Most companies report measurable benefits from AI where it has been deployed; however, much work remains to scale impact, manage risks, and retrain the workforce. A group of high performers shows the way, as explained in this article by Arif Cam, Michael Chui and Bryce Hall of McKinsey. Adoption of artificial intelligence (AI) continues to increase, and the technology is generating returns. The findings of the latest McKinsey Global Survey on the subject show a nearly 25 percent year-over-year increase in the use of AI2 in standard business processes, with a sizable jump from the past year in companies using AI across multiple areas of their business. A majority of executives whose companies have adopted AI report that it has provided an uptick in revenue in the business areas where it is used, and 44 percent say AI has reduced costs.

Through play children (and adults) learn how to use their imaginations, to experiment with different ways of doing things. This might seem like it has relevance only for their self-development, but it's also through imagination and experimentation that the human race as a collective arrives at the solutions to its problems. As such, it's vital that we encourage children and people more generally to use their imaginations and to experiment, and it's to this end that LEGO, of all things, has an important role to play in nurturing the next generation of engineers, scientists and problem solvers. And we're not just talking about informal play with LEGO here, since one organization in particular has taken it upon itself to incorporate the famous Danish toy in competitions and workshops, all of which aim to instil a love for science and engineering in children. This organization is FIRST (For Inspiration and Recognition of Science and Technology), a not-for-profit public charity based in New Hampshire that works to inspire young people to pursue careers and education in STEM (science, technology, engineering and mathematics) subjects. Beginning in 1999, it partnered with the LEGO Group itself to launch the FIRST LEGO League, tapping into the LEGO brand to bring children to science.

New York, January 07, 2020: Based on Deployment, the global Enterprise Artificial Intelligence market is segmented in Cloud and On-Premises. The report also bifurcates the global Enterprise Artificial Intelligence market based on Solution in Business Intelligence, Customer Management, Sales & Marketing, Finance & Operations, Digital Commerce, and Others. The global Enterprise Artificial Intelligence market is segregated on the basis of Deployment as Cloud and On-Premises. Based on Service the global Enterprise Artificial Intelligence market is segmented in Professional Service and Managed Service. Based on End User the global Enterprise Artificial Intelligence market is segmented in Automotive, Media and Entertainment, Healthcare, Retail, IT & Telecommunication, BFSI, and Aerospace.

It is crucial for the wine industry to have methods like electronic nose systems (E-Noses) for real-time monitoring thresholds of acetic acid in wines, preventing its spoilage or determining its quality. In this paper, we prove that the portable and compact self-developed E-Nose, based on thin film semiconductor (SnO2) sensors and trained with an approach that uses deep Multilayer Perceptron (MLP) neural network, can perform early detection of wine spoilage thresholds in routine tasks of wine quality control. To obtain rapid and online detection, we propose a method of rising-window focused on raw data processing to find an early portion of the sensor signals with the best recognition performance. Our approach was compared with the conventional approach employed in E-Noses for gas recognition that involves feature extraction and selection techniques for preprocessing data, succeeded by a Support Vector Machine (SVM) classifier. The results evidence that is possible to classify three wine spoilage levels in 2.7 seconds after the gas injection point, implying in a methodology 63 times faster than the results obtained with the conventional approach in our experimental setup.

Whether enjoying the lucid prose of a favorite author or slogging through some other writer's cumbersome, heavy-set prattle (full of parentheses, em dashes, compound adjectives, and Oxford commas), readers will notice stylistic signatures not only in word choice and grammar, but also in punctuation itself. Indeed, visual sequences of punctuation from different authors produce marvelously different (and visually striking) sequences. Punctuation is a largely overlooked stylistic feature in "stylometry", the quantitative analysis of written text. In this paper, we examine punctuation sequences in a corpus of literary documents and ask the following questions: Are the properties of such sequences a distinctive feature of different authors? Is it possible to distinguish literary genres based on their punctuation sequences? Do the punctuation styles of authors evolve over time? Are we on to something interesting in trying to do stylometry without words, or are we full of sound and fury (signifying nothing)?

Bayesian nonparametric (BNP) models provide elegant methods for discovering underlying latent features within a data set, but inference in such models can be slow. We exploit the fact that completely random measures, which commonly used models like the Dirichlet process and the beta-Bernoulli process can be expressed as, are decomposable into independent sub-measures. We use this decomposition to partition the latent measure into a finite measure containing only in-stantiated components, and an infinite measure containing all other components. We then select different inference algorithms for the two components: uncollapsed samplers mix well on the finite measure, while collapsed samplers mix well on the infinite, sparsely occupied tail. The resulting hybrid algorithm can be applied to a wide class of models, and can be easily distributed to allow scalable inference without sacrificing asymptotic convergence guarantees.

Ensembles of models have been empirically shown to improve predictive performance and to yield robust measures of uncertainty. However, they are expensive in computation and memory. Therefore, recent research has focused on distilling ensembles into a single compact model, reducing the computational and memory burden of the ensemble while trying to preserve its predictive behavior. Most existing distillation formulations summarize the ensemble by capturing its average predictions. As a result, the diversity of the ensemble predictions, stemming from each individual member, is lost. Thus, the distilled model cannot provide a measure of uncertainty comparable to that of the original ensemble. To retain more faithfully the diversity of the ensemble, we propose a distillation method based on a single multi-headed neural network, which we refer to as Hydra. The shared body network learns a joint feature representation that enables each head to capture the predictive behavior of each ensemble member. We demonstrate that with a slight increase in parameter count, Hydra improves distillation performance on classification and regression settings while capturing the uncertainty behaviour of the original ensemble over both in-domain and out-of-distribution tasks.

W e present a general algorithm to order moves so as to speedup exact game solvers. It uses online learning of playout policies and Monte Carlo Tree Search. The learned policy and the information in the Monte Carlo tree are used to order moves in game solvers. They improve greatly the solving time for multiple games.

With the rapid development of online education system, knowledge tracing which aims at predicting students' knowledge state is becoming a critical and fundamental task in personalized education. Traditionally, existing methods are domain-specified. However, there are a larger number of domains (e.g., subjects, schools) in the real world and the lacking of data in some domains, how to utilize the knowledge and information in other domains to help train a knowledge tracing model for target domains is increasingly important. We refer to this problem as domain adaptation for knowledge tracing (DAKT) which contains two aspects: (1) how to achieve great knowledge tracing performance in each domain. (2) how to transfer good performed knowledge tracing model between domains. To this end, in this paper, we propose a novel adaptable framework, namely adaptable knowledge tracing (AKT) to address the DAKT problem. Specifically, for the first aspect, we incorporate the educational characteristics (e.g., slip, guess, question texts) based on the deep knowledge tracing (DKT) to obtain a good performed knowledge tracing model. For the second aspect, we propose and adopt three domain adaptation processes. First, we pre-train an auto-encoder to select useful source instances for target model training. Second, we minimize the domain-specific knowledge state distribution discrepancy under maximum mean discrepancy (MMD) measurement to achieve domain adaptation. Third, we adopt fine-tuning to deal with the problem that the output dimension of source and target domain are different to make the model suitable for target domains. Extensive experimental results on two private datasets and seven public datasets clearly prove the effectiveness of AKT for great knowledge tracing performance and its superior transferable ability.

NIRSpectroscopy NIRS Sensors NearInfrared Analyzers DigitalTransformation QualityControl foodtech machinelearning AI datascience LINK SAFE COST IN MAINTAINING NIR-SPECTROSCOPY METHODS NIRSpectroscopy NIRS Spectroscopy DigitalTransformation Analysis Lab Laboratory Application Quantitative Analysis Methods Measurements Analytical Parameters Spectrometer Quality Accuracy LINK Do you develop NIR / NIRS calibrations by yourself? Check out their product page … link Get the Chemometrics and Spectroscopy News in real time on Twitter @ CalibModel and follow us. Near Infrared "Study of chemical compound spatial distribution in biodegradable active films using NIR hyperspectral imaging and multivariate curve resolution" LINK "Advances in Near Infrared Spectroscopy and Related Computational Methods" MDPI Books – Pages: 496 OpenAccess NIRSpectroscopy NIRS NIR LINK " Ampliación de una librería espectral de mezclas unifeed analizadas en un instrumento NIRS de laboratorio" LINK "Applied Sciences, Vol. 9, Pages 5058: Single-Kernel FT-NIR Spectroscopy for Detecting Maturity of Cucumber Seeds Using a Multiclass Hierarchical Classification Strategy" LINK " Visible-near Infrared (VIS-NIR) Spectroscopy as a Rapid Measurement Tool to Assess the Effect of Tillage on Oil Contaminated Sites" LINK "Non-invasive measurements of'Yunhe'pears by vis-NIRS technology coupled with deviation fusion modeling approach" LINK "Standard Analytical Methods, Sensory Evaluation, NIRS and Electronic Tongue for Sensing Taste Attributes of Different Melon Varieties." LINK "Control of ascorbic acid in fortified powdered soft drinks using near-infrared spectroscopy (NIRS) and multivariate analysis" LINK "Prediction Model of the Key Components for Lodging Resistance in Rapeseed Stalk Using Near-Infrared Reflectance Spectroscopy (NIRS)" LINK "NIR spectroscopic determination of urine components in spot urine: preliminary investigation towards optical point-of-care test." LINK "O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression."