception," the company said in the announcement.

AI is the greatest IT disruption of our time, promising to transform society and industry. I've never seen a technology with as much potential to boost the security, health and prosperity of our country. It's estimated to make an economic impact measured in trillions of dollars. The U.S. federal government has been moving quickly, especially this past year, to help advance our nation's adoption of this transformative technology. From the White House to agency leaders and department heads in dozens of federal organizations, the government is acutely aware of the competitive international environment, with more than 35 countries that have already announced AI strategies.

A computer algorithm developed by scientists at the University of California, San Francisco (UCSF), and UC Berkeley bested two out of four expert radiologists at finding tiny brain hemorrhages in head scans -- an advance that one day may help doctors treat patients with traumatic brain injuries, strokes and aneurysms. Radiologists typically look at thousands of brain images each day, searching for tiny abnormalities that can signal life-threatening emergencies. A single, three-dimensional, computed tomography scan can produce a stack of 30 or more images, each of which must be reviewed by a radiologist. The researchers created their algorithm to see if artificial intelligence could more efficiently and accurately pick out images with significant abnormalities to help radiologists focus on the most important images and examine them more closely. "We wanted something that was practical, and for this technology to be useful clinically, the accuracy level needs to be close to perfect," said study co-author Esther Yuh, an associate professor of radiology at UCSF. "The performance bar is high for this application, due to the potential consequences of a missed abnormality, and people won't tolerate less than human performance or accuracy."

Database activity monitoring (DAM) systems are commonly used by organizations to protect the organizational data, knowledge and intellectual properties. In order to protect organizations database DAM systems have two main roles, monitoring (documenting activity) and alerting to anomalous activity. Due to high-velocity streams and operating costs, such systems are restricted to examining only a sample of the activity. Current solutions use policies, manually crafted by experts, to decide which transactions to monitor and log. This limits the diversity of the data collected. Bandit algorithms, which use reward functions as the basis for optimization while adding diversity to the recommended set, have gained increased attention in recommendation systems for improving diversity. In this work, we redefine the data sampling problem as a special case of the multi-armed bandit (MAB) problem and present a novel algorithm, which combines expert knowledge with random exploration. We analyze the effect of diversity on coverage and downstream event detection tasks using a simulated dataset. In doing so, we find that adding diversity to the sampling using the bandit-based approach works well for this task and maximizing population coverage without decreasing the quality in terms of issuing alerts about events.

This work presents an explicit-implicit procedure that combines an offline trained neural network with an online primal active set solver to compute a model predictive control (MPC) law with guarantees on recursive feasibility and asymptotic stability. The neural network improves the suboptimality of the controller performance and accelerates online inference speed for large systems, while the primal active set method provides corrective steps to ensure feasibility and stability. We highlight the connections between MPC and neural networks and introduce a primal-dual loss function to train a neural network to initialize the online controller. We then demonstrate online computation of the primal feasibility and suboptimality criteria to provide the desired guarantees. Next, we use these neural network and criteria measures to accelerate an online primal active set method through warm starts and early termination. Finally, we present a data set generation algorithm that is critical for successfully applying our approach to high dimensional systems. The primary motivation is developing an algorithm that scales to systems that are challenging for current approaches, involving state and input dimensions as well as planning horizons in the order of tens to hundreds.

Predictive policing is a data-based, predictive analytical technique used in law enforcement. In this paper, we give an overview of the current situation in Austria and discuss technical, sociopolitical and legal questions raised by the use of PP, such as the lack of awareness of discriminatory structures in society, the biases in data underlying PP and the lack of reflection on the basic premises and feedback mechanisms of PP. Violations of fundamental rights without cause are not allowed by the Austrian Code of Criminal Procedure (Strafproze{\ss}ordnung, StPO), the Security Police Act (Sicherheitspolizeigesetz, SPG) or the Act concerning Police Protection of the State (Polizeiliches Staatsschutzgesetz, PStSG); the principle of allowing police intervention only on the basis of concrete threats or suspicion must remain absolute. Considering the numerous problems (not least from the point of view of legal policy), we conclude that the use of PP should be eschewed and that resources and planning should instead be focussed on solving the social problems which actually cause crime. ----- Predictive Policing ist ein datenbasiertes und prognosegetriebenes Modell f\"ur Polizeiarbeit. Wir geben in diesem Artikel einen \"Uberblick \"uber den aktuellen Stand in \"Osterreich und diskutieren technische, politisch-gesellschaftliche und rechtliche Probleme, die sich daraus ergeben -- etwa das mangelhafte Bewusstsein f\"ur Prozesse gesellschaftlicher Diskriminierung, die verzerrte Datenbasis, die PP zugrundeliegt, und fehlende Reflexion \"uber zugrundeliegende Annahmen und R\"uckkopplungseffekte. Anlasslose Grundrechtseingriffe sind weder durch die StPO noch das SPG oder das PStSG gedeckt; dem Grundgedanken, dass Polizei erst bei konkreter Gefahrenlage oder Tatverdacht t\"atig werden darf, muss weiterhin Rechnung getragen werden. Aus unserer Sicht sollte angesichts der zahlreichen Probleme (und auch aus rechtspolitischen Erw\"agungen) auf PP verzichtet werden und stattdessen Ressourcen und \"Uberlegung in die L\"osung jener gesellschaftlicher Probleme investiert werden, die zu Kriminalit\"at f\"uhren.

Machine learning (ML) in the representation of molecular-orbital-based (MOB) features has been shown to be an accurate and transferable approach to the prediction of post-Hartree-Fock correlation energies. Previous applications of MOB-ML employed Gaussian Process Regression (GPR), which provides good prediction accuracy with small training sets; however, the cost of GPR training scales cubically with the amount of data and becomes a computational bottleneck for large training sets. In the current work, we address this problem by introducing a clustering/regression/classification implementation of MOB-ML. In a first step, regression clustering (RC) is used to partition the training data to best fit an ensemble of linear regression (LR) models; in a second step, each cluster is regressed independently, using either LR or GPR; and in a third step, a random forest classifier (RFC) is trained for the prediction of cluster assignments based on MOB feature values. Upon inspection, RC is found to recapitulate chemically intuitive groupings of the frontier molecular orbitals, and the combined RC/LR/RFC and RC/GPR/RFC implementations of MOB-ML are found to provide good prediction accuracy with greatly reduced wall-clock training times. For a dataset of thermalized geometries of 7211 organic molecules of up to seven heavy atoms, both implementations reach chemical accuracy (1 kcal/mol error) with only 300 training molecules, while providing 35000-fold and 4500-fold reductions in the wall-clock training time, respectively, compared to MOB-ML without clustering. The resulting models are also demonstrated to retain transferability for the prediction of large-molecule energies with only small-molecule training data. Finally, it is shown that capping the number of training datapoints per cluster leads to further improvements in prediction accuracy with negligible increases in wall-clock training time.

Machine reading comprehension(MRC) has attracted significant amounts of research attention recently, due to an increase of challenging reading comprehension datasets. In this paper, we aim to improve a MRC model's ability to determine whether a question has an answer in a given context (e.g. the recently proposed SQuAD 2.0 task). Our solution is a relation module that is adaptable to any MRC model. The relation module consists of both semantic extraction and relational information. We first extract high level semantics as objects from both question and context with multi-head self-attentive pooling. These semantic objects are then passed to a relation network, which generates relationship scores for each object pair in a sentence. These scores are used to determine whether a question is non-answerable. We test the relation module on the SQuAD 2.0 dataset using both BiDAF and BERT models as baseline readers. We obtain 1.8% gain of F1 on top of the BiDAF reader, and 1.0% on top of the BERT base model. These results show the effectiveness of our relation module on MRC

We present a novel language adaptable spell checking system which detects spelling errors and suggests context sensitive corrections in real-time. We show that our system can be extended to new languages with minimal language-specific processing. Available literature majorly discusses spell checkers for English but there are no publicly available systems which can be extended to work for other languages out of the box. Most of the systems do not work in real-time. We explain the process of generating a language's word dictionary and n-gram probability dictionaries using Wikipedia-articles data and manually curated video subtitles. We present the results of generating a list of suggestions for a misspelled word. We also propose three approaches to create noisy channel datasets of real-world typographic errors. We compare our system with industry-accepted spell checker tools for 11 languages. Finally, we show the performance of our system on synthetic datasets for 24 languages.